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Academic literature on the topic 'Advanced prognostic model'

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Published: 10 March 2023

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Journal articles on the topic "Advanced prognostic model"

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Uneno, Yu, Tadayuki Kou, Masashi Kanai, Michio Yamamoto, Peng Xue, Yukiko Mori, Yasushi Kudo, et al. "Prognostic model for survival in patients with advanced pancreatic cancer receiving palliative chemotherapy." Journal of Clinical Oncology 33, no. 3_suppl (January 20, 2015): 248. http://dx.doi.org/10.1200/jco.2015.33.3_suppl.248.

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248 Background: The prognosis of patients with advanced pancreatic cancer (APC) is extremely poor. Several clinical and laboratory factors have been known to be associated with prognosis of APC patients. However, there are few clinically available prognostic models predicting survival in APC patients receiving palliative chemotherapy. Methods: To construct a prognostic model to predict survival in APC patients receiving palliative chemotherapy, we analyzed the clinical data from 306 consecutive patients with pathologically confirmed APC who received palliative chemotherapy. We selected six independent prognostic factors which remained independent prognostic factors after multivariate analysis. Thereafter, we rounded the regression coefficient (β) for each independent prognostic factor derived from the Cox regression equation (HR = eβ) and developed a prognostic index (PI). Results: Developed prognostic index (PI) was as follows: PI = 2 (if performance status score 2–3) + 1 (if metastatic disease) + 1 (if initially unresectable disease) + 1 (if carcinoembryonic antigen level ≥5.0 ng/ml) + 1 (if carbohydrate antigen 19-9 level ≥1000 U/ml) + 2 (if neutrophil–lymphocyte ratio ≥5). The patients were classified into three prognostic groups: favorable (PI 0–1, n = 73), intermediate (PI 2–3, n = 145), and poor prognosis (PI 4–8, n = 88). The median overall survival for each prognostic group was 16.5, 12.3 and 6.2 months, respectively, and the 1-year survival rates were 67.3%, 51.3%, and 19.1%, respectively (P < 0.01). The c index of the model was 0.658. This model was well calibrated to predict 1-year survival, in which overestimation (2.4% and 0.2% in the favorable and poor prognosis groups, respectively) and underestimation (3.6% in the intermediate prognosis group) were observed. Conclusions: This prognostic model based on readily available clinical factors would help clinicians in estimating the overall survival in APC patients receiving palliative chemotherapy.
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Hum, Allyn, Yoko Kin Yoke Wong, Choon Meng Yee, Chung Seng Lee, Huei Yaw Wu, and Mervyn Yong Hwang Koh. "PROgnostic Model for Advanced Cancer (PRO-MAC)." BMJ Supportive & Palliative Care 10, no. 4 (April 4, 2019): e34-e34. http://dx.doi.org/10.1136/bmjspcare-2018-001702.

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ObjectiveTo develop and validate a simple prognostic tool for early prediction of survival of patients with advanced cancer in a tertiary care setting.DesignProspective cohort study with 2 years’ follow-up.SettingSingle tertiary teaching hospital in Singapore.ParticipantsThe study includes consecutive patients diagnosed with advanced cancer who were referred to a palliative care unit between 2013 and 2015 (N=840). Data were randomly split into training (n=560) and validation (n=280) sets.Results743 (88.5%) patients died with a mean follow-up of 97.0 days (SD 174.0). Cox regression modelling was used to build a prognostic model, cross-validating with six randomly split dataset pairs. Predictor variables for the model included functional status (Palliative Performance Scale, PPS V.2), symptoms (Edmonton Symptom Assessment System, ESASr), clinical assessment (eg, the number of organ systems with metastasis, serum albumin and total white cell count level) and patient demographics. The area under the receiver operating characteristic curve using the final averaged prognostic model was between 0.69 and 0.75. Our model classified patients into three prognostic groups, with a median survival of 79.0 days (IQR 175.0) for the low-risk group (0–1.5 points), 42.0 days (IQR 75.0) for the medium-risk group (2.0–5.5 points), and 15.0 days (IQR 28.0) for the high-risk group (6.0–10.5 points).ConclusionsPROgnostic Model for Advanced Cancer (PRO-MAC) takes into account patient and disease-related factors and identify high-risk patients with 90-day mortality. PPS V.2 and ESASr are important predictors. PRO-MAC will help physicians identify patients earlier for supportive care, facilitating multidisciplinary, shared decision-making.
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Liu, Lin, Karen Messer, John A. Baron, David A. Lieberman, Elizabeth T. Jacobs, Amanda J. Cross, Gwen Murphy, Maria Elena Martinez, and Samir Gupta. "A prognostic model for advanced colorectal neoplasia recurrence." Cancer Causes & Control 27, no. 10 (August 12, 2016): 1175–85. http://dx.doi.org/10.1007/s10552-016-0795-5.

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Kim, Jung Hoon, Sung Yong Oh, Jung Hun Kang, Myoung-Hee Kang, Chi-Young Jeong, and Jun Ho Ji. "The prognostic significance of the advanced lung cancer inflammation index(ALI) in patients with advanced biliary tract cancer: A retrospective study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16613-e16613. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16613.

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e16613 Background: Intrahepatic cholangiocarcinoma is the second most common primary cancer, but the prognosis is poor and aggressive therapies provide only limited benefit when it is in advanced stage. Many studies were conducted to find significant prognostic factors to predict clinical outcomes, but there is no definitive parameter or scoring model yet in this disease. This study is a retrospective analysis to test various kinds of prognostic scoring systems including the advanced lung cancer inflammation (ALI) index, a new prognostic model, for patients treated for advanced intrahepatic cholangiocarcinoma. Methods: We retrospectively searched medical records of patients who were diagnosed with advanced intrahepatic cholangiocarcinoma between January 2012 and December 2017 and actively treated at a single tertiary regional cancer center. Patients who received only supportive care, or had active infection concomitantly were excluded. 60 patients were identified, and we reviewed and analyzed their baseline characteristics and clinical outcomes. Various types of inflammation or nutritional scoring systems, neutrophil to lymphocyte ratio (NLR), Onodera’s prognostic nutritional index (OPNI), the advanced lung cancer inflammation (ALI) index were concurrently calculated and significance for patients’ survival was evaluated. Results: In 50 patients with metastatic stage and 10 patients with locally advanced disease, the median overall survival was 13.97 months (95% confidence interval (CI) 7.99 – 19.95). ALI of 28.5 was determined as the optimal cut-off value for prediction of 1-year survival by receiver operating characteristics (ROC) curve with an AUC value of 0.671 (sensitivity and specificity were 77.8% and 60.6%, respectively). 26 patients with ALI > 28.5 had significantly better overall survival compared with 34 patients with ALI≤28.5 (19.2 months vs. 8.73 months, p= .007). NLR above 2.8 only showed mildly significant difference for overall survival (OS), and the OPNI failed to predict prognosis of these patients. In multivariate analysis, there was no independent prognostic factor observed to have association with overall survival. Conclusions: The advanced lung cancer inflammation (ALI) index showed a potential to be a new prognostic model for advanced intrahepatic cholangiocarcinoma. Further cohort studies to expand population size may be worth.
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Pellegrini, Fabio, Massimiliano Copetti, Maria Pia Sormani, Francesca Bovis, Carl de Moor, Thomas PA Debray, and Bernd C. Kieseier. "Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling." Multiple Sclerosis Journal 26, no. 14 (November 5, 2019): 1828–36. http://dx.doi.org/10.1177/1352458519887343.

