Daniel Simmons, John White, Valery Walker, Stephanie V Blank, Jiefen Munley, Kimmie McLaurin
{"title":"使用病历回顾验证基于行政索赔的卵巢癌妇女治疗路线算法。","authors":"Daniel Simmons, John White, Valery Walker, Stephanie V Blank, Jiefen Munley, Kimmie McLaurin","doi":"10.1007/s12325-025-03174-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>New maintenance therapies to treat advanced ovarian cancer have added complexity to identifying lines of therapy (LOTs) for real-world evidence (RWE) studies. This study evaluated the performance of a claims-based algorithm that identifies LOTs among patients with ovarian cancer using medical chart review validation.</p><p><strong>Methods: </strong>The algorithm was developed previously utilizing the Optum Research Database (ORD), a US database that contains administrative claims data. To validate the algorithm, LOT results generated using claims data vs chart data were compared at the patient level by calculating the percent agreement between total number of active and maintenance LOTs, type of therapy (neoadjuvant vs adjuvant classification), and type of regimen (individual drugs). Patients with a diagnosis of ovarian cancer who initiated chemotherapy between December 1, 2014, and September 15, 2017, were included in the study. We report descriptive statistics, the percentage correspondence between medical records and claims data, and kappa statistics to measure the magnitude of agreement.</p><p><strong>Results: </strong>A total of 294 patients were included in the analysis; 164 received only chemotherapy and no maintenance, 77 received bevacizumab, and 53 patients received poly (ADP-ribose) polymerase inhibitors (PARPi). Mean age was 64.9 years, and 47.3% had stage III cancer. The algorithm demonstrated substantial agreement between claims and medical records for total number of lines of active and maintenance therapy (weighted kappa 0.65 and 0.62 p < 0.0001). There was moderate-to-substantial agreement for neoadjuvant and adjuvant therapy (kappa 0.56 and 0.62 p < 0.0001). The algorithm performed best at identifying early treatment with a regimen match of 82% and 88% agreement for first-line active and first-line maintenance, respectively.</p><p><strong>Conclusion: </strong>We validated an administrative claims-based algorithm that demonstrates strong concordance with medical records for identifying LOT among patients with ovarian cancer. The algorithm can be applied in future studies to analyze treatment patterns and outcomes.</p>","PeriodicalId":7482,"journal":{"name":"Advances in Therapy","volume":" ","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validation of an Administrative Claims-based Line of Therapy Algorithm for Women with Ovarian Cancer Using Medical Chart Review.\",\"authors\":\"Daniel Simmons, John White, Valery Walker, Stephanie V Blank, Jiefen Munley, Kimmie McLaurin\",\"doi\":\"10.1007/s12325-025-03174-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>New maintenance therapies to treat advanced ovarian cancer have added complexity to identifying lines of therapy (LOTs) for real-world evidence (RWE) studies. This study evaluated the performance of a claims-based algorithm that identifies LOTs among patients with ovarian cancer using medical chart review validation.</p><p><strong>Methods: </strong>The algorithm was developed previously utilizing the Optum Research Database (ORD), a US database that contains administrative claims data. To validate the algorithm, LOT results generated using claims data vs chart data were compared at the patient level by calculating the percent agreement between total number of active and maintenance LOTs, type of therapy (neoadjuvant vs adjuvant classification), and type of regimen (individual drugs). Patients with a diagnosis of ovarian cancer who initiated chemotherapy between December 1, 2014, and September 15, 2017, were included in the study. We report descriptive statistics, the percentage correspondence between medical records and claims data, and kappa statistics to measure the magnitude of agreement.</p><p><strong>Results: </strong>A total of 294 patients were included in the analysis; 164 received only chemotherapy and no maintenance, 77 received bevacizumab, and 53 patients received poly (ADP-ribose) polymerase inhibitors (PARPi). Mean age was 64.9 years, and 47.3% had stage III cancer. The algorithm demonstrated substantial agreement between claims and medical records for total number of lines of active and maintenance therapy (weighted kappa 0.65 and 0.62 p < 0.0001). There was moderate-to-substantial agreement for neoadjuvant and adjuvant therapy (kappa 0.56 and 0.62 p < 0.0001). The algorithm performed best at identifying early treatment with a regimen match of 82% and 88% agreement for first-line active and first-line maintenance, respectively.</p><p><strong>Conclusion: </strong>We validated an administrative claims-based algorithm that demonstrates strong concordance with medical records for identifying LOT among patients with ovarian cancer. The algorithm can be applied in future studies to analyze treatment patterns and outcomes.</p>\",\"PeriodicalId\":7482,\"journal\":{\"name\":\"Advances in Therapy\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12325-025-03174-y\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12325-025-03174-y","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Validation of an Administrative Claims-based Line of Therapy Algorithm for Women with Ovarian Cancer Using Medical Chart Review.
Introduction: New maintenance therapies to treat advanced ovarian cancer have added complexity to identifying lines of therapy (LOTs) for real-world evidence (RWE) studies. This study evaluated the performance of a claims-based algorithm that identifies LOTs among patients with ovarian cancer using medical chart review validation.
Methods: The algorithm was developed previously utilizing the Optum Research Database (ORD), a US database that contains administrative claims data. To validate the algorithm, LOT results generated using claims data vs chart data were compared at the patient level by calculating the percent agreement between total number of active and maintenance LOTs, type of therapy (neoadjuvant vs adjuvant classification), and type of regimen (individual drugs). Patients with a diagnosis of ovarian cancer who initiated chemotherapy between December 1, 2014, and September 15, 2017, were included in the study. We report descriptive statistics, the percentage correspondence between medical records and claims data, and kappa statistics to measure the magnitude of agreement.
Results: A total of 294 patients were included in the analysis; 164 received only chemotherapy and no maintenance, 77 received bevacizumab, and 53 patients received poly (ADP-ribose) polymerase inhibitors (PARPi). Mean age was 64.9 years, and 47.3% had stage III cancer. The algorithm demonstrated substantial agreement between claims and medical records for total number of lines of active and maintenance therapy (weighted kappa 0.65 and 0.62 p < 0.0001). There was moderate-to-substantial agreement for neoadjuvant and adjuvant therapy (kappa 0.56 and 0.62 p < 0.0001). The algorithm performed best at identifying early treatment with a regimen match of 82% and 88% agreement for first-line active and first-line maintenance, respectively.
Conclusion: We validated an administrative claims-based algorithm that demonstrates strong concordance with medical records for identifying LOT among patients with ovarian cancer. The algorithm can be applied in future studies to analyze treatment patterns and outcomes.
期刊介绍：
Advances in Therapy is an international, peer reviewed, rapid-publication (peer review in 2 weeks, published 3–4 weeks from acceptance) journal dedicated to the publication of high-quality clinical (all phases), observational, real-world, and health outcomes research around the discovery, development, and use of therapeutics and interventions (including devices) across all therapeutic areas. Studies relating to diagnostics and diagnosis, pharmacoeconomics, public health, epidemiology, quality of life, and patient care, management, and education are also encouraged.
The journal is of interest to a broad audience of healthcare professionals and publishes original research, reviews, communications and letters. The journal is read by a global audience and receives submissions from all over the world. Advances in Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. This inclusive approach allows the journal to assist in the dissemination of all scientifically and ethically sound research.
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