Pharmacoepidemiology and drug safety · 2025
Diagnostic Performance of Prostate Cancer Disease-Specific Phenotypes Identified Using Real-World Databases: A Systematic Review.
СтатьяСистематический обзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Vyas Ami, Kamat Shweta, Thomas Sadie, Gambino Connor, Brown Britny R, Raval Amit D
Аннотация
BACKGROUND: Research using real-world databases (RWD) often requires the development of computable phenotypes based on clinical reasoning-based algorithms or prediction models with validation through a reference standard such as chart review. While there are studies reporting different phenotypes for key prostate cancer (PC) disease or outcomes, these have not been summarized systematically.
OBJECTIVES: To conduct a systematic review (SR) to summarize validation statistics on PC-specific phenotypes, including metastasis, biochemical recurrence (BCR), castration-resistant prostate cancer (CRPC), hormone-sensitive prostate cancer (HSPC), progression-free survival, and performance status.
METHODS: We conducted a SR in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies guidelines. We systematically searched PubMed/Medline and EMBASE for studies reporting algorithms and prediction models for PC phenotypes based on structured RWD published between 2012 and 2024. A summary of algorithms and prediction models, along with their respective estimates of diagnostic accuracy compared to reference standards and/or measures of uncertainty, was provided. An area under the curve (AUC) > 0.7 was considered an acceptable phenotype.
RESULTS: Out of 7427 retrieved citations, 29 unique retrospective studies (31 citations) were included. Both claims-based codes and prediction model-based classification for any metastasis and bone metastases had an acceptable performance with high AUC (0.88 and > 0.7, respