The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Accuracy of ICD-9-CM Codes for Identifying Cardiovascular and Stroke Risk Factors
Abstract
OBJECTIVES: We sought to determine which ICD-9-CM codes in Medicare Part A data identify cardiovascular and stroke risk factors. DESIGN AND PARTICIPANTS: This was a cross-sectional study comparing ICD-9-CM data to structured medical record review from 23,657 Medicare beneficiaries aged 20 to 105 years who had atrial fibrillation. MEASUREMENTS: Quality improvement organizations used standardized abstraction instruments to determine the presence of 9 cardiovascular and stroke risk factors. Using the chart abstractions as the gold standard, we assessed the accuracy of ICD-9-CM codes to identify these risk factors. MAIN RESULTS: ICD-9-CM codes for all risk factors had high specificity (>0.95) and low sensitivity (< or =0.76). The positive predictive values were greater than 0.95 for 5 common, chronic risk factors-coronary artery disease, stroke/transient ischemic attack, heart failure, diabetes, and hypertension. The sixth common risk factor, valvular heart disease, had a positive predictive value of 0.93. For all 6 common risk factors, negative predictive values ranged from 0.52 to 0.91. The rare risk factors-arterial peripheral embolus, intracranial hemorrhage, and deep venous thrombosis-had high negative predictive value (> or =0.98) but moderate positive predictive values (range, 0.54-0.77) in this population. CONCLUSIONS: Using ICD-9-CM codes alone, heart failure, coronary artery disease, diabetes, hypertension, and stroke can be ruled in but not necessarily ruled out. Where feasible, review of additional data (eg, physician notes or imaging studies) should be used to confirm the diagnosis of valvular disease, arterial peripheral embolus, intracranial hemorrhage, and deep venous thrombosis.
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Citations
Cites
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“confidence intervals for likelihood ratios based on the normal approximation.(24) We computed 95% confidence intervals for LR of individual ICD-9-CM codes by generating 1000 bootstrapped samples for each code.”
MethodThe accuracy of Medicare's hospital claims data: progress has been made, but problems remain.
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Accuracy of Diagnostic Coding for Medicare Patients under the Prospective-Payment System
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Cited by
Gage, B. F., Yan, Y., Milligan, P. E., Waterman, A. D., Culverhouse, R., Rich, M. W., & Radford, M. J. (2006) · American Heart Journal · doi:10.1016/j.ahj.2005.04.017 · Cited by 1,008
Self-citationPiccini, J. P., Hammill, B. G., Sinner, M. F., Jensen, P. N., Hernandez, A. F., Heckbert, S. R., Benjamin, E. J., & Curtis, L. H. (2012) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.111.962688 · Cited by 565
Network-based approach to prediction and population-based validation of in silico drug repurposing
Cheng, F., Desai, R. J., Handy, D. E., Wang, R., Schneeweiss, S., Barabási, A.-L., & Loscalzo, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05116-5 · Cited by 552
Hammill, B. G., Curtis, L. H., Schulman, K. A., & Whellan, D. J. (2010) · Circulation · doi:10.1161/circulationaha.109.876383 · Cited by 543
Graham, D. J., Ouellet-Hellstrom, R., MaCurdy, T. E., Ali, F., Sholley, C., Worrall, C., & Kelman, J. A. (2010) · JAMA · doi:10.1001/jama.2010.920 · Cited by 512
Metadata
- Authors
- Birman-Deych, E., Waterman, A. D., Yan, Y., Nilasena, D. S., Radford, M. J., & Gage, B. F.
- Venue
- Medical Care
- Published
- 2005-05
- Volume
- 43
- Issue
- 5
- Pages
- 480-485
- ISSN
- 0025-7079 (ISSN-L)
- OpenAlex
- W2012487908
- Cited by
- 719
- References
- 24
- FWCI
- 38.20
- Type
- journal article
- Language
- en
- Open access
- closed
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