Ramtin Hakimjavadi, Shahin Basiratzadeh, Eugene K. Wai, Natalie Baddour, Stephen Kingwell, Wojtek Michalowski, Alexandra Stratton, Eve Tsai, Herna Viktor, Philippe Phan
{"title":"外伤性脊髓损伤的多变量预测模型:系统综述","authors":"Ramtin Hakimjavadi, Shahin Basiratzadeh, Eugene K. Wai, Natalie Baddour, Stephen Kingwell, Wojtek Michalowski, Alexandra Stratton, Eve Tsai, Herna Viktor, Philippe Phan","doi":"10.46292/sci23-00010","DOIUrl":null,"url":null,"abstract":"Background Traumatic spinal cord injuries (TSCI) greatly affect the lives of patients and their families. Prognostication may improve treatment strategies, health care resource allocation, and counseling. Multivariable clinical prediction models (CPMs) for prognosis are tools that can estimate an absolute risk or probability that an outcome will occur. Objectives We sought to systematically review the existing literature on CPMs for TSCI and critically examine the predictor selection methods used. Methods We searched MEDLINE, PubMed, Embase, Scopus, and IEEE for English peer-reviewed studies and relevant references that developed multivariable CPMs to prognosticate patient-centered outcomes in adults with TSCI. Using narrative synthesis, we summarized the characteristics of the included studies and their CPMs, focusing on the predictor selection process. Results We screened 663 titles and abstracts; of these, 21 full-text studies (2009-2020) consisting of 33 distinct CPMs were included. The data analysis domain was most commonly at a high risk of bias when assessed for methodological quality. Model presentation formats were inconsistently included with published CPMs; only two studies followed established guidelines for transparent reporting of multivariable prediction models. Authors frequently cited previous literature for their initial selection of predictors, and stepwise selection was the most frequent predictor selection method during modelling. Conclusion Prediction modelling studies for TSCI serve clinicians who counsel patients, researchers aiming to risk-stratify participants for clinical trials, and patients coping with their injury. Poor methodological rigor in data analysis, inconsistent transparent reporting, and a lack of model presentation formats are vital areas for improvement in TSCI CPM research.","PeriodicalId":46769,"journal":{"name":"Topics in Spinal Cord Injury Rehabilitation","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review\",\"authors\":\"Ramtin Hakimjavadi, Shahin Basiratzadeh, Eugene K. Wai, Natalie Baddour, Stephen Kingwell, Wojtek Michalowski, Alexandra Stratton, Eve Tsai, Herna Viktor, Philippe Phan\",\"doi\":\"10.46292/sci23-00010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background Traumatic spinal cord injuries (TSCI) greatly affect the lives of patients and their families. Prognostication may improve treatment strategies, health care resource allocation, and counseling. Multivariable clinical prediction models (CPMs) for prognosis are tools that can estimate an absolute risk or probability that an outcome will occur. Objectives We sought to systematically review the existing literature on CPMs for TSCI and critically examine the predictor selection methods used. Methods We searched MEDLINE, PubMed, Embase, Scopus, and IEEE for English peer-reviewed studies and relevant references that developed multivariable CPMs to prognosticate patient-centered outcomes in adults with TSCI. Using narrative synthesis, we summarized the characteristics of the included studies and their CPMs, focusing on the predictor selection process. Results We screened 663 titles and abstracts; of these, 21 full-text studies (2009-2020) consisting of 33 distinct CPMs were included. The data analysis domain was most commonly at a high risk of bias when assessed for methodological quality. Model presentation formats were inconsistently included with published CPMs; only two studies followed established guidelines for transparent reporting of multivariable prediction models. Authors frequently cited previous literature for their initial selection of predictors, and stepwise selection was the most frequent predictor selection method during modelling. Conclusion Prediction modelling studies for TSCI serve clinicians who counsel patients, researchers aiming to risk-stratify participants for clinical trials, and patients coping with their injury. Poor methodological rigor in data analysis, inconsistent transparent reporting, and a lack of model presentation formats are vital areas for improvement in TSCI CPM research.\",\"PeriodicalId\":46769,\"journal\":{\"name\":\"Topics in Spinal Cord Injury Rehabilitation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topics in Spinal Cord Injury Rehabilitation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46292/sci23-00010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REHABILITATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topics in Spinal Cord Injury Rehabilitation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46292/sci23-00010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REHABILITATION","Score":null,"Total":0}
Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review
Background Traumatic spinal cord injuries (TSCI) greatly affect the lives of patients and their families. Prognostication may improve treatment strategies, health care resource allocation, and counseling. Multivariable clinical prediction models (CPMs) for prognosis are tools that can estimate an absolute risk or probability that an outcome will occur. Objectives We sought to systematically review the existing literature on CPMs for TSCI and critically examine the predictor selection methods used. Methods We searched MEDLINE, PubMed, Embase, Scopus, and IEEE for English peer-reviewed studies and relevant references that developed multivariable CPMs to prognosticate patient-centered outcomes in adults with TSCI. Using narrative synthesis, we summarized the characteristics of the included studies and their CPMs, focusing on the predictor selection process. Results We screened 663 titles and abstracts; of these, 21 full-text studies (2009-2020) consisting of 33 distinct CPMs were included. The data analysis domain was most commonly at a high risk of bias when assessed for methodological quality. Model presentation formats were inconsistently included with published CPMs; only two studies followed established guidelines for transparent reporting of multivariable prediction models. Authors frequently cited previous literature for their initial selection of predictors, and stepwise selection was the most frequent predictor selection method during modelling. Conclusion Prediction modelling studies for TSCI serve clinicians who counsel patients, researchers aiming to risk-stratify participants for clinical trials, and patients coping with their injury. Poor methodological rigor in data analysis, inconsistent transparent reporting, and a lack of model presentation formats are vital areas for improvement in TSCI CPM research.
期刊介绍:
Now in our 22nd year as the leading interdisciplinary journal of SCI rehabilitation techniques and care. TSCIR is peer-reviewed, practical, and features one key topic per issue. Published topics include: mobility, sexuality, genitourinary, functional assessment, skin care, psychosocial, high tetraplegia, physical activity, pediatric, FES, sci/tbi, electronic medicine, orthotics, secondary conditions, research, aging, legal issues, women & sci, pain, environmental effects, life care planning