Dissertations / Theses on the topic 'Mortality and Length of stay Prediction'
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Shin, Jung-Ho. "New outcome-specific comorbidity scores excelled in predicting in-hospital mortality and healthcare charges in administrative databases." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263579.
Full textCissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. https://publication-theses.unistra.fr/restreint/theses_doctorat/2024/CISSOKO_MamadouBenHamidou_2024_ED269.pdf.
Full textIn personalized predictive medicine, accurately modeling a patient's illness and care processes is crucial due to the inherent long-term temporal dependencies. However, Electronic Health Records (EHRs) often consist of episodic and irregularly timed data, stemming from sporadic hospital admissions, which create unique patterns for each hospital stay. Consequently, constructing a personalized predictive model necessitates careful consideration of these factors to accurately capture the patient's health journey and assist in clinical decision-making. LSTM networks are effective for handling sequential data like EHRs, but they face two significant limitations: the inability to interpret prediction results and to take into account irregular time intervals between consecutive events. To address limitations, we introduce novel deep dynamic memory neural networks called Multi-Way Adaptive and Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM and AMITA) designed for irregularly collected sequential data. The primary objective of both models is to leverage medical records to memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power
Lipovich, Carol Jean. "Analysis of Ventilator Associated Pneumonia Patients' Hospital and Intensive Care Charges, Length of Stay and Mortality." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366228755.
Full textSundareshan, Padma. "Clostridium difficile Infection (CDI) Incidence Rate and CDI-Associated Length of Stay, Total Hospital Charges and Mortality." The University of Arizona, 2009. http://hdl.handle.net/10150/623982.
Full textOBJECTIVES: The purpose of the study was to determine the rate of Clostridium difficile infections (CDI) in hospitalized patients and the various factors that were associated with the risk of developing CDI by examining patient discharge data for hospitals in 37 states in the United States using Healthcare Cost and Utilization Project (HCUP). METHODS: Patient discharge information for all patients obtained using HCUP census for the years 2002-2005, either for primary or secondary (all-listed) occurrences of CDI using the ICD-9-CM code (008.45) specific for intestinal infections due to C. difficile, were included in the study. Regression analysis, either Generalized Linear Model log-link or power-link, or a logistic regression was employed to control for the multiple independent variables. RESULTS: The incidence rate for CDI was 9.4% for the years 2002-2005. Among the concomitant diagnoses and procedures, essential hypertension, volume depletion, congestive heart failure, urinary tract infection and venous catheterization were the top 5. The length of stay (LOS) for CDI was associated with being Black, Hispanic or Other race category, number of diagnoses and procedures, primary expected payer of Medicaid, private insurance and other (including worker’s compensation, CHAMPUS,CHAMPVA etc), and all groups classified based on median household income category for patient’s zip code. Predictors of CDI related to inpatient total hospital charges were being female, race (other than black), number of diagnoses and procedures, Death, LOS, patient location and with self-pay and no charge categories as primary expected payer. Predictors of higher CDI related inpatient hospital deaths were age, female sex, Hispanic race, number of diagnoses and procedures, LOS and having Medicaid, self-pay or other as primary expected payer. CONCLUSIONS: LOS, inpatient total hospital charges, and inpatient mortality were dependent on several patient and other characteristics.
Spencer, Patricia L. "The influence of specialized cancer hospitals in Florida on mortality, length of stay, and charges of care." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002725.
Full textFletcher, Emily A., and Robert S. Lawson. "Characteristics of Hospital Inpatient Charges, Length of Stay, and Inpatient Mortality in Patients with Ovarian Cancer from 2002-2005." The University of Arizona, 2009. http://hdl.handle.net/10150/623991.
Full textOBJECTIVES: To determine and characterize the relative impact of patient demographics on hospital inpatient charges, length of stay, and inpatient mortality in patients with ovarian cancer from 2002-2005. METHODS: A retrospective database analysis of AHRQ’s Health Care Cost and Utilization Project (HCUP) Nationwide Inpatient Sample databases was conducted spanning from January 1, 2002, to December 31, 2005.Data were collected regarding age, race, payer status, median household income, location of hospital (urban/rural), comorbidities, procedures, total charges, length of stay, and inpatient mortality. Multivariate and gamma regression methods were utilized to examine incremental risks associated with length of stay, total charges, and inpatient mortality, after controlling for all other variables. RESULTS: Overall, data from 246,012 hospital admissions were obtained. The average length of stay of patients was 6.58 days (SD = 7.22), the average number of diagnoses was 7.18 (SD = 3.36), the average number of procedures performed was 2.71 (SD = 2.66). A total of 14,485 (5.9%) patients died during hospitalization. The average total charge was $29,698 (SD = $42,951). The IRR was 0.886 (95%CI, -0.105 to -0.04) for patients who were Hispanic, and 1.089 (95%CI, 0.017–0.153) for patients who were Black compared to patients who were white. When compared to patients who lived in large, metropolitan areas, the IRR was 0.88 (95%CI, -0.146 to - 0.109) for patients located in smaller, metropolitan areas, and the IRR was 0.74 (95%CI, -0.335 to -0.268) for patients located in non- urban areas. CONCLUSIONS: Patient demographics were found to have associations, both directly and indirectly, with length o
Pattakos, Gregory. "Predicting Length of Stay and Non-Home Discharge: A Novel Approach to Reduce Wasted Resources after Cardiac Surgery." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1291145768.
Full textLeitch, David B. "Predictive patterns of institutional misconduct, pro-social behavior, and length of stay of incarcerated youth in a secure, long-term, juvenile rehabilitation facility." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1529614192152508.
Full textStraathof, Bas Theodoor. "A Deep Learning Approach to Predicting the Length of Stay of Newborns in the Neonatal Intensive Care Unit." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282873.
Full textFramstegen inom maskininlärning och det utbredda införandet av elektroniska hälsoregister har möjliggjort genombrott för flera prediktiva modelleringsuppgifter inom sjukvården. En sådan uppgift som har sett betydande förbättringar förknippade med djupa neurala nätverk är förutsägelsens av vistelsetid på sjukhus, men forskningen har främst inriktats på vuxna patienter i intensivvården. Den här avhandlingen använder multivariata tidsserier extraherade från den offentligt tillgängliga databasen Medical Information Mart for Intensive Care III för att undersöka potentialen för djup inlärning att klassificera återstående vistelsetid för nyfödda i den neonatala intensivvårdsavdelningen (neonatal-IVA) vid varje timme av vistelsen. Denna avhandling beskriver experiment genomförda med olika djupinlärningsmodeller, inklusive longshort-term memory, gated recurrent units, fully-convolutional networks och flera sammansatta nätverk. Detta arbete visar att modellering av återstående vistelsetid för nyfödda i neonatal-IVA som ett multivariat tidsserieklassificeringsproblem på ett naturligt sätt underlättar upprepade förutsägelser över tid och gör det möjligt för avancerade djupa inlärningsmodeller att överträffaen multinomial logistisk regressionsbaslinje tränad på handgjorda funktioner. Dessutom visar det vikten av den nyfödda graviditetsåldern och binära masker som indikerar saknade värden som variabler för att förutsäga den återstående vistelsetiden.
Oliveira, Ana Rita Castelo Branco. "Pneumonias adquiridas durante o internamento hospitalar : impacte na saúde e implicação nos custos." Master's thesis, Universidade Nova de Lisboa. Escola Nacional de Saúde Pública, 2012. http://hdl.handle.net/10362/9702.
Full textABSTRACT - Introduction: The main goal of this study is to analyze the health and the costs due to acquired Pneumonia during hospital stay. There is evidence that hospital infections are a public health problem in hospitals worldwide. Methods: The population analyzed is 97,033 hospital admissions, occurred in 10 hospitals in the year 2010. The work comprises three phases: i) characterization of the population, ii) identification of variables that influence health outcomes, iii) estimating the costs of acquired Pneumonia. Results: Admissions with acquired Pneumonia are more frequent on males (58.1%). The most relevant age group was from 80 to 89 years. The prevalence rate was 4.16% and the in-hospital mortality rate was 34.56%. The patients with acquired Pneumonia had an increase of the length of stay circa 13 days compared with patients without acquired Pneumonia for the same set of GDH. The males and admissions on non-teaching hospitals lead to an increased risk of hospital death. Moreover larger length of stay and higher number of comorbidities had decreased the risk of hospital death. The increase on admissions costs due to acquired Pneumonia were circa 18 million euros. Conclusions: The study presents some poor health outcomes, as well as costs increase due to acquired Pneumonia in Portuguese public hospitals. These results should be considered as a real problem in Portugal, and therefore it is necessary to be more evidenced based on hospital guidelines definition and in clinical management practice in order to increase hospital’s effectiveness and efficiency.
Baeza, Fernanda Lucia Capitanio. "Desfechos negativos entre pacientes internados em unidade psiquiátrica de hospital geral : um estudo longitudinal." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/172516.
