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The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients

BACKGROUND: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functio...

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Autores principales: Jepma, Patricia, Verweij, Lotte, Tijssen, Arno, Heymans, Martijn W., Flierman, Isabelle, Latour, Corine H. M., Peters, Ron J. G., Scholte op Reimer, Wilma J. M., Buurman, Bianca M., ter Riet, Gerben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105911/
https://www.ncbi.nlm.nih.gov/pubmed/33964888
http://dx.doi.org/10.1186/s12877-021-02243-5
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author Jepma, Patricia
Verweij, Lotte
Tijssen, Arno
Heymans, Martijn W.
Flierman, Isabelle
Latour, Corine H. M.
Peters, Ron J. G.
Scholte op Reimer, Wilma J. M.
Buurman, Bianca M.
ter Riet, Gerben
author_facet Jepma, Patricia
Verweij, Lotte
Tijssen, Arno
Heymans, Martijn W.
Flierman, Isabelle
Latour, Corine H. M.
Peters, Ron J. G.
Scholte op Reimer, Wilma J. M.
Buurman, Bianca M.
ter Riet, Gerben
author_sort Jepma, Patricia
collection PubMed
description BACKGROUND: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. AIM: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. METHODS: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (P(HL)) to describe predictive performance in terms of discrimination and calibration. RESULTS: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56–0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63–0.73; P(HL) was 0.658). DISCUSSION: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02243-5.
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spelling pubmed-81059112021-05-10 The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients Jepma, Patricia Verweij, Lotte Tijssen, Arno Heymans, Martijn W. Flierman, Isabelle Latour, Corine H. M. Peters, Ron J. G. Scholte op Reimer, Wilma J. M. Buurman, Bianca M. ter Riet, Gerben BMC Geriatr Research BACKGROUND: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. AIM: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. METHODS: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (P(HL)) to describe predictive performance in terms of discrimination and calibration. RESULTS: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56–0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63–0.73; P(HL) was 0.658). DISCUSSION: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02243-5. BioMed Central 2021-05-08 /pmc/articles/PMC8105911/ /pubmed/33964888 http://dx.doi.org/10.1186/s12877-021-02243-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Jepma, Patricia
Verweij, Lotte
Tijssen, Arno
Heymans, Martijn W.
Flierman, Isabelle
Latour, Corine H. M.
Peters, Ron J. G.
Scholte op Reimer, Wilma J. M.
Buurman, Bianca M.
ter Riet, Gerben
The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
title The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
title_full The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
title_fullStr The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
title_full_unstemmed The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
title_short The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
title_sort performance of the dutch safety management system frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105911/
https://www.ncbi.nlm.nih.gov/pubmed/33964888
http://dx.doi.org/10.1186/s12877-021-02243-5
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