Nader Markazi-Moghaddam1, Mohammad Fathi2, Azra Ramezankhani3. 1. Department of Public Health, School of Medicine, AJA University of Medical Sciences, Tehran, Iran; Critical Care Quality Improvement Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2. Critical Care Quality Improvement Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Anesthesiology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 3. Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: az.ramezankhani@sbmu.ac.ir.
Abstract
OBJECTIVE: We conducted a systematic review of primary models to predict intensive care unit (ICU) readmission. REVIEW METHODS: We searched MEDLINE, PubMed, Scopus, and Embase for studies on the development of ICU readmission prediction models that are published until January 2017. Data were extracted on the source of data, participants, outcomes, candidate predictors, sample size, missing data, methods for model development, and measures of model performance and model evaluation. The quality and applicability of the included studies were assessed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. RESULTS: We identified five studies describing the development of the primary prediction models of ICU readmission. Studies ranged in size from 343 to 704,963 patients with the mean age of 58.0-68.9 years. The proportion of readmission ranged from 2.5% to 9.6%. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.66-0.81. None of the studies performed external validations. The quality scores ranged from 42 to 54 out of 62, and the applicability scores from 24 to 32 out of 38. CONCLUSION: We identified five prediction models for ICU readmission. However, owing to the numerous methodological and reporting deficiencies in the included studies, physicians using these models should interpret the predictions with precautions until an external validation study shows the acceptable level of calibration and accuracy of these models.
OBJECTIVE: We conducted a systematic review of primary models to predict intensive care unit (ICU) readmission. REVIEW METHODS: We searched MEDLINE, PubMed, Scopus, and Embase for studies on the development of ICU readmission prediction models that are published until January 2017. Data were extracted on the source of data, participants, outcomes, candidate predictors, sample size, missing data, methods for model development, and measures of model performance and model evaluation. The quality and applicability of the included studies were assessed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. RESULTS: We identified five studies describing the development of the primary prediction models of ICU readmission. Studies ranged in size from 343 to 704,963 patients with the mean age of 58.0-68.9 years. The proportion of readmission ranged from 2.5% to 9.6%. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.66-0.81. None of the studies performed external validations. The quality scores ranged from 42 to 54 out of 62, and the applicability scores from 24 to 32 out of 38. CONCLUSION: We identified five prediction models for ICU readmission. However, owing to the numerous methodological and reporting deficiencies in the included studies, physicians using these models should interpret the predictions with precautions until an external validation study shows the acceptable level of calibration and accuracy of these models.
Authors: M Fathi; F Taghizadeh; H Mojtahedi; S Zargar Balaye Jame; N Markazi Moghaddam Journal: Rev Neurol (Paris) Date: 2021-09-13 Impact factor: 4.313
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