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Background: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS). Objective: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression. Methods: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance. Results: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. Overall model discrimination performance was relatively poor (all C-indices ⩽ 0.65) across all models and across different definitions of progression. Conclusion: Inconsistency of prognostic factor importance ranking confirmed the relatively poor prediction ability of baseline factors in modeling disease progression in MS. Our findings underline the importance to explore alternative predictors as well as alternative definitions of commonly used endpoints.
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Chen, Chen Hsiu, Su Ching Kuo, and Siew Tzuh Tang. "Current status of accurate prognostic awareness in advanced/terminally ill cancer patients: Systematic review and meta-regression analysis." Palliative Medicine 31, no. 5 (August 4, 2016): 406–18. http://dx.doi.org/10.1177/0269216316663976.

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Background: No systematic meta-analysis is available on the prevalence of cancer patients’ accurate prognostic awareness and differences in accurate prognostic awareness by publication year, region, assessment method, and service received. Aim: To examine the prevalence of advanced/terminal cancer patients’ accurate prognostic awareness and differences in accurate prognostic awareness by publication year, region, assessment method, and service received. Design: Systematic review and meta-analysis. Methods: MEDLINE, Embase, The Cochrane Library, CINAHL, and PsycINFO were systematically searched on accurate prognostic awareness in adult patients with advanced/terminal cancer (1990–2014). Pooled prevalences were calculated for accurate prognostic awareness by a random-effects model. Differences in weighted estimates of accurate prognostic awareness were compared by meta-regression. Results: In total, 34 articles were retrieved for systematic review and meta-analysis. At best, only about half of advanced/terminal cancer patients accurately understood their prognosis (49.1%; 95% confidence interval: 42.7%–55.5%; range: 5.4%–85.7%). Accurate prognostic awareness was independent of service received and publication year, but highest in Australia, followed by East Asia, North America, and southern Europe and the United Kingdom (67.7%, 60.7%, 52.8%, and 36.0%, respectively; p = 0.019). Accurate prognostic awareness was higher by clinician assessment than by patient report (63.2% vs 44.5%, p < 0.001). Conclusion: Less than half of advanced/terminal cancer patients accurately understood their prognosis, with significant variations by region and assessment method. Healthcare professionals should thoroughly assess advanced/terminal cancer patients’ preferences for prognostic information and engage them in prognostic discussion early in the cancer trajectory, thus facilitating their accurate prognostic awareness and the quality of end-of-life care decision-making.
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Graham, Jeffrey, Daniel Y. C. Heng, James Brugarolas, and Ulka Vaishampayan. "Personalized Management of Advanced Kidney Cancer." American Society of Clinical Oncology Educational Book, no. 38 (May 2018): 330–41. http://dx.doi.org/10.1200/edbk_201215.

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The treatment of renal cell carcinoma represents one of the great success stories in translational cancer research, with the development of novel therapies targeting key oncogenic pathways. These include drugs that target the VEGF and mTOR pathways, as well as novel immuno-oncology agents. Despite the therapeutic advancements, there is a paucity of well-validated prognostic and predictive biomarkers in advanced kidney cancer. With a number of highly effective therapies available across multiple lines, it will become increasingly important to develop a more tailored approach to treatment selection. Prognostic clinical models, such the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model, are routinely used for prognostication in clinical practice. The IMDC model has demonstrated a predictive capability in the context of these treatments including immune checkpoint inhibition. A number of promising molecular markers and gene expression signatures are being explored as prognostic and predictive biomarkers, but none are ready to be widely used for treatment selection. In this review, we will explore the current landscape of personalized care in metastatic renal cell carcinoma. This will include a focus on both prognostic and predictive factors as well as clinical applications of biology in kidney cancer.
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Redman, J. R., G. R. Petroni, P. E. Saigo, N. L. Geller, and T. B. Hakes. "Prognostic factors in advanced ovarian carcinoma." Journal of Clinical Oncology 4, no. 4 (April 1986): 515–23. http://dx.doi.org/10.1200/jco.1986.4.4.515.

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Nineteen factors were analyzed for prognostic significance in a series of 89 women with advanced (stage III or IV) ovarian carcinoma treated with chemotherapy after initial debulking surgery. Seventy-eight of these women received cyclophosphamide, Adriamycin (Adria Laboratories, Columbus, Ohio), and cisplatin (CAP) treatment, and 11 received cyclophosphamide initially with Adriamycin and cisplatin administered at the time of recurrence. Median survival and remission duration were 25 and 19 months, respectively. Using survival as an end point, significant prognostic factors in univariate analyses included the total residual mass after debulking (P = .0007), largest residual mass after debulking (P = .0008), and stage (P = .0098). Using remission duration as an end point, significant prognostic factors in univariate analyses included total residual mass after debulking (P = .007) and the largest residual mass after debulking (P = .0020). The prognostic variables were then considered as possible predictors of survival in a multivariate analysis using the Cox proportional hazards model resulting in the following expression: lambda i(t)/lambda o(t) = exp(0.5928 (log TRM - 1.8117) + 0.6450 (stage - 0.3827) + 0.6673 (C4 - 0.4198) - 0.8596 (CAP - 0.8642)), where lambda i(t)/lambda o(t) is the risk of dying for a particular patient compared with the average risk of the entire group; log TRM is the log of the volume of the total residual mass in cm3 plus 1.0; stage = 0 if stage III, 1 if stage IV; C4 = 0 if cytologic grade is 1, 2, or 3 and 1 if grade 4; CAP = 0 if treatment is cyclophosphamide and 1 if CAP. Median survival times of patients with relative risk greater than 1 and less than 1 are 43 and 19 months respectively. If this model is confirmed in a prospective study, then it could be used to assign risk and assess treatment options for similar patients at diagnosis.
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Schmidt, Rebecca J., Daniel L. Landry, Lewis Cohen, Alvin H. Moss, Cheryl Dalton, Brian H. Nathanson, and Michael J. Germain. "Derivation and validation of a prognostic model to predict mortality in patients with advanced chronic kidney disease." Nephrology Dialysis Transplantation 34, no. 9 (November 5, 2018): 1517–25. http://dx.doi.org/10.1093/ndt/gfy305.