Full textIntroduction: In the last decades, several factors have determined significant changes in the way to provide psychiatric care. These include changes in the way we understand mental disorders, advances and improvements in the availability of psychiatric treatments, increased political interest in mental health, and emphasis on health care costs. With this, psychiatric hospitalization started to represent a smaller portion of the resources used in mental health care. It fulfills the function of diagnosing and treating acute symptoms with the purpose of avoiding risks, being focused on stabilization, patient safety and short stay. In this context, the study of negative outcomes among patients hospitalized in the psychiatric beds of general hospital becomes more and more necessary. Objectives: To identify determinants of adverse outcomes in patients admitted in psychiatric beds of a general hospital, defined a priori as longer hospital stay, rehospitalization and death from any cause one year after discharge. Methods: This is a naturalistic, longitudinal and prospective study carried out in a psychiatric unit of a general university-level tertiary care hospital. Patients admitted between June 2011 and December 2013 aged 18 years and over were considered eligible, except those who had substance use disorders as the main diagnosis, severe psychomotor agitation in the first 72 hours of admission, cognitive impairment sufficient to compromise the evaluation or refusal to participate in the research. Sociodemographic and clinical data were collected on admission, discharge and one year after discharge. Results: In article 1, six variables explained 14,6% of the variability of length-of-stay: absence of own income, history of psychiatric hospitalizations in the last two years, the total score of Brief Psychiatric Rating Scale and Clinical Global Impression, Schizophrenia diagnosis and history of suicide attempts. Article 2 reaffirmed the role of previous admissions in predicting future hospitalizations. Also, for patients admitted in a depressive episode, not being in remission at discharge increases the chance to be readmitted (OR 2.40; CI 1.14-5.07), as well as higher scores in the Brief Psychiatric Rating Scale at discharge for patients with Schizophrenia (OR 1.28, CI 1.11-1.48). Article 3 reported the mortality among patients followed up, more than three times greater than the mortality of the general population for the same period and geographical area. Discussion: The three studies produced by this thesis collaborate to the body of evidence about adverse outcomes among patients admitted in psychiatric beds of a general hospital. Contemporary psychiatry widely advocates the model of psychiatric hospitalization in general hospital as the best for treatment of acute mental disorder exacerbations. However, it is not the majority model in Brazil as in the world. Therefore, the results of this thesis reflect the outcomes of a recommended, but not predominant, care model. Final considerations: We still lack researches that focus on evaluating negative outcomes in general hospital psychiatric hospitalization.
Inocencio, Timothy. "The Economic Burden of Opioid Poisoning in the United States and Determinants of Increased Costs in Opioid Poisoning." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2930.
Full textBaffoe, Seth Kojo Ananse. "Comparing Outcomes of Laparoscopic Adjustable Banding and Laparoscopic Sleeve Gastrectomy Bariatric Surgery." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4996.
Full textArbuckle, Lon Michel Luk. "Short-Term Occupancy Prediction at the Ottawa Hospital Using Time-Series Data for Admissions and Longitudinal Patient Data for Discharge." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20545.
Full textPhromjuang, Kornwika. "The Relationship between Personal Demographic Components, Health Status, Discharge Status, and Mortality among Asian Pacific Islander Elders." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1207269544.
Full textShiramizo, Sandra Christina Pereira Lima. "O fluxo de paciente séptico dentro da instituição como fator prognóstico independente de letalidade." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/5/5137/tde-08122014-151547/.
Full textSepsis is a common cause of death. Several predictors of hospital mortality have been identified. However, it is possible that the route the septic patient takes within the hospital may also affect endpoints. Thus, our main objective was to verify whether the routes of septic patients before being admitted to ICU affect their in-hospital mortality. Methods Retrospective cohort study of 489 patients with severe sepsis or septic shock (age >= 18 years) admitted to the Intensive Care Unit. We analyzed the impact of route on in-hospital mortality using Cox regression with robust variance. Results Of 489 patients, 207 (42.3%) presented with severe sepsis in the ED, 185 (37.8%) were diagnosed with severe sepsis in the ward, 56 (13.3%) in the step down unit and 32 (6.5%) in the ICU. The mortality rate was 41.9%. The mean age was 66.3 years, and 56.6% were men. APACHE II scores were >25 in 39.8% of patients, and 77.5% were diagnosed with septic shock. In the multivariate analysis, with adjustment for several prognostic factors including length of hospital stay before ICU admission, there was no statistically significant difference in the risk of death between patients who had severe sepsis diagnosed in the ED compared to CMC (relative risk [RR] 1,36; IC 95% 1,00 a 1,83). However, the risk of death was increased in patients who had severe sepsis diagnosed in the step-down unit or ICU (RR 1,64; IC 95% 1,20 a 2,25). Conclusion Patients who have severe sepsis or septic shock diagnosed in the CMC have in-hospital mortality similar to those who present with severe sepsis or septic shock in the ED. However, patients who develop severe sepsis in the step-down unit or ICU have higher mortality
Eriksson, Thomas. "Närståendes besök hos patienter som vårdas på intensivvårdsavdelning." Doctoral thesis, Göteborgs universitet, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3631.
Full textAkademisk avhandling som för avläggande av filosofie doktorsexamen vid Sahlgrenska akademin vid Göteborgs universitet kommer att offentligt försvaras i hörsal 2118, Institutionen för vårdvetenskap och hälsa, Arvid Wallgrens backe, Hus 2, Göteborg, fredagen den 19 oktober 2012 kl. 09.00
Barbosa, Mariana Raslan Paes. "Desempenho de testes de rastreamento e avaliação nutricional como preditores de desfechos clínicos negativos em pacientes hospitalizados." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/5/5154/tde-08062010-161230/.
Full textINTRODUCTION: The diagnosis of nutritional status by nutritional screening and assessment tools detects malnutrition and is associated with negative clinical outcomes in adult hospitalized patients. OBJECTIVE: To identify the most appropriate tool for analysis of nutritional risk and malnutrition in relation to adverse clinical outcomes in adult hospitalized patients, and to investigate the complementarity of the nutritional screening (NRS 2002) and nutritional assessment (SGA) tests. METHODS: A prospective, sequential, non-interventional study, conducted in 705 adult patients of both sexes, from different wards in the Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo. Within 48 hours of admission, all the patients were submitted to four nutritional screening and assessment tests (NRS 2002: Nutritional Risk Screening 2002, MUST: Malnutrition Universal Screening Tool, MNA-SF: Mini Nutritional Assessment Short Form and SGA: Subjective Global Assessment). Patients were followed until the final outcome, obtaining clinical outcomes of complications, length of hospital stay and death. The performance of the tests was analyzed by the ROC (Receiver Operating Characteristic) curve and likelihood ratio (LR). The complementarity of screening and assessment tools was analyzed by logistic regression, and the number of patients required to screen was obtained by calculating the number needed to screen (NNS). RESULTS: NRS 2002 detected 27.9% (n = 197) of nutritional risk, MUST 39.6% (n = 279), MNA-SF 73.2% (n = 516), and SGA detected moderate or severe malnutrition in 38.9% of the patients (n = 274). NRS 2002 and SGA had a better performance in predicting adverse clinical outcomes than MUST and MNA-SF confirmed by ROC curve. NRS 2002 had higher positive LR compared to the other tests for all the clinical outcomes. According to the logistic regression analysis, 13% (CI 10.0-17.0%) of the patients may have length of hospital stay, 9% (CI 7.0-12.0%) moderate or severe complications and 1% (CI 0.3 - 2.1%) mortality. For length of hospital stay, malnourished patients by SGA, class B (SGA B) increase this probability in 1.9 times (CI 1.2-3.2 times, p = 0.008) and SGA class C (SGA C) in 3.8 times (CI 2.0-7.2 times, p <0.0001). For patients nutritionally at risk by NRS 2002 (NRS +), the probability of moderate and severe complication increase in 1.9 times (CI 1.1-3.5 times, p = 0.03), in 1.9 times (CI 1.1-3.4 times, p = 0.02) for SGA B patients and 17.8 times (CI 1.4-5.8 times, p = 0.003) for SGA C patients. The probability of mortality increase in 3.9 times (CI 1.2-13.1 times, p = 0.03) for NRS+ patients. The NNS calculated for all adverse clinical outcomes in patients NRS+ & SGA C (at nutritional risk by NRS 2002 and severe malnourished by SGA), was lower than for the test separately. CONCLUSIONS: NRS 2002 is the best test for nutritional risk screening. The application of SGA in nutritionally at risk patients by NRS 2002 increases the predictive capacity of malnutrition in relation to adverse clinical outcomes.
Franzosi, Oellen Stuani. "Comparação das estratégias de terapia nutricional enteral hipocalóricas versus normocalóricas em pacientes críticos com insuficiência respiratória aguda : revisão sistemática e metanálise de ensaios clínicos randomizados." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/115074.
