Evidence map›Paper›PMID 31801537›Full record

ArticleBMC health services research2019

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

Shawn Choon Wee Ng, Yu Heng Kwan, Shi Yan, Chuen Seng Tan, Lian Leng Low

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Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

  1. Article
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  3. Categorizing Comorbid Risk for People Living With HIV: A Latent Profile Analysis.Journal of acquired immune deficiency syndromes (1999) · 2022
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shawn Choon Wee NgDuke-NUS Medical School, Singapore, Singapore.
Yu Heng KwanDuke-NUS Medical School, Singapore, Singapore.
Shi YanDuke-NUS Medical School, Singapore, Singapore.
Chuen Seng TanSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Lian Leng LowSingHealth Regional Health System, Singapore Health Services, Singapore, Singapore. low.lian.leng@sgh.com.sg.ORCID http://orcid.org/0000-0003-4228-2862

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-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.

methodsWe 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.

resultsAmong 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).

conclusionsWe 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.

Indexed as

Transitional CareAdultAgedAnalysis of VarianceCognitive DysfunctionFemaleFrail ElderlyHumansLatent Class AnalysisLogistic ModelsLongitudinal StudiesMaleMiddle AgedMortalityPatient Acceptance of Health CarePatient ReadmissionHigh-risk healthcare utilizersHospital readmissionsIntegrated careLatent class analysisMortalityTransitional care program

Identifiers

PMID31801537
PMCPMC6894210

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