Translational andrology and urology · 2026
Electrolyte trajectory-based subphenotypes and their association with hospital length of stay in prostate cancer patients.
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Shen Yujiao, Xuan Haojie, Wang Jie, Hu Yue, Zhang Yanling
Аннотация
BACKGROUND: Hospital length of stay (LOS) is an important indicator of postoperative recovery and healthcare efficiency, and prolonged LOS increases the risk of adverse events and healthcare burden. Electrolyte disturbances influence both complications and LOS. This study aimed to identify potential electrolyte trajectory subtypes in postoperative prostate cancer patients and to evaluate their association with LOS.
METHODS: A total of 818 prostate cancer patients who underwent surgery were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Consensus clustering and group-based multi-trajectory modeling (GBMTM) were applied to classify patients based on electrolyte levels, and baseline characteristics across subphenotypes were compared. Key covariates related to LOS were screened using univariate regression and machine learning methods. Finally, regression models were constructed to analyze the association between subphenotypes and LOS.
RESULTS: Four electrolyte trajectory subphenotypes were identified. Among these, Cluster 2 represented younger patients with favorable electrolyte homeostasis, while Cluster 4 consisted of older patients with multiple comorbidities, impaired renal function, and severe electrolyte disturbances. Compared with Cluster 2, patients in Clusters 1, 3, and 4 showed progressively prolonged LOS (β=0.174, 0.242, and 0.341, all P<0.001), and the results remained significant after multivariable adjustment.
CONCLUSIONS: Electrolyte trajectory-based subtyping effectively identifies risk stratification in postoperative prostate cancer patients. Cluster 2 represents a low-risk group, whereas Cluster 4 is a high-risk group, suggesting that these subphenotypes may provide a valuable tool for perioperative management and prognosis prediction in postoperative prostate cancer patients.
METHODS: A total of 818 prostate cancer patients who underwent surgery were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Consensus clustering and group-based multi-trajectory modeling (GBMTM) were applied to classify patients based on electrolyte levels, and baseline characteristics across subphenotypes were compared. Key covariates related to LOS were screened using univariate regression and machine learning methods. Finally, regression models were constructed to analyze the association between subphenotypes and LOS.
RESULTS: Four electrolyte trajectory subphenotypes were identified. Among these, Cluster 2 represented younger patients with favorable electrolyte homeostasis, while Cluster 4 consisted of older patients with multiple comorbidities, impaired renal function, and severe electrolyte disturbances. Compared with Cluster 2, patients in Clusters 1, 3, and 4 showed progressively prolonged LOS (β=0.174, 0.242, and 0.341, all P<0.001), and the results remained significant after multivariable adjustment.
CONCLUSIONS: Electrolyte trajectory-based subtyping effectively identifies risk stratification in postoperative prostate cancer patients. Cluster 2 represents a low-risk group, whereas Cluster 4 is a high-risk group, suggesting that these subphenotypes may provide a valuable tool for perioperative management and prognosis prediction in postoperative prostate cancer patients.