Full textContext: Controversy exists regarding the optimal amount of calories that critically ill patients with acute respiratory failure should consume as far as clinical outcomes and gastrointestinal tolerability are concerned. Objective: To compare the effect of two enteral nutrition strategies (underfeeding versus fullfeeding) on clinical outcomes and gastrointestinal tolerability in critically ill patients with acute respiratory failure. Data Sources: MEDLINE, EMBASE, SCOPUS and the Cochrane Central Register of Controlled Trials up to August 2014. Study Selection: Randomized Controlled Trials that compared the effects of underfeeding with full-feeding strategies on major clinical outcomes (ICU and overall mortality, ICU and hospital length of stay and mechanical ventilation) and gastrointestinal signs and symptoms (regurgitation, aspiration, vomiting, diarrhea, constipation, abdominal distention, elevated gastric residual volume and use of prokinetic agents). Data extraction: Studies’ information, patient’s characteristics and outcomes were extracted. Risk ratio (RR) and Mean Difference (MD) estimates were synthesized under a randomeffects model. Heterogeneity was evaluated using the Q test and I2. A sensitivity analysis on overall mortality was conducted, wherein the groups were classified according to the feeding strategy used (trophic versus hypocaloric nutrition). Meta-analyses were performed using RevMan v5.3 analysis software. Data synthesis: Among the 798 studies retrieved, four studies of 1540 patients were included. Interventional studies comparing underfeeding with full-feeding were not associated with significant difference in overall mortality (RR, 0.92; 95% CI, 0.73 – 1,19; I2 31% p=0.23 for heterogeneity). Subgroup analysis of the groups according to the amount of delivered calories showed that the overall mortality was significantly lower in the subgroup that achieved 59-72% of energy intake than in the full-feeding group (RR, 0.72; 95% CI, 0.53 – 0.98; I2 0% p=0.78 for heterogeneity). No differences were found between the underfeeding versus full-feeding groups regarding in the ICU mortality, ICU and hospital length of stay and duration of mechanical ventilation. As far as gastrointestinal tolerability is concerned, the underfeeding group showed lower occurrence of vomiting, regurgitation, use of prokinetic agents, elevated gastric residual volume occurrence, diarrhea and constipation when compared with the full-feeding strategy. No differences between the two groups were found for aspiration and abdominal distention. Conclusion: The underfeeding strategy was associated with lower overall mortality in the subgroup that achieved initial moderate intake. Gastrointestinal tolerability was improved by the underfeeding strategy. Initial moderate intake should be preferred rather than trophic or full-feeding in critically ill patients.
Zambiazi, Reisi Weber. "Complicações respiratórias no pós-operatório de cirurgia abdominal : fatores de risco e implicações." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/179818.
Full textIntroduction: Postoperative respiratory complications are common after abdominal surgeries. Identify risk factors helps the health team to adopt protective measures in order to reduce the chance of complications and its implications. Objective: Identify risk factors for postoperative respiratory complications after abdominal surgeries. Methodology: A retrospective cohort study was carried out by searching electronic medical records of adult subjects submitted to abdominal surgery from January to July 2016. Data were analyzed using statistical software SPSS 20.0. For the normality test, Shapiro-Wilk was used to compare groups of categorical variables. X² test was used and for continuous variables, t test for independent variables and multivariate logistic regression was used to calculate odds ratios. Significant p<0.05 was considered. Results: During the study period, 1586 surgeries were performed, 55.7% female patients with a mean age of 52.12±16.56 years. After surgery, 17.7% of the patients presented one or more respiratory complications; the most common was atelectasis. Independent risk factors identified were open surgery, emergency surgery, chronic lung disease, ASA≥3, supraumbilical incision, BMI≤21kg/m², smoking, age and surgery time. Subjects with respiratory complications presented higher length of stay and mortality. Conclusion: Abdominal surgeries performed by laparoscopy are related to a lower risk of respiratory complications, while the presence of chronic lung disease is the main risk factor among comorbidities. Respiratory complications increase length of hospital stay and mortality.
Rosa, Regis Goulart. "Desfechos clínicos em neutropenia febril." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/119418.
Full textFebrile neutropenia (FN) is a common complication of cancer chemotherapy and is associated with high morbidity and mortality rates. Recognition of the main factors associated with the development of adverse clinical outcomes in FN is crucial, given that these factors can be used as prognostic markers or therapeutic targets. This study aims to determine the main factors associated with mortality, length of hospital stay, incidence of bacteremia by multidrug-resistant pathogens and incidence of septic shock at the onset of fever in hospitalized patients with FN secondary to cancer cytotoxic chemotherapy. In the present prospective cohort of 305 FN episodes (in 169 cancer patients) conducted at a tertiary hospital from October 2009 to August 2011, the following research questions were evaluated: impact of time to antibiotic administration on 28-day mortality; factors associated with length of hospital stay; impact of microbiological factors of bacteremia on the development of septic shock at the onset of FN; risk factors for bacteremia by multidrug-resistant pathogens; impact of coagulasenegative Staphylococcus bacteremia on 28-day mortality. In 5 distinct publications, the following results were noted: delay of antibiotic administration is associated with higher 28-day mortality rates; hematologic malignancy, high-dose chemotherapy regimens, duration of neutropenia and bacteremia by multidrug-resistant Gram-negative bacteria are associated with prolonged length of hospital stay; polymicrobial bloodstream infection, bacteremia by Escherichia coli, and bacteremia by viridans sreptococci are associated with septic shock at the onset of FN; advanced age, duration of neutropenia and presence of indwelling central venous catheters are associated with bacteremia by multidrug-resistant pathogens; coagulase-negative Staphylococcus bacteremia is associated with lower 28-day mortality rates compared with bacteremia by other pathogens.
Kinnunen, T. (Tuija). "Keuhkoahtaumataudin sairaalahoito Suomessa: hoitoajan pituus ja sen yhteys ennusteeseen." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514283864.
Full textTiivistelmä Tutkimuksen tarkoituksena oli selvittää valtakunnallisen hoitoilmoitusrekisterin ja kuolemansyytilaston avulla keuhkoahtaumataudista (KAT) aiheutunutta sairaalahoitoa Suomessa 1972–2001: sairaalapalvelujen käyttöä, hoitojakson pituuteen vaikuttavia tekijöitä sekä hoitoajan yhteyttä ennusteeseen. Lähdeaineistosta valittiin erilaisia ajanjaksoja tutkimusasetelman mukaan. Tulokset viittaavat siihen, että hoitoajan pituus vaihtelee Suomessa maantieteellisesti ja vuodenaikojen mukaan: lyhyin hoitoaika on Pohjois-Suomessa kesällä. Ilmiötä selittänevät pääosin terveydenhuollon resurssien ja hoitokäytäntöjen alueelliset erot sekä ilmasto-olosuhteiden vaihtelu. Vuosina 1987–1998 keskimääräinen hoitoaika koko aineistossa oli yhdeksän vuorokautta. Jos potilaalla oli samanaikaisina sairauksina keuhkokuume tai aivoverenkiertohäiriö, nämä johtivat pisimpiin hoitoaikoihin. KAT:n pahenemisvaiheen hoitoaika lyheni kaksi vuorokautta vuodesta 1993 vuoteen 2001. Iäkkäitten naisten hoitoajat olivat pisimmät. Viikon pituinen hoitoaika nykyisillä hoitomuodoilla oli optimaalinen, sillä tällöin aika seuraavan pahenemisvaiheen hoitojakson alkuun oli pisin: vähän yli puoli vuotta. Kaikista päivystyshoitojaksoista potilaan kuolemaan päättyi kolmisen prosenttia. Yleisimmin tällainen hoitojakso päättyi potilaan kuolemaan perjantaisin ja todennäköisimmin talvella tai keväällä. Viikonloppuna sairaalaan tulleista potilaista kuoli ensimmäisen vuorokauden aikana enemmän kuin arkipäivinä tulleista. Keskimääräinen hoitoaika oli pisin ja sairaalahoito runsainta sairauden loppuvaiheessa kuoleman lähestyessä. Ensimmäisen KAT:n aiheuttaman hoitojakson jälkeen noin neljännes potilaista oli kuollut vuoden sisällä ja viiden vuoden kuluessa noin puolet. Keuhkoahtaumataudin sairaalahoito on tehostunut Suomessa 1990-luvulla sairaansijojen vähentyessä. Hoitoajat ovat lyhentyneet ja pahenemisvaiheiden sairaalakuolleisuus on vähäistä. Väestön ikääntyminen on kuitenkin ennakoitava ja sairaalaa korvaavia hoitomuotoja kehitettävä taudista aiheutuneiden kustannusten hillitsemiseksi. Varhaisdiagnostiikkaa ja avokuntoutusta on kehitettävä ja erityinen huomio kiinnitettävä sairauden loppuvaiheen asianmukaiseen hoitoon
Pepler, Pieter Theo. "The identification and application of common principal components." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96101.
