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Accuracy of Medicare Claim–based Algorithm to Detect Breast, Prostate, or Lung Cancer Bone Metastases

BACKGROUND:We had previously developed an algorithm for Medicare claims data to detect bone metastases associated with breast, prostate, or lung cancer. This study was conducted to examine whether this algorithm accurately documents bone metastases on the basis of diagnosis codes in Medicare claims...

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Published in:Medical care 2017-12, Vol.55 (12), p.e144-e149
Main Authors: Sathiakumar, Nalini, Delzell, Elizabeth, Yun, Huifeng, Jooste, Rene, Godby, Kelly, Falkson, Carla, Yong, Mellissa, Kilgore, Meredith L
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cited_by cdi_FETCH-LOGICAL-c4329-183c1b545c58f52ec09350d8fcfd5ec3b288d2c6488ae9e97580d33e6cbed1893
cites cdi_FETCH-LOGICAL-c4329-183c1b545c58f52ec09350d8fcfd5ec3b288d2c6488ae9e97580d33e6cbed1893
container_end_page e149
container_issue 12
container_start_page e144
container_title Medical care
container_volume 55
creator Sathiakumar, Nalini
Delzell, Elizabeth
Yun, Huifeng
Jooste, Rene
Godby, Kelly
Falkson, Carla
Yong, Mellissa
Kilgore, Meredith L
description BACKGROUND:We had previously developed an algorithm for Medicare claims data to detect bone metastases associated with breast, prostate, or lung cancer. This study was conducted to examine whether this algorithm accurately documents bone metastases on the basis of diagnosis codes in Medicare claims data. METHODS:We obtained data from Medicare claims and electronic medical records of patients 65 years or older with a breast, prostate, or lung cancer diagnosis at a teaching hospital and/or affiliated clinics during 2005 or 2006. We calculated the sensitivity and positive predictive value (PPV) of our algorithm using medical records as the “gold standard.” The κ statistic was used to measure agreement between claims and medical record data. RESULTS:The agreement between claims and medical record data for bone metastases among breast, prostate, and lung cancer patients was 0.93, 0.90, and 0.69, respectively. The sensitivities of our algorithm for bone metastasis in patients with breast, prostate, and lung were 96.8% [95% confidence interval (CI)=83.8% to 99.4%], 91.7% (95% CI=78.2% to 97.1%), and 74.1% (95% CI=55.3% to 86.8%), respectively; and the PPVs were 90.9% (95% CI=76.4% to 96.9%), 91.7% (95% CI=78.2% to 97.1%), and 71.4% (95% CI=52.9% to 84.8%), respectively. CONCLUSIONS:The algorithm for detecting bone metastases in claims data had high sensitivity and PPV for breast and prostate cancer patients. Sensitivity and PPV were lower but still moderate for lung cancer patients.
doi_str_mv 10.1097/MLR.0000000000000539
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This study was conducted to examine whether this algorithm accurately documents bone metastases on the basis of diagnosis codes in Medicare claims data. METHODS:We obtained data from Medicare claims and electronic medical records of patients 65 years or older with a breast, prostate, or lung cancer diagnosis at a teaching hospital and/or affiliated clinics during 2005 or 2006. We calculated the sensitivity and positive predictive value (PPV) of our algorithm using medical records as the “gold standard.” The κ statistic was used to measure agreement between claims and medical record data. RESULTS:The agreement between claims and medical record data for bone metastases among breast, prostate, and lung cancer patients was 0.93, 0.90, and 0.69, respectively. The sensitivities of our algorithm for bone metastasis in patients with breast, prostate, and lung were 96.8% [95% confidence interval (CI)=83.8% to 99.4%], 91.7% (95% CI=78.2% to 97.1%), and 74.1% (95% CI=55.3% to 86.8%), respectively; and the PPVs were 90.9% (95% CI=76.4% to 96.9%), 91.7% (95% CI=78.2% to 97.1%), and 71.4% (95% CI=52.9% to 84.8%), respectively. CONCLUSIONS:The algorithm for detecting bone metastases in claims data had high sensitivity and PPV for breast and prostate cancer patients. Sensitivity and PPV were lower but still moderate for lung cancer patients.