Data from: External validation of an electronic health record-based diagnostic model for histological acute tubulointerstitial nephritis

Background Accurate diagnosis of acute tubulointerstitial nephritis (AIN)
often requires a kidney biopsy. We previously developed a diagnostic
statistical model for predicting biopsy-confirmed AIN by combining four
laboratory tests after evaluating over 150 potential predictors from the
electronic health record. Here, we validate this diagnostic model in two
biopsy-based cohorts at Johns Hopkins Hospital (JHH) and Yale, which were
geographically and temporally distinct from the development cohort,
respectively. Methods We analyzed patients who underwent kidney biopsy at
JHH and Yale University (2019-2023). We assessed discrimination (AUC) and
calibration using previously derived model coefficients and recalibrated
the model using an intercept correction factor that accounted for
differences in baseline prevalence of AIN between development and
validation cohorts. Results We included 1982 participants: 1454 at JHH and
528 at Yale. JHH (5%) and Yale (17%) had lower proportions of biopsies
with AIN than the development set (23%). The AUC was 0.73 (0.66-0.79) at
JHH and 0.73 (0.67-0.78) at Yale, similar to the development set (0.73
(0.64-0.81)). Calibration was imperfect in validation cohorts,
particularly at JHH, but improved with application of intercept correction
factor. The model increased AUC of clinicians' prebiopsy suspicion
for AIN by 0.10 to 0.77 (0.71-0.82). Conclusion AIN diagnostic model
retained discrimination in two validation cohorts but needed recalibration
to account for local AIN prevalence. The model improved clinicians’
ability to predict AIN.

背景 急性肾小管间质肾炎（acute tubulointerstitial nephritis, AIN）的准确诊断通常需要肾活检。我们此前通过评估电子健康记录中的150余个潜在预测因子，结合四项实验室检查，开发出一种预测活检证实AIN的诊断统计模型。本研究在约翰斯·霍普金斯医院（Johns Hopkins Hospital, JHH）和耶鲁大学的两个基于活检的队列中验证该诊断模型，这两个队列分别在地理和时间上与开发队列存在差异。

方法 我们分析了2019-2023年期间在JHH和耶鲁大学接受肾活检的患者。采用先前推导的模型系数评估区分度（AUC）和校准度，并使用截距校正因子对模型进行重新校准，该因子可解释开发队列与验证队列之间AIN基线患病率的差异。

结果 本研究共纳入1982名参与者：JHH为1454名，耶鲁大学为528名。JHH（5%）和耶鲁大学（17%）的AIN活检比例均低于开发队列（23%）。JHH的AUC为0.73（0.66-0.79），耶鲁大学为0.73（0.67-0.78），与开发队列（0.73（0.64-0.81））相似。验证队列的校准效果欠佳，尤其在JHH队列中，但应用截距校正因子后有所改善。该模型将临床医生活检前对AIN的怀疑的AUC提高了0.10，达到0.77（0.71-0.82）。

结论 AIN诊断模型在两个验证队列中保持了良好的区分度，但需重新校准以适应局部AIN患病率。该模型提升了临床医生预测AIN的能力。