TY - JOUR
T1 - Prediction models for hospital readmissions in patients with heart disease: A systematic review and meta-analysis
AU - van Grootven, Bastiaan
AU - Jepma, Patricia
AU - Rijpkema, Corinne
AU - Verweij, Lotte
AU - Leeflang, Mariska
AU - Daams, Joost
AU - Deschodt, Mieke
AU - Milisen, Koen
AU - Flamaing, Johan
AU - Buurman, Bianca
N1 - Funding Information: Funding This work was partly supported by the Research Foundation Flanders (FWO) fellowship grant (grant number 1165518N (BVG)), and by the Dutch Research Council (NWO) (grant number 023.009.036 (PJ)). The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. Publisher Copyright: © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - Objective To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions. Design Systematic review and meta-analysis. Data source Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020. Eligibility criteria for selecting studies Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled. Results Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was 0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability. PROSPERO registration number CRD42020159839.
AB - Objective To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions. Design Systematic review and meta-analysis. Data source Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020. Eligibility criteria for selecting studies Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled. Results Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was 0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability. PROSPERO registration number CRD42020159839.
KW - adverse events
KW - cardiology
KW - risk management
UR - https://pure.hva.nl/ws/files/18293785/e047576.full_3_.pdf
UR - http://www.scopus.com/inward/record.url?scp=85113500925&partnerID=8YFLogxK
U2 - https://doi.org/10.1136/bmjopen-2020-047576
DO - https://doi.org/10.1136/bmjopen-2020-047576
M3 - Article
C2 - 34404703
SN - 2044-6055
VL - 11
SP - 1
EP - 18
JO - BMJ Open
JF - BMJ Open
IS - 8
M1 - e047576
ER -