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Journal articles on the topic 'Predictive factors'

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Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Chocce, John, Donald A. Johnson, and Yossiri Yossatorn. "Predictive Factors of Freshmen’s Intercultural Sensitivity." International Journal of Information and Education Technology 5, no. 10 (2015): 778–82. http://dx.doi.org/10.7763/ijiet.2015.v5.610.

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Al-Nefaie, Mamdouh Mohammed Zowaid. "Prevalence and Predictive Risk Factors of Hypertension." International Journal Of Pharmaceutical And Bio-Medical Science 02, no. 11 (November 17, 2022): 518–23. http://dx.doi.org/10.47191/ijpbms/v2-i11-08.

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Worldwide, the prevalence of diseases caused by and related to hypertension is rising. The goal of the current study was to investigate the causes of hypertension in hospital inpatients receiving tertiary care. Patient information was gathered, including demographics, laboratory results, and the final diagnosis. The six-month study, which involved 160 patients overall, was conducted. 20% did not have hypertension, making up the remaining 80%. Between the hypertensive and non-hypertensive population, risk factors for hypertension such as smoking, alcohol use, demographics, socioeconomic status, diet, family history, family size, education level, salt intake, lifestyle, and basic metabolic index were compared. In the study population, it was discovered that drinking alcohol, smoking, and eating a varied diet were significant risk factors for hypertension. As a result, these factors can be taken into account when creating effective prevention strategies and management guidelines for hypertension at the study site.

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van Leeuwen, R. B., P. H. Hoogland, and A. W. de Weerd. "Chemonucleolysis; Predictive Factors." Spine 17, no. 7 (July 1992): 838–41. http://dx.doi.org/10.1097/00007632-199207000-00019.

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Köhne, Claus-Henning, Udo Vanhoefer, and Gernot Hartung. "Clinical predictive factors." European Journal of Cancer 45 (September 2009): 43–49. http://dx.doi.org/10.1016/s0959-8049(09)70015-2.

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Wang, Hsin-Yao, Yu-Hsin Liu, Yi-Ju Tseng, Chia-Ru Chung, Ting-Wei Lin, Jia-Ruei Yu, Yhu-Chering Huang, and Jang-Jih Lu. "Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance." Diagnostics 12, no. 2 (February 5, 2022): 413. http://dx.doi.org/10.3390/diagnostics12020413.

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The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.

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M. Ahmed, Shyaw, Lusan A. Arkawazi, and Kawa A. Mahmood. "SURGICAL SPERM RETRIEVAL IN AZOOSPERMIA: OUTCOME AND PREDICTIVE FACTORS." Journal of Sulaimani Medical College 11, no. 2 (June 21, 2021): 129–37. http://dx.doi.org/10.17656/jsmc.10295.

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Dixon, A. A., R. O. Holness, W. J. Howes, and J. B. Garner. "Spontaneous Intracerebral Haemorrhage: An Analysis of Factors Affecting Prognosis." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 12, no. 3 (August 1985): 267–71. http://dx.doi.org/10.1017/s0317167100047144.

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ABSTRACT:A retrospective study of 100 patients with spontaneous intracerebral haemorrhage was carried out, to identify clinical factors which have a predictive value for outcome. Numerical equivalents for the admission level of consciousness (the Glasgow Coma Scale), ventricular rupture, partial pressure of oxygen in the blood, the electrocardiogram, clot location, and clot size were combined into equations predicting outcome. The best single parameter for prediction was the Glasgow Coma Scale.

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Dragicevic, S., D. Djordjevic, V. Andrejevic, D. Perovic, N. Lalic, and S. Micic. "S65 VARICOCELETOMY: PREDICTIVE FACTORS." European Urology Supplements 11, no. 4 (October 2012): 149. http://dx.doi.org/10.1016/s1569-9056(13)60259-6.

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9

Gafarov, F. M., Ya B. Rudneva, and U. Yu Sharifov. "Predictive Modeling in Higher Education: Determining Factors of Academic Performance." Vysshee Obrazovanie v Rossii = Higher Education in Russia 32, no. 1 (January 21, 2023): 51–70. http://dx.doi.org/10.31992/0869-3617-2023-32-1-51-70.

