In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

Affiliations

¹ Research Fellow, Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
² Research Fellow, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
³ Lecturer in Epidemiology and Health Informatics, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
⁴ Emeritus Director, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
⁵ Professor of Artificial Intelligence, Nuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
⁶ Director, Health Data Science, AstraZeneca, 1 Francis Crick Avenue, Cambridge, Biomedical Campus, Cambridge, CB2 0AA, UK.
⁷ MRC Investigator, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
⁸ Professor of Public Health and Health Informatics, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
⁹ Professor of General Practice, Primary Care Unit, Strangeways Research Laboratory Worts Causeway Cambridge CB1 8RN, UK.
¹⁰ Senior Clinical Research Associate, Primary Care Unit, Primary Care Unit, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK.
¹¹ Professor of Public Health and Primary Care, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK. Electronic address: T.P.Marshall@bham.ac.uk.

PMID: 36228971
PMCID: PMC7613854
DOI: 10.1016/j.jclinepi.2022.10.011

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

Linda Nichols et al. J Clin Epidemiol. 2022 Dec.

. 2022 Dec:152:164-175.

doi: 10.1016/j.jclinepi.2022.10.011. Epub 2022 Oct 11.

Authors

Affiliations

¹ Research Fellow, Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
² Research Fellow, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
³ Lecturer in Epidemiology and Health Informatics, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
⁴ Emeritus Director, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
⁵ Professor of Artificial Intelligence, Nuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
⁶ Director, Health Data Science, AstraZeneca, 1 Francis Crick Avenue, Cambridge, Biomedical Campus, Cambridge, CB2 0AA, UK.
⁷ MRC Investigator, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
⁸ Professor of Public Health and Health Informatics, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK.
⁹ Professor of General Practice, Primary Care Unit, Strangeways Research Laboratory Worts Causeway Cambridge CB1 8RN, UK.
¹⁰ Senior Clinical Research Associate, Primary Care Unit, Primary Care Unit, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK.
¹¹ Professor of Public Health and Primary Care, Institute of Applied Health Research, University of Birmingham, B15 2TT, UK. Electronic address: T.P.Marshall@bham.ac.uk.

PMID: 36228971
PMCID: PMC7613854
DOI: 10.1016/j.jclinepi.2022.10.011

Abstract

Background and objectives: To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering.

Methods: We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples.

Results: In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54).

Conclusion: LCA achieved higher aRI than other clustering algorithms.

Keywords: Clustering methods; Electronic medical records; Hierarchical cluster analysis; K-means; Latent class analysis; Multimorbidity; Multiple correspondence analysis.

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Conflict of interest statement

Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Tom Marshall reports financial support was provided by UKRI - research grant from NIHR-MRC for BIRMCAM study. Jessica Barrett reports financial support was provided by Medical Research Council (Biostatistics Unit). Sylvia Richardson reports financial support was provided by Medical Research Council (Biostatistics Unit). Paul Kirk reports was provided by Medical Research Council (Biostatistics Unit). Linda Nichols reports financial support was provided by UKRI - research grant from NIHR-MRC for BIRMCAM study. Tom Taverner reports financial support was provided by UKRI - research grant from NIHR-MRC for BIRMCAM study. Krishnarajah Nirantharakumar reports financial support was provided by Health Data Research UK - fellowship. Paul Kirk reports a relationship with Director, Health Data Science, AstraZeneca that includes: employment.

Figures

**Fig. 1**
Simulated dataset of patients with two or more conditions in 3 clusters, within cluster disease prevalence approximately 15%, noise approximately 0.5%, overlap of diseases between clusters: examining the effect of varying correlation of diseases within a cluster. Error bars show interquartile range (IQR).

**Fig. 2**
Simulated dataset of patients with two or more conditions in 3 clusters, within cluster disease prevalence approximately 15%, correlation = 0.5, overlap of diseases between clusters: examining the effect of varying the amount of noise.

**Fig. 3**
Simulated dataset of patients with two or more conditions in 3 clusters, noise approximately 4%, correlation = 0.5, overlap of diseases between clusters: examining the effect of varying within cluster prevalence of disease.

**Fig. 4**
Simulated dataset of patients with two or more conditions in 4 clusters, within cluster disease prevalence approximately 24%, noise approximately 0.5%, correlation = 0.5, overlap of diseases between clusters: examining the effect of varying the number of clusters algorithm is asked to find.

See this image and copyright information in PMC

References

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In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

Affiliations

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Research Materials