Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors.
ABSTRACT We sought to determine which ICD-9-CM codes in Medicare Part A data identify cardiovascular and stroke risk factors.
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.
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.
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.
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.
Full-textDOI: · Available from: Amy D Waterman, Jun 16, 2015
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ABSTRACT: Purpose. Acute healthcare utilization of stroke and bleeding has been previously examined among patients with nonvalvular atrial fibrillation (NVAF). The long-term cost of such outcomes over several years is not well understood. Methods. Using 1999-2009 Medicare medical and enrollment data, we identified incident NVAF patients without history of stroke or bleeding. Patients were followed from the first occurrence of ischemic stroke, major bleeding, or intracranial hemorrhage (ICH) resulting in hospitalization. Those with events were matched with 1-5 NVAF patients without events. Total incremental costs of events were calculated as the difference between costs for patients with events and matched controls for up to 3 years. Results. Among the 25,465 patients who experienced events, 94.5% were successfully matched. In the first year after event, average incremental costs were $32,900 for ischemic stroke, $23,414 for major bleeding, and $47,640 for ICH. At 3 years after these events, costs remained elevated by $3,156-$5,400 per annum. Conclusion. While the costs of stroke and bleeding among patients with NVAF are most dramatic in the first year, utilization remained elevated at 3 years. Cost consequences extend beyond the initial year after these events and should be accounted for when assessing the cost-effectiveness of treatment regimens for stroke prevention.10/2012; 2012:645469. DOI:10.1155/2012/645469
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ABSTRACT: While developed for managing individuals with atrial fibrillation, risk stratification schemes for stroke, such as CHADS2, may be useful in population-based studies, including those assessing process of care. We investigated how certain decisions in identifying diagnoses from administrative data affect the apparent prevalence of CHADS2-associated diagnoses and distribution of scores. Two sets of ICD-9 codes (more restrictive/ more inclusive) were defined for each CHADS2-associated diagnosis. For stroke/transient ischemic attack (TIA), the more restrictive set was applied to only inpatient data. We varied the number of years (1-3) in searching for relevant codes, and, except for stroke/TIA, the number of instances (1 vs. 2) that diagnoses were required to appear. The impact of choices on apparent disease prevalence varied by type of choice and condition, but was often substantial. Choices resulting in substantial changes in prevalence also tended to be associated with more substantial effects on the distribution of CHADS2 scores.01/2012; 2(3):184-91.
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ABSTRACT: To determine the positive predictive value of International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) discharge codes for acute deep vein thrombosis or pulmonary embolism. Retrospective review of 3456 cases hospitalized between 2005 and 2007 that had a discharge code for venous thromboembolism, using 3 sample populations: a single academic hospital, 33 University HealthSystem Consortium hospitals, and 35 community hospitals in a national Joint Commission study. Analysis was stratified by position of the code in the principal versus a secondary position. Among 1096 cases that had a thromboembolism code in the principal position the positive predictive value for any acute venous thrombosis was 95% (95%CI:93-97), whereas among 2360 cases that had a thromboembolism code in a secondary position the predictive value was lower, 75% (95%CI:71-80). The corresponding positive predictive values for lower extremity deep-vein thrombosis or pulmonary embolism were 91% (95%CI:86-95) and 50% (95%CI:41-58), respectively. More highly defined codes had higher predictive value. Among codes in a secondary position that were false positive, 22% (95%CI:16-27) had chronic/prior venous thrombosis, 15% (95%CI:10-19) had an upper extremity thrombosis, 6% (95%CI:4-8) had a superficial vein thrombosis, and 7% (95%CI:4-13) had no mention of any thrombosis. ICD-9-CM codes for venous thromboembolism had high predictive value when present in the principal position, and lower predictive value when in a secondary position. New thromboembolism codes that were added in 2009 that specify chronic thrombosis, upper extremity thrombosis and superficial venous thrombosis should reduce the frequency of false-positive thromboembolism codes.Thrombosis Research 07/2010; 126(1):61-7. DOI:10.1016/j.thromres.2010.03.009 · 2.43 Impact Factor