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Abstract Background Guiding patients with advanced chronic kidney disease (CKD) through advance care planning about future treatment obliges an assessment of prognosis. A patient-specific integrated model to predict mortality could inform shared decision-making for patients with CKD. Methods Patients with Stages 4 and 5 CKD from Massachusetts (749) and West Virginia (437) were prospectively evaluated for clinical parameters, functional status [Karnofsky Performance Score (KPS)] and their provider’s response to the Surprise Question (SQ). A predictive model for 12-month mortality was derived with the Massachusetts cohort and then validated externally on the West Virginia cohort. Logistic regression was used to create the model, and the c-statistic and Hosmer–Lemeshow statistic were used to assess model discrimination and calibration, respectively. Results In the derivation cohort, the SQ, KPS and age were most predictive of 12-month mortality with odds ratios (ORs) [95% confidence interval (CI)] of 3.29 (1.87–5.78) for a ‘No’ response to the SQ, 2.09 (95% CI 1.19–3.66) for fair KPS and 1.41 (95% CI 1.15–1.74) per 10-year increase in age. The c-statistic for the 12-month mortality model for the derivation cohort was 0.80 (95% CI 0.75–0.84) and for the validation cohort was 0.74 (95% CI 0.66–0.83). Conclusions Our integrated prognostic model for 12-month mortality in patients with advanced CKD had good discrimination and calibration. This model provides prognostic information to aid nephrologists in identifying and counseling advanced CKD patients with poor prognosis who are facing the decision to initiate dialysis or pursue medical management without dialysis.
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Chen, Zhan-Hong, Jin-Xiang Lin, Qu Lin, Xing Li, Ying-Fen Hong, and Xiang-yuan Wu. "A new prognostic model based on total tumor volume to predict survival rate in locally advanced hepatocellular carcinoma patients." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15622-e15622. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15622.

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e15622 Background: Many HCC patients are diagnosed as locally advanced and loco-regional therapies improved the prognosis of these patients. Survival rates of these patients vary differently. We want to research on the prognostic factors and establish a new prognostic model based on total tumor volume(TTV) to predict the survival rate in locally advanced hepatocellular carcinoma patients who receive loco-regional therapy. Methods: 214 locally advanced HCC patients who received TACE or PEJ were retrospectively studied. Univariate and multivariate analyses were used to assess the variables. A new prognostic model based on TTV was developed with independent prognostic factors. The predictive value was evaluated using receiver operator characteristic curve (ROC) analyses. Results: The median survival is 23.4 months. Univariate and multivariate analyses showed that total tumor volume (TTV), child-pugh garde, protal vein thrombosis and antivirus therapy were independent factors of overall survival. A risk model was establsihed and it is consisted of 4 factors mentioned above. 214 patients were classified into 5 stages by the new staging system. We researched on the predictive value of the new prognostic system by comparing it with CLIP, BCLC and TNM. When predicting 1-year OS, AUC of the new prognostic system, CLIP, BCLC and TNM is 0.620, 0.735, 0.527 and 0.655, respectively; CLIP is best when predicting 1-year os. When predicting 2-year OS, AUC of the new prognostic system, CLIP, BCLC and TNM is 0.623, 0.671, 0.516 and 0.577, respectively;The new prognostic system is as good as CLIP. When predicting 3-year OS, AUC of the new prognostic system, CLIP, BCLC and TNM is 0.623, 0.645, 0.504 and 0.593, respectively; The new prognostic system is as good as CLIP and is better than BCLC(P < 0.05). Conclusions: Total tumor volume is independent prognotic factors of locally advanced hepatocellular carcinoma patients who received locoregional therapies.The new prognostic system based on TTV is as good as CLIP in predicting 2-year OS and 3-year OS. A new prognostic system based on TTV TTV(CM3) < 22.5(0) ≥22.5(1) Antivirus therapy No(0) Yes(1) Child pugh Grade A(0) B(1) Portal vein thrombosis No(0) Yes(1)
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Dissertations / Theses on the topic "Advanced prognostic model"

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Liu, Lin, Karen Messer, John A. Baron, David A. Lieberman, Elizabeth T. Jacobs, Amanda J. Cross, Gwen Murphy, Maria Elena Martinez, and Samir Gupta. "A prognostic model for advanced colorectal neoplasia recurrence." SPRINGER, 2016. http://hdl.handle.net/10150/621531.

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Following colonoscopic polypectomy, US Multisociety Task Force (USMSTF) guidelines stratify patients based on risk of subsequent advanced neoplasia (AN) using number, size, and histology of resected polyps, but have only moderate sensitivity and specificity. We hypothesized that a state-of-the-art statistical prediction model might improve identification of patients at high risk of future AN and address these challenges. Data were pooled from seven prospective studies which had follow-up ascertainment of metachronous AN within 3-5 years of baseline polypectomy (combined n = 8,228). Pooled data were randomly split into training (n = 5,483) and validation (n = 2,745) sets. A prognostic model was developed using best practices. Two risk cut-points were identified in the training data which achieved a 10 percentage point improvement in sensitivity and specificity, respectively, over current USMSTF guidelines. Clinical benefit of USMSTF versus model-based risk stratification was then estimated using validation data. The final model included polyp location, prior polyp history, patient age, and number, size and histology of resected polyps. The first risk cut-point improved sensitivity but with loss of specificity. The second risk cut-point improved specificity without loss of sensitivity (specificity 46.2 % model vs. 42.1 % guidelines, p < 0.001; sensitivity 75.8 % model vs. 74.0 % guidelines, p = 0.64). Estimated AUC was 65 % (95 % CI: 62-69 %). This model-based approach allows flexibility in trading sensitivity and specificity, which can optimize colonoscopy over- versus underuse rates. Only modest improvements in prognostic power are possible using currently available clinical data. Research considering additional factors such as adenoma detection rate for risk prediction appears warranted.
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Abou, Jaoudé Abdo. "Advanced Analytical Model for the Prognostic of Industrial Systems Subject to Fatigue." Phd thesis, Aix-Marseille Université, 2012. http://tel.archives-ouvertes.fr/tel-00874624.

