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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 |
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container_title | Medical care |
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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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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 & 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. All rights reserved</general><general>Lippincott Williams & Wilkins Ovid Technologies</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>201712</creationdate><title>Accuracy of Medicare Claim–based Algorithm to Detect Breast, Prostate, or Lung Cancer Bone Metastases</title><author>Sathiakumar, Nalini ; Delzell, Elizabeth ; Yun, Huifeng ; Jooste, Rene ; Godby, Kelly ; Falkson, Carla ; Yong, Mellissa ; Kilgore, Meredith L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4329-183c1b545c58f52ec09350d8fcfd5ec3b288d2c6488ae9e97580d33e6cbed1893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Applied Methods</topic><topic>Bone cancer</topic><topic>Breast</topic><topic>Breast cancer</topic><topic>Cancer</topic><topic>Confidence intervals</topic><topic>Diagnosis</topic><topic>Electronic health records</topic><topic>Electronic medical records</topic><topic>Government programs</topic><topic>Health care</topic><topic>Lung cancer</topic><topic>Medicare</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Older people</topic><topic>Patients</topic><topic>Prostate cancer</topic><topic>Sensitivity</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Medical care</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sathiakumar, Nalini</au><au>Delzell, Elizabeth</au><au>Yun, Huifeng</au><au>Jooste, Rene</au><au>Godby, Kelly</au><au>Falkson, Carla</au><au>Yong, Mellissa</au><au>Kilgore, Meredith L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accuracy of Medicare Claim–based Algorithm to Detect Breast, Prostate, or Lung Cancer Bone Metastases</atitle><jtitle>Medical care</jtitle><addtitle>Med Care</addtitle><date>2017-12</date><risdate>2017</risdate><volume>55</volume><issue>12</issue><spage>e144</spage><epage>e149</epage><pages>e144-e149</pages><issn>0025-7079</issn><eissn>1537-1948</eissn><abstract>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.</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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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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