Abstract：
BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time.
METHODS: We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation.
RESULTS: The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months).
CONCLUSIONS: These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.
METHODS: We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation.
RESULTS: The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months).
CONCLUSIONS: These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.
摘要：
背景：自动诊断风险计算器可以改善对有精神病风险的个体的检测。然而,它们依赖于单个时间点评估，并且可以使用动态建模技术进行改进，该技术可以考虑风险随时间的变化。
方法：我们纳入了n=158,139名患者（n=5,007例事件），这些患者在2008年1月1日至2021年10月8日之间接受了SALMNHS基金会信托的电子健康记录中首次诊断为非器质性和非精神病性精神障碍。根据TRIPOD声明，开发了动态Cox界标模型来估计患精神病的2年风险。动态模型包括在9个地标点提取的24个预测因子(基线，0、6、12、24、30、36、42和48个月)：三个人口统计，一个临床，和20种基于自然语言处理(NLP)的症状和物质使用预测因子。将性能与静态Cox回归模型进行比较，所有预测因子仅在基线评估，通过辨别索引(C指数)，校准（校准图），以及内部-外部验证中的潜在临床效用(决策曲线)。
结果：与基线（动态：C指数=0.9；静态：C指数=0.87）到最终界标点（动态：C指数=0.79；静态：C指数=0.76）的静态模型相比，动态模型提高了辨别性能。在较晚的界标点（≥24个月），动态模型的校准（校准斜率=0.97-1.1）也明显优于静态模型。在较晚的地标点(≥24个月)，动态的净收益高于静态模型。
结论：这些发现表明，动态预测模型可以改善二级精神保健中精神病风险个体的检测。
方法：我们纳入了n=158,139名患者（n=5,007例事件），这些患者在2008年1月1日至2021年10月8日之间接受了SALMNHS基金会信托的电子健康记录中首次诊断为非器质性和非精神病性精神障碍。根据TRIPOD声明，开发了动态Cox界标模型来估计患精神病的2年风险。动态模型包括在9个地标点提取的24个预测因子(基线，0、6、12、24、30、36、42和48个月)：三个人口统计，一个临床，和20种基于自然语言处理(NLP)的症状和物质使用预测因子。将性能与静态Cox回归模型进行比较，所有预测因子仅在基线评估，通过辨别索引(C指数)，校准（校准图），以及内部-外部验证中的潜在临床效用(决策曲线)。
结果：与基线（动态：C指数=0.9；静态：C指数=0.87）到最终界标点（动态：C指数=0.79；静态：C指数=0.76）的静态模型相比，动态模型提高了辨别性能。在较晚的界标点（≥24个月），动态模型的校准（校准斜率=0.97-1.1）也明显优于静态模型。在较晚的地标点(≥24个月)，动态的净收益高于静态模型。
结论：这些发现表明，动态预测模型可以改善二级精神保健中精神病风险个体的检测。
