Lu Yanyan, Wang Qiang, Liu Xuzhen, Gao Shuzhan, Ni Sulin, Sun Jing, Xu Xijia
Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, People's Republic of China.
Department of Medical Psychology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, People's Republic of China.
Neuropsychiatr Dis Treat. 2024 Oct 27;20:2029-2037. doi: 10.2147/NDT.S470127. eCollection 2024.
Acute and transient psychotic disorder (ATPD), a psychosis frequently diagnosed, can potentially evolve into chronic conditions like schizophrenia (SCZ) and other mental disorders. This study aimed to develop a predictive model based on clinical data to forecast the transition from ATPD to SCZ and to identify the predictive factors.
According to the diagnostic criteria issued by the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10), 396 inpatients diagnosed with ATPD were collected in this study. The Cox proportional-hazards regression model was performed using demographic data, clinical characteristics, and inflammatory markers to identify independent predictors for subsequent diagnostic transition (SDT) to SCZ.
During the follow-up period, 43.69% (n = 173) of ATPD patients had their diagnoses revised to SCZ. The multivariate Cox regression analysis identified post-treatment monocyte count, post-treatment monocyte/lymphocyte ratio (MLR), and the presence of schizophreniform symptoms as significant predictors for the diagnostic revision. Time-dependent receiver operating characteristic (TimeROC) analyses were developed. The AUC value at the 5-year follow-up was 0.728 for combined predictors, 0.702 for post-treatment monocyte count, 0.764 for post-treatment MLR, and 0.535 for the presence of schizophreniform symptoms.
The combined predictors had good predictive ability for the diagnostic transition from acute and transient psychotic disorder to schizophrenia.
急性短暂性精神病性障碍(ATPD)是一种常被诊断出的精神病,有可能演变成精神分裂症(SCZ)等慢性疾病以及其他精神障碍。本研究旨在基于临床数据建立一个预测模型,以预测从ATPD向SCZ的转变,并确定预测因素。
根据《国际疾病分类及相关健康问题统计分类第十次修订版》(ICD - 10)发布的诊断标准,本研究收集了396例被诊断为ATPD的住院患者。使用人口统计学数据、临床特征和炎症标志物进行Cox比例风险回归模型分析,以确定后续诊断转变(SDT)为SCZ的独立预测因素。
在随访期间,43.69%(n = 173)的ATPD患者诊断被修订为SCZ。多变量Cox回归分析确定治疗后单核细胞计数、治疗后单核细胞/淋巴细胞比值(MLR)以及精神分裂症样症状的存在是诊断修订的显著预测因素。开展了时间依赖性受试者工作特征(TimeROC)分析。联合预测指标在5年随访时的AUC值为0.728,治疗后单核细胞计数为0.702,治疗后MLR为0.764,精神分裂症样症状的存在为0.535。
联合预测指标对急性短暂性精神病性障碍向精神分裂症的诊断转变具有良好的预测能力。