Cargando…

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

BACKGROUND: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we s...

Descripción completa

Detalles Bibliográficos
Autores principales: Ng, Shawn Choon Wee, Kwan, Yu Heng, Yan, Shi, Tan, Chuen Seng, Low, Lian Leng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894210/
https://www.ncbi.nlm.nih.gov/pubmed/31801537
http://dx.doi.org/10.1186/s12913-019-4769-7
_version_ 1783476343008133120
author Ng, Shawn Choon Wee
Kwan, Yu Heng
Yan, Shi
Tan, Chuen Seng
Low, Lian Leng
author_facet Ng, Shawn Choon Wee
Kwan, Yu Heng
Yan, Shi
Tan, Chuen Seng
Low, Lian Leng
author_sort Ng, Shawn Choon Wee
collection PubMed
description BACKGROUND: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. METHODS: We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. RESULTS: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p <  0.05), 30- and 90-day readmission (p <  0.05) and mortality (p <  0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21–3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76–27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57–29.02, p <  0.001). CONCLUSIONS: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.
format Online
Article
Text
id pubmed-6894210
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-68942102019-12-11 The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns Ng, Shawn Choon Wee Kwan, Yu Heng Yan, Shi Tan, Chuen Seng Low, Lian Leng BMC Health Serv Res Research Article BACKGROUND: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. METHODS: We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. RESULTS: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p <  0.05), 30- and 90-day readmission (p <  0.05) and mortality (p <  0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21–3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76–27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57–29.02, p <  0.001). CONCLUSIONS: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort. BioMed Central 2019-12-04 /pmc/articles/PMC6894210/ /pubmed/31801537 http://dx.doi.org/10.1186/s12913-019-4769-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ng, Shawn Choon Wee
Kwan, Yu Heng
Yan, Shi
Tan, Chuen Seng
Low, Lian Leng
The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
title The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
title_full The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
title_fullStr The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
title_full_unstemmed The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
title_short The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
title_sort heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894210/
https://www.ncbi.nlm.nih.gov/pubmed/31801537
http://dx.doi.org/10.1186/s12913-019-4769-7
work_keys_str_mv AT ngshawnchoonwee theheterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT kwanyuheng theheterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT yanshi theheterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT tanchuenseng theheterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT lowlianleng theheterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT ngshawnchoonwee heterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT kwanyuheng heterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT yanshi heterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT tanchuenseng heterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns
AT lowlianleng heterogeneoushealthstateprofilesofhighriskhealthcareutilizersandtheirlongitudinalhospitalreadmissionandmortalitypatterns