使用锚定阳性和未标记患者的电子健康记录表型分析和内部评估性能（PhIAP）
Electronic Health Record Phenotyping with Internally Assessable Performance (PhIAP) using Anchor-Positive and Unlabeled Patients
摘要：
使用电子健康记录（EHR）数据构建表型模型通常需要手动标记病例和对照。荧光标记是劳动密集型的，对于某些表型，鉴定金标准对照是禁止的。为了促进全面的临床决策支持和研究，我们试图开发一种准确的EHR表型分析方法，在没有验证集的情况下评估其性能。我们的框架依赖于指定一个随机的病例子集，可能使用一个锚变量，具有良好的阳性预测值和独立于预测因子的灵敏度。我们开发了一种新的极大似然方法，有效地利用锚定阳性和未标记患者的数据来开发逻辑斯谛回归表型模型。此外，我们描述了新的统计方法，用于估计表型流行率和评估模型校准和预测性能指标。理论和模拟研究表明，我们的方法产生准确的预测概率，导致优秀的歧视和校准，和相合估计的表型患病率和锚敏感性。该方法对轻微失拟具有稳健性，拟定的校准评估检测到严重失拟。我们将我们的方法应用于EHR数据，以开发用于识别原发性醛固酮增多症患者的初步模型，该模型的AUC为0.99，PPV为0.8。我们开发了新的统计方法，用于精确的模型开发和验证，最大限度地减少手动标记，促进可扩展，可转移，半自动化的案例标记和特定于实践的模型的开发。我们的EHR表型分析方法减少了劳动密集型的手动表型分析和注释，这应该能够为EHR临床决策支持和研究提供更广泛的模型开发和传播。
英文摘要：
Building phenotype models using electronic health record (EHR) data conventionally requires manually labeled cases and controls. Assigning labels is labor intensive and, for some phenotypes, identifying gold-standard controls is prohibitive. To facilitate comprehensive clinical decision support and research, we sought to develop an accurate EHR phenotyping approach that assesses its performance without a validation set. Our framework relies on specifying a random subset of cases, potentially using an anchor variable that has excellent positive predictive value and sensitivity that is independent of predictors. We developed a novel maximum likelihood approach that efficiently leverages data from anchor-positive and unlabeled patients to develop logistic regression phenotyping models. Additionally, we described novel statistical methods for estimating phenotyping prevalence and assessing model calibration and predictive performance measures. Theoretical and simulation studies indicated our method generates accurate predicted probabilities, leading to excellent discrimination and calibration, and consistent estimates of phenotype prevalence and anchor sensitivity. The method appeared robust to minor lack-of-fit and the proposed calibration assessment detected major lack-of-fit. We applied our method to EHR data to develop a preliminary model for identifying patients with primary aldosteronism, which achieved an AUC of 0.99 and PPV of 0.8. We developed novel statistical methods for accurate model development and validation with minimal manual labeling, facilitating development of scalable, transferable, semi-automated case labeling and practice-specific models. Our EHR phenotyping approach decreases labor-intensive manual phenotyping and annotation, which should enable broader model development and dissemination for EHR clinical decision support and research.
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