Japan Drug Development and Medical Affairs, Eli Lilly Japan K.K., Kobe, Hyogo Prefecture, Japan.
Real World Data Co., Ltd., Nakagyo Ward, Kyoto, Kyoto Prefecture, Japan.
PLoS One. 2023 Aug 9;18(8):e0289840. doi: 10.1371/journal.pone.0289840. eCollection 2023.
The purpose of this study was to evaluate the performance of algorithms for identifying cases of severe hypoglycemia in Japanese hospital administrative data.
This was a multicenter, retrospective, observational study conducted at 3 acute-care hospitals in Japan. The study population included patients aged ≥18 years with diabetes who had an outpatient visit or hospital admission for possible hypoglycemia. Possible cases of severe hypoglycemia were identified using health insurance claims data and Diagnosis Procedure Combination data. Sixty-one algorithms using combinations of diagnostic codes and prescription of high concentration (≥20% mass/volume) injectable glucose were used to define severe hypoglycemia. Independent manual chart reviews by 2 physicians at each hospital were used as the reference standard. Algorithm validity was evaluated using standard performance metrics.
In total, 336 possible cases of severe hypoglycemia were identified, and 260 were consecutively sampled for validation. The best performing algorithms included 6 algorithms that had sensitivity ≥0.75, and 6 algorithms that had positive predictive values ≥0.75 with sensitivity ≥0.30. The best-performing algorithm with sensitivity ≥0.75 included any diagnoses for possible hypoglycemia or prescription of high-concentration glucose but excluded suspected diagnoses (sensitivity: 0.986 [95% confidence interval 0.959-1.013]; positive predictive value: 0.345 [0.280-0.410]). Restricting the algorithm definition to those with both a diagnosis of possible hypoglycemia and a prescription of high-concentration glucose improved the performance of the algorithm to correctly classify cases as severe hypoglycemia but lowered sensitivity (sensitivity: 0.375 [0.263-0.487]; positive predictive value: 0.771 [0.632-0.911]).
The case-identifying algorithms in this study showed moderate positive predictive value and sensitivity for identification of severe hypoglycemia in Japanese healthcare data and can be employed by future pharmacoepidemiological studies using Japanese hospital administrative databases.
本研究旨在评估用于识别日本医院行政数据中严重低血糖病例的算法性能。
这是一项在日本 3 家急症医院进行的多中心、回顾性、观察性研究。研究人群包括年龄≥18 岁、因疑似低血糖而接受门诊或住院治疗的糖尿病患者。使用医疗保险索赔数据和诊断程序组合数据识别严重低血糖疑似病例。使用 61 种算法，通过诊断代码和高浓度（≥20%质量/体积）注射用葡萄糖的处方组合来定义严重低血糖。每个医院由 2 名医生进行独立的手动图表审查，作为参考标准。使用标准性能指标评估算法的有效性。
共确定了 336 例疑似严重低血糖病例，其中 260 例连续采样进行验证。表现最好的算法包括 6 种灵敏度≥0.75 的算法，以及 6 种灵敏度≥0.30、阳性预测值≥0.75 的算法。表现最好的灵敏度≥0.75 的算法包括任何疑似低血糖的诊断或高浓度葡萄糖的处方，但排除了疑似诊断（灵敏度：0.986[95%置信区间 0.959-1.013]；阳性预测值：0.345[0.280-0.410]）。将算法定义限制为既有疑似低血糖诊断又有高浓度葡萄糖处方的算法，可以提高将病例正确分类为严重低血糖的性能，但降低了灵敏度（灵敏度：0.375[0.263-0.487]；阳性预测值：0.771[0.632-0.911]）。
本研究中的病例识别算法在日本医疗保健数据中识别严重低血糖具有中等的阳性预测值和灵敏度，可用于未来使用日本医院行政数据库的药物流行病学研究。