</description><identifier>ISSN: 0025-7079</identifier><identifier>EISSN: 1537-1948</identifier><identifier>DOI: 10.1097/MLR.0000000000000539</identifier><identifier>PMID: 27111753</identifier><language>eng</language><publisher>United States: Wolters Kluwer Health, Inc</publisher><subject>Algorithms ; Applied Methods ; Bone cancer ; Breast ; Breast cancer ; Cancer ; Confidence intervals ; Diagnosis ; Electronic health records ; Electronic medical records ; Government programs ; Health care ; Lung cancer ; Medicare ; Metastases ; Metastasis ; Older people ; Patients ; Prostate cancer ; Sensitivity ; Statistical analysis</subject><ispartof>Medical care, 2017-12, Vol.55 (12), p.e144-e149</ispartof><rights>Copyright © 2016 Wolters Kluwer Health, Inc.</rights><rights>Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.</rights><rights>Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.</rights><rights>Copyright Lippincott Williams &amp; Wilkins Ovid Technologies Dec 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4329-183c1b545c58f52ec09350d8fcfd5ec3b288d2c6488ae9e97580d33e6cbed1893</citedby><cites>FETCH-LOGICAL-c4329-183c1b545c58f52ec09350d8fcfd5ec3b288d2c6488ae9e97580d33e6cbed1893</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26418508$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26418508$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,58238,58471</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27111753$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sathiakumar, Nalini</creatorcontrib><creatorcontrib>Delzell, Elizabeth</creatorcontrib><creatorcontrib>Yun, Huifeng</creatorcontrib><creatorcontrib>Jooste, Rene</creatorcontrib><creatorcontrib>Godby, Kelly</creatorcontrib><creatorcontrib>Falkson, Carla</creatorcontrib><creatorcontrib>Yong, Mellissa</creatorcontrib><creatorcontrib>Kilgore, Meredith L</creatorcontrib><title>Accuracy of Medicare Claim–based Algorithm to Detect Breast, Prostate, or Lung Cancer Bone Metastases</title><title>Medical care</title><addtitle>Med Care</addtitle><description>BACKGROUND:We had previously developed an algorithm for Medicare claims data to detect bone metastases associated with breast, prostate, or lung cancer. This study was conducted to examine whether this algorithm accurately documents bone metastases on the basis of diagnosis codes in Medicare claims data. METHODS:We obtained data from Medicare claims and electronic medical records of patients 65 years or older with a breast, prostate, or lung cancer diagnosis at a teaching hospital and/or affiliated clinics during 2005 or 2006. We calculated the sensitivity and positive predictive value (PPV) of our algorithm using medical records as the “gold standard.” The κ statistic was used to measure agreement between claims and medical record data. RESULTS:The agreement between claims and medical record data for bone metastases among breast, prostate, and lung cancer patients was 0.93, 0.90, and 0.69, respectively. The sensitivities of our algorithm for bone metastasis in patients with breast, prostate, and lung were 96.8% [95% confidence interval (CI)=83.8% to 99.4%], 91.7% (95% CI=78.2% to 97.1%), and 74.1% (95% CI=55.3% to 86.8%), respectively; and the PPVs were 90.9% (95% CI=76.4% to 96.9%), 91.7% (95% CI=78.2% to 97.1%), and 71.4% (95% CI=52.9% to 84.8%), respectively. CONCLUSIONS:The algorithm for detecting bone metastases in claims data had high sensitivity and PPV for breast and prostate cancer patients. Sensitivity and PPV were lower but still moderate for lung cancer patients.