Data Sheet 1_Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information database network.pdf

IntroductionThis study aimed to evaluate the validity of algorithms based on electronic health data in identifying cases of acute heart failure and acute exacerbation of chronic heart failure at multiple institutions using the Medical Information Database Network (MID-NET®) in Japan.
MethodsData were collected from March 8, 2021 to March 31, 2021, from the data source of three hospitals among the MID-NET® cooperating medical institutions. All Possible Cases were defined by combining ICD-10 codes related to acute heart failure and abnormal values of serum B-type natriuretic peptide (BNP) or N-terminal pro-brain natriuretic peptide (NT-proBNP). Eighteen algorithms were created using various data sources in MID-NET®, including electronic medical records, diagnostic procedure combination (DPC) data, and health insurance claims data. True cases were determined by reviewing medical records obtained independently by two experienced physicians.
ResultsThe kappa coefficient among the three medical institutions was 0.94 (95% confidence interval: 0.90–0.98). Among the 18 algorithms, the highest positive predictive value (PPV) of the three medical institutions was 77.78% for Algorithm 8 which was constructed using ICD-10 codes in DPC disease data, moderate or high range of abnormal BNP (≥100 pg/mL) or NT-proBNP (≥400 pg/mL), and medications for acute heart failure. The highest sensitivity at 89.53% was observed for Algorithm 9. This algorithm was constructed using a combination of disease codes entered in electronic medical records, DPC, or health insurance claims data, abnormal BNP values in the moderate or high range (≥100 pg/mL), and medications for acute heart failure. However, its PPV was the lowest among 18 algorithms, generally reflecting the inverse relationship between PPV and sensitivity. The same tendency was seen in the sensitivity study. Cases with stable chronic heart failure, renal insufficiency, assessment for cardiac function, or severe circulatory failure inflated false-positive cases in this study.
ConclusionValidated algorithms for identifying acute heart failure and acute exacerbation of chronic heart failure were successfully established. Using these algorithms should facilitate more appropriate pharmacoepidemiological studies related to acute heart failure and contribute to better drug safety assessments based on real-world data in Japan.

**引言**
本研究旨在依托日本医疗信息数据库网络（Medical Information Database Network, MID-NET®），评估基于电子健康数据的算法在多机构场景下识别急性心力衰竭及慢性心力衰竭急性加重病例的有效性。

**方法**
本研究数据采集时间为2021年3月8日至2021年3月31日，来源于日本MID-NET®合作医疗机构中的3家医院。所有候选病例通过结合与急性心力衰竭相关的ICD-10编码，以及血清B型钠尿肽（B-type natriuretic peptide, BNP）或N末端B型脑钠肽前体（N-terminal pro-brain natriuretic peptide, NT-proBNP）的异常检测值进行定义。本研究依托MID-NET®中的多种数据源（包括电子病历、诊断组合（Diagnostic Procedure Combination, DPC）数据及健康保险索赔数据）构建了18种算法。金标准病例由两名经验丰富的医师独立审阅病历后确定。

**结果**
三家医疗机构间的kappa系数为0.94（95%置信区间：0.90~0.98）。在18种算法中，算法8在三家机构中的阳性预测值（Positive Predictive Value, PPV）最高，达77.78%；该算法通过结合DPC疾病数据中的ICD-10编码、BNP（≥100 pg/mL）或NT-proBNP（≥400 pg/mL）的中高异常值，以及急性心力衰竭治疗药物构建而成。算法9的灵敏度最高，为89.53%；该算法通过结合电子病历、DPC或健康保险索赔数据中录入的疾病编码、BNP（≥100 pg/mL）的中高异常值，以及急性心力衰竭治疗药物构建而成。但该算法的PPV为18种算法中最低，这普遍反映了PPV与灵敏度之间的负相关关系，本次灵敏度分析也观察到了相同趋势。本研究中，合并慢性稳定性心力衰竭、肾功能不全、需行心功能评估或重度循环衰竭的病例会导致假阳性病例数增加。

**结论**
本研究成功构建了可用于识别急性心力衰竭及慢性心力衰竭急性加重病例的验证算法。应用该类算法可助力开展更规范的急性心力衰竭相关药物流行病学研究，并有助于基于日本真实世界数据开展更优质的药物安全性评估。