Sandip Pravin Patel, Rongrong Wang, Summera Qiheng Zhou, Daniel Sheinson, Ann Johnson, Janet Shin Lee
{"title":"验证在行政索赔数据库中识别非小细胞肺癌患者的最新算法。","authors":"Sandip Pravin Patel, Rongrong Wang, Summera Qiheng Zhou, Daniel Sheinson, Ann Johnson, Janet Shin Lee","doi":"10.1200/CCI.23.00165","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Real-world lung cancer data in administrative claims databases often lack staging information and specific diagnostic codes for lung cancer histology subtypes. This study updates and validates Turner's 2017 treatment-based algorithm using more recent claims and electronic health record (EHR) data.</p><p><strong>Methods: </strong>This study used Optum's deidentified Market Clarity Data of linked medical and pharmacy claims with EHR data. Eligible patients had an incident lung cancer diagnosis (January 2014-December 2020) and ≥one valid histology code for lung cancer 30 days before to 60 days after diagnosis. Histology and stage information from the EHR were used to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated the Turner algorithm using cohort 1 patients diagnosed between June 2014 and October 2015 (step 1) and between November 2015 and December 2020 after approval of immunotherapies (step 2). Next, we evaluated cohort 2 patients diagnosed between November 2015 and December 2020 using an updated algorithm incorporating the latest US treatment guidelines (step 3), and compared the results for cohort 2 (Turner algorithm, step 2 patients). Furthermore, an algorithm to determine early NSCLC (eNSCLC; stage I-III) versus metastatic or advanced/metastatic non-small cell lung cancer (stage IV) was evaluated among patients with available histology and stage information.</p><p><strong>Results: </strong>A total of 5,012 patients were included (cohort 1, step 1: n = 406; cohort 1, step 2: n = 2,573; cohort 2, step 3: n = 2,744). The updated algorithm showed improved performance relative to the previous Turner algorithm for sensitivity (0.920-0.932), specificity (0.865-0.923), PPV (0.976-0.988), and NPV (0.640-0.673). The eNSCLC algorithm showed high specificity (0.874) and relatively low sensitivity (0.539).</p><p><strong>Conclusion: </strong>An updated treatment-based algorithm identifying patients with incident NSCLC was validated using EHR data and distinguished lung cancer subtypes in claims databases when EHR data were not available.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10965218/pdf/","citationCount":"0","resultStr":"{\"title\":\"Validation of an Updated Algorithm to Identify Patients With Incident Non-Small Cell Lung Cancer in Administrative Claims Databases.\",\"authors\":\"Sandip Pravin Patel, Rongrong Wang, Summera Qiheng Zhou, Daniel Sheinson, Ann Johnson, Janet Shin Lee\",\"doi\":\"10.1200/CCI.23.00165\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Real-world lung cancer data in administrative claims databases often lack staging information and specific diagnostic codes for lung cancer histology subtypes. This study updates and validates Turner's 2017 treatment-based algorithm using more recent claims and electronic health record (EHR) data.</p><p><strong>Methods: </strong>This study used Optum's deidentified Market Clarity Data of linked medical and pharmacy claims with EHR data. Eligible patients had an incident lung cancer diagnosis (January 2014-December 2020) and ≥one valid histology code for lung cancer 30 days before to 60 days after diagnosis. Histology and stage information from the EHR were used to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated the Turner algorithm using cohort 1 patients diagnosed between June 2014 and October 2015 (step 1) and between November 2015 and December 2020 after approval of immunotherapies (step 2). Next, we evaluated cohort 2 patients diagnosed between November 2015 and December 2020 using an updated algorithm incorporating the latest US treatment guidelines (step 3), and compared the results for cohort 2 (Turner algorithm, step 2 patients). Furthermore, an algorithm to determine early NSCLC (eNSCLC; stage I-III) versus metastatic or advanced/metastatic non-small cell lung cancer (stage IV) was evaluated among patients with available histology and stage information.</p><p><strong>Results: </strong>A total of 5,012 patients were included (cohort 1, step 1: n = 406; cohort 1, step 2: n = 2,573; cohort 2, step 3: n = 2,744). The updated algorithm showed improved performance relative to the previous Turner algorithm for sensitivity (0.920-0.932), specificity (0.865-0.923), PPV (0.976-0.988), and NPV (0.640-0.673). The eNSCLC algorithm showed high specificity (0.874) and relatively low sensitivity (0.539).</p><p><strong>Conclusion: </strong>An updated treatment-based algorithm identifying patients with incident NSCLC was validated using EHR data and distinguished lung cancer subtypes in claims databases when EHR data were not available.</p>\",\"PeriodicalId\":51626,\"journal\":{\"name\":\"JCO Clinical Cancer Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10965218/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCO Clinical Cancer Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1200/CCI.23.00165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO Clinical Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1200/CCI.23.00165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Validation of an Updated Algorithm to Identify Patients With Incident Non-Small Cell Lung Cancer in Administrative Claims Databases.
Purpose: Real-world lung cancer data in administrative claims databases often lack staging information and specific diagnostic codes for lung cancer histology subtypes. This study updates and validates Turner's 2017 treatment-based algorithm using more recent claims and electronic health record (EHR) data.
Methods: This study used Optum's deidentified Market Clarity Data of linked medical and pharmacy claims with EHR data. Eligible patients had an incident lung cancer diagnosis (January 2014-December 2020) and ≥one valid histology code for lung cancer 30 days before to 60 days after diagnosis. Histology and stage information from the EHR were used to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated the Turner algorithm using cohort 1 patients diagnosed between June 2014 and October 2015 (step 1) and between November 2015 and December 2020 after approval of immunotherapies (step 2). Next, we evaluated cohort 2 patients diagnosed between November 2015 and December 2020 using an updated algorithm incorporating the latest US treatment guidelines (step 3), and compared the results for cohort 2 (Turner algorithm, step 2 patients). Furthermore, an algorithm to determine early NSCLC (eNSCLC; stage I-III) versus metastatic or advanced/metastatic non-small cell lung cancer (stage IV) was evaluated among patients with available histology and stage information.
Results: A total of 5,012 patients were included (cohort 1, step 1: n = 406; cohort 1, step 2: n = 2,573; cohort 2, step 3: n = 2,744). The updated algorithm showed improved performance relative to the previous Turner algorithm for sensitivity (0.920-0.932), specificity (0.865-0.923), PPV (0.976-0.988), and NPV (0.640-0.673). The eNSCLC algorithm showed high specificity (0.874) and relatively low sensitivity (0.539).
Conclusion: An updated treatment-based algorithm identifying patients with incident NSCLC was validated using EHR data and distinguished lung cancer subtypes in claims databases when EHR data were not available.
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