Center for Health System Sciences (CHASSIS), Atrium Health, Charlotte, NC, United States.
Statistics and Data Management, Elanco, Greenfield, IN, United States.
JMIR Med Inform. 2024 Oct 28;12:e58732. doi: 10.2196/58732.
Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention.
We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded).
Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record-based predictors were based on the 1-year period before a patient's index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses.
In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71-0.72) and 0.015 (95% CI 0.012-0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79-0.82) and 0.009 (95% CI 0.007-0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69-0.71) and 0.79 (95% CI 0.78-0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively.
An electronic health record-based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension.
评估未控制高血压患者的疾病进展对于发现干预机会非常重要。
我们旨在开发和验证 2 种模型，一种用于预测持续的、未控制的高血压（≥2 次血压读数≥140/90mmHg 或≥1 次血压读数≥180/120mmHg），另一种用于预测高血压危象（≥1 次血压读数≥180/120mmHg），预测时间为指数就诊（记录未控制血压读数的门诊或动态就诊）后 1 年内。
使用 2018 年 Atrium Health Greater Charlotte 中 142897 例未控制高血压患者的数据。基于电子健康记录的预测因子基于患者就诊前 1 年的时间段。数据集随机分为（80：20）训练集和验证集。总共考虑了 4 种机器学习框架：L2 正则化逻辑回归、多层感知机、梯度提升机和随机森林。使用 10 折交叉验证进行模型选择。使用决策曲线分析评估最终模型的区分度（C 统计量）、校准（例如，综合校准指数）和净收益。此外，在县级进行内部-外部交叉验证，使用新的人群评估性能，并使用随机效应荟萃分析进行总结。
在内部验证中，持续未控制高血压模型的 C 统计量和综合校准指数分别为 0.72（95%CI 0.71-0.72）和 0.015（95%CI 0.012-0.020），高血压危象模型分别为 0.81（95%CI 0.79-0.82）和 0.009（95%CI 0.007-0.011）。与默认策略（即治疗所有和不治疗所有）相比，这些模型在不同决策阈值下均具有更高的净收益。在内部-外部交叉验证中，汇总性能与内部验证结果一致；特别是，持续未控制高血压模型和高血压危象模型的汇总 C 统计量分别为 0.70（95%CI 0.69-0.71）和 0.79（95%CI 0.78-0.81）。
基于电子健康记录的模型在内部和内部-外部验证中合理地预测了高血压危象。该模型可能有助于支持人群健康监测和高血压管理。需要进一步研究来提高预测持续未控制高血压的能力。