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This thesis is dedicated to the prognostic evaluation of dynamic systems. The work presented here aims at developing an advanced tool to treat the prognostic evaluation in linear and nonlinear deterministic context in a first part as well as in the stochastic context in a second part. Our purpose is to prepare a general prognostic tool that can be capable of well predicting the RUL of a system based on an analytical damage accumulation law in either a deterministic or a stochastic context.
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Abou, Jaoude Abdo. "Advanced analytical model for the prognostic of industrial systems subject to fatigue." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4331/document.

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La disponibilité élevée des systèmes technologiques comme l'aérospatial, la défense, la pétrochimie et l'automobile, est un but important des nouveaux développements de la technologie de conception des systèmes sachant que la défaillance onéreuse survient, en général, soudainement. Afin de rendre les stratégies classiques de maintenance plus efficaces et pour prendre en considération l'état et l'environnement évolutifs du produit, un nouveau modèle de pronostic analytique est développé en tant que complément des stratégies de maintenance existantes. Ce nouveau modèle est appliqué aux systèmes mécaniques soumis à la défaillance par fatigue sous charge cyclique répétitive. Sachant que l'effet de fatigue va initier des microfissures qui peuvent se propager soudainement et conduire à la défaillance. Ce modèle est basé sur des lois d'endommagement existantes dans la mécanique de la rupture comme la loi de propagation de fissures de Paris-Erdogan à côté de la loi de cumul de dommage de Palmgren-Miner. A partir d'un seuil prédéfini de dégradation DC, la durée de vie résiduelle (RUL) est estimée à l'aide de ce modèle de pronostic. Les dommages peuvent être cumulés linéairement (Loi de Palmgren-Miner) et aussi non linéairement afin de prendre en compte un comportement plus complexe des chargements et des matériaux. Le modèle de dégradation développé dans ce travail est basé sur une sommation d'une mesure de dommage D à la suite de chaque cycle de chargement. Quand cette mesure devient égale à un seuil prédéfini DC, le système est considéré dans l'état de panne. En plus, l'influence stochastique est incluse dans notre modèle pour le rendre plus précis et réaliste
The high availability of technological systems like aerospace, defense, petro-chemistry and automobile, is an important goal of earlier recent developments in system design technology knowing that the expensive failure can generally occur suddenly. To make the classical strategies of maintenance more efficient and to take into account the evolving product state and environment, a new analytic prognostic model is developed as a complement of existent maintenance strategies. This new model is applied to mechanical systems that are subject to fatigue failure under repetitive cyclic loading. Knowing that, the fatigue effects will initiate micro-cracks that can propagate suddenly and lead to failure. This model is based on existing damage laws in fracture mechanics, such as the crack propagation law of Paris-Erdogan beside the damage accumulation law of Palmgren-Miner. From a predefined threshold of degradation DC, the Remaining Useful Lifetime (RUL) is estimated by this prognostic model. Damages can be assumed to be accumulated linearly (Palmgren-Miner's law) and also nonlinearly to take into consideration the more complex behavior of loading and materials. The degradation model developed in this work is based on the accumulation of a damage measurement D after each loading cycle. When this measure reaches the predefined threshold DC, the system is considered in wear out state. Furthermore, the stochastic influence is included to make the model more accurate and realistic
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Siddiqui, Muhammad A. "Development of a prognostic model for fistula maturation in patients with advanced renal failure." Thesis, Queen Margaret University, 2014. https://eresearch.qmu.ac.uk/handle/20.500.12289/7432.

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Introduction: A suitable type of vascular access has to be created to establish a connection between the circulation system of the patient and the haemodialysis cycle. The arteriovenous fistula (AVF) is considered to provide the best long-term functional vascular access, with reduced risk of thrombosis or infection and cost-effective. However, significant numbers of AVF, which fail to develop sufficiently for dialysis, are 28-53% of cases. This study aimed to explore the potential influence of blood markers and factors on the maturation of AVF, in patients who have undergone vascular access surgery and to develop and validate a prognostic model to determine the success of AVF maturation. Methods: Data from 300 patients was retrieved who had undergone AVF surgery between the years 2006 and 2009, from the Royal Infirmary of Edinburgh. A prognostic model was developed for the prediction of maturation of AVF using backward stepwise logistical regression. This data was analysed using univariable, multivariable logistic regression. Model performance was assessed, using the receiver operating characteristics (ROC) curve and Hosmer and Lemeshow goodness of fit test. A prognostic model was validated with the prospective data of 100 patients who had undergone AVF surgery between the years 2009 and 2011, from the Royal Infirmary of Edinburgh. Results: Three variables were identified, which independently influenced fistula maturation. Males were twice as likely to undergo fistula maturation, compared to that of females (odds ratio (OR) 0.514; 95% confidence interval (CI) 0.308 to 0.857). Patients with no evidence of Peripheral Vascular Disease (PVD) were three times more likely to mature their fistula (OR 3.140; 95% CI 1.596 to 6.177). A pre-operative vein diameter greater than 2.5mm resulted in a fivefold increase in fistula maturation compared to a vein size less than 2.5mm (OR 4.532; 95% CI 2.063 to 9.958). The model for fistula maturation had good discrimination as indicated by area under the ROC curve 0.677 and calibration as indicated by Hosmer and Lemeshow test (p = 0.79). The model discriminatory power was confirmed in the prospective study (validation data set) with area under the receiver operating curve was 0.59 and calibration indicated by Hosmer and Lemeshow test (p > 0.05). Conclusion: Successful vascular access provision is the foundation on which successful haemodialysis is built. This study has found that female gender, history of PVD and vein diameter less than 2.5 mm are the negative significant independent clinical predictors of maturation of arteriovenous fistula.
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Gwilliam, Bridget. "The development of prognostic models for predicting survival in patients with advanced cancer." Thesis, St George's, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546796.

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Smith, Ann. "Characterisation of condition monitoring information for diagnosis and prognosis using advanced statistical models." Thesis, University of Huddersfield, 2017. http://eprints.hud.ac.uk/id/eprint/32609/.