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For several decades in the field of data mining in education (EDM), predictive learning has remained one of the most popular and internationally discussed research topics. Specifically, data mining is used to predict educational outcomes such as academic performance, retention, success, satisfaction, achievement and dropout rates. In the management practice of higher education institutions, on the basis of an operational forecast, measures are developed and implemented to support those students who fall into the risk group.Our study is aimed at substantiating a model for predicting the early departure of students using an artificial neural network and analyzing predictors that increase the accuracy of predicting successful graduation from a Russian university. This work will expand the international practice of comparative research in higher education.The paper confirms the already existing hypotheses about the influence of a number of factors on the prediction of academic performance and suggests the need to test their universality or specificity in a particular institution of higher education. We also proved that an artificial neural network model with a certain set of attributes can be applied in the context of a single higher education institution, regardless of specialization. To determine the potential risk group of students, a binary classification prediction model is used. The overall prediction accuracy of a neural network with combined data reaches 88%. For this neural network model, the basic predictors that affect the accuracy of the forecast are the cumulative average level of achievement (CGPA) and the year of admission to the university.

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Mehta, Nirali, R. G. Bhatt, Hetal Vora, and Dlvya Reddy. "Predischarge risk factors for predicting significant hyperbilirubinemia in term of infants." International Journal of Contemporary Pediatrics 6, no. 2 (February 23, 2019): 315. http://dx.doi.org/10.18203/2349-3291.ijcp20190526.

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Background: The objective of the study to compare the predictive ability of predischarge serum total bilirubin (STB) and clinical factors for significant hyperbilirubinemia (SHB) in newborn to observe the prediction of the hyperbilirubinemia.Methods: In the prospective study, enlist of healthy newborn infants with >35 weeks gestation, in a tertiary hospital in western India. The serum bilirubin between 36-48 hours of age and risk factors for SHB were identified before discharge. SHB was distinct as a bilirubin level that exceed or was within 1mg/dL (17µmol/L) of the hour-specific phototherapy conduct threshold recommended by American Academy of Pediatrics (AAP) guideline on the management of neonatal hyperbilirubinemia.Results: Of 505 infants, 380 infants were included in final analysis, among which 70 babies (22.5%) developed SHB. On univariate analysis STB, gestational age (GA) and percentage of weight loss were found to be predictive of SHB. On multiple logistic regressions, the prognostic ability of predischarge STB is higher than that of percentage of weight loss and GA. The predictive accurateness of predischarge (<48 hours) STB level was comparable to that of percentage of weight loss (AUC=0.88, 95% CI 0.84-0.93). However, the prediction model that combined multiple risk factors such as predischarge STB, GA and percentage of weight loss have the best accuracy for predicting SHB.Conclusions: Combination of specific clinical factors (gestational age and percentage of weight loss) with predischarge serum total bilirubin described best predicts development of considerable hyperbilirubinemia.

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Park, HyeongKyu, Da Young Lee, So young Park, Jiyoung Min, Jiwon Shinn, Dae Ho Lee, Soon Hyo Kwon, Hun-Sung Kim, and Nan Hee Kim. "Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting It." Cardiovascular Prevention and Pharmacotherapy 3, no. 4 (October 31, 2021): 106–14. http://dx.doi.org/10.36011/cpp.2021.3.e14.

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Background: Glycated hemoglobin (HbA1c), which reflects the patient's blood sugar level, can only be measured in a hospital setting. Therefore, we developed a model predicting HbA1c using personal information and self-monitoring of blood glucose (SMBG) data solely obtained by a patient.Methods: Leave-one-out cross-validation (LOOCV) was performed at two university hospitals. After measuring the baseline HbA1c level before SMBG (Pre_HbA1c), the SMBG was recorded over a 3-month period. Based on these data, an HbA1c prediction model was developed, and the actual HbA1c value was measured after 3 months. The HbA1c values of the prediction model and actual HbA1c values were compared. Personal information was used in addition to SMBG data to develop the HbA1c predictive model.Results: Thirty model training sessions and evaluations were conducted using LOOCV. The average mean absolute error of the 30 models was 0.659 (range, 0.005–2.654). Pre_HbA1c had the greatest influence on HbA1c prediction after 3 months, followed by post-breakfast blood glucose level, oral hypoglycemic agent use, fasting glucose level, height, and weight, while insulin use had a limited effect on HbA1c values.Conclusions: The patient's SMBG data and personal information strongly influenced the HbA1c predictive model. In the future, it will be necessary to develop sophisticated predictive models using large samples for stable SMBG in patients.

