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. 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. 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). These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

中文翻译：

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基于自然语言处理的动态和跨诊断风险计算器，用于预测二级心理保健中的精神病：开发和内部-外部验证队列研究


自动跨诊断风险计算器可以提高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的评估，并且可以通过考虑风险随时间变化的动态建模技术进行完善。我们纳入了 158,139 名患者（5007 起事件），他们在 2008 年 1 月 1 日至 2021 年 10 月 8 日期间在南伦敦和莫兹利国家卫生服务基金会信托基金的电子健康记录中接受了非器质性和非精神病性精神障碍的首次索引诊断。 Cox 地标模型是根据 TRIPOD（个人预后或诊断多变量预测模型的透明报告）声明开发的，用于估计 2 年发生精神病的风险。动态模型包括在 9 个里程碑点（基线、0、6、12、24、30、36、42 和 48 个月）提取的 24 个预测因子：3 个人口统计因子、1 个临床因子和 20 个基于自然语言处理的症状和物质使用因子预测因子。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时进行评估，并通过内部-外部验证中的判别（C 指数）、校准（校准图）和潜在临床效用（决策曲线）进行索引。与静态模型（动态：C-index = 0.9；静态：C-index = 0.87）和最终标志点（动态：C-index = 0.79；静态：C-index = 0.76）。在后来的里程碑点（≥24 个月），动态模型的校准也明显优于静态模型（校准斜率 = 0.97-1.1）。在后来的里程碑点（≥24 个月），动态模型的净效益高于静态模型。 这些发现表明，动态预测模型可以改善二级精神卫生保健机构中对精神病风险个体的检测。