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This research focuses on classification of categorical events using advanced statistical models. Primarily utilised to detect and identify individual component faults and deviations from normal healthy operation of reciprocating compressors. Effective monitoring of condition ensuring optimal efficiency and reliability whilst maintaining the highest possible safety standards and reducing costs and inconvenience due to impaired performance. Variability of operating conditions being revealed through examination of vibration signals recorded at strategic points of the process. Analysis of these signals informing expectations with respect to tolerable degrees of imperfection in specific components. Isolating inherent process variability from extraneous variability affords reliable means of ascertaining system health and functionality. Vibration envelope spectra offering highly responsive model parameters for diagnostic purposes. This thesis examines novel approaches to alleviating the computational burdens of large data analysis through investigation of the potential input variables. Three methods are investigated as follows: Method one employs multivariate variable clustering to ascertain homogeneity amongst input variables. A series of heterogeneous groups being formed from each of which explanatory input variables are selected. Data reduction techniques, method two, offer an alternative means of constructing predictive classifiers. A reduced number of reconstructed explanatory variables provide enhanced modelling capabilities ensuring algorithmic convergence. The final novel approach proposed combines both these methods alongside wavelet data compression techniques. Simplifying number of input parameters and individual signal volume whilst retaining crucial information for deterministic supremacy.
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Isaksson, Olle. "Model-based Diagnosis of a Satellite Electrical Power System with RODON." Thesis, Linköping University, Linköping University, Vehicular Systems, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16763.

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As space exploration vehicles travel deeper into space, their distance to earth increases.The increased communication delays and ground personnel costs motivatea migration of the vehicle health management into space. A way to achieve thisis to use a diagnosis system. A diagnosis system uses sensor readings to automaticallydetect faults and possibly locate the cause of it. The diagnosis system usedin this thesis is a model-based reasoning tool called RODON developed by UptimeSolutions AB. RODON uses information of both nominal and faulty behavior ofthe target system mathematically formulated in a model.The advanced diagnostics and prognostics testbed (ADAPT) developed at theNASA Ames Research Center provides a stepping stone between pure researchand deployment of diagnosis and prognosis systems in aerospace systems. Thehardware of the testbed is an electrical power system (EPS) that represents theEPS of a space exploration vehicle. ADAPT consists of a controlled and monitoredenvironment where faults can be injected into a system in a controlled manner andthe performance of the diagnosis system carefully monitored. The main goal of thethesis project was to build a model of the ADAPT EPS that was used to diagnosethe testbed and to generate decision trees (or trouble-shooting trees).The results from the diagnostic analysis were good and all injected faults thataffected the actual function of the EPS were detected. All sensor faults weredetected except faults in temperature sensors. A less detailed model would haveisolated the correct faulty component(s) in the experiments. However, the goal wasto create a detailed model that can detect more than the faults currently injectedinto ADAPT. The created model is stationary but a dynamic model would havebeen able to detect faults in temperature sensors.Based on the presented results, RODON is very well suited for stationary analysisof large systems with a mixture of continuous and discrete signals. It is possibleto get very good results using RODON but in turn it requires an equally goodmodel. A full analysis of the dynamic capabilities of RODON was never conductedin the thesis which is why no conclusions can be drawn for that case.

 

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Cordoba, Arenas Andrea Carolina. "Aging Propagation Modeling and State-of-Health Assessment in Advanced Battery Systems." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385967836.

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Navicelli, Andrea, Mario Tucci, and Filippo De Carlo. "Analisi ed applicazione di modelli diagnostici e prognostici per guasti e prestazioni di componenti di impianti industriali nell’era I4.0." Doctoral thesis, 2021. http://hdl.handle.net/2158/1234822.

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Il ruolo fondamentale che la manutenzione gioca nei costi di esercizio e nella produttività degli impianti industriali ha portato le aziende e i ricercatori a spostare il loro interesse su questo tema. L'ultima frontiera dell'innovazione in campo manutentivo, resa possibile anche dall'avvento della quarta rivoluzione industriale che promuove la sensorizzazione e l’interconnessione di tutti i macchinari di impianto, è la manutenzione predittiva. Essa mira ad ottenere una previsione accurata della vita utile dei componenti degli impianti industriali al fine di ottimizzare la schedulazione degli interventi sul campo. Lo studio parte da una accurata revisione della letteratura scientifica di settore riguardante le tecniche diagnostiche e prognostiche applicate a componenti di impianti industriali, necessaria alla comprensione dei diversi modelli sviluppati in funzione della tipologia di componente e modo di guasto in analisi. Successivamente ho spostato l’attenzione sul concetto di manutenzione 4.0 al fine di mappare tutte le caratteristiche associate al paradigma dell'Industria 4.0 e le loro possibili applicazioni alla manutenzione. Lo studio condotto ha portato poi alla progettazione, sviluppo e validazione delle metodologie necessarie all’applicazione in real-time di modelli diagnostici e prognostici avanzati, sia statistici che machine learning, necessari all’implementazione sul campo di un sistema di manutenzione predittiva. Grazie all’applicazione delle metodologie proposte ad un caso studio è stato possibile non solo validare i modelli proposti ma anche definire l’architettura informatica necessaria alla loro corretta implementazione sul sistema distribuito di controllo (Distributed Control System - DCS) di impianto in funzione della tipologia del componente e del guasto in analisi. I modelli testati e validati hanno mostrato elevate prestazioni diagnostiche soprattutto per quanto riguarda i modelli ML che sfruttano le Support Vector Machine (SVM). In definitiva, questo lavoro di tesi mostra nel dettaglio tutti i passaggi necessari allo sviluppo di un sistema di manutenzione predittiva efficace in impianto: partendo dall’analisi dei modi di guasto e dalla sensorizzazione dei componenti, passando poi allo sviluppo dei modelli diagnostici e prognostici real-time fino alla costruzione dell’interfaccia di visualizzazione dei risultati delle analisi svolte, analizzando anche l’architettura informatica necessaria al suo corretto funzionamento. The fundamental role that maintenance plays in the operating costs and productivity of industrial plants has led companies and researchers to shift their interest in this issue. The last frontier of innovation in the maintenance field, made possible also by the advent of the fourth industrial revolution which promotes the sensorisation and interconnection of all plant machinery, is predictive maintenance. It aims to obtain an accurate forecast of the useful life of the industrial plants’ components in order to optimise the scheduling of interventions in the field. The study starts from an accurate review of the scientific literature concerning the diagnostic and prognostic techniques applied to industrial plant components, necessary to understand the different models developed according to the type of component and failure mode under analysis. Subsequently I shifted the focus to the maintenance 4.0 concept in order to map all the characteristics associated with the Industry 4.0 paradigm and their possible applications to maintenance operations. The study then led to the design, development and validation of the methodologies necessary for the real-time application of advanced diagnostic and prognostic models, both statistical and machine learning, necessary for the field implementation of a predictive maintenance system. Thanks to the application of the proposed methodologies to a case study, it was possible not only to validate the proposed models but also to define the IT architecture necessary for their correct implementation on the plant's Distributed Control System (DCS) according to the type of component and the fault under analysis. The tested and validated models showed high diagnostic performance, especially regarding the Support Vector Machine (SVM) Machine Learning models. Ultimately, this thesis shows in detail all the steps necessary for the development of an effective predictive maintenance system in the plant: starting from the analysis of failure modes and component sensorisation, then moving on to the development of real-time diagnostic and prognostic models up to the build-up of the interface for visualising the results of the analyses carried out, also analysing the IT architecture necessary for its correct operation.
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Books on the topic "Advanced prognostic model"