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Darves-Bornoz, JM, JP Lépine, M. Choquet, C. Berger, A. Degiovanni, and P. Gaillard. "Predictive factors of chronic Post-Traumatic Stress Disorder in rape victims." European Psychiatry 13, no. 6 (September 1998): 281–87. http://dx.doi.org/10.1016/s0924-9338(98)80045-x.

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SummaryThis study aimed to investigate the psychological disorders following rape as well as the course of Post-Traumatic Stress Disorder (PTSD), and to determine clinical factors predictive of chronic PTSD. Seventy-three rape victims were observed in a systematic follow-up study over 1 year following rape using structured interview schedules. The frequency of PTSD was massive. The early disorders predicting PTSD 1 year after rape included somatoform and dissociative disorders, agoraphobia and specific phobias as well as depressive and gender identity disorders and alcohol abuse. Through stepwise logistic regressions, the following were found to be good models of prediction of chronic PTSD 1 year after rape: for the characteristics of the traumas, intrafamily rape, being physically assaulted outside rape, and added physical violence during rape; for the early psychological and behavioural attitudes, low self-esteem, permanent feelings of emptiness and running away; and for early mental disorders, agoraphobia and depressive disorders. Finally, among all these predictive factors, added physical violence during rape, low self-esteem, permanent feelings of emptiness and agoraphobia were shown to constitute a strong model of predictors. People presenting features such as the predictive factors of chronic PTSD found in the study should be asked about a history of rape and symptoms of PTSD.

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13

Millichap, J. Gordon. "Factors Predictive of Epilepsy Remission." Pediatric Neurology Briefs 14, no. 12 (December 1, 2000): 89. http://dx.doi.org/10.15844/pedneurbriefs-14-12-1.

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14

Anderson, Joseph C., Catherine R. Messina, William Cohn, Edward Gottfried, Scott Ingber, Gary Bernstein, Eugene Coman, and Joseph Polito. "Factors predictive of difficult colonoscopy." Gastrointestinal Endoscopy 54, no. 5 (November 2001): 558–62. http://dx.doi.org/10.1067/mge.2001.118950.

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15

Leonardson, G., R. Lapierre, and D. Hollingsworth. "Factors predictive of physician location." Academic Medicine 60, no. 1 (January 1985): 37–43. http://dx.doi.org/10.1097/00001888-198501000-00006.

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16

Opstelten, Wim, and Karel G. M. Moons. "Predictive Factors for Postherpetic Neuralgia." Clinical Journal of Pain 29, no. 1 (January 2013): 92. http://dx.doi.org/10.1097/ajp.0b013e31824e329c.

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17

Meding, Birgitta, and Gunnar Swanbeck. "Predictive factors for hand eczema." Contact Dermatitis 23, no. 3 (September 1990): 154–61. http://dx.doi.org/10.1111/j.1600-0536.1990.tb04776.x.

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18

Coimbra, Cristiana, Francisco Monteiro, Pedro Oliveira, Leandro Ribeiro, Mário Giesteira de Almeida, and Artur Condé. "Hypoparathyroidism following thyroidectomy: Predictive factors." Acta Otorrinolaringológica Española 68, no. 2 (March 2017): 106–11. http://dx.doi.org/10.1016/j.otorri.2016.06.008.

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Coimbra, Cristiana, Francisco Monteiro, Pedro Oliveira, Leandro Ribeiro, Mário Giesteira de Almeida, and Artur Condé. "Hypoparathyroidism following thyroidectomy: Predictive factors." Acta Otorrinolaringologica (English Edition) 68, no. 2 (March 2017): 106–11. http://dx.doi.org/10.1016/j.otoeng.2016.06.001.

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20

James, Adrian. "Predictive factors for recurrent cholesteatoma." Journal of Laryngology & Otology 130, S3 (May 2016): S102—S103. http://dx.doi.org/10.1017/s002221511600387x.

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21

Salkovskis, Paul M., and Katharine A. Rimes. "Predictive genetic testing: Psychological factors." Journal of Psychosomatic Research 43, no. 5 (November 1997): 477–87. http://dx.doi.org/10.1016/s0022-3999(97)00170-0.

