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Development and validation of case‐finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data

Purpose To determine the positive predictive values (PPVs) of ICD‐9, ICD‐10, and current procedural terminology (CPT)‐based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration. Methods We...

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Published in:Pharmacoepidemiology and drug safety 2021-09, Vol.30 (9), p.1184-1191
Main Authors: Weinstein, Erica J., Stephens‐Shields, Alisa, Loabile, Bogadi, Yuh, Tiffany, Silibovsky, Randi, Nelson, Charles L., O'Donnell, Judith A., Hsieh, Evelyn, Hanberg, Jennifer S., Akgün, Kathleen M., Tate, Janet P., Lo Re, Vincent
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creator Weinstein, Erica J.
Stephens‐Shields, Alisa
Loabile, Bogadi
Yuh, Tiffany
Silibovsky, Randi
Nelson, Charles L.
O'Donnell, Judith A.
Hsieh, Evelyn
Hanberg, Jennifer S.
Akgün, Kathleen M.
Tate, Janet P.
Lo Re, Vincent
description Purpose To determine the positive predictive values (PPVs) of ICD‐9, ICD‐10, and current procedural terminology (CPT)‐based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration. Methods We identified patients with: (1) hospital discharge ICD‐9 or ICD‐10 diagnosis of PJI, (2) ICD‐9, ICD‐10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X‐ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD‐9 and ICD‐10‐based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD‐9 and ICD‐10 PJI algorithms were calculated. Results Among a sample of 80 patients meeting the ICD‐9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%–84.0%]) had confirmed PJI. Among 80 patients who met the ICD‐10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%–92.0%]) had a confirmed diagnosis. Conclusions An algorithm consisting of an ICD‐9 or ICD‐10 PJI diagnosis following a TKA code combined with CPT codes for a knee X‐ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD‐9) and 85.0% (ICD‐10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.
doi_str_mv 10.1002/pds.5316
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Methods We identified patients with: (1) hospital discharge ICD‐9 or ICD‐10 diagnosis of PJI, (2) ICD‐9, ICD‐10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X‐ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD‐9 and ICD‐10‐based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD‐9 and ICD‐10 PJI algorithms were calculated. Results Among a sample of 80 patients meeting the ICD‐9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%–84.0%]) had confirmed PJI. Among 80 patients who met the ICD‐10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%–92.0%]) had a confirmed diagnosis. Conclusions An algorithm consisting of an ICD‐9 or ICD‐10 PJI diagnosis following a TKA code combined with CPT codes for a knee X‐ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD‐9) and 85.0% (ICD‐10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.</description><identifier>ISSN: 1053-8569</identifier><identifier>EISSN: 1099-1557</identifier><identifier>DOI: 10.1002/pds.5316</identifier><identifier>PMID: 34170057</identifier><language>eng</language><publisher>Chichester, UK: John Wiley &amp; Sons, Inc</publisher><subject>Algorithms ; Arthroplasty (knee) ; Arthroplasty, Replacement, Knee - adverse effects ; Blood culture ; Databases, Factual ; Diagnosis ; epidemiologic methods ; Humans ; Infectious diseases ; Joint diseases ; Joint replacement surgery ; Joint surgery ; Medical records ; outcomes ; Patients ; pharmacoepidemiology ; Prostheses ; Prosthesis-Related Infections - diagnosis ; Prosthesis-Related Infections - epidemiology ; Prosthesis-Related Infections - etiology ; prosthetic joint infection ; Retrospective Studies ; Terminology ; total knee arthroplasty ; validation studies ; veteran ; Veterans Health</subject><ispartof>Pharmacoepidemiology and drug safety, 2021-09, Vol.30 (9), p.1184-1191</ispartof><rights>2021 John Wiley &amp; Sons Ltd.</rights><rights>2021 John Wiley &amp; Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4386-8cde229b5cb46a7730d5e2e8f83d5a312a503e027501ec632091cdb731b6b85d3</citedby><cites>FETCH-LOGICAL-c4386-8cde229b5cb46a7730d5e2e8f83d5a312a503e027501ec632091cdb731b6b85d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34170057$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Weinstein, Erica J.