Full textENGLISH ABSTRACT: When estimating the covariance matrices of two or more populations, the covariance matrices are often assumed to be either equal or completely unrelated. The common principal components (CPC) model provides an alternative which is situated between these two extreme assumptions: The assumption is made that the population covariance matrices share the same set of eigenvectors, but have di erent sets of eigenvalues. An important question in the application of the CPC model is to determine whether it is appropriate for the data under consideration. Flury (1988) proposed two methods, based on likelihood estimation, to address this question. However, the assumption of multivariate normality is untenable for many real data sets, making the application of these parametric methods questionable. A number of non-parametric methods, based on bootstrap replications of eigenvectors, is proposed to select an appropriate common eigenvector model for two population covariance matrices. Using simulation experiments, it is shown that the proposed selection methods outperform the existing parametric selection methods. If appropriate, the CPC model can provide covariance matrix estimators that are less biased than when assuming equality of the covariance matrices, and of which the elements have smaller standard errors than the elements of the ordinary unbiased covariance matrix estimators. A regularised covariance matrix estimator under the CPC model is proposed, and Monte Carlo simulation results show that it provides more accurate estimates of the population covariance matrices than the competing covariance matrix estimators. Covariance matrix estimation forms an integral part of many multivariate statistical methods. Applications of the CPC model in discriminant analysis, biplots and regression analysis are investigated. It is shown that, in cases where the CPC model is appropriate, CPC discriminant analysis provides signi cantly smaller misclassi cation error rates than both ordinary quadratic discriminant analysis and linear discriminant analysis. A framework for the comparison of di erent types of biplots for data with distinct groups is developed, and CPC biplots constructed from common eigenvectors are compared to other types of principal component biplots using this framework. A subset of data from the Vermont Oxford Network (VON), of infants admitted to participating neonatal intensive care units in South Africa and Namibia during 2009, is analysed using the CPC model. It is shown that the proposed non-parametric methodology o ers an improvement over the known parametric methods in the analysis of this data set which originated from a non-normally distributed multivariate population. CPC regression is compared to principal component regression and partial least squares regression in the tting of models to predict neonatal mortality and length of stay for infants in the VON data set. The tted regression models, using readily available day-of-admission data, can be used by medical sta and hospital administrators to counsel parents and improve the allocation of medical care resources. Predicted values from these models can also be used in benchmarking exercises to assess the performance of neonatal intensive care units in the Southern African context, as part of larger quality improvement programmes.
AFRIKAANSE OPSOMMING: Wanneer die kovariansiematrikse van twee of meer populasies beraam word, word dikwels aanvaar dat die kovariansiematrikse of gelyk, of heeltemal onverwant is. Die gemeenskaplike hoofkomponente (GHK) model verskaf 'n alternatief wat tussen hierdie twee ekstreme aannames gele e is: Die aanname word gemaak dat die populasie kovariansiematrikse dieselfde versameling eievektore deel, maar verskillende versamelings eiewaardes het. 'n Belangrike vraag in die toepassing van die GHK model is om te bepaal of dit geskik is vir die data wat beskou word. Flury (1988) het twee metodes, gebaseer op aanneemlikheidsberaming, voorgestel om hierdie vraag aan te spreek. Die aanname van meerveranderlike normaliteit is egter ongeldig vir baie werklike datastelle, wat die toepassing van hierdie metodes bevraagteken. 'n Aantal nie-parametriese metodes, gebaseer op skoenlus-herhalings van eievektore, word voorgestel om 'n geskikte gemeenskaplike eievektor model te kies vir twee populasie kovariansiematrikse. Met die gebruik van simulasie eksperimente word aangetoon dat die voorgestelde seleksiemetodes beter vaar as die bestaande parametriese seleksiemetodes. Indien toepaslik, kan die GHK model kovariansiematriks beramers verskaf wat minder sydig is as wanneer aanvaar word dat die kovariansiematrikse gelyk is, en waarvan die elemente kleiner standaardfoute het as die elemente van die gewone onsydige kovariansiematriks beramers. 'n Geregulariseerde kovariansiematriks beramer onder die GHK model word voorgestel, en Monte Carlo simulasie resultate toon dat dit meer akkurate beramings van die populasie kovariansiematrikse verskaf as ander mededingende kovariansiematriks beramers. Kovariansiematriks beraming vorm 'n integrale deel van baie meerveranderlike statistiese metodes. Toepassings van die GHK model in diskriminantanalise, bi-stippings en regressie-analise word ondersoek. Daar word aangetoon dat, in gevalle waar die GHK model toepaslik is, GHK diskriminantanalise betekenisvol kleiner misklassi kasie foutkoerse lewer as beide gewone kwadratiese diskriminantanalise en line^ere diskriminantanalise. 'n Raamwerk vir die vergelyking van verskillende tipes bi-stippings vir data met verskeie groepe word ontwikkel, en word gebruik om GHK bi-stippings gekonstrueer vanaf gemeenskaplike eievektore met ander tipe hoofkomponent bi-stippings te vergelyk. 'n Deelversameling van data vanaf die Vermont Oxford Network (VON), van babas opgeneem in deelnemende neonatale intensiewe sorg eenhede in Suid-Afrika en Namibi e gedurende 2009, word met behulp van die GHK model ontleed. Daar word getoon dat die voorgestelde nie-parametriese metodiek 'n verbetering op die bekende parametriese metodes bied in die ontleding van hierdie datastel wat afkomstig is uit 'n nie-normaal verdeelde meerveranderlike populasie. GHK regressie word vergelyk met hoofkomponent regressie en parsi ele kleinste kwadrate regressie in die passing van modelle om neonatale mortaliteit en lengte van verblyf te voorspel vir babas in die VON datastel. Die gepasde regressiemodelle, wat maklik bekombare dag-van-toelating data gebruik, kan deur mediese personeel en hospitaaladministrateurs gebruik word om ouers te adviseer en die toewysing van mediese sorg hulpbronne te verbeter. Voorspelde waardes vanaf hierdie modelle kan ook gebruik word in normwaarde oefeninge om die prestasie van neonatale intensiewe sorg eenhede in die Suider-Afrikaanse konteks, as deel van groter gehalteverbeteringprogramme, te evalueer.
Campos, Edvaldo Vieira de. "Uso de banco de dados para caracterização de pacientes queimados internados em unidade de terapia intensiva de um hospital acadêmico terciário." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/5/5165/tde-11052017-162206/.
Full textIntroduction: Patients with large total burn surface area (TBSA) are critically ill and need support in the intensive care unit (ICU). However, the characteristics of burn patients who require ICU admission are scarce in the literature. Objective: To characterize the patients from an epidemiological point of view and clinical outcomes who were admitted to a burn ICU, defining the factors associated with mortality and morbidity. Methods: From a database collected prospectively, informations from patients older than 16 years with hospital admissions for burns and admitted to the ICU during a period of 60 months were collected. We collected epidemiological and clinical data regarding the Intensive care support in the first seven days in the ICU, the fourteenth, twentyfirst and twenty-eighth day of ICU hospitalization if the patient still remained hospitalized in the unit. Results: One-hundred sixty-three patients were included. The hospital mortality was 42%. The median hospital stay was 29 [11, 50] days, the age was 34 [25.47] years, the TBSA was 29 [18.43]% and the SAPS 3 was 41 [34, 54]. Lethal burn area at which fifty percent of patients died (LA50%) was 36.5%. The value of total SOFA score was higher in nonsurvivors at all timepoints analyzed compared with survivors. The median maximum value of the total SOFA score came at an early stage and on the second day of hospitalization for survivors and fourth day for non-survivors. The area under the ROC curve of the total SOFA score was 0.887 and the respiratory components, cardiovascular, renal, hematologic, hepatic and neurological were respectively 0.814, 0.811, 0.738, 0.738, 0.526 and 0.569. We found a significant association between hospital mortality and SAPS3 [OR(95%CI) = 1.114(1.062-1.168)], TBSA [OR(95%CI) = 1.043(1.010-1.076)], suicide attempts [OR(95%CI) = 8.126(2.284-28.907)], and cumulative fluid balance per liter within the first week [OR(95%CI) = 1.090(1.030-1.154)]. Inhalation injury was present in 45% of patients, and it was not significantly associated with hospital mortality. Conclusions: The factors independently associated with mortality during admission were increased TBSA, suicide attempt and higher SAPS 3. A higher accumulative fluid balance within the first seven days after ICU admission was also independently associated with hospital mortality. The implementation of judicious fluid management after an acute resuscitation phase may help to improve outcomes in this scenario. The organ dysfunction is present early in burn patients. Respiratory, cardiovascular, renal and hematological dysfunctions are associated with a higher mortality in this population. Thus, efforts should be targeted for early detection and intervention in organ dysfunction
Uroš, Batranović. "Prognostički značaj venoarterijskog gradijenta ugljen-dioksida u teškoj sepsi." Phd thesis, Univerzitet u Novom Sadu, Medicinski fakultet u Novom Sadu, 2017. http://www.cris.uns.ac.rs/record.jsf?recordId=104158&source=NDLTD&language=en.
