ArticleBMC health services research2020
Tailoring integrated care services for high-risk patients with multiple chronic conditions: a risk stratification approach using cluster analysis.
Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of disease clusters in multimorbidity.Nature communications · 2025Pooled it
- Healthcare utilization and chronic condition clusters in multimorbidity patients using weighted k-means: a register-based study in Denmark.Health information science and systems · 2026Article
- Identifying and characterizing subgroups of medically complex older patients in community-based intermediate care: a latent class analysis of Danish municipal temporary stay patients.BMC health services research · 2026Article
- Co-Design of a New Integrated Care Model With People Affected by Huntington's Disease: A Mixed Methods Study.Health expectations : an international journal of public participation in health care and health policy · 2026Article
- Deep representation learning for clustering longitudinal survival data from electronic health records.Nature communications · 2025Article
- Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19.Frontiers in public health · 2025Article
- Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling.BMC medical research methodology · 2024Article
- Multimorbidity clusters in adults 50 years or older with and without a history of cancer: National Health Interview Survey, 2018.BMC geriatrics · 2024Article
- The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function.BMC medicine · 2022Article
- Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning.Life (Basel, Switzerland) · 2022Article
- Variability in Healthcare Expenditure According to the Stratification of Adjusted Morbidity Groups in the Canary Islands (Spain).International journal of environmental research and public health · 2022Observational
- Identifying risk patterns in older adults with atrial fibrillation by hierarchical cluster analysis: A retrospective approach based on the risk probability for clinical events.International journal of cardiology. Heart & vasculature · 2021Article
- Profile of multimorbidity in outpatients attending public healthcare settings: A descriptive cross-sectional study from Odisha, India.Journal of family medicine and primary care · 2021Article
- Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles.JAMA network open · 2020Article
- Maternal multimorbidity and chronic conditions as predictors of preterm birth: A matched case-control study in Cyprus.Journal of multimorbidity and comorbidityArticle
- Multimorbidity and acute infectious diseases in urban and semi-urban settings in Peru: A mixed-methods study.Journal of multimorbidity and comorbidityArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to produce a risk stratification within a population of high-risk patients with multiple chronic conditions who are currently treated under a case management program and to explore the existence of different risk subgroups. Different care strategies were then suggested for healthcare reform according to the characteristics of each subgroup.
methodsAll high-risk multimorbid patients from a case management program in the Navarra region of Spain were included in the study (n = 885). A 1-year mortality risk score was estimated for each patient by logistic regression. The population was then divided into subgroups according to the patients' estimated risk scores. We used cluster analysis to produce the stratification with Ward's linkage hierarchical algorithm. The characteristics of the resulting subgroups were analyzed, and post hoc pairwise tests were performed.
resultsThree distinct risk strata were found, containing 45, 38 and 17% of patients. Age increased from cluster to cluster, and functional status, clinical severity, nursing needs and nutritional values deteriorated. Patients in cluster 1 had lower renal deterioration values, and patients in cluster 3 had higher rates of pressure skin ulcers, higher rates of cerebrovascular disease and dementia, and lower prevalence rates of chronic obstructive pulmonary disease.
conclusionsThis study demonstrates the existence of distinct subgroups within a population of high-risk patients with multiple chronic conditions. Current case management integrated care programs use a uniform treatment strategy for patients who have diverse needs. Alternative treatment strategies should be considered to fit the needs of each patient subgroup.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.