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22

Welling, D. Bradley, James A. Merrell, Meredith Merz, and Edward E. Dodson. "Predictive Factors in Pediatric Stapedectomy." Laryngoscope 113, no. 9 (September 2003): 1515–19. http://dx.doi.org/10.1097/00005537-200309000-00018.

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23

Alarcón, Graciela S. "Predictive factors in rheumatoid arthritis." American Journal of Medicine 103, no. 6 (December 1997): S19—S24. http://dx.doi.org/10.1016/s0002-9343(97)90004-8.

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24

Monraats, P. S., W. R.P. Agema, and J. W. Jukema. "Genetic predictive factors in restenosis." Pathologie Biologie 52, no. 4 (May 2004): 186–95. http://dx.doi.org/10.1016/j.patbio.2004.02.003.

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Hayes, Daniel F. "Prognostic and predictive factors revisited." Breast 14, no. 6 (December 2005): 493–99. http://dx.doi.org/10.1016/j.breast.2005.08.023.

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Felip, E. "4IN PREDICTIVE FACTORS IN NSCLC." Lung Cancer 64 (May 2009): S3—S4. http://dx.doi.org/10.1016/s0169-5002(09)70127-8.

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Keck and Breckwoldt. "Predictive factors determining menopausal age." Therapeutische Umschau 59, no. 4 (April 1, 2002): 189–92. http://dx.doi.org/10.1024/0040-5930.59.4.189.

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Das durchschnittliche Menopausealter liegt in Mitteleuropa bei 52 Jahren. Damit beträgt die postmenopausale Phase im Leben einer Frau rund 30 Jahre. Es hätte für die individuelle Lebensplanung enorme Konsequenzen, wenn es möglich wäre, den wahrscheinlichen Zeitpunkt der Menopause vorherzusagen. Dies könnte Entscheidungen zur Familienplanung, zur Sterilitätstherapie bzw. zum Beginn einer Hormonersatztherapie (Hormonal Replacement Therapy, HRT) beeinflussen. Es gibt zahlreiche Assoziationen zwischen dem Menopausealter und sozioökonomischen/epidemiologischen Faktoren. So besteht ein gesicherter Zusammenhang zwischen Zigarettenkonsum und dem frühzeitigen Eintritt der Menopause. Ebenso ist das Menopausealter vom Ernährungsstgatus abhängig. Strittig ist die Assoziation zu generativen Faktoren und sozioökonomischen Faktoren wie Verheirateten-Status und Zugehörigkeit zu einer Gesellschaftsschicht.

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Amano, T., K. Kunimi, S. Tokunaga, and M. Ohkawa. "Refractory haemospermia: Any predictive factors?" International Urology and Nephrology 27, no. 3 (May 1995): 335–39. http://dx.doi.org/10.1007/bf02564772.

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Campos, Guilherme M. R. "Predictive Factors of Barrett Esophagus." Archives of Surgery 136, no. 11 (November 1, 2001): 1267. http://dx.doi.org/10.1001/archsurg.136.11.1267.

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Sathana, Boonyapipat, and Sukkasame Pichamon. "Prevalence, Predictive Factors and Nomogram of Residual Disease Following Cervical Conization for Adenocarcinoma in Situ." Asian Pacific Journal of Cancer Care 6, no. 3 (September 29, 2021): 317–22. http://dx.doi.org/10.31557/apjcc.2021.6.3.317-322.

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Objectives: To evaluate the prevalence of residual disease after conization in AIS women, and to develop a nomogram for predicting residual disease in such patients. Methods: Eighty-three cervical adenocarcinoma in situ (AIS) patients were retrospectively reviewed. Patient data concerning: demographic characteristics, colposcopic findings and diagnosis, type of conization, size of pathologic specimens, pathological characteristics, endocervical curettage (ECC) pathology and subsequent procedures, were collected. The rate of residual disease after conization and predictive factors for residual disease in subsequent hysterectomy were analyzed, and a predictive nomogram for residual disease was developed, based on the multivariate analysis results. The statistical significance was set at a p-value of <0.05.Results: The prevalence of residual disease in hysterectomy specimens following conization was 31.8%. Five (5.7%) women with AIS, who underwent subsequent hysterectomy, were found to have invasive adenocarcinoma. According to the multivariate analysis results, the predictive factors for residual disease were a positive endocervical margin status [OR 22.5 (95% CI 4.74, 106.79)] and a depth of specimen of < 8 mm [OR 8.11 (95% CI 1.12, 58.95)]. A nomogram for the prediction of residual disease in AIS women was developed, based on these predictive factors. After bootstrapping 1000 times, the bootstrap-corrected concordance index value for predicting residual disease was 0.852. Conclusion: The residual disease was found in 31.8% of hysterectomy specimens after conization for AIS. Residual disease was strongly associated with a positive endocervical margin and a depth of specimen of <8 mm. This study reports a feasible nomogram, with an acceptable level of accuracy for predicting the individual risk of residual disease; which may be beneficial in proper management decision-making.

