Department of General Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, China.
Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, 214151, China.
BMC Psychiatry. 2024 Aug 22;24(1):573. doi: 10.1186/s12888-024-06024-3.
Schizophrenia is a pervasive and severe mental disorder characterized by significant disability and high rates of recurrence. The persistently high rates of readmission after discharge present a serious challenge and source of stress in treating this population. Early identification of this risk is critical for implementing targeted interventions. The present study aimed to develop an easy-to-use predictive instrument for identifying the risk of readmission within 1-year post-discharge among schizophrenia patients in China.
A prediction model, based on static factors, was developed using data from 247 schizophrenia inpatients admitted to the Mental Health Center in Wuxi, China, from July 1 to December 31, 2020. For internal validation, an additional 106 patients were included. Multivariate Cox regression was applied to identify independent predictors and to create a nomogram for predicting the likelihood of readmission within 1-year post-discharge. The model's performance in terms of discrimination and calibration was evaluated using bootstrapping with 1000 resamples.
Multivariate cox regression demonstrated that involuntary admission (adjusted hazard ratio [aHR] 4.35, 95% confidence interval [CI] 2.13-8.86), repeat admissions (aHR 3.49, 95% CI 2.08-5.85), the prescription of antipsychotic polypharmacy (aHR 2.16, 95% CI 1.34-3.48), and a course of disease ≥ 20 years (aHR 1.80, 95% CI 1.04-3.12) were independent predictors for the readmission of schizophrenia patients within 1-year post-discharge. The area under the curve (AUC) and concordance index (C-index) of the nomogram constructed from these four factors were 0.820 and 0.780 in the training set, and 0.846 and 0.796 for the validation set, respectively. Furthermore, the calibration curves of the nomogram for both the training and validation sets closely approximated the ideal diagonal line. Additionally, decision curve analyses (DCAs) demonstrated a significantly better net benefit with this model.
A nomogram, developed using pre-discharge static factors, was designed to predict the likelihood of readmission within 1-year post-discharge for patients with schizophrenia. This tool may offer clinicians an accurate and effective way for the timely prediction and early management of psychiatric readmissions.
精神分裂症是一种普遍且严重的精神障碍，其特征是显著的残疾和高复发率。出院后持续高的再入院率是治疗该人群的严重挑战和压力源。早期识别这种风险对于实施有针对性的干预措施至关重要。本研究旨在开发一种易于使用的预测工具，用于识别中国精神分裂症患者出院后 1 年内再入院的风险。
使用 2020 年 7 月 1 日至 12 月 31 日期间从中国无锡精神卫生中心入院的 247 例精神分裂症住院患者的数据，基于静态因素建立预测模型。为了内部验证，还纳入了另外 106 例患者。采用多变量 Cox 回归分析确定独立预测因素，并创建预测出院后 1 年内再入院可能性的列线图。通过 1000 次重采样的 bootstrap 方法评估模型在区分度和校准度方面的性能。
多变量 Cox 回归分析显示，非自愿入院（调整后的危险比[aHR]4.35，95%置信区间[CI]2.13-8.86）、再次入院（aHR 3.49，95% CI 2.08-5.85）、抗精神病药联合用药（aHR 2.16，95% CI 1.34-3.48）和病程≥20 年（aHR 1.80，95% CI 1.04-3.12）是精神分裂症患者出院后 1 年内再入院的独立预测因素。从这四个因素构建的列线图的曲线下面积（AUC）和一致性指数（C-index）在训练集中分别为 0.820 和 0.780，在验证集中分别为 0.846 和 0.796。此外，训练集和验证集的列线图校准曲线均与理想的对角线非常接近。此外，决策曲线分析（DCAs）表明，该模型具有显著更好的净效益。
本研究开发了一种基于出院前静态因素的列线图，用于预测精神分裂症患者出院后 1 年内再入院的可能性。该工具可为临床医生提供一种准确有效的方法，及时预测和早期管理精神科再入院。