Full textCentral venous-arterial CO2 difference (Pv-aCO2) reflects adequacy of microcirculatory venous flow. Widening of Pv-aCO2 due to CO2-stagnant phenomenon is described in the low flow states. Pv-aCO2 was proposed as an additional resuscitation target for patients with septic shock.The aim of this study was to examine correlation between changes in Pv-aCO2 and SOFA score as well as different blood flow indices (lactate, mixed venous oxygen saturation) 12 hours after onset of resuscitation in patients with sepsis or septic shock. Secondary aim was to evaluate association of delta CO2 6 hours after onset of resuscitation and patient outcomes (length of stay in the ICU, mortality). Prospective observational study included 150 patients with sepsis. Simultaneous measurements of lactate, mixed venous oxygen saturation (ScvO2) and delta PCO2 were performed at onset of resuscitation (T0) and after 6 hours (T6). Delta PCO2 was calculated as a difference between arterial PCO2 and PCO2 from mixed venous blood. Organ dysfunction was evaluated with the Sequential Organ Failure Assessment (SOFA) score at T0 and after 48 hours (T48). Mortality was assessed after 28 days. For data analysis purposes two groups were created based on delta SOFA [(1) patients with SOFA score decrease (delta SOFA <0); (2) patients without SOFA score decrease (delta SOFA ≥ 0)] and based on Pv-aCO2 [(1) patients with high Pv-aCO2 (≥0.8 kPa); (2) patients with normal Pv-aCO2 (<0.8 kPa). Patients with high and normal Pv-aCO2 differed only with respect to highest respiratory SOFA score (p=0.01) Change in Pv-aCO2 between T0 and T6 was not in correlation with change in SOFA score between T0 and T48 (p=0.12). Moderate statistically significant correlation was found between Pv-aCO2 and lactate at T6 (r=0.2), and moderate inverse correlation between Pv-aCO2 and ScvO2 at T0 (r=-0.4) and T12 (r=-0.25) and ScvO2 and lactate at T0 (r=-0.27) and T12 (r=-0.18). Pv-aCO2 at T6 was not associated with 28-day mortality and length of stay in the ICU.
Nicolau, José Eduardo. ""O transplante de medula óssea alogênico de curto período de internação"." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/5/5136/tde-08082005-114337/.
Full textWe analyzed the results of a retrospective study of 100 patients with chronic myelogenous leukemia submitted to allogeneic stem cell transplantation in one of two settings: Group I, with 49 patients, transplanted in the traditional inpatient and group II, with 51 patients, in partial outpatient. We compared the median number of days spent in hospital, morbidity and mortality within 100 after bone marrow transplantation. We concluded that there was a significant reduction in the median of hospital length of stay in the partial outpatient group, without increasing morbidity and mortality
Lim, Yen Peng. "Malnutrition and clinical outcomes in elderly patients from a Singapore acute hospital." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/44143/1/Yen_Peng_Lim_Thesis.pdf.
Full textNouri, Golmaei Sara. "Improving the Performance of Clinical Prediction Tasks by using Structured and Unstructured Data combined with a Patient Network." Thesis, 2021. http://dx.doi.org/10.7912/C2/41.
Full textWith the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses mainly on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task. To evaluate the generalization of DeepNote and patient network modules on new prediction tasks, we create a multimodal model and train it on structured and unstructured data of MIMIC-III dataset to predict patient mortality and Length of Stay (LOS). Our proposed multimodal model consists of four components: DeepNote, patient network, DeepTemporal, and score aggregation. While DeepNote keeps its functionality and extracts representations of clinical notes, we build a DeepTemporal module using a fully connected layer stacked on top of a one-layer Gated Recurrent Unit (GRU) to extract the deep representations of temporal signals. Independent to DeepTemporal, we extract feature vectors of temporal signals and use them to build a patient network. Finally, the DeepNote, DeepTemporal, and patient network scores are linearly aggregated to fit the multimodal model on downstream prediction tasks. Our results are very competitive to the baseline model. The multimodal model analysis reveals that unstructured text data better help to estimate predictions than temporal signals. Moreover, there is no limitation in applying a patient network on structured data. In comparison to other modules, the patient network makes a more significant contribution to prediction tasks. We believe that our efforts in this work have opened up a new study area that can be used to enhance the performance of clinical predictive models.
Lopes, Cláudia Rodrigues. "Computational Intelligence Models for Length of Stay Prediction." Master's thesis, 2020. http://hdl.handle.net/10316/92126.
Full textA previsão do tempo de internamento dos pacientes é de grande importância para os hospitais, uma vez que pode determinar a utilização de recursos, melhorar o agendamento de futuros internamentos e cirurgias, e auxiliar no planeamento dos cuidados de saúde dos pacientes, desde a admissão até à alta. Consequentemente, uma melhor qualidade dos cuidados de saúde prestados pode ser proporcionada aos pacientes, sendo este o principal objetivo dos hospitais. Neste projecto, quatro abordagens diferentes foram implementadas para desenvolver modelos de previsão de tempo de internamento: i) exploração de modelos de risco existentes (SCORE), ii) aplicação de modelos típicos de inteligência computacional (Random Forest, Support Vector Machine e Multilayer Perceptron), iii) desenvolvimento de um modelo interpretável e personalizável ao paciente com base em regras e iv) integração de dados dinâmicos (sinais vitais) nos modelos anteriores. Os dados clínicos usados neste trabalho foram fornecidos pelo CHUC (Centro Hospitalar e Universitário de Coimbra) e pela Philips Electronics Nederland B.V., compreendendo 1544 pacientes admitidos na unidade de cuidados intensivos de cardiologia do Hospital dos Covões (Coimbra) e 189 pacientes bariátricos admitidos para cirurgia no Catharina Hospital (Eindhoven), respetivamente.O conjunto inicial de variáveis dos pacientes cardíacos foi obtido através de uma revisão da literatura e do conhecimento clínico de um cardiologista da unidade de cuidados intensivos de cardiologia do CHUC. Para os pacientes bariátricos, este conjunto resultou de uma revisão da literatura para a determinação das variáveis relevantes. Posteriormente, as variáveis de entrada dos modelos de previsão de tempo de internamento foram selecionadas desse conjunto inicial usando o coeficiente de correlação tau de Kendall. Adicionalmente, as variáveis de entrada selecionadas para os pacientes cardíacos foram também validadas pelo cardiologista. O desempenho dos modelos referidos, medido através da média geométrica (GE) e do F1 score, foi determinado aplicando este conjunto final de variáveis de entrada a cada um deles.Finalmente, através da aplicação do teste Friedman e do correspondente teste post-hoc Nemenyi, foi possível ordenar os modelos em função do seu desempenho.A performance do modelo baseado no SCORE foi significativamente baixa, obtendo uma GE de 0.50. Assim, apesar deste modelo de risco ser de grande importância na prática cardiológica europeia, não é adequado para estimar o tempo de internamento hospitalar. A segunda abordagem (modelo Black-box) superou o modelo anterior. Os melhores resultados foram obtidos pelo Multilayer perceptron com uma GE de 0.62 ± 0.03 para os pacientes cardíacos e 0.64 ± 0.08 para os bariátricos, respetivamente. Verificou-se ainda que o desempenho do modelo interpretável e personalizável foi superior ao modelo Black-box, para os dois tipos de pacientes, com uma GE de 0.66 ± 0.02 para os pacientes cardíacos e 0.83 ± 0.05 para os pacientes bariátricos. Adicionalmente, a inclusão de sinais vitais aos modelos de previsão mostrou-se vantajosa por levar a um aumento da performance em todos os classificadores. Estes resultados sugerem que a incorporação de dados dinâmicos em modelos de previsão de tempo de internamento deve ser explorada de forma aprofundada em estudos posteriores.A análise dos resultados permitiu-nos concluir que, apesar de aceitável, a performance dos modelos desenvolvidos não parece ser adequada para o seu uso na prática clínica (GE máxima de 0.66 e 0.83 para os pacientes cardíacos e bariátricos, respetivamente). Este facto pode-se justificar pela dificuldade e complexidade que o problema apresenta. O estudo de outras variáveis, não só determinadas aquando a admissão, mas durante as primeiras horas ou no primeiro dia de internamento do doente, poderia ser uma estratégia a explorar no futuro.
Predicting the patients' length of stay (LOS) is of major importance for hospitals, since it can determine the resource utilization, improve the scheduling of admissions and surgeries and helping in the development of effective clinical pathways. Consequently, a better quality of care can be provided to the patients, which is the main goal of the hospitals.In this project, four different approaches were implemented to develop LOS prediction models: i) exploration of available risk tools (SCORE), ii) application of typical computational intelligence models (Random Forest, Support Vector Machine and Multilayer Perceptron), iii) development of an interpretable and patient customized model based on rules and iv) integration of dynamic data (vital signs) in the previous models. The clinical data used in this work was provided by the CHUC (Coimbra Hospital and University Center) and by Philips Electronics Nederland B.V., comprising 1544 patients admitted in the cardiac intensive care unit of Hospital dos Covões (Coimbra) and 189 bariatric patients admitted to surgery in Catharina Hospital (Eindhoven), respectively.The initial set of features of the cardiac patients was obtained through a literature review and the clinical knowledge of an ICU cardiologist of CHUC. For the bariatric patients, this set resulted from a literature review for the determination of the relevant features. Then, the input features of the LOS prediction models were selected from this initial set using the Kendall's tau coefficient correlation. Moreover, the selected input features for the cardiac patients were also validated by the cardiologist. The performance of the referred models, measured in terms of the geometric mean (GE) and F1 score, was determined by employing this final set of input variables to each one of them. Finally, through the application of the Friedman test and the corresponding post-hoc Nemenyi test, it was possible to order the models according to their performance.The SCORE model performance was significantly low, achieving a geometric mean (GE) of 0.50. Thus, although this risk tool is of high importance in the European cardiology practice, it is not sufficiently accurate to estimate the actual LOS. The second approach (Black-box model) outperformed the previous model. The best results were achieved by the multilayer perceptron with a GE of 0.62 ± 0.03 for the cardiac patients and 0.64 ± 0.08 for the bariatric ones. Furthermore, we verified that the performance of the interpretable and customized model was higher than the Black-box model, for both types of patients, obtaining a GE of 0.66 ± 0.02 for the cardiac patients and 0.83 ± 0.05 for the bariatric patients. Moreover, the addition of the vital signs to the prediction models was proved to be advantageous since it leaded to an increase of performance in all the classifiers. These results suggest that the incorporation of dynamic data in LOS prediction models is worthy of further exploratory studies.The analysis of the results allowed us to conclude that, although acceptable, the performance of the developed models does not seem to be adequate for their use in clinical practice (maximum GE of 0.66 and 0.83 for the cardiac and bariatric patients, respectively). This fact may be justified by the difficulty and complexity that the problem presents. The study of other variables, not only determined at admission time, but during the first hours or on the first day of the patient's stay, could be a strategy to explore in the future.
