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Background <p>Ovarian cancer (OC) patients admitted to the Intensive Care Unit (ICU) represent a critically ill population with significant heterogeneity, posing challenges for prognosis and personalized management. A robust subtyping system integrating multidimensional clinical data is urgently needed.</p> Objectives <p>This study aimed to identify and characterize distinct clinical phenotypes of OC in critical care settings and investigate their associations with outcomes and subtype-specific treatment responses.</p> Methods <p>This retrospective cohort study analyzed data from 448 ICU patients with OC across two critical care databases (MIMIC-IV and eICU). We employed a Multiple Clustering Algorithms for OC Subtyping (MCAOCS) approach to identify phenotypes. The robustness of subtypes was assessed through internal and external application. Survival analysis and Inverse Probability of Treatment Weighting (IPTW) were used to evaluate mortality and treatment-exposure association.</p> Results <p>We identified two distinct clinical phenotypes from the MIMIC-IV discovery cohort (<i>n</i> = 259): Critical OC (<i>n</i> = 118) characterized by hemodynamic instability and high disease severity, and Stable OC (<i>n</i> = 141) with preserved organ function. The robustness and reproducibility of these phenotypes were assessed in the external eICU application cohort (<i>n</i> = 189). The Critical OC subtype demonstrated significantly higher mortality from 28 days to one year. Exploratory treatment-exposure analyses suggested subtype-specific associations: several ICU support and medication exposures were associated with lower 28-day mortality in the Critical OC subtype, whereas in the Stable OC subtype, only insulin and phenylephrine exposures retained statistical significance after multiple-testing correction. These findings should be interpreted as hypothesis-generating rather than causal.</p> Conclusions <p>The MCAOCS-based binary phenotype system is robust and may have potential clinical utility, effectively stratifying ICU OC patients by mortality risk and suggesting subtype-specific treatment associations, thereby potentially facilitating precision critical care.</p>

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Multiple Clustering Algorithms for Ovarian Cancer Subtyping (MCAOCS): identifying clinical phenotypes and treatment response in critical care

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Yamin Qiu,
  • Zhengwen Qin,
  • Huixian Zheng,
  • Tao Wang,
  • Runhong Liu,
  • Jing Yang

摘要

Background

Ovarian cancer (OC) patients admitted to the Intensive Care Unit (ICU) represent a critically ill population with significant heterogeneity, posing challenges for prognosis and personalized management. A robust subtyping system integrating multidimensional clinical data is urgently needed.

Objectives

This study aimed to identify and characterize distinct clinical phenotypes of OC in critical care settings and investigate their associations with outcomes and subtype-specific treatment responses.

Methods

This retrospective cohort study analyzed data from 448 ICU patients with OC across two critical care databases (MIMIC-IV and eICU). We employed a Multiple Clustering Algorithms for OC Subtyping (MCAOCS) approach to identify phenotypes. The robustness of subtypes was assessed through internal and external application. Survival analysis and Inverse Probability of Treatment Weighting (IPTW) were used to evaluate mortality and treatment-exposure association.

Results

We identified two distinct clinical phenotypes from the MIMIC-IV discovery cohort (n = 259): Critical OC (n = 118) characterized by hemodynamic instability and high disease severity, and Stable OC (n = 141) with preserved organ function. The robustness and reproducibility of these phenotypes were assessed in the external eICU application cohort (n = 189). The Critical OC subtype demonstrated significantly higher mortality from 28 days to one year. Exploratory treatment-exposure analyses suggested subtype-specific associations: several ICU support and medication exposures were associated with lower 28-day mortality in the Critical OC subtype, whereas in the Stable OC subtype, only insulin and phenylephrine exposures retained statistical significance after multiple-testing correction. These findings should be interpreted as hypothesis-generating rather than causal.

Conclusions

The MCAOCS-based binary phenotype system is robust and may have potential clinical utility, effectively stratifying ICU OC patients by mortality risk and suggesting subtype-specific treatment associations, thereby potentially facilitating precision critical care.