利用电子病历中的诊断代码对结节病进行计算表型分析算法的性能：来自2个退伍军人事务医疗中心的病例验证研究

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers.

作者信息

Seedahmed Mohamed I, Mogilnicka Izabella, Zeng Siyang, Luo Gang, Whooley Mary A, McCulloch Charles E, Koth Laura, Arjomandi Mehrdad

机构信息

Division of Pulmonary, Critical Care, Allergy and Immunology, and Sleep, Department of Medicine, University of California San Francisco, San Francisco, CA, United States.

San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States.

出版信息

JMIR Form Res. 2022 Mar 2;6(3):e31615. doi: 10.2196/31615.

DOI:10.2196/31615

PMID:35081036

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8928044/

Abstract

BACKGROUND

Electronic medical records (EMRs) offer the promise of computationally identifying sarcoidosis cases. However, the accuracy of identifying these cases in the EMR is unknown.

OBJECTIVE

The aim of this study is to determine the statistical performance of using the International Classification of Diseases (ICD) diagnostic codes to identify patients with sarcoidosis in the EMR.

METHODS

We used the ICD diagnostic codes to identify sarcoidosis cases by searching the EMRs of the San Francisco and Palo Alto Veterans Affairs medical centers and randomly selecting 200 patients. To improve the diagnostic accuracy of the computational algorithm in cases where histopathological data are unavailable, we developed an index of suspicion to identify cases with a high index of suspicion for sarcoidosis (confirmed and probable) based on clinical and radiographic features alone using the American Thoracic Society practice guideline. Through medical record review, we determined the positive predictive value (PPV) of diagnosing sarcoidosis by two computational methods: using ICD codes alone and using ICD codes plus the high index of suspicion.

RESULTS

Among the 200 patients, 158 (79%) had a high index of suspicion for sarcoidosis. Of these 158 patients, 142 (89.9%) had documentation of nonnecrotizing granuloma, confirming biopsy-proven sarcoidosis. The PPV of using ICD codes alone was 79% (95% CI 78.6%-80.5%) for identifying sarcoidosis cases and 71% (95% CI 64.7%-77.3%) for identifying histopathologically confirmed sarcoidosis in the EMRs. The inclusion of the generated high index of suspicion to identify confirmed sarcoidosis cases increased the PPV significantly to 100% (95% CI 96.5%-100%). Histopathology documentation alone was 90% sensitive compared with high index of suspicion.

CONCLUSIONS

ICD codes are reasonable classifiers for identifying sarcoidosis cases within EMRs with a PPV of 79%. Using a computational algorithm to capture index of suspicion data elements could significantly improve the case-identification accuracy.

摘要

背景

电子病历（EMR）有望通过计算识别结节病病例。然而，在电子病历中识别这些病例的准确性尚不清楚。

目的

本研究的目的是确定使用国际疾病分类（ICD）诊断代码在电子病历中识别结节病患者的统计性能。

方法

我们通过搜索旧金山和帕洛阿尔托退伍军人事务医疗中心的电子病历并随机选择200名患者，使用ICD诊断代码来识别结节病病例。为了在缺乏组织病理学数据的情况下提高计算算法的诊断准确性，我们根据美国胸科学会的实践指南，开发了一种怀疑指数，仅基于临床和影像学特征来识别高度怀疑结节病（确诊和可能）的病例。通过病历审查，我们通过两种计算方法确定了诊断结节病的阳性预测值（PPV）：仅使用ICD代码以及使用ICD代码加高度怀疑指数。

结果

在200名患者中，158名（79%）高度怀疑结节病。在这158名患者中，142名（89.9%）有非坏死性肉芽肿的记录，证实为经活检证实的结节病。仅使用ICD代码识别结节病病例的PPV为79%（95%CI 78.6%-80.5%），在电子病历中识别经组织病理学证实的结节病的PPV为71%（95%CI 64.7%-77.3%）。纳入生成的高度怀疑指数以识别确诊的结节病病例，PPV显著提高到100%（95%CI 96.5%-100%）。与高度怀疑指数相比，仅组织病理学记录的敏感性为90%。