Key result
Renal complication cluster linked to ~9.9-fold higher in-hospital mortality vs CTT cluster.
Why the study?
With diabetes reaching epidemic proportions in Pakistan, this study applied machine learning techniques to identify comorbidity-based and diabetic complications-based clusters in hospitalised patients with diabetes and examine their association with in-hospital mortality.
Does comorbidity cluster membership identified via K-Modes machine learning predict in-hospital mortality in hospitalised adults with diabetes mellitus in Pakistan?
Cross-Sectional (n=78,271)
No
Does comorbidity cluster membership identified via K-Modes machine learning predict in-hospital mortality in hospitalised adults with diabetes mellitus in Pakistan?
Answers are grounded in this paper and cite their sources.
Unsupervised K-Modes clustering of hospitalised diabetic patients in Pakistan identified four distinct multimorbidity phenotypes, revealing that patients in the renal complication cluster faced an 18.1% in-hospital mortality rate and over six-fold higher adjusted odds of death compared with the lowest-risk cluster.
BACKGROUND: Diabetes has reached epidemic proportions in Pakistan. This study applied machine learning (ML) techniques to identify comorbidity-based and diabetic complications-based clusters in hospitalised patients with diabetes and examine their association with in-hospital mortality. DESIGN: Retrospective cross-sectional study. SETTING: Aga Khan University Hospital, Pakistan. PARTICIPANTS: Adult patients (≥18 years) with diabetes admitted between 2008 and 2021. METHODS: Diagnoses were extracted using International Classification of Diseases (ICD-9 and ICD-10) codes. Data was integrated from the Hospital Information Management Systems (HIMS). The K-Modes clustering algorithm was applied to categorical diagnostic data, with the optimal number of clusters determined using elbow curve and silhouette score analysis. Latent-Dirichlet Allocation and Term Frequency-Inverse Document Frequency were applied to identify frequent terms from each cluster followed by expert validation. Logistic regression was performed to assess the association between cluster membership and in-hospital mortality. RESULTS: Among 78 271 patients, four clusters were identified with varying mortality risks: (1) cardio, tumour and tobacco (CTT), (2) renal complication cluster (RCC), (3) cardiovascular cluster (CVC) and (4) uncontrolled diabetes mellitus, hypertension, stroke and kidney (HSK). The within cluster mortality was highest in the RCC cluster (682; 18.1%), followed by CVC (543; 4.4%), HSK (1,379; 4%) and CTT (603; 2.2%). Compared with CTT, the unadjusted odds of in-hospital mortality were significantly higher in RCC (OR=9.9, 95% CI 8.9 to 11.2, p<0.001), CVC (OR=2.1, 95% CI 1.9 to 2.3, p<0.001) and HSK (OR=1.9, 95% CI 1.7 to 2.1, p<0.001). CONCLUSION: Among hospitalised patients with diabetes, the RCC cluster demonstrated the highest mortality risk, comprising predominantly older patients with severe complications. These findings highlight the utility of unsupervised ML approaches for identifying high-risk clinical phenotypes and informing risk stratification in resource-constrained settings. Longitudinal studies are warranted to evaluate progression and long-term outcomes across clusters.
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Aziz et al. (2026) conducted a cross-sectional in Hospitalised patients with diabetes mellitus (n=78,271). Renal complication cluster (RCC) membership (vs CVC, HSK, and CTT clusters identified via K-Modes machine learning clustering) vs. Cardio, tumour and tobacco (CTT) cluster (lowest-risk reference cluster) was evaluated on In-hospital mortality during the index admission (OR 9.9, 95% CI 8.9-11.2, p=<0.001). Membership in the renal complication cluster was associated with nearly 10-fold higher odds of in-hospital mortality (18.1% vs 2.2%; OR 9.9, 95% CI 8.9-11.2, p<0.001) compared with the CTT cluster.
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