Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis
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, E...
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| Format: | Article |
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BMJ Publishing Group
2021-08-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/11/8/e047576.full |
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| author | Bianca Buurman Patricia Jepma Bastiaan Van Grootven Koen Milisen Mieke Deschodt Mariska Leeflang Joost Daams Corinne Rijpkema Lotte Verweij |
| author_facet | Bianca Buurman Patricia Jepma Bastiaan Van Grootven Koen Milisen Mieke Deschodt Mariska Leeflang Joost Daams Corinne Rijpkema Lotte Verweij |
| author_sort | Bianca Buurman |
| collection | DOAJ |
| description | 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 <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >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. |
| format | Article |
| id | doaj-art-7c01b14011aa4d8b88b346512fdd186f |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2021-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-7c01b14011aa4d8b88b346512fdd186f2024-12-08T19:50:12ZengBMJ Publishing GroupBMJ Open2044-60552021-08-0111810.1136/bmjopen-2020-047576Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysisBianca Buurman0Patricia Jepma1Bastiaan Van Grootven2Koen Milisen3Mieke Deschodt4Mariska Leeflang5Joost Daams6Corinne Rijpkema7Lotte Verweij8Faculty of Science, Amsterdam UMC Locatie AMC, Amsterdam, NetherlandsDepartment of Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The NetherlandsDepartment of Public Health and Primary Care, Katholieke Universiteit Leuven, Leuven, BelgiumDepartment of Public Health and Primary Care, KU Leuven – University of Leuven, Leuven, BelgiumDepartment of Public Health and Primary Care, KU Leuven – University of Leuven, Leuven, BelgiumFaculty of Science, Amsterdam UMC Locatie AMC, Amsterdam, Netherlands4 Medical Library, Amsterdam UMC Locatie AMC, Amsterdam, The NetherlandsFaculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, NetherlandsInstitute for Implementation Science in Health Care, University of Zurich Faculty of Medicine, Zurich, SwitzerlandObjective 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 <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >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.https://bmjopen.bmj.com/content/11/8/e047576.full |
| spellingShingle | Bianca Buurman Patricia Jepma Bastiaan Van Grootven Koen Milisen Mieke Deschodt Mariska Leeflang Joost Daams Corinne Rijpkema Lotte Verweij Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis BMJ Open |
| title | Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis |
| title_full | Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis |
| title_fullStr | Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis |
| title_full_unstemmed | Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis |
| title_short | Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis |
| title_sort | prediction models for hospital readmissions in patients with heart disease a systematic review and meta analysis |
| url | https://bmjopen.bmj.com/content/11/8/e047576.full |
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