Barbosa, Teresa do Carmo Sousa Magalhães. "Estudo de fatores preditivos das diferenças nos resultados de duração de internamento e de mortalidade em doentes com enfarte agudo do miocárdio." Doctoral thesis, 2016. http://hdl.handle.net/10362/20058.
Full textABSTRACT - Background: Appropriate hospital resource use is an important factor to control the expenditure and improve the results. The aim of this study is to understand the factors that explain the length of hospital stay (LOS) and the mortality results in acute myocardial infarction (AMI) by observing the associated waste through unjustified variability and identify the predictive factors for those measures. Methods: To pursue the objectives, the following analysis were performed: 1) the study of AMI in LOS and mortality at hospital level with the inpatient discharged from NHS hospitals between 2011/2013, based on administrative data (AD) and 2) the study of the predictive factors on LOS and mortality in AMI patients at the time of inpatient admission, discharged from one hospital between 2010/2011 based on AD and laboratory data (LD). Results: In the first study, there were differences identified in LOS and mortality, with a total waste of 2392 days of stay and 137 avoidable deaths. In the second study, predictive factors with both DA and DL variables were identified, showing excellent validation results. Conclusions: There is variability in AMI treatment practices and this fact has particular incidence in mortality. We defend that is necessary to do more in the inpatient management at AMI treatment in several domains to reduce associated hospital waste and the models generated from the predictive factors study contribute to that improvement. Emerging topics for future investigation are the evaluation of waste in other diseases, implementation of a disease support system and predictive factors generalization studies.
Hsu, Yi-Chen, and 徐宜蓁. "Effects of Length of Stay in Emergency Department on Inpatient Utilization and Mortality." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/u8gq74.
Full textShiang-ying, Huang, and 黃香螢. "Prediction of patient length of stay: the comparison of three methods." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/90361277684392637210.
Full text國立臺灣大學
公共衛生學研究所
84
To understand the difference among three methods (MD method, DRG method and ICD-9-CM method) in predicting patients'' length of sta y, 707 cases were chosen purposely. These patients'' admission ICD -9-CM codes were classfied as MDC6 or MDC7 and they also admitted in five units of a major medical center during Apr. 1- Jul. 31, 1995. After dealing with inappropriate data, 564 cases were analy zed with Wilcoxon rank sum test. The author found that DRG method is the most precise one among three methods. The MD method has lower precision in surgical groups and the ICD-9-CM method has lower precision in tumorous groups. Because of the uncer- tainty about the causes in medical cases, it''s usually harder to predict patient length of stay in medical than surgical groups. The reason why MD method has lower precision in surgical groups in this study needs further study. The research also found that in all admission diagnoses, 72.16% are equal to discharge diagnoses, 22.34% are related to discharge diagnoses, and 5.5% are unrelated to discharge diagnoses. This result manifests the DRG method may be used practically. Due to numerous uncertain factors, prediction of patients'' length of stay needs to be more studied. If the DRGs/PPS payment system is implemented, the design of prediction method should be further discussed.
Shen, I.-Ching, and 沈怡菁. "Prediction of Ischemic Stroke Length of Stay Using Data Mining Technique." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/74158583648800210645.
Full text國立陽明大學
衛生資訊與決策研究所
95
The stroke is a cardiovascular disease. The annual incidence of stroke is approximately 700,000 per year in the United States, and Ischemic stroke accounts for 80% of them. In Taiwan 51,000 people suffer from stroke each year, and 15,000 of them resulted in death. From 1993 to 2005 stroke is the second most common cause of mortality, and 71% was due to ischemic stroke in Taiwan. In America, the annual cost on health care of stroke is near 40 billion USD, while in Taiwan the monthly cost is 15-40 thousand NTD. That was to make the nation, the patients and their family’s financial and psychological burden. So, the win-win situation could not be accomplished to the patient and nation. Therefore, if information techniques can be used to predict the hospitalization length of stay of ischemic stroke patients. And to discover the effect variables of ischemic stroke length of stay. It can provide for the medical personnel to make the medical plan. We hold that can expect the increased usage of acute wards and decrease the cost of medical expenses. In our study used the 441 patient data records and 14 variables. The variables are included age, sex, Diabetes Mellitus, Hypertension, heart disease, smoking, alcohol, hyperlipemia, old CVA, LOS, admission and discharge BI score and admission of NIHS score. This study applied C4.5 decision tree, ANN and logical regression with past data to establish the prediction model and to predict the patients’ length of stay. In the analysis from the C4.5 decision tree one can see that the key variables in the LOS of ischemic stroke patients are BI score, age, hyperlipidemia, sex, alcohol, HTN, heart disease, NIHSS at admission hospital, in which the BI during hospitalization is the most important. We found that the C4.5 decision tree model out performs ANN and logistic regression in terms of accuracy. So, it points out if applying the C4.5 decision tree and ANN to predict the ischemic stroke length of stay, it has high of predictive accuracy. In future studies the model should be applied in clinical practice.
Yuan, Chi-Chuan, and 袁繼銓. "Neural Network Approach for Length of Hospital Stay Prediction of Burn Patients." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/21402548517491529233.
Full text國立中山大學
資訊管理學系研究所
91
A burn injury is a disastrous trauma and can have very wide ranging impacts, including individual, family, and social. Burns patients generally have a long period of hospital stay whose accurate prediction can not only facilitate allocations of scarce medical resources but also help clinicians to counsel patients and relatives at an early stage of care. Besides prediction accuracy, prediction timing of length of hospital stay (LOS) for burn patients is also critical. Early prediction has profound effects on more efficient and effective medical resource allocations and better patient care and management. Hence, the objective of this study is to apply a backpropagation neural network (BPNN) for predicting length of hospital stay (LOS) for burn patients at early stages of care. Specifically, we defined two early-prediction timing, including admission and initial treatment stages. Prediction timing at the admission stage is to predict a burn patient’s LOS when the patient is admitted into the Burns Unit. Prediction at the initial treatment stage refers to the timing right after the first surgery for burn wound excision and skin graft is performed (typically within 72 hours of injury if the patient’s condition allows). Experimentally, we evaluated the prediction accuracy of these two stages, using that achieved at the post-treatment stage (referring to the timing when all surgeries for burn wound excision and skin graft are performed) as benchmarks. The evaluation results showed that prediction LOS at the admission and the initial treatment stages could attain an accuracy of 50.37% and 57.22%, respectively. Compared to the accuracy of 62.13% achieved by the post-treatment stage, the performance reached by the initial treatment stage would consider satisfactory.
Hu, Chu-hsuan, and 胡竹瑄. "Locations of fracture, length of stay, mortality and related factors among hospitalized elderly fracture patients." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/31710136010799925638.
Full text國防醫學院
公共衛生學研究所
87
Abstract The proportion of elderly people in our population is increasing. In addition to the threat of the chronic disease, injury is another major cause of death for elderly people. Elderly people are at high risk of fracture during the occurrence of injuries. Research article indicated that there are differences between the urban and the rural areas on the incidence of fracture. Since Taipei City and Hualien County are good representatives of urban and rural areas in Taiwan, the study was designed to compare between Taipei and Hualien on fracture locations, fracture types, length of stay and mortality among hospitalized fracture patients at age 65 or above. Data come from National Health Insurance Data base, covering 22 months from January 1996, to October 1997. The main findings are- 1. The percentages of fractures with external causes among were 47.1 in Taipei and 76 in Hualien. Among all external causes, the percentage of falls was higher in Taipei, whereas the percentage of motor vehicle accidents was higher in Hualien. 2. Hip fractures were the most frequently seen cases both in Taipei and Hualien. However, among all kinds of fractures the percentage of hip fractures was higher in Taipei, whereas the percentage of skull fracture was higher in Hualien. 3. Less than 8% of elderly fractures were compound fractures in both areas. Patients with compound fracture were mostly male, younger and injured by motor vehicle accidents. 4. Both in Taipei and Hualien mortality cases were counted for less than 2% among elderly fracture inpatients. Factors related to mortality of fracture inpatients identified from Taipei data were male, older age, skull fracture, hip fracture and compound fractures. There was no significant risk factor of fracture death identified from Hualien data. Keywords: elderly people、fracture、hospitalization
Gothen, Margarida de Castro. "Does the hospital length of stay until admission to Intensive Care impact mortality? A observational study." Master's thesis, 2021. https://hdl.handle.net/10216/134262.
