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Vagliano I, Kingma MY, Dongelmans DA, de Lange DW, de Keizer NF, Schut MC. Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU. Comput Biol Med 2023; 163:107146. [PMID: 37356293 PMCID: PMC10266884 DOI: 10.1016/j.compbiomed.2023.107146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND - Subgroup discovery (SGD) is the automated splitting of the data into complex subgroups. Various SGD methods have been applied to the medical domain, but none have been extensively evaluated. We assess the numerical and clinical quality of SGD methods. METHOD - We applied the improved Subgroup Set Discovery (SSD++), Patient Rule Induction Method (PRIM) and APRIORI - Subgroup Discovery (APRIORI-SD) algorithms to obtain patient subgroups on observational data of 14,548 COVID-19 patients admitted to 73 Dutch intensive care units. Hospital mortality was the clinical outcome. Numerical significance of the subgroups was assessed with information-theoretic measures. Clinical significance of the subgroups was assessed by comparing variable importance on population and subgroup levels and by expert evaluation. RESULTS - The tested algorithms varied widely in the total number of discovered subgroups (5-62), the number of selected variables, and the predictive value of the subgroups. Qualitative assessment showed that the found subgroups make clinical sense. SSD++ found most subgroups (n = 62), which added predictive value and generally showed high potential for clinical use. APRIORI-SD and PRIM found fewer subgroups (n = 5 and 6), which did not add predictive value and were clinically less relevant. CONCLUSION - Automated SGD methods find clinical subgroups that are relevant when assessed quantitatively (yield added predictive value) and qualitatively (intensivists consider the subgroups significant). Different methods yield different subgroups with varying degrees of predictive performance and clinical quality. External validation is needed to generalize the results to other populations and future research should explore which algorithm performs best in other settings.
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Affiliation(s)
- I Vagliano
- Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Postbus 7057, 1007 MB, Amsterdam, the Netherlands.
| | - M Y Kingma
- Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, the Netherlands
| | - D A Dongelmans
- Amsterdam Public Health (APH), Postbus 7057, 1007 MB, Amsterdam, the Netherlands; Dept. of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, the Netherlands; National Intensive Care Evaluation (NICE) Foundation, Postbus 23640, 1100 EC, Amsterdam, the Netherlands
| | - D W de Lange
- National Intensive Care Evaluation (NICE) Foundation, Postbus 23640, 1100 EC, Amsterdam, the Netherlands; Dept. of Intensive Care, University Medical Center Utrecht, University Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - N F de Keizer
- Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Postbus 7057, 1007 MB, Amsterdam, the Netherlands; National Intensive Care Evaluation (NICE) Foundation, Postbus 23640, 1100 EC, Amsterdam, the Netherlands
| | - M C Schut
- Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Postbus 7057, 1007 MB, Amsterdam, the Netherlands; Dept. of Clinical Chemistry, Amsterdam UMC, Vrije Universiteit Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, the Netherlands
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