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Kang, Xue-ran, Bin Chen, Yi-sheng Chen, Bin Yi, Xiaojun Yan, Chenyan Jiang, Shulun Wang, Lixing Lu, and Runjie Shi. "A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study." PeerJ 8 (September 11, 2020): e9890. http://dx.doi.org/10.7717/peerj.9890.

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Background To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. Methods A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

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Holgers, Kajsa-Mia, Soly I. Erlandsson, and Marie-Louise Barrenäs. "Predictive Factors for the Severity of Tinnitus: Factores predictivos de la severidad del tinnitus." International Journal of Audiology 39, no. 5 (January 2000): 284–91. http://dx.doi.org/10.3109/00206090009073093.

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Zhu, Difeng, Guojiang Shen, Duanyang Liu, Jingjing Chen, and Yijiang Zhang. "FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data." Sensors 19, no. 22 (November 14, 2019): 4967. http://dx.doi.org/10.3390/s19224967.

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The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.

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Ooka, Tadao, Hisashi Johno, Kazunori Nakamoto, Yoshioki Yoda, Hiroshi Yokomichi, and Zentaro Yamagata. "Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan." BMJ Nutrition, Prevention & Health 4, no. 1 (March 11, 2021): 140–48. http://dx.doi.org/10.1136/bmjnph-2020-000200.

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IntroductionEarly intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model.Research design and methodsThis study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c <6.5%. The objective variable was the change in HbA1c in the next year. Each prediction model was created with 51 health-check items and part of their change values from the previous year. We used two analytical methods to compare the predictive powers: RF as a new model and multivariate logistic regression (MLR) as a conventional model. We also created models excluding the change values to determine whether it positively affected the predictions. In addition, variable importance was calculated in the RF analysis, and standard regression coefficients were calculated in the MLR analysis to identify the predictors.ResultsThe RF model showed a higher predictive power for the change in HbA1c than MLR in all models. The RF model including change values showed the highest predictive power. In the RF prediction model, HbA1c, fasting blood glucose, body weight, alkaline phosphatase and platelet count were factors with high predictive power.ConclusionsCorrect use of the RF method may enable highly accurate risk prediction for the change in HbA1c and may allow the identification of new diabetes risk predictors.

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Mkrtchyan, Siranush А., Razmik А. Dunamalyan, Lilit E. Ghukasyan, and Marine А. Mardiyan. "SOCIO-DEMOGRAPHIC AND MEDICO-BIOLOGICAL FACTORS AS PROGNOSTIC INDICATORS OF QUALITY OF LIFE IN EARLY CHILDHOOD." Proceedings of the YSU B: Chemical and Biological Sciences 55, no. 1 (254) (April 28, 2021): 75–84. http://dx.doi.org/10.46991/pysu:b/2021.55.1.075.

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Patient’s quality of life (QL) measures are endowed with independent predictive value and these factors are considered to be more distinct than patient’s general somatic condition for predicting patient’s health condition. However, the number of researches devoted to QL prediction in the field of medical science is low. The aim of research is evaluation of predictive measure of QL of early aged children. Prospective observational study was carried out. The objects of the research were 2362 early age children (3months-3years old) from pediatric polyclinics of Yerevan. QL of children was evaluated with the international questionnaire “QUALIN”. Wald’s analytical method has been applied for predictive evaluation of QL criteria and formation of risk group. For the analysis and evaluation of the statistical material used SPSS Statistics software package. In social-hygienic factors more important were: family type, conflicts in family, disabled child and frequent morbidity families, presence of artificial nutrition since birthday. Among medico-biological factors the presence of two or more diseases in neonatal period, low and high levels of physical development, weight deficit and obesity, child’s health group and respiratory, nervous and digestive system diseases were more significant. In terms of predictive evaluation of QL, it can be stated that a number of medico-biological and socio-hygienic factors affect the overall formation of QL. By means of predictive evaluation of QL one can originally set apart targeted risk groups and if the score of predictive evaluation is +13 and higher, implement health measures, which may provide with improvements of QL criteria.