</creatorcontrib><creatorcontrib>Stephens‐Shields, Alisa</creatorcontrib><creatorcontrib>Loabile, Bogadi</creatorcontrib><creatorcontrib>Yuh, Tiffany</creatorcontrib><creatorcontrib>Silibovsky, Randi</creatorcontrib><creatorcontrib>Nelson, Charles L.</creatorcontrib><creatorcontrib>O'Donnell, Judith A.</creatorcontrib><creatorcontrib>Hsieh, Evelyn</creatorcontrib><creatorcontrib>Hanberg, Jennifer S.</creatorcontrib><creatorcontrib>Akgün, Kathleen M.</creatorcontrib><creatorcontrib>Tate, Janet P.</creatorcontrib><creatorcontrib>Lo Re, Vincent</creatorcontrib><title>Development and validation of case‐finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data</title><title>Pharmacoepidemiology and drug safety</title><addtitle>Pharmacoepidemiol Drug Saf</addtitle><description>Purpose To determine the positive predictive values (PPVs) of ICD‐9, ICD‐10, and current procedural terminology (CPT)‐based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration. Methods We identified patients with: (1) hospital discharge ICD‐9 or ICD‐10 diagnosis of PJI, (2) ICD‐9, ICD‐10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X‐ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD‐9 and ICD‐10‐based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD‐9 and ICD‐10 PJI algorithms were calculated. Results Among a sample of 80 patients meeting the ICD‐9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%–84.0%]) had confirmed PJI. Among 80 patients who met the ICD‐10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%–92.0%]) had a confirmed diagnosis. Conclusions An algorithm consisting of an ICD‐9 or ICD‐10 PJI diagnosis following a TKA code combined with CPT codes for a knee X‐ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD‐9) and 85.0% (ICD‐10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.</description><subject>Algorithms</subject><subject>Arthroplasty (knee)</subject><subject>Arthroplasty, Replacement, Knee - adverse effects</subject><subject>Blood culture</subject><subject>Databases, Factual</subject><subject>Diagnosis</subject><subject>epidemiologic methods</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Joint diseases</subject><subject>Joint replacement surgery</subject><subject>Joint surgery</subject><subject>Medical records</subject><subject>outcomes</subject><subject>Patients</subject><subject>pharmacoepidemiology</subject><subject>Prostheses</subject><subject>Prosthesis-Related Infections - diagnosis</subject><subject>Prosthesis-Related Infections - epidemiology</subject><subject>Prosthesis-Related Infections - etiology</subject><subject>prosthetic joint infection</subject><subject>Retrospective Studies</subject><subject>Terminology</subject><subject>total knee arthroplasty</subject><subject>validation studies</subject><subject>veteran</subject><subject>Veterans Health</subject><issn>1053-8569</issn><issn>1099-1557</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kcuKFDEUhgtRnIuCTyABN25qTCqVumyEYcZxhAEFL9twKjnVnTaVlEm6pXc-gg_hk_kkpqfbQQVXCZwvH__JXxRPGD1jlFYvZh3PBGfNveKY0b4vmRDt_d1d8LITTX9UnMS4ojTP-vphccRr1lIq2uPixyVu0Pp5QpcIOE02YI2GZLwjfiQKIv789n00Thu3IGAXPpi0nCJJnhidH5lxS-bgY1piMoqsvMki40ZUO0ckMCYMmU5gyWeHSCCkZfCzhZi2GSSfMAOQyWsEm5bkXE_GmZjCPkTOAo-KByPYiI8P52nx8erVh4vr8ubt6zcX5zelqnnXlJ3SWFX9INRQN9C2nGqBFXZjx7UAzioQlCOtWkEZqoZXtGdKDy1nQzN0QvPT4uXeO6-HCbXK6wWwcg5mgrCVHoz8e-LMUi78Rna85r1os-D5QRD8lzXGJCcTFVoLDv06ykrUQvSiZlVGn_2Drvw6uLxepkRb5YZuqYNQ5S-OAce7MIzKXfUyVy931Wf06Z_h78DfXWeg3ANfjcXtf0Xy3eX7W-EvbTG9vw</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Weinstein, Erica J.</creator><creator>Stephens‐Shields, Alisa</creator><creator>Loabile, Bogadi</creator><creator>Yuh, Tiffany</creator><creator>Silibovsky, Randi</creator><creator>Nelson, Charles L.</creator><creator>O'Donnell, Judith A.</creator><creator>Hsieh, Evelyn</creator><creator>Hanberg, Jennifer S.</creator><creator>Akgün, Kathleen M.</creator><creator>Tate, Janet P.</creator><creator>Lo Re, Vincent</creator><general>John Wiley &amp; Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TK</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>202109</creationdate><title>Development and validation of case‐finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data</title><author>Weinstein, Erica J. ; Stephens‐Shields, Alisa ; Loabile, Bogadi ; Yuh, Tiffany ; Silibovsky, Randi ; Nelson, Charles L. ; O'Donnell, Judith A. ; Hsieh, Evelyn ; Hanberg, Jennifer S. ; Akgün, Kathleen M. ; Tate, Janet P. ; Lo Re, Vincent</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4386-8cde229b5cb46a7730d5e2e8f83d5a312a503e027501ec632091cdb731b6b85d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Arthroplasty (knee)</topic><topic>Arthroplasty, Replacement, Knee - adverse effects</topic><topic>Blood culture</topic><topic>Databases, Factual</topic><topic>Diagnosis</topic><topic>epidemiologic methods</topic><topic>Humans</topic><topic>Infectious diseases</topic><topic>Joint diseases</topic><topic>Joint replacement surgery</topic><topic>Joint surgery</topic><topic>Medical records</topic><topic>outcomes</topic><topic>Patients</topic><topic>pharmacoepidemiology</topic><topic>Prostheses</topic><topic>Prosthesis-Related Infections - diagnosis</topic><topic>Prosthesis-Related Infections - epidemiology</topic><topic>Prosthesis-Related Infections - etiology</topic><topic>prosthetic joint infection</topic><topic>Retrospective Studies</topic><topic>Terminology</topic><topic>total knee arthroplasty</topic><topic>validation studies</topic><topic>veteran</topic><topic>Veterans Health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weinstein, Erica J.</creatorcontrib><creatorcontrib>Stephens‐Shields, Alisa</creatorcontrib><creatorcontrib>Loabile, Bogadi</creatorcontrib><creatorcontrib>Yuh, Tiffany</creatorcontrib><creatorcontrib>Silibovsky, Randi</creatorcontrib><creatorcontrib>Nelson, Charles L.</creatorcontrib><creatorcontrib>O'Donnell, Judith A.</creatorcontrib><creatorcontrib>Hsieh, Evelyn</creatorcontrib><creatorcontrib>Hanberg, Jennifer S.</creatorcontrib><creatorcontrib>Akgün, Kathleen M.</creatorcontrib><creatorcontrib>Tate, Janet P.</creatorcontrib><creatorcontrib>Lo Re, Vincent</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Pharmacoepidemiology and drug safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Weinstein, Erica J.</au><au>Stephens‐Shields, Alisa</au><au>Loabile, Bogadi</au><au>Yuh, Tiffany</au><au>Silibovsky, Randi</au><au>Nelson, Charles L.</au><au>O'Donnell, Judith A.</au><au>Hsieh, Evelyn</au><au>Hanberg, Jennifer S.</au><au>Akgün, Kathleen M.</au><au>Tate, Janet P.</au><au>Lo Re, Vincent</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of case‐finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data</atitle><jtitle>Pharmacoepidemiology and drug safety</jtitle><addtitle>Pharmacoepidemiol Drug Saf</addtitle><date>2021-09</date><risdate>2021</risdate><volume>30</volume><issue>9</issue><spage>1184</spage><epage>1191</epage><pages>1184-1191</pages><issn>1053-8569</issn><eissn>1099-1557</eissn><abstract>Purpose To determine the positive predictive values (PPVs) of ICD‐9, ICD‐10, and current procedural terminology (CPT)‐based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration. Methods We identified patients with: (1) hospital discharge ICD‐9 or ICD‐10 diagnosis of PJI, (2) ICD‐9, ICD‐10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X‐ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD‐9 and ICD‐10‐based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD‐9 and ICD‐10 PJI algorithms were calculated. Results Among a sample of 80 patients meeting the ICD‐9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%–84.0%]) had confirmed PJI. Among 80 patients who met the ICD‐10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%–92.0%]) had a confirmed diagnosis. Conclusions An algorithm consisting of an ICD‐9 or ICD‐10 PJI diagnosis following a TKA code combined with CPT codes for a knee X‐ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD‐9) and 85.0% (ICD‐10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.</abstract><cop>Chichester, UK</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>34170057</pmid><doi>10.1002/pds.5316</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record>
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source Wiley-Blackwell Read & Publish Collection
subjects Algorithms
Arthroplasty (knee)
Arthroplasty, Replacement, Knee - adverse effects
Blood culture
Databases, Factual
Diagnosis
epidemiologic methods
Humans
Infectious diseases
Joint diseases
Joint replacement surgery
Joint surgery
Medical records
outcomes
Patients
pharmacoepidemiology
Prostheses
Prosthesis-Related Infections - diagnosis
Prosthesis-Related Infections - epidemiology
Prosthesis-Related Infections - etiology
prosthetic joint infection
Retrospective Studies
Terminology
total knee arthroplasty
validation studies
veteran
Veterans Health
title Development and validation of case‐finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data
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