External validation of a claims-based algorithm for classifying kidney-cancer surgeries

<p>Abstract</p> <p>Background</p> <p>Unlike other malignancies, there is no literature supporting the accuracy of medical claims data for identifying surgical treatments among patients with kidney cancer. We sought to validate externally a previously published Medicare-...

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Main Authors: Deapen Dennis, Leventhal Meryl, Warren Joan L, Saigal Christopher S, Miller David C, Banerjee Mousumi, Lai Julie, Hanley Jan, Litwin Mark S
Format: Article
Language:English
Published: BMC 2009-06-01
Series:BMC Health Services Research
Online Access:http://www.biomedcentral.com/1472-6963/9/92
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spelling doaj-a3c1a96ebfca44868c6b577953b79f992020-11-24T22:17:23ZengBMCBMC Health Services Research1472-69632009-06-01919210.1186/1472-6963-9-92External validation of a claims-based algorithm for classifying kidney-cancer surgeriesDeapen DennisLeventhal MerylWarren Joan LSaigal Christopher SMiller David CBanerjee MousumiLai JulieHanley JanLitwin Mark S<p>Abstract</p> <p>Background</p> <p>Unlike other malignancies, there is no literature supporting the accuracy of medical claims data for identifying surgical treatments among patients with kidney cancer. We sought to validate externally a previously published Medicare-claims-based algorithm for classifying surgical treatments among patients with early-stage kidney cancer. To achieve this aim, we compared procedure assignments based on Medicare claims with the type of surgery specified in SEER registry data and clinical operative reports.</p> <p>Methods</p> <p>Using linked SEER-Medicare data, we calculated the agreement between Medicare claims and SEER data for identification of cancer-directed surgery among 6,515 patients diagnosed with early-stage kidney cancer. Next, for a subset of 120 cases, we determined the agreement between the claims algorithm and the medical record. Finally, using the medical record as the reference-standard, we calculated the sensitivity, specificity, and positive and negative predictive values of the claims algorithm.</p> <p>Results</p> <p>Among 6,515 cases, Medicare claims and SEER data identified 5,483 (84.1%) and 5,774 (88.6%) patients, respectively, who underwent cancer-directed surgery (observed agreement = 93%, κ = 0.69, 95% CI 0.66 – 0.71). The two data sources demonstrated 97% agreement for classification of partial versus radical nephrectomy (κ = 0.83, 95% CI 0.81 – 0.86). We observed 97% agreement between the claims algorithm and clinical operative reports; the positive predictive value of the claims algorithm exceeded 90% for identification of both partial nephrectomy and laparoscopic surgery.</p> <p>Conclusion</p> <p>Medicare claims represent an accurate data source for ascertainment of population-based patterns of surgical care among patients with early-stage kidney cancer.</p> http://www.biomedcentral.com/1472-6963/9/92
collection DOAJ
language English
format Article
sources DOAJ
author Deapen Dennis
Leventhal Meryl
Warren Joan L
Saigal Christopher S
Miller David C
Banerjee Mousumi
Lai Julie
Hanley Jan
Litwin Mark S
spellingShingle Deapen Dennis
Leventhal Meryl
Warren Joan L
Saigal Christopher S
Miller David C
Banerjee Mousumi
Lai Julie
Hanley Jan
Litwin Mark S
External validation of a claims-based algorithm for classifying kidney-cancer surgeries
BMC Health Services Research
author_facet Deapen Dennis
Leventhal Meryl
Warren Joan L
Saigal Christopher S
Miller David C
Banerjee Mousumi
Lai Julie
Hanley Jan
Litwin Mark S
author_sort Deapen Dennis
title External validation of a claims-based algorithm for classifying kidney-cancer surgeries
title_short External validation of a claims-based algorithm for classifying kidney-cancer surgeries
title_full External validation of a claims-based algorithm for classifying kidney-cancer surgeries
title_fullStr External validation of a claims-based algorithm for classifying kidney-cancer surgeries
title_full_unstemmed External validation of a claims-based algorithm for classifying kidney-cancer surgeries
title_sort external validation of a claims-based algorithm for classifying kidney-cancer surgeries
publisher BMC
series BMC Health Services Research
issn 1472-6963
publishDate 2009-06-01
description <p>Abstract</p> <p>Background</p> <p>Unlike other malignancies, there is no literature supporting the accuracy of medical claims data for identifying surgical treatments among patients with kidney cancer. We sought to validate externally a previously published Medicare-claims-based algorithm for classifying surgical treatments among patients with early-stage kidney cancer. To achieve this aim, we compared procedure assignments based on Medicare claims with the type of surgery specified in SEER registry data and clinical operative reports.</p> <p>Methods</p> <p>Using linked SEER-Medicare data, we calculated the agreement between Medicare claims and SEER data for identification of cancer-directed surgery among 6,515 patients diagnosed with early-stage kidney cancer. Next, for a subset of 120 cases, we determined the agreement between the claims algorithm and the medical record. Finally, using the medical record as the reference-standard, we calculated the sensitivity, specificity, and positive and negative predictive values of the claims algorithm.</p> <p>Results</p> <p>Among 6,515 cases, Medicare claims and SEER data identified 5,483 (84.1%) and 5,774 (88.6%) patients, respectively, who underwent cancer-directed surgery (observed agreement = 93%, κ = 0.69, 95% CI 0.66 – 0.71). The two data sources demonstrated 97% agreement for classification of partial versus radical nephrectomy (κ = 0.83, 95% CI 0.81 – 0.86). We observed 97% agreement between the claims algorithm and clinical operative reports; the positive predictive value of the claims algorithm exceeded 90% for identification of both partial nephrectomy and laparoscopic surgery.</p> <p>Conclusion</p> <p>Medicare claims represent an accurate data source for ascertainment of population-based patterns of surgical care among patients with early-stage kidney cancer.</p>
url http://www.biomedcentral.com/1472-6963/9/92
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