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Steinhauser, Karen E., and James A. Tulsky. Defining a ‘good’ death. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656097.003.0008.

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Although any outcome of an advanced illness can be predicted, in palliative care settings the word ‘prognosis’ usually means the estimated time to death. Prognosis is an important but challenging set of clinical skills for palliative medicine clinicians to master. It is important because patients and families want to know what to expect, it influences clinical decision-making, and it may determine eligibility for services. It is challenging because of the inherent uncertainty of making predictions and because dying is not an easy topic to discuss. Advances in statistical computing have allowed the development of mathematical models and predictive tools that are now more accurate than clinical estimates. A large section of this chapter is devoted to presenting and evaluating several of these models, although prognostic uncertainty remains a significant issue even with them, and survival estimates should never drive clinical decision-making alone.
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Boland, Lawrence A. Epilogue. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190274320.003.0017.

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This chapter considers whether new efforts provided by behavioural, evolutionary and complexity economics have any chance of changing how economics is taught and practiced in the future. The main question at issue is whether the new views based on experimental behavioural, evolutionary and complexity models have any hope of displacing textbook equilibrium economic explanations. The prognosis is they do not at the introductory level, but perhaps might at the advanced undergraduate and the graduate levels. The chapter concludes by stressing the need to understand the role of learning in the process of equilibrium attainment if equilibrium models are ever to be useful for understanding real world economies and guiding policy makers.
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Roth, Katalin. Bioethical Issues in Integrative Geriatrics. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190466268.003.0030.

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Many older persons use complementary and alternative medicine (CAM), and an integrative approach is very consistent with the holistic model of geriatric “slow medicine.” Ethical practice requires an understanding of the patient’s values and goals of care. The core ethical principles of beneficence, autonomy, and justice are applied to geriatric concerns such as decision-making capacity, prognosis, and advance care planning. Informed consent requires that patients understand the goals of treatment, conventional options, and the evidence and safety of CAM therapies. Legal issues affecting CAM providers such as licensing, referrals, and malpractice are reviewed.
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Guo, Yong, and Claudia F. Lucchinetti. Taking a Microscopic Look at Multiple Sclerosis. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199341016.003.0005.

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The pathology of multiple sclerosis is complex, extends beyond the white matter plaque, and is influenced by stage of demyelinating activity, clinical course, disease duration, and treatment. Technological advances in immunology, molecular biology, and “omic” biology have provided novel insights into the mechanisms for development of white matter plaques, axonal damage, cortical demyelination, and disease progression. Detailed, systematic, and statistically rigorous pathological studies on clinically well-characterized MS cohorts have helped define the heterogeneous pathological substrates of MS and unravel the complex molecular pathogenic mechanisms, with the ultimate goal of identifying targets for therapeutic interventions. It is increasingly clear that the use of human tissues is imperative to improve current diagnostic, prognostic, and therapeutic modalities. Preclinical animal models have been invaluable for discovery of key immune processes, basic disease mechanisms, and candidate immune targeting strategies, but the conclusions have yet be reconciled with the essential features of the human disease.
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Ugarte-Gil, Manuel F., and Graciela S. Alarcón. History of systemic lupus erythematosus. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198739180.003.0001.

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The first description of cutaneous ulcerations consistent with systemic lupus erythematosus (SLE) has been attributed to Hippocrates. The term lupus first appeared in English literature in the tenth century. Until the nineteenth century, however, this term was used to describe different conditions. Osler first recognized that organ involvement may occur with or without skin involvement. With the discovery of LE cells and autoantibodies, the use of lupus murine models, and the recognition of familial aggregation and the importance of genetic factors, the pathogenesis of SLE started to be unravelled and allowed the definition of classification criteria. In parallel, the discovery of cortisone, the use of immunosuppressive drugs and antimalarials, the control of hypertension, and the availability of renal replacement therapy improved the prognosis of SLE from a 4-year survival of 51% to a 5-year survival >90%. Advances in genetics and targeted therapies will lead to better intermediate and long-term outcomes.
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Kissane, David W., Barry D. Bultz, Phyllis N. Butow, Carma L. Bylund, Simon Noble, and Susie Wilkinson, eds. Oxford Textbook of Communication in Oncology and Palliative Care. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198736134.001.0001.

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This textbook integrates clinical wisdom with empirical findings, drawing upon the history of communication science, providing a comprehensive curriculum for applied communication skills training for specialist oncologists, surgeons, nurses, psychosocial care providers and other members of the multidisciplinary team. This new edition presents a curriculum for nurses, which discusses needs of pre-registration to advanced trainees, including the ‘SAGE & THYME’ training programme, chronic disease, responding to depressed patients, the last hours and days of life, family care, facilitation training, and e-learning. The core curriculum ranges from breaking bad news, discussing risk and prognosis, achieving shared treatment decisions, responding to difficult emotions, dealing with denial, communicating with relatives and conducting a family meeting, helping patients cope with survivorship, deal with recurrence, transition to palliative care, and talk openly about death and dying. Modules offer guidelines about key skills, essential tasks, effective strategies, and scenarios for training sessions with simulated patients. The communication science section covers the history and models of communication skills training, the art of facilitating skill development, ethics, gender, power, the internet, audio-recording significant consultations, decision aides, and shared treatment decisions, medical student training, and enhancing patient participation in consultations. Specialty issues are explored, including enrolling in clinical trials, working in teams, discussing genetic risk, reconstructive and salvage surgery, among many other important issues. Variations in clinical disciplines are also discussed, including chapters for social workers, radiologists, surgical oncologists, medical and radiation oncologists, palliative medicine, pastoral care, pharmacy, paediatrics, and the elderly.
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Book chapters on the topic "Advanced prognostic model"

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Bobrowski, Leon. "Prognostic Models Based on Linear Separability." In Advances in Data Mining. Applications and Theoretical Aspects, 11–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23184-1_2.