</description><subject>Algorithms</subject><subject>Applied Methods</subject><subject>Bone cancer</subject><subject>Breast</subject><subject>Breast cancer</subject><subject>Cancer</subject><subject>Confidence intervals</subject><subject>Diagnosis</subject><subject>Electronic health records</subject><subject>Electronic medical records</subject><subject>Government programs</subject><subject>Health care</subject><subject>Lung cancer</subject><subject>Medicare</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Older people</subject><subject>Patients</subject><subject>Prostate cancer</subject><subject>Sensitivity</subject><subject>Statistical analysis</subject><issn>0025-7079</issn><issn>1537-1948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFkctu1DAUhi0EokPhDQBZYsOiKce3xF5Oh6s0FQjBOnKck5kMSdzajqru-g59Q54ElykXdQGWLMvS93869k_IUwbHDEz16nT9-Rj-XkqYe2TBlKgKZqS-TxYAXBUVVOaAPIpxB8AqofhDcsArxlilxIJsls7NwbpL6jt6im3vbEC6Gmw_fr-6bmzEli6HjQ992o40efoaE7pETwLamI7op-BjsgmPqA90PU8burKTw0BP_IRZmDKVJfExedDZIeKT2_OQfH375svqfbH--O7DarkunBTcFEwLxxollVO6UxwdGKGg1Z3rWoVONFzrlrtSam3RoKmUhlYILF2DLdNGHJKXe-9Z8OczxlSPfXQ4DHZCP8eamVJWIABERl_cQXd-DlOeLlMajBGyvBHKPeXyS2PArj4L_WjDZc2gvimizkXUd4vIsee38rkZsf0d-vXzGdB74MIPCUP8NswXGOot2iFt_-eW_4j-pEoFBc91M55vRd6c59izfWwXkw9_Jiol0wq0-AHtuq0K</recordid><startdate>201712</startdate><enddate>201712</enddate><creator>Sathiakumar, Nalini</creator><creator>Delzell, Elizabeth</creator><creator>Yun, Huifeng</creator><creator>Jooste, Rene</creator><creator>Godby, Kelly</creator><creator>Falkson, Carla</creator><creator>Yong, Mellissa</creator><creator>Kilgore, Meredith L</creator><general>Wolters Kluwer Health, Inc</general><general>Copyright Wolters Kluwer Health, Inc. 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This study was conducted to examine whether this algorithm accurately documents bone metastases on the basis of diagnosis codes in Medicare claims data. METHODS:We obtained data from Medicare claims and electronic medical records of patients 65 years or older with a breast, prostate, or lung cancer diagnosis at a teaching hospital and/or affiliated clinics during 2005 or 2006. We calculated the sensitivity and positive predictive value (PPV) of our algorithm using medical records as the “gold standard.” The κ statistic was used to measure agreement between claims and medical record data. RESULTS:The agreement between claims and medical record data for bone metastases among breast, prostate, and lung cancer patients was 0.93, 0.90, and 0.69, respectively. The sensitivities of our algorithm for bone metastasis in patients with breast, prostate, and lung were 96.8% [95% confidence interval (CI)=83.8% to 99.4%], 91.7% (95% CI=78.2% to 97.1%), and 74.1% (95% CI=55.3% to 86.8%), respectively; and the PPVs were 90.9% (95% CI=76.4% to 96.9%), 91.7% (95% CI=78.2% to 97.1%), and 71.4% (95% CI=52.9% to 84.8%), respectively. CONCLUSIONS:The algorithm for detecting bone metastases in claims data had high sensitivity and PPV for breast and prostate cancer patients. Sensitivity and PPV were lower but still moderate for lung cancer patients.</abstract><cop>United States</cop><pub>Wolters Kluwer Health, Inc</pub><pmid>27111753</pmid><doi>10.1097/MLR.0000000000000539</doi></addata></record>
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source JSTOR Archival Journals and Primary Sources Collection
subjects Algorithms
Applied Methods
Bone cancer
Breast
Breast cancer
Cancer
Confidence intervals
Diagnosis
Electronic health records
Electronic medical records
Government programs
Health care
Lung cancer
Medicare
Metastases
Metastasis
Older people
Patients
Prostate cancer
Sensitivity
Statistical analysis
title Accuracy of Medicare Claim–based Algorithm to Detect Breast, Prostate, or Lung Cancer Bone Metastases
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