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Emsley, R., P. Oosthuizen, D. Niehaus, L. Koen, and B. Chiliza. "Changing the course of schizophrenia - predictors of treatment outcome revisited." South African Journal of Psychiatry 13, no. 1 (February 1, 2007): 5. http://dx.doi.org/10.4102/sajpsychiatry.v13i1.4.

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<p>Multiple factors play a role in determining the outcome of schizophrenia. However, the role of these factors is poorly understood, and research findings so far have been inconclusive and sometimes contradictory. Various demographic and baseline clinical factors have been reported to be associated with treatment outcome. Also, early symptom reduction after initiation of antipsychotic therapy is closely related to later treatment response. However, associations as such do not necessarily imply predictive value, and none of these factors can be regarded as clinically useful in predicting treatment outcome. This article discusses selected aspects of treatment outcome and its prediction in schizophrenia, focusing particularly on early treatment response, ethnicity, neurological soft signs, and the predictive value of a discriminant functional analysis model utilising a combination of putative predictors. Such a model holds promise, and it is to be hoped that future refinements will lead to a clinically useful model for predicting outcome.</p>

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Pinheiro, Liana, Ilka Lopes Santoro, João Aléssio Juliano Perfeito, Meyer Izbicki, Roberta Pulcheri Ramos, and Sonia Maria Faresin. "Preoperative predictive factors for intensive care unit admission after pulmonary resection." Jornal Brasileiro de Pneumologia 41, no. 1 (February 2015): 31–38. http://dx.doi.org/10.1590/s1806-37132015000100005.

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Objective: To determine whether the use of a set of preoperative variables can predict the need for postoperative ICU admission. Methods: This was a prospective observational cohort study of 120 patients undergoing elective pulmonary resection between July of 2009 and April of 2012. Prediction of ICU admission was based on the presence of one or more of the following preoperative characteristics: predicted pneumonectomy; severe/very severe COPD; severe restrictive lung disease; FEV1 or DLCO predicted to be < 40% postoperatively; SpO2 on room air at rest < 90%; need for cardiac monitoring as a precautionary measure; or American Society of Anesthesiologists physical status ≥ 3. The gold standard for mandatory admission to the ICU was based on the presence of one or more of the following postoperative characteristics: maintenance of mechanical ventilation or reintubation; acute respiratory failure or need for noninvasive ventilation; hemodynamic instability or shock; intraoperative or immediate postoperative complications (clinical or surgical); or a recommendation by the anesthesiologist or surgeon to continue treatment in the ICU. Results: Among the 120 patients evaluated, 24 (20.0%) were predicted to require ICU admission, and ICU admission was considered mandatory in 16 (66.6%) of those 24. In contrast, among the 96 patients for whom ICU admission was not predicted, it was required in 14 (14.5%). The use of the criteria for predicting ICU admission showed good accuracy (81.6%), sensitivity of 53.3%, specificity of 91%, positive predictive value of 66.6%, and negative predictive value of 85.4%. Conclusions: The use of preoperative criteria for predicting the need for ICU admission after elective pulmonary resection is feasible and can reduce the number of patients staying in the ICU only for monitoring.

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Tunthanathip, Thara, Sanguansin Ratanalert, Sakchai Sae-heng, Thakul Oearsakul, Ittichai Sakaruncchai, Anukoon Kaewborisutsakul, Thirachit Chotsampancharoen, Utcharee Intusoma, Amnat Kitkhuandee, and Tanat Vaniyapong. "Prognostic Factors and Nomogram Predicting Survival in Diffuse Astrocytoma." Journal of Neurosciences in Rural Practice 11, no. 01 (January 2020): 135–43. http://dx.doi.org/10.1055/s-0039-3403446.