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LeBlanc, Michael, and John Crowley. "A review of tree-based prognostic models." In Recent Advances in Clinical Trial Design and Analysis, 113–24. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2009-2_6.

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Papaioannou, Ioannis, Ioanna Roussaki, and Miltiades Anagnostou. "Multi-modal Opponent Behaviour Prognosis in E-Negotiations." In Advances in Computational Intelligence, 113–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21501-8_15.

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Bobrowski, L. "Interval Uncertainty in CPL Models for Computer Aided Prognosis." In Advances in Intelligent and Soft Computing, 443–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23172-8_29.

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ArunKumar, K., and S. Vasundra. "Prognostic Outcome Prediction on Patient Treatment Trajectory Data Using PSO Optimization on LTSM-RNN Model." In Advances in Intelligent Systems and Computing, 1045–61. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7330-6_78.

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Ceriani, Roberto L., Frank Baratta, Ramon J. Gaslonde, Carolyn M. De Rosa, and Luciano Ozzello. "Multivariate Prognostic Model for Infiltrating Ductal Carcinoma of the Breast in the Axillary Node-Free Patient." In Advances in Experimental Medicine and Biology, 155–67. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2443-4_15.

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Korfiati, Aigli, Giorgos Livanos, Christos Konstantinou, Sophia Georgiou, and George Sakellaropoulos. "ebioMelDB: Multi-modal Database for Melanoma and Its Application on Estimating Patient Prognosis." In IFIP Advances in Information and Communication Technology, 33–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79150-6_3.

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Carvalho, Rafaela, João Pedrosa, and Tudor Nedelcu. "Multimodal Multi-tasking for Skin Lesion Classification Using Deep Neural Networks." In Advances in Visual Computing, 27–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90439-5_3.

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AbstractSkin cancer is one of the most common types of cancer and, with its increasing incidence, accurate early diagnosis is crucial to improve prognosis of patients. In the process of visual inspection, dermatologists follow specific dermoscopic algorithms and identify important features to provide a diagnosis. This process can be automated as such characteristics can be extracted by computer vision techniques. Although deep neural networks can extract useful features from digital images for skin lesion classification, performance can be improved by providing additional information. The extracted pseudo-features can be used as input (multimodal) or output (multi-tasking) to train a robust deep learning model. This work investigates the multimodal and multi-tasking techniques for more efficient training, given the single optimization of several related tasks in the latter, and generation of better diagnosis predictions. Additionally, the role of lesion segmentation is also studied. Results show that multi-tasking improves learning of beneficial features which lead to better predictions, and pseudo-features inspired by the ABCD rule provide readily available helpful information about the skin lesion.
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Mechri, Walid, Hai-Canh Vu, Phuc Do, Timothee Klingelschmidt, Flavien Peysson, and Didier Theilliol. "A Study on Health Diagnosis and Prognosis of an Industrial Diesel Motor: Hidden Markov Models and Particle Filter Approach." In Advances in Intelligent Systems and Computing, 380–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64474-5_32.

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Reddy Chimmula, Vinay Kumar, Amit Kumar Yadav, and Hasmat Malik. "Novel Application of Relief Algorithm in Cascade ANN Model for Prognosis of Photovoltaic Maximum Power Under Sunny Outdoor Condition of Sikkim India: A Case Study." In Advances in Intelligent Systems and Computing, 387–405. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1532-3_17.

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Conference papers on the topic "Advanced prognostic model"

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Roemer, Michael J., and Gregory J. Kacprzynski. "Advanced Diagnostic and Prognostic Technologies for Gas Turbine Engine Risk Assessment." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0030.

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Real-time, integrated health monitoring of gas turbine engines that can detect, classify, and predict developing engine faults is critical to reducing operating and maintenance costs while optimizing the life of critical engine components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technologies presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices. This paper describes some novel diagnostic and prognostic technologies for dedicated, real-time sensor analysis, performance anomaly detection and diagnosis, vibration fault detection, and component prognostics. The technologies have been developed for gas turbine engine health monitoring and prediction applications which includes an array of intelligent algorithms for assessing the total ‘health’ of an engine, both mechanically and thermodynamically. This includes the ability to account for uncertainties from engine transient conditions, random measurement fluctuations and modeling errors associated with model-based diagnostic and prognostic procedures. The implementation of probabilistic methods in the diagnostic and prognostic methodology is critical to accommodating for these types of uncertainties.
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Orsagh, Rolf F., Jeremy Sheldon, and Christopher J. Klenke. "Prognostics/Diagnostics for Gas Turbine Engine Bearings." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38075.

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Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.
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Roy, S., G. Dib, P. Ramuhalli, E. H. Hirt, M. S. Prowant, L. Luzi, A. F. Pardini, and S. G. Pitman. "Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation." In 2015 IEEE Conference on Prognostics and Health Management (PHM). IEEE, 2015. http://dx.doi.org/10.1109/icphm.2015.7245059.

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Yu, Ting-ting, Sai-kit Lam, Lok-hang To, Ka yan Tse, Nong-yi Cheng, Yeuk-nam Fan, Cheuk-lai Lo, et al. "Constructing Novel Prognostic Biomarkers of Advanced Nasopharyngeal Carcinoma from Multiparametric MRI Radiomics Using Ensemble-Model Based Iterative Feature Selection." In 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE). IEEE, 2019. http://dx.doi.org/10.1109/icmipe47306.2019.9098211.

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Sampath, Suresh, Ankush Gulati, and Riti Singh. "Fault Diagnostics Using Genetic Algorithm for Advanced Cycle Gas Turbine." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30021.

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This paper describes a new approach to the development of a fault diagnostics and prognostic capability for an advanced cycle gas turbine. It is based on techniques using sensor based and model based information. Sensor based information is the actual information obtained from the real engine and the model based information comes from the data obtained from engine performance model simulation with a permutation of implanted faults taking into account sensor noise and bias. The approach adopted here is to minimize an objective function which represents the difference between the actual and simulated data and the minimized objective function allows us identify the nature of fault. After the initial success with simple cycle engines, it was decided to extend this technique to advanced cycle engines. The technique is being tested on an in-house model of an intercooled recuperated engine with variable geometry similar to the ICR-WR21cycle. A detailed analysis of the technique applied to simple cycle and advanced cycle will be presented.
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Littles, Jerrol W., Robert J. Morris, Richard Pettit, David M. Harmon, Michael F. Savage, and Sharayu Tulpule. "Materials and Structures Prognosis for Gas Turbine Engines." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91203.

