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

Ryusuke Inoue^1*Masaharu Nakayama²Hideki Ota³Naoki Nakamura³Susumu Fujii³Akira Ishii⁴Atsuko Saito⁴Takahiro Suzuki⁴Hiroko Nomura⁵Natsuko Goto⁶Shinya Watanabe⁶Hotaka Maruyama⁶Mayu Nozawa⁶Yoshiaki Uyama⁷

• ¹Medical Information Technology Center, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-Ku, Sendai, Miyagi, Japan
• ²Department of Medical Informatics, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-Ku, Sendai, Miyagi, Japan
• ³Medical Information Technology Center, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-Ku, Sendai, Japan
• ⁴Division of Medical Informatics and Management, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba-shi, Chiba, Japan
• ⁵Tokushukai General Incorporated Association Osaka Headquarters, 1-3-1 Umeda, Kita-Ku, Osaka, Japan
• ⁶Office of Pharmacovigilance I, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo, Japan
• ⁷Center for Regulatory Science, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo, Japan

This 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. Data 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. The 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. In conclusion, validated 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.