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Abstract Background Prognosis of low-grade glioma are currently determined by genetic markers that are limited in some countries. This study aimed to use clinical parameters to develop a nomogram to predict survival of patients with diffuse astrocytoma (DA) which is the most common type of low-grade glioma. Materials and Methods Retrospective data of adult patients with DA from three university hospitals in Thailand were analyzed. Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Cox’s regression analyses were performed to determine associated factors. Significant associated factors from the Cox regression model were subsequently used to develop a nomogram for survival prediction. Performance of the nomogram was then tested for its accuracy. Results There were 64 patients with DA with a median age of 39.5 (interquartile range [IQR] = 20.2) years. Mean follow-up time of patients was 42 months (standard deviation [SD] = 34.3). After adjusted for three significant factors associated with survival were age ≥60 years (hazard ratio [HR] = 5.8; 95% confidence interval [CI]: 2.09–15.91), motor response score of Glasgow coma scale < 6 (HR = 75.5; 95% CI: 4.15–1,369.4), and biopsy (HR = 0.45; 95% CI: 0.21–0.92). To predict 1-year mortality, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve our nomogram was 1.0, 0.50, 0.45, 1.0, 0.64, and 0.75, respectively. Conclusions This study provided a nomogram predicting prognosis of DA. The nomogram showed an acceptable performance for predicting 1-year mortality.

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Kao, Hao-Yun, Chi-Chang Chang, Chin-Fang Chang, Ying-Chen Chen, Chalong Cheewakriangkrai, and Ya-Ling Tu. "Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease." International Journal of Environmental Research and Public Health 19, no. 3 (January 22, 2022): 1219. http://dx.doi.org/10.3390/ijerph19031219.

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Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.

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Garg, Pankaj Kumar, Deepti Choudhary, and Niladhar S. Hadke. "Hemostatic factors in breast cancer as prognostic/predictive factors." European Journal of Internal Medicine 20, no. 5 (September 2009): e130. http://dx.doi.org/10.1016/j.ejim.2008.11.006.

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Strudel, Martina, Lucia Festino, Vito Vanella, Massimiliano Beretta, Francesco M. Marincola, and Paolo A. Ascierto. "Melanoma: Prognostic Factors and Factors Predictive of Response to Therapy." Current Medicinal Chemistry 27, no. 17 (June 4, 2020): 2792–813. http://dx.doi.org/10.2174/0929867326666191205160007.

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Background: A better understanding of prognostic factors and biomarkers that predict response to treatment is required in order to further improve survival rates in patients with melanoma. Predictive Biomarkers: The most important histopathological factors prognostic of worse outcomes in melanoma are sentinel lymph node involvement, increased tumor thickness, ulceration and higher mitotic rate. Poorer survival may also be related to several clinical factors, including male gender, older age, axial location of the melanoma, elevated serum levels of lactate dehydrogenase and S100B. Predictive Biomarkers: Several biomarkers have been investigated as being predictive of response to melanoma therapies. For anti-Programmed Death-1(PD-1)/Programmed Death-Ligand 1 (PD-L1) checkpoint inhibitors, PD-L1 tumor expression was initially proposed to have a predictive role in response to anti-PD-1/PD-L1 treatment. However, patients without PD-L1 expression also have a survival benefit with anti-PD-1/PD-L1 therapy, meaning it cannot be used alone to select patients for treatment, in order to affirm that it could be considered a correlative, but not a predictive marker. A range of other factors have shown an association with treatment outcomes and offer potential as predictive biomarkers for immunotherapy, including immune infiltration, chemokine signatures, and tumor mutational load. However, none of these have been clinically validated as a factor for patient selection. For combined targeted therapy (BRAF and MEK inhibition), lactate dehydrogenase level and tumor burden seem to have a role in patient outcomes. Conclusions: With increasing knowledge, the understanding of melanoma stage-specific prognostic features should further improve. Moreover, ongoing trials should provide increasing evidence on the best use of biomarkers to help select the most appropriate patients for tailored treatment with immunotherapies and targeted therapies.

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Lu, Shubiao, Yuwen Zhou, Xuejuan Huang, Jinsong Lin, Yingyu Wu, and Zhiqiao Zhang. "Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study." PeerJ 10 (December 6, 2022): e14457. http://dx.doi.org/10.7717/peerj.14457.

