Huang Xinru, Zhang Ting, Li Bin, Meng Yahui, Li Fen
School of Management, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Medical Affairs Department, Huai'an No.3 People's Hospital, Hui'an, China.
Front Psychiatry. 2025 Jul 25;16:1619051. doi: 10.3389/fpsyt.2025.1619051. eCollection 2025.
Participants with schizophrenia face the dual impacts of mental illness and physical diseases, which significantly affect their clinical prognosis and quality of life.
This study, which is based on multivariate clinical data, using data-driven approaches (statistical analysis and data mining techniques) to systematically characterize the comorbidities landscape of schizophrenia patients identifying key patterns and clinical profiles to inform optimized treatment strategies and health management interventions.
It was found that the comorbidity rate among hospitalized schizophrenia participants is notably high in China. Notably, significant variations in comorbidity profiles were identified across diverse demographic and clinical variables, including age, occupational status, marital status, duration of hospital stay, frequency of hospitalizations, and health insurance status. The comorbidities in schizophrenia participants primarily include acute upper respiratory infections (J00-J06), metabolic disorders (E70-E90), hypertension (I10-I15), and diabetes (E1 0-E14). Furthermore, four strongly associated comorbidities patterns were identified: the metabolic-cardiovascular comorbidities cluster, the co-disease cluster of respiratory system infection, gut respiratory symptom cluster, electrolyte imbalance infection trigger cluster were identified.
The exploration of comorbidity characteristics and patterns in schizophrenia provides a quantifiable tool for enhancing treatment and health management outcomes for participants while also offering a reference for advancing the application of precision medicine in the treatment and management of schizophrenia.
精神分裂症患者面临精神疾病和躯体疾病的双重影响,这显著影响他们的临床预后和生活质量。
本研究基于多变量临床数据,采用数据驱动方法(统计分析和数据挖掘技术)系统地描述精神分裂症患者的共病情况,识别关键模式和临床特征,为优化治疗策略和健康管理干预提供依据。
研究发现,中国住院精神分裂症患者的共病率显著较高。值得注意的是,在不同的人口统计学和临床变量中,包括年龄、职业状况、婚姻状况、住院时间、住院频率和医疗保险状况,共病情况存在显著差异。精神分裂症患者的共病主要包括急性上呼吸道感染(J00-J06)、代谢紊乱(E70-E90)、高血压(I10-I15)和糖尿病(E10-E14)。此外,还识别出四种强相关的共病模式:代谢-心血管共病集群、呼吸系统感染共病集群、肠道呼吸症状集群、电解质失衡感染触发集群。
对精神分裂症共病特征和模式的探索为提高患者的治疗和健康管理效果提供了一种可量化的工具,同时也为推进精准医学在精神分裂症治疗和管理中的应用提供了参考。