收稿日期: 2025-01-08
网络出版日期: 2025-04-30
基金资助
河南省医学科技攻关计划省部共建项目(SBGJ202103094)
Establishment of a nomogram model for comorbid cardiovascular disease in schizophrenia based on electrocardiogram parameters and kynurenine metabolites
Received date: 2025-01-08
Online published: 2025-04-30
目的 探讨精神分裂症共患心血管疾病影响因素，并构建列线图模型。 方法 回顾性收集2023年6月至2024年10月医院586例精神分裂症患者临床资料，包含100例共患心血管疾病，486例未共患心血管疾病，分别设为共患组（n = 100）和非共患组（n = 486），采用倾向性评分匹配法（PSM）降低组间偏倚。统计两组临床资料、心电图参数［经心率校正的QT（QTc）、经心率校正的Tp-Te间期（TP-Tec）］、犬尿氨酸（KYN）代谢物［色氨酸（TRP）、KYN、犬尿喹啉酸（KYNA）］，采用logistic回归方程筛选精神分裂症共患心血管疾病影响因素，构建列线图模型并验证。 结果 （1）BMI、吸烟、饮酒、睡眠障碍、QTc、TP-Tec、TRP、KYN、KYNA均是精神分裂症共患心血管疾病的高危因素（P < 0.05）；（2）列线图模型诊断AUC为0.876，诊断结果与观察结果之间具有较好一致性；阈概率为5% ~ 100%时，患者净获益率较高。 结论 基于QTc、TP-Tec、TRP、KYN、KYNA等构建的列线图模型有助于提高精神分裂症共患心血管疾病诊断价值，指导临床诊治，降低心血管疾病。
郭蕊 , 刘涛 , 苏玺 . 基于心电图参数、犬尿氨酸代谢物建立精神分裂症共患心血管疾病的列线图模型[J]. 实用医学杂志, 2025 , 41(8) : 1205 -1211 . DOI: 10.3969/j.issn.1006-5725.2025.08.017
Objective To explore the factors influencing comorbid cardiovascular disease in schizophrenia and to construct a column-line graphical model. Methods Clinical data of 586 schizophrenic patients in the hospital from June 2023 to October 2024 were retrospectively collected. The study included 100 patients with comorbid cardiovascular disease and 486 patients without co-morbid cardiovascular disease， which were divided into the comorbidity group （n = 100） and the non-comorbidity group （n = 486）. Propensity score matching （PSM） was used to reduce between-group bias. The clinical data， electrocardiogram parameters ［QT corrected by heart rate （QTc）， Tp-Te interval corrected by heart rate （TP-Tec）］， and Kynurenine （KYN） metabolites ［tryptophan （TRP）， KYN， and kynurenic acid （KYNA）］ were statistically analyzed in both groups. Logistic regression equation was used to screen the influencing factors of comorbid cardiovascular disease in schizophrenia. A nomogram model was constructed and validated. Results （1） BMI， smoking， alcohol consumption， sleep disorders， QTc， TP-Tec， TRP， KYN， and KYNA were all high-risk factors for co-morbid cardiovascular disease in schizophrenia （P < 0.05）. （2） The diagnostic AUC for the nomogram model was 0.876， and there was a good concordance between diagnostic and observational results； and the threshold probability of a net patient benefit was higher when the threshold probability was between 5% and 100%. Conclusion The nomogram model constructed based on QTc， TP-Tec， TRP， KYN， KYNA， etc. can help improve the diagnostic value of schizophrenia comorbid cardiovascular disease， guide clinical diagnosis and treatment， and reduce cardiovascular disease.
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