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Multimorbidity patterns with K-means nonhierarchical cluster analysis.

Concepción Violán1,2, Albert Roso-Llorach3,4, Quintí Foguet-Boreu3,4,5

  • 1Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Gran Via Corts Catalanes, 587 àtic, 08007, Barcelona, Spain. cviolan@idiapjgol.org.

BMC Family Practice
|July 5, 2018
PubMed
Summary
This summary is machine-generated.

This study identified distinct multimorbidity patterns in adult primary care patients using cluster analysis. These patterns align with clinical practice, helping to define patient subgroups.

Keywords:
Cluster analysisDiseasesElectronic health recordsK-means clusteringMultimorbidityMultiple correspondence analysisPrimary health care

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Area of Science:

  • Primary Care Research
  • Health Services Research
  • Epidemiology

Background:

  • Multimorbidity presents a significant challenge in adult primary care.
  • Understanding patterns of co-occurring conditions is crucial for effective patient management.

Purpose of the Study:

  • To identify and characterize multimorbidity patterns in adult patients attending primary care centers in Catalonia.
  • To apply non-hierarchical cluster analysis for pattern discovery.

Main Methods:

  • Cross-sectional study of 523,656 adult patients (45-64 years) using electronic health records from 2010.
  • Employed multiple correspondence analysis followed by k-means clustering to identify multimorbidity patterns.
  • Analysis was stratified by sex.

Main Results:

  • Analysis included 408,994 patients meeting multimorbidity criteria.
  • Six distinct multimorbidity patterns were identified for each sex.
  • The most prevalent patterns, common to both sexes, included metabolic, hypertensive, substance use, musculoskeletal, and soft tissue disorders.

Conclusions:

  • Non-hierarchical cluster analysis effectively identified clinically relevant multimorbidity patterns.
  • These findings help in defining phenotypic subgroups of patients with multimorbidity.
  • The identified patterns can inform clinical practice and patient care strategies.