Accepted for/Published in: JMIR Cancer
Date Submitted: Jan 30, 2025
Open Peer Review Period: Jan 30, 2025 - Mar 27, 2025
Date Accepted: May 26, 2025
(closed for review but you can still tweet)
Comorbidity clusters and mortality in patients with cancer: Predictive modeling using machine learning approaches of data from the US and Hong Kong
ABSTRACT
Background:
Multimorbidity is common among patients with cancer; however, research on prognosis has predominantly focused on cancers in isolation.
Objective:
This study investigated comorbidity clusters among patients with cancer using machine learning and examined their associations with survival outcomes in two large representative samples of the US and Hong Kong.
Methods:
This study used data from the National Health and Nutrition Examination Survey (NHANES) and the Hospital Authority Data Collaboration Laboratory (HADCL). Individuals aged ≥20 years with a history of cancer were included. Bernoulli mixture model and other clustering approaches were used to identify comorbidity clusters. Cox proportional hazards models were used to analyze the associations between comorbidity clusters and mortality outcomes.
Results:
The study included 4,390 individuals in NHANES and 12,484 individuals in HADCL. Four comorbidity clusters were identified: Low Comorbidity, Metabolic, Cardiovascular disease [CVD], Respiratory. In NHANES, individuals in the Respiratory Cluster had the highest risk of all-cause mortality (aHR=1.62, 95%CI=1.26-2.08, P<.001), followed by the CVD Cluster (aHR=1.50, 95%CI=1.26-1.80, P<.001) compared to the Low Comorbidity Cluster. The three clusters were associated with higher risks of CVD-related mortality (aHR=1.48-3.05, P <.003). The effects of comorbidity clusters on mortality were modified by income-to-poverty ratio (P for interaction=.04), diet quality (P for interaction=.02), and cancer prognosis (P for interaction=.005). In the HADCL (validation) cohort, individuals in the Respiratory and CVD Clusters had a higher risk of all-cause mortality.
Conclusions:
High comorbidity burden clusters showed increased all-cause and CVD-related mortality in patients with cancer. These findings highlight the significance of considering comorbidity burden in cancer care, including the importance of managing metabolic risk factors. Machine learning approaches can provide valuable insights into complex multimorbidity profiles. Further research is needed to deepen understanding of the relationships between multimorbidity and cancer-specific outcomes.
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