Vin-Cent Wu1, Ya-Hui Hu2, Che-Hsiung Wu3, Chih-Chin Kao4, Cheng-Yi Wang5, Wei-Shen Yang6, Hsiu-Hao Lee7, Yuan-Shian Chang8, Yen-Hung Lin1, Shuo-Meng Wang9, Likwang Chen10, Kwan-Dun Wu1. 1. Department of Internal Medicine, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei, Taiwan. 2. Division of Endocrinology, Taipei Tzu Chi General Hospital, 289 Jianguo Road, Xindian, Taiwan. 3. Division of Nephrology, Taipei Tzu Chi General Hospital, 289 Jianguo Road, Xindian, Taiwan. 4. Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, 250 Wu-Hsing Street, Taipei, Taiwan. 5. Department of Internal Medicine and Medical Research Center, Cardinal Tien Hospital, 362 Zhongzheng Road, Xindian, Taiwan. 6. Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, 25 Jingguo Road, Hsinchu, Taiwan. 7. Department of Internal Medicine, Taipei City Hospital, Zhongxing Branch, 145 Zhengzhou Road, Taipei, Taiwan. 8. Department of Internal Medicine, Postal Hospital, 14 Fu-Chuo Street, Taipei, Taiwan. 9. Department of Urology, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei, Taiwan. Electronic address: likwang@nhri.org.tw. 10. Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Taiwan. Electronic address: dturo62@yahoo.com.tw.
Abstract
OBJECTIVES: To develop algorithms of locating patients with primary aldosteronism (PA) using insurance reimbursement data and to validate the algorithms using medical charts. STUDY DESIGN AND SETTING: We extracted National Health Insurance (NHI) reimbursement data and medical charts in seven enrolled hospitals and analyzed diagnosis-related information for 1999-2010. The NHI codes PA as 255.1x, using the International Classification of Diseases, Ninth Revision, Clinical Modification. Confirmation of PA was based on suppression tests. RESULTS: We reviewed medical charts for 1,094 cases with at least one PA diagnosis. PA was confirmed for 563 cases. Compared with patients with essential hypertension, PA patients had higher systolic blood pressure, higher aldosterone, lower renin activity, and lower potassium level (all P-values <0.05). An algorithm based on PA diagnosis reported in at least one hospital stay or three outpatient visits had modest performance (sensitivity = 0.94 and specificity = 0.20). The best additional condition for the algorithm was use of mineralocorticoid receptor antagonist (MRA; sensitivity = 0.89 and specificity = 0.88). CONCLUSION: Using information on PA diagnosis and MRA prescription reported in insurance claims data can precisely locate PA patients in high-risk groups. This algorithm can construct a reliable PA sample for conducting research in various fields, including epidemiology and clinical practice.
OBJECTIVES: To develop algorithms of locating patients with primary aldosteronism (PA) using insurance reimbursement data and to validate the algorithms using medical charts. STUDY DESIGN AND SETTING: We extracted National Health Insurance (NHI) reimbursement data and medical charts in seven enrolled hospitals and analyzed diagnosis-related information for 1999-2010. The NHI codes PA as 255.1x, using the International Classification of Diseases, Ninth Revision, Clinical Modification. Confirmation of PA was based on suppression tests. RESULTS: We reviewed medical charts for 1,094 cases with at least one PA diagnosis. PA was confirmed for 563 cases. Compared with patients with essential hypertension, PA patients had higher systolic blood pressure, higher aldosterone, lower renin activity, and lower potassium level (all P-values <0.05). An algorithm based on PA diagnosis reported in at least one hospital stay or three outpatient visits had modest performance (sensitivity = 0.94 and specificity = 0.20). The best additional condition for the algorithm was use of mineralocorticoid receptor antagonist (MRA; sensitivity = 0.89 and specificity = 0.88). CONCLUSION: Using information on PA diagnosis and MRA prescription reported in insurance claims data can precisely locate PA patients in high-risk groups. This algorithm can construct a reliable PA sample for conducting research in various fields, including epidemiology and clinical practice.