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Background Chronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population. Patients and methods The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors. Results Cox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155–1.348]); albumin (HR 0.952 [95% CI 0.931–0.974); age (HR 1.033 [95% CI 1.022–1.044]); globulin (HR 1.057 [95% CI 1.038–1.077]); smoking years (HR 1.011 [95% CI 1.006–1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007–1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010–1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017–1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the “Survival Curve Prediction System for COPD patients”, was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time. Conclusion The current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).

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Nordin, Noratikah, Zurinahni Zainol, Mohd Halim Mohd Noor, and Chan Lai Fong. "A comparative study of machine learning techniques for suicide attempts predictive model." Health Informatics Journal 27, no. 1 (January 2021): 146045822198939. http://dx.doi.org/10.1177/1460458221989395.

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Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts.

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Awad, Esmail Tharwat Kamel, Mohammed Abdel Fattah Abdel Rahim, and Ahmed Mohamed Hassan. "Predictive Factors of Difficult Laparoscopic Cholecystectomy." Egyptian Journal of Hospital Medicine 82, no. 1 (January 1, 2021): 67–73. http://dx.doi.org/10.21608/ejhm.2021.137933.

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Thabet, H., M. Slim, W. Dahmani, A. Ben Slama, M. Kacem, A. Meddeb, S. Dardouri, et al. "Cirrhotic cardiomyopathy: Prevalence and predictive factors." Archives of Cardiovascular Diseases Supplements 13, no. 1 (January 2021): 43. http://dx.doi.org/10.1016/j.acvdsp.2020.10.123.

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Yalav, Orcun, and Ugur Topal. "Predictive factors associated mortality after gastrectomy." Annals of Medical Research 27, no. 1 (2020): 326. http://dx.doi.org/10.5455/annalsmedres.2019.11.692.

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Imaz Fernandez, E., C. Corchuelo-Maillo, E. Argüelles-Salido, P. Campoy-Sánchez, and R. A. Medina-López. "Impaction predictive factors in ureteral lithiasis." European Urology Open Science 39 (May 2022): S205. http://dx.doi.org/10.1016/s2666-1683(22)00247-6.

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Pramono, Ardi, Yunita Widyastuti, Yati Soenarto, Erna Rochmawati, and Sudadi. "Predictive Factors for Cardiopulmonary Resuscitation Failure." Indian Journal of Palliative Care 27 (November 9, 2021): 426–30. http://dx.doi.org/10.25259/ijpc_447_20.

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Objectives: Patients with chronic diseases are often admitted to the hospital through the emergency room of the hospital because of complaints of dyspnoea, urinary retention, decreased consciousness and cardiac arrest requiring resuscitation. The purpose of this study is to find predictive factors for failure of cardiopulmonary resuscitation (CPR) in patients of chronic diseases. Materials and Methods: This cross-sectional study took medical records of patients who were carried out from primary healthcare center in Yogyakarta from 2017 to 2019. Bivariate statistical analysis used Fisher’s exact test to determine the relative risk; if P < 0.25, then multivariate analysis with logistic regression continued with the backward method to obtain the odds ratio (OR). Results: The results indicate that cardiac arrest patients with sepsis are most likely to fail at CPR, whereas male patients are 9.1 times (OR 9.1); patients with acidosis, 8.1 times (OR 8.1); and patients with asystole heart rhythm, 7.8 times (OR 7.8, P < 0.05). We can conclude that male patients with sepsis, acidosis or asystole heart rhythm will almost certainly fail to receive resuscitation. Conclusion: Sepsis or septic shock, the male gender, acidosis, and asystole rhythm can be determinants of mortality in patients with chronic diseases who undergo CPR. It is necessary for one to test the application of the checklist or data from other hospitals and score the predictive factors to make the determination of the success of CPR easier.

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Ducray, François, Ahmed Idbaih, Xiao-Wei Wang, Caroline Cheneau, Marianne Labussiere, and Marc Sanson. "Predictive and prognostic factors for gliomas." Expert Review of Anticancer Therapy 11, no. 5 (May 2011): 781–89. http://dx.doi.org/10.1586/era.10.202.

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Budisteanu, Magdalena, Emanuela Andrei, Florentina Linca, Diana Hulea, Alexandra Velicu, Ilinca Mihailescu, Sorin Riga, et al. "Predictive factors in early onset schizophrenia." Experimental and Therapeutic Medicine 20, no. 6 (October 14, 2020): 1. http://dx.doi.org/10.3892/etm.2020.9340.

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