Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.
Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.
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DOI：
10.37737/ace.22004
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发表时间：
2022-01-01
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影响因子：
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通讯作者：
Koide, Daisuke
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作者：
Nishikawa, Atsushi;Yoshinaga, Eiko;Koide, Daisuke
背景技术背景：这项回顾性观察性研究验证了日本行政医疗保健database.METHODS中恶性肿瘤和严重感染的病例发现算法：从参与Medical Data Vision Co.，使用ICD-10诊断代码和其他程序/计费代码的组合识别MDV数据库。对于每种疾病，两名医生通过病历审查从可能病例的随机样本中确定真实病例;第三名医生在两名医生不同意的情况下做出最终决定。病例发现算法的准确性进行了评估，使用阳性预测值（PPV）和灵敏度。结果：有2,940例可能的恶性肿瘤，180例被随机选择，108例被确定为真实病例后，病历审查。一种病例发现算法给出了高PPV（64.1%），而灵敏度（90.7%）没有实质性损失，并包括恶性肿瘤和摄影/成像的ICD-10代码。严重感染可能病例3,559例;随机抽取200例，经病历审核确定为真实病例167例。两种病例发现算法给出了高PPV（85.6%），灵敏度没有损失（100%）。这两个病例发现算法包括相关的诊断代码和免疫感染测试/其他相关的测试，其中，一个还包括1个月内的病理诊断hospitalizations.CONCLUSIONS：在这项研究中的病例发现算法显示出良好的PPV和敏感性的情况下，恶性肿瘤和严重感染的行政医疗保健数据库在日本的识别。
BACKGROUND: This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database.METHODS: Random samples of possible cases of each disease (January 2015-January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identified true cases among the random samples of possible cases by medical record review; a third physician made the final decision in cases where the two physicians disagreed. The accuracy of case-finding algorithms was assessed using positive predictive value (PPV) and sensitivity.RESULTS: There were 2,940 possible cases of malignant tumor; 180 were randomly selected and 108 were identified as true cases after medical record review. One case-finding algorithm gave a high PPV (64.1%) without substantial loss in sensitivity (90.7%) and included ICD-10 codes for malignancy and photographing/imaging. There were 3,559 possible cases of serious infection; 200 were randomly selected and 167 were identified as true cases after medical record review. Two case-finding algorithms gave a high PPV (85.6%) with no loss in sensitivity (100%). Both case-finding algorithms included the relevant diagnostic code and immunological infection test/other related test and, of these, one also included pathological diagnosis within 1 month of hospitalization.CONCLUSIONS: The case-finding algorithms in this study showed good PPV and sensitivity for identification of cases of malignant tumors and serious infections from an administrative healthcare database in Japan.