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Gas turbine engine diagnostic systems often utilize data trending and anomaly detection to provide a measure of system health. These systems provide significant benefits for trending shifts in engine performance and diagnosing system degradation that requires some maintenance action. However, this approach may be limited in the ability to uniquely identify damage for select components and failure modes. Advanced prognostic systems are being developed to work symbiotically with state of the art diagnostic techniques in use today; these advanced systems use advanced material and component damage evolution modelling linked with system-level structural analyses to intelligently guide the health management system to search for specific signatures that would be expected from key changes in component and system health [1,2,3,4]. Material damage models, advanced component models, and novel system-level structural analyses are being used to generate newly defined “structural transfer functions” (STFs) that provide a link between sensed parameters and the remaining capability of specific components, and the system. The characteristic damage signatures vary by component type and failure mode, and hence the specific STF approach varies among component types. An initial STF approach was developed and demonstrated for a specific component and damage type [5] under an initial feasibility program. This STF-based prognosis approach is fundamentally different from the traditional modal analysis based NDE approach used for crack detection. This presentation will review this novel STF-based prognosis approach, and consider examples of STFs characteristic of specific components and damage types, as well as progress towards the development of tools that are enabling system-level STF development [6].
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Tamilselvan, Prasanna, Yibin Wang, and Pingfeng Wang. "Prognosis Informed Design Framework for Operation and Maintenance of Wind Turbines." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70792.

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Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.
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Tamilselvan, Prasanna, Yibin Wang, Pingfeng Wang, and Janet M. Twomey. "Prognosis Informed Stochastic Decision Making Framework for Operation and Maintenance of Wind Turbines." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7168.

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Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.
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Almeida, Raissa Janine de, Carolina Terra de Moraes Luizaga, José Eluf-Neto, Eduardo Carvalho Pessoa, Amanda de Moraes Mamede Chiarotti, Rainer de Almeida Souza, and Cristiane Murta Nascimento. "THE IMPACT OF EDUCATION ON BREAST CANCER SURVIVAL IN THE STATE OF SÃO PAULO." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2108.

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Objectives: To estimate 5- and 10-year breast cancer–specific survival probabilities of patients admitted in the hospitalbased cancer registry (HBCR) of the Fundação Oncocentro de São Paulo (FOSP, in Portuguese) and to assess the prognostic factors for this neoplasm. Methods: Historical cohort study that included women with breast cancer included in HBCRFOSP and diagnosed between 2002 and 2012. The event of interest was breast cancer–specific mortality. Living cases at the end of follow-up (December 31, 2017), loss to follow-up, and death other than that due to breast cancer were considered censored on the date of the last contact or date of death. Descriptive analysis and survival analysis were performed using the Kaplan–Meyer method. Survival curves were compared using the log-rank test. HR and 95%CI were estimated using Cox proportional hazards model. This study was approved by the Human Research Ethics Committee of the Botucatu Medical School, São Paulo State University, Brazil. Results: The HBCR-FOSP registered 53,146 cases of invasive breast cancer between 2002 and 2012. The median age at diagnosis was 55.9 years. The 5- and 10-year breast cancer–specific survival for the entire cohort was 76.1% (95%CI 75.7–76.5) and 64.8% (95%CI 64.2–65.3), respectively. In the multivariate analysis, the factors clinical stage and educational level were with the greatest impact on survival. The other factors associated with prognosis were age at diagnosis, histological type, and year of diagnosis. Conclusion: The results show that patients in more advanced stages and with less level of education have a higher risk of death from breast cancer. Besides, these findings may contribute to the development of policies for the identification of breast tumors at earlier stages.
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Hoyle, Christopher, Irem Y. Tumer, Tolga Kurtoglu, and Wei Chen. "Multi-Stage Uncertainty Quantification for Verifying the Correctness of Complex System Designs." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47888.

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Designing complex systems for mission-critical applications requires a design process with focus upon maximizing the probability of meeting design requirements. Typically the design process for these systems consists of filtering and refining an initial set of conceptual designs to produce a final set of detailed designs. This final set of designs is presented to the system contracting agency or management team for design selection. In this work, a framework is presented for a multi-stage design process in which the Probability of Correctness (PoC) is utilized as a metric to sequentially filter designs from the abstract conceptual phase through the detailed design phase. This framework utilizes methods for uncertainty propagation (UP) from reliability engineering, which are organized within the framework to match the UP method with the model fidelity and data type available at each stage of the process. A case study using the Advanced Diagnostic and Prognostic Testbed (ADAPT) Electric Power System (EPS) is presented to illustrate both the verification process utilizing multiple UP methods, and also the use of the OpenModelica environment for system design. A discussion presents a generalization of the framework and the future work needed to realize the comprehensive framework for system design.
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Reports on the topic "Advanced prognostic model"

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Neodo, Anna, Fiona Augsburger, Jan Waskowski, Joerg C. Schefold, and Thibaud Spinetti. Monocytic HLA-DR expression and clinical outcomes in adult ICU patients with sepsis – a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0119.

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Review question / Objective: The scope of this review was defined using PICOTS framework where 1) population: adult critically ill patients with sepsis or septic shock; 2) index prognostic factor: cell surface protein expression of mHLA-DR in blood; 3) comparative factor: none; 4) outcomes to be predicted: mortality, secondary infections, length of stay, and organ dysfunction score (sequential organ failure assessment [SOFA], multiple organ dysfunction score [MODS], logistic organ dysfunction score [LODS]), composite outcomes where component endpoints consist of at least one of the outcomes stated above (e.g., “adverse outcome” defined as death or secondary infection), 5) timing (of the prediction horizon and the moment of prognosis): any; and 6) setting: ICU. Condition being studied: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to severe infections. It can further progress to septic shock, which includes hemodynamic failure and increased mortality rates. A recent worldwide epidemiological study estimated 48.9 million sepsis cases and 11 million of sepsis-related deaths (~20% of global deaths in 2017). Although its management has advanced considerably, sepsis remains deadly and challenging to treat. The 28/30-day mortality averages around 25% for sepsis and 38% for septic shock in high-income countries. Current models describe the underlying pathophysiologic mechanisms of sepsis as an interplay between concurrent dysfunctional pro- and anti-inflammatory immune response.
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