TY - JOUR T1 - Predicting falls in community-dwelling older adults: a systematic review of prognostic models JF - BMJ Open JO - BMJ Open DO - 10.1136/bmjopen-2020-044170 VL - 11 IS - 5 SP - e044170 AU - Gustav Valentin Gade AU - Martin Grønbech Jørgensen AU - Jesper Ryg AU - Johannes Riis AU - Katja Thomsen AU - Tahir Masud AU - Stig Andersen Y1 - 2021/05/01 UR - http://bmjopen.bmj.com/content/11/5/e044170.abstract N2 - Objective To systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteria Prospective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.Information source MEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.Data extraction and risk of bias Two authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.Results After screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models’ The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.Conclusions An abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.PROSPERO registration number CRD42019124021.All data relevant to the study are included in the article or uploaded as online supplemental information. The study protocol is available online at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124021. All information extracted for the included studies in the review is available as a data supplement. ER -