Full textGothen, Margarida de Castro. "Does the hospital length of stay until admission to Intensive Care impact mortality? A observational study." Dissertação, 2021. https://hdl.handle.net/10216/134262.
Full textMsibi, Sithembiso Sifiso, and 史飛碩. "Cardiovascular diseases (CVDs) patients’ in-hospital mortality rate and length of hospital stay in Swaziland: 2001-2013." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/59388964739003430236.
Full text臺北醫學大學
醫務管理學研究所
103
Background: Cardiovascular diseases (CVDs) are one of the leading non-communicable diseases (NCDs). A global analysis reveals that some of the poorest countries in the world have among the highest age-standardized mortality rates of CVDs. They are the leading cause of death globally. An estimated 17.5 million people died from CVDs in 2012, representing 31% of all global deaths [1]. Objectives: 1. Calculate and analyze the trend of in-hospital mortality rate of CVDs patients in public health facilities over a period of 13 years 2. Analyze the trend of length of hospital stay of patients with CVDs. 3. Analyze the association between the CVDs mortality and the independent variables (gender, age, and region). 4. Analyze the association between the length of hospital stay and independent variables Methods: Government data from 21 health facilities that offer in-patient services all over Swaziland was analyzed. Data for Cardiovascular Disease (CVD) in-patients, from 2001 to 2013, was used for the purpose of this study. 17809 patients were admitted for CVDs in the above-mentioned timeline and of those, there were 2955 in-hospital deaths. The study focuses on the CVD deaths. The association between outcome variables and the patients’ independent variables. Trend analysis on in-hospital CVD mortality and length of stay (LOS) over the 13 year period. Results: There was a significant rise on the CVD deaths over the years (p= 0.007). More patients are being admitted and the deaths also increase. For the LOS, there was no significant increase or decrease over the years. Region (urbanization) was found to be a significant variable for all outcome variables, i.e. CVD mortality, LOS and Discharge status with p= 0.01, 0.011 and 0 respectively. CVD mortality was also dependent on gender. There were significantly more female cases than males. Conclusion: Different studies, including this one, have shown that CVDs together with other non-communicable diseases are on the rise in lower middle income countries (LMIC) like Swaziland. The country has been plagued by the double burden of infectious diseases and NCDs. A lot of work has been done in combating most infectious diseases, more attention through government intervention policies, should be afforded to these NCDs.
YEN-YU, CHEN, and 陳彥佑. "Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/bmkwv7.
Full text國立臺北科技大學
工業工程與管理系碩士班
103
The major concern for most hospitalists in Taiwan is to serve the aging population with the limited healthcare budget provided by the National Health Insurance Administration. As hospital beds are scarce, high occupancy rates are often preferred. To develop efficient admission policy and optimize bed management, it would be beneficial to investigate the critical factors which might determine the length of stay (LOS) in the early stage of admission. This research is to use artificial neural network (ANN) models to predict the LOS for patients in cardiology department during the pre-admission stage. After comparing with the regression model, the ANN models are able to predict patients with longer LOS.
Lin, Chien-Lin, and 林千琳. "The related factors and prediction models of length of stay in rehabilitation ward for stroke patients." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/16185160620543423304.
Full text臺中健康暨管理學院
健康管理研究所
93
Abstract A good prediction model of length of stay for stroke patients in rehabilitation ward can be used as the basis of reimbursement for stroke rehabilitation in global budget and case payment system . 645 patient’s data from a rehabilitation ward in a Medical Center of mid-Taiwan were used retrospectively to create a prediction model , based on Cox’s proportional hazard model . The model requires information about patient’s demographic variables and functional independence ability which was evaluated by Functional Independence Measure and Modified Barthel Index .We also discuss the risk factors affecting the length of stay. We conclude that Cox’s proportional hazard model is easy to implement and interpret ,using various statistical package , and is efficient in the terms of shorter confidence intervals. The model can also be used to facilitate management of rehabilitation units ,set goal for length of stay and discharge planning . Key Word: stroke, rehabilitation therapy , length of stay , length of stay efficiency, Functional Independence Measure(FIM) , Modified Barthel Index(MBI), Cox’s proportional hazard model
Lee, Hsuei-Chen, and 李雪楨. "Prediction of Inpatient Length of Stay and Costs for Acute Stroke in Taiwan: Clinical and Policy Implications for Stroke Care." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/90468932205512219984.
Full text國立陽明大學
公共衛生研究所
95
Background and Objectives: As a major cause of mortality, prolonged hospitalization, and chronic disability, stroke imposed considerable physical and socioeconomic burden. The direct cost of stroke was largely dependent on the length of initial hospital stay. It is important to understand the determinants of inpatient length of stay (LOS) and costs for acute stroke, and how they may be modified by specific treatments, care arrangement or payment schemes. This study aimed to analyze inpatient length of stay, costs and their predictive factors systematically for acute hospitalized stroke patients in Taiwan. The magnitude and associated factors for prolonged hospital stay (LOS ³ 23 days) during acute care phase of stroke were also examined. Methods: Patients admitted consecutively between 1 January 1997 and 31 December 2002 with the principal diagnoses of acute cerebrovascular accidents were identified from the claims data of a nationally representative cohort of 200,000 National Health Insurance (NHI) enrollees. Dependent variables were inpatient length of stay and medical costs for acute stroke. Independent variables included patient demographics, clinical characteristics and hospital characteristics. Univariate and multivariate analyses were performed to analyze the main predictors of LOS and costs during an acute stroke admission. Results: In total, 2639 stroke subjects were eligible for analysis. The mean acute LOS was 15.3 days for SAH, 17.2 days for ICH, 11.8 days for CI, and 6.1 days for other unspecified CVD. The mean total LOS (combined acute and rehabilitation stays) was 44.0 days for SAH, 49.1 days for ICH, 37.4 days for CI, and 27.0 days for other unspecified CVD. Multi-variate analysis revealed 41.3% of the total variance in acute LOS and 54.5% of the total variance in acute costs for stroke was explained by each stepwise regression model. After adjusting for other factors, coding of physical/ADL dependency, surgical operation, in-hospital death (negative factor), occurrence of infection or aspiration pneumonia, and admission to a hospital located in Taipei region were the strongest predictors for the acute LOS. Furthermore, surgical fee, ICH, coding of physical/ADL dependency, admission to a medical center, and occurrence of infection or aspiration pneumonia were the strongest predictors for the acute care costs. Other factors, such as: age, gender, use of mechanical ventilation, coding of speech/swallowing disorders, comorbidity, and hospital ownership had significant but less important impact on acute hospital use. Of the sub-sample of 2358 subjects who admitted to acute wards without further transfer to other wards or hospitals, only 245 subjects (10.4%) had prolonged hospital stay (LOS³ 23 days), but they accounted for 38.9% of the total person-hospital days and 47.8% of the total inpatient medical expenses of all the stroke subjects. Conclusions and Recommendations: An early, well-organized multidisciplinary team care is postulated for its potential to minimize functional dependency, prevent complications; and hence reduce the LOS and subsequent social-economic burden of stroke. It is suggested that the degree of neurological impairment or functional disability be incorporated into the Taiwan-DRG based prospective payment scheme for acute stroke care to ensure fairer reimbursement. NHI payment reform as well as establishment of a more integrated and affordable post-acute or long-term care system should be policy priorities to effectively reduce unnecessary acute hospital use and to ensure a seamless stroke care. Good discharge planning is crucial for efficient hospital use and successful reintegration into the community.
Lin, Yi-Ching, and 林怡卿. "Prediction on Hospital Length of Stay in Case Payment by Using Data Mining Technique- A Case Study of Total Joint Replacement." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/87458643472316126910.
Full text國立陽明大學
衛生資訊與決策研究所
94
Since 1995, The National Health Insurance Program (NHI) that is one of the most significant public policies in recent decades has implemented in Taiwan; this program not only changed the medical environment, but also improved health quality and patients’ right. However, this program is also facing the cost-increasing problem as all other countries. One of the strategies to control cost is to change the payment method. Taiwan government has tried to carry out the "Case Payment System" which is a prospective payment system resemblance to the Diagnosis-Related Groups to replace the current Fee For Service (FFS) payment method for inpatient services. Among the great deal of health insurance expenses, the ratio of hospitalization is 32%, and above 50% of hospitalization fee is related to days to hospital stay. In response to rapidly rising costs, governments and providers have become aggressive in searching for mechanisms to control their expenditures. And one of the most important reasons why budget of hospitalization is pruned off is often due to the long days to hospital stay. Many hospitals devoted themselves to manage patients’ stay length decreasing the costs and promoting health providers’ service to higher qualities. This study carried out to the analysis of the payment data (patients with Total Knee Replacement and Total Hip Replacement) using individual cases of the hospital database and the data elements were explored as a mechanism useful in the prediction of patient length of stay (LOS). This study applied C4.5-based data mining with the past data and neural network technologies to establish the prediction model and to detect if the patients’ length of stay exceeded the average length of stay. This can help health providers to better understand patients’ recovery and have an early prediction profound effect on more efficient and effective medical resource deliveries. This study adopted attribute selective procedures to reduce unnecessary properties. Making the model construction more efficient and to understand the simplified classification rules. The reduction of attribute numbers will also increase the efficiency of the neural network. This study recommends that using a proper attribute selective procedure to figure out some important attributes. And then applying this two data mining technologies to setup the predictive model will be a more efficient way to set up a model and to bring about the classification rules. Futures studies should assess the feasibility of implementing model in clinical practice.
CHEN, PO-CHIA, and 陳柏嘉. "A Study for the Healthcare Management System and Length of Stay Prediction – from Case Hospital to the National-level Database in Taiwan." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fpayz9.
Full text國立臺北科技大學
管理學院管理博士班
107
Most developed countries face the continuous pressure to keep offering the best and affordable health care services to every citizen, especially for counties with universal health care coverage. One of the most challenging issue in the definition of service quality and the cost structure in the healthcare industry lies in the lack of transparency or even common ground when comparing among clinics and hospitals. For acute-care inpatient facilities, the length of stay (LOS) is considered as a general measure associated with the complexity of treatment needed for individual patients. In this research, a questionnaire-based approach is first proposed to evaluate the use of HIS in terms of usability factors. The perception of the satisfaction and expectation in these factors is then investigated using the generalized version of importance-performance analysis, or referred to as dynamic importance-performance analysis. The artificial neural network (ANN) models is constructed to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei. The National Health Insurance Research Database (NHIRD) of Taiwan is then applied to demonstrate the feasibility and the predictivity of the national-level LOS prediction model.
Benson, Cedric. "Effect of time to the operating room on hospital length of stay, postoperative complications, & in-hospital mortality in patients who require emergency general surgery." Thesis, 2016. https://hdl.handle.net/2144/16779.
Full textFunenga, Inês Sofia Azinhal. "Efeito fim-de-semana e noite : impacto do momento de admissão sobre a mortalidade e a demora média no internamento." Master's thesis, 2014. http://hdl.handle.net/10362/14644.
Full textABSTRACT - Context: The existence of variations is common in the number and level of experience of health professionals available in hospitals during the night and weekend. The consequences of this situation on the quality of the healthcare provided to the patients, reveals the importance of this study, conducted with the aim of assessing the impact of the moment of admission on mortality and length of stay. Methodology: This study included 201 369 urgent admissions, with a primary diagnosis of the 36 selected, according to the information in the database of abstracts of the year 2012. The moment of admission was defined by period (weekdays/weekends) and time (day/night), and the weekend and night effects were accessed by logistic regression models. Results: There was an increase of 3% in the risk of death in patients admitted on weekends. There were no differences between patients admitted at day or night. Regarding the length of stay, there was a 3% increase in the likelihood of patients admitted during the weekend to have an average delay of superior relocation. As well as patients admitted overnight showed an increase of 2,9% on the probability of having a longer average internment delay. Conclusion: The data presented here allow a better knowledge about the influence of variation in hospital activity throughout the day and the week in Portuguese hospitals. It identifies the need to extend this matter research and to implement measures to eliminate the weekend and night effects.
YU, JU YI, and 游茹貽. "A Study of Using Grey Prediction GM(1, 1) in Forecasting Tourists, Length of Stay, Tourism Receipts and Maximum Capacity: A Case of New Southbound Policy′s Countries." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8e257t.
Full text大葉大學
休閒事業管理學系碩士班
107
The GM(1, 1) of grey system theory has been adopted in a variety of ranges, and it features forecasting through small amounts of data. This research predicts the New Southbound Policy's countries of tourists, length of stay, and tourism receipts by using GM(1, 1) and other four kinds modified GM(1, 1), which are modified 1-years-period GM(1, 1) model, modified 2-years-period GM(1, 1) model, modified 3-years-period GM(1, 1) and modified 4-years-period GM(1, 1) model. Meanwhile, forecasting the maximum capacity of three major regions tourists and tourism receipts by using Verhulst method. Among these five methods, the result of modified 1-years-period GM(1, 1) by starting in odd years is the best, and the mean error is 3.68%. The Verhulst method indicates that the maximum capacity of tourists and tourism receipts have reached the peak. The major finding of this study are summarized as follows: First, GM(1, 1) prediction is precise in forecasting tourists, length of stay, and tourism receipts. Second, forecasting through the modified 1-years-period GM(1, 1) by starting in odd years. All countries of tourists most have a growing trend. Above of then, New Zealand and Australia have the highest growth rate. Third, regarding the length of stay. Tour group of staying 8-15 days has the highest increase in the future. Fourth, the Verhulst method indicates that the maximum capacity of tourists has reached saturation from 2019 to 2020. Finally, the Verhulst model displays the maximum capacity of tourism receipts will be a supersaturated state and negative growth in 2019. Therefore, there is room for improvement in the government's New Southbound Policy. Hoping the results of this study will provide the tourism industry and relevant government department as references for planning the tourism policy and related strategy. Key Words : grey prediction, the New Southbound Policy, length of stay, tourism receipts, maximum capacity
Mohammed, Mohammed A., J. J. Deeks, A. J. Girling, G. M. Rudge, M. Carmalt, A. J. Stevens, and R. J. Lilford. "Evidence of methodological bias in hospital standardised mortality ratios: retrospective database study of English hospitals." 2009. http://hdl.handle.net/10454/6101.
Full textLavinha, Pedro Henrique Pires. "A importância do pré-hospitalar em Portugal : via verde do acidente vascular cerebral." Master's thesis, 2019. http://hdl.handle.net/10362/82176.
Full textABSTRACT - Introduction: Stroke is the leading cause of long-term morbidity and disability around the world, with a strong impact on health systems as well as family and professional lives. For this reason, the role of pre-hospital emergency appears to be paramount in managing a comprehensive response to an acute stroke. The objective of this work is to characterize pre-hospital management in the context of the retrospective INEM registration of these episodes (2015-2016) in Portugal. Methodology: A transversal and retrospective study was carried out via a descriptive and analytical approach to describe the episodes of the pre-hospital stroke pathway and hospitalization for cerebrovascular disease, analysis and comparison of its geographic distribution and analysis of their correlations. In order to perform the study, the data base of the pre-hospital stroke pathway, the hospital morbidity database and the Disease Staging classification system were used. Results: The stroke pathway has been in place since 2006, but the percentage of use is very low (12.97%). The average age of patients was 72 years. The period between the identification of stroke symptoms and contact with emergency services was, on average, 59,57 minutes. Considering the therapeutic window of 3 hours - 4,5 hours as good practice, the time from the onset of symptoms and the arrival at the hospital was, on average, 119,51 minutes. More episodes were observed with the presence of two or more symptoms (61.2%) - that is; indicating a higher probability of experiencing a stroke. The municipalities with distances above the average of 28,79 Km are those that least use emergency services. Considering the variability in the data, the users of the stroke pathway verified a lower-length stay in 57,04% of the episodes and a mortality rate higher than the average in 53,33%. However, the association between the variable percentage of utilization of the stroke pathway and the variables of access and results are weak - even when analyzed with socio-economic variables - with the exception of the variable of the number of symptoms, in which the association was moderate. Conclusion: Portugal has implemented and organized a system of response to stroke, but its use is low and does not translate into response and the desired results. Permanent awareness, education and evaluation measures are needed to make the comprehensive management strategy sustainable. These measures are corroborated by other international studies.
Silvester, K. M., Mohammed A. Mohammed, P. Harriman, A. Girolami, and T. W. Downes. "Timely care for frail older people referred to hospital improves efficiency and reduces mortality without the need for extra resources." 2014. http://hdl.handle.net/10454/9937.
Full textHospitals are under pressure to reduce waiting times and costs. One strategy that may be effective focuses on optimising the flow of emergency patients. We undertook a patient flow analysis of older emergency patients to identify and address delays in ensuring timely care, without additional resources. Prospective systems redesign study over 2 years. The Geriatric Medicine Directorate in an acute hospital (Sheffield Teaching Hospitals NHS Foundation Trust) with 1920 beds. Older patients admitted as emergencies. Diagnostic patient flow analysis followed by a series of Plan Do Study Act cycles to test and implement changes by a multidisciplinary team using time series run charts. 60% of patients aged 75+ years arrived in the Emergency Department during office hours, but two-thirds of the admissions to GM wards were outside office hours highlighting a major delay. Three changes were undertaken to address this, Discharge to Assess, Seven Day Working and the establishment of a Frailty Unit. Average bed occupancy fell by 20.4 beds (95% confidence interval (CI) -39.6 to -1.2, P = 0.037) for similar demand. The risk of hospital mortality also fell by 2.25% (before 11.4% (95% CI 10.4-12.4%), after 9.15% (95% CI 7.6-10.7%) which equates to a number needed to treat of 45 and a 19.7% reduction in relative risk of mortality. The risk of re-admission remained unchanged. Redesigning the system of care for older emergency patients led to reductions in bed occupancy and mortality without affecting re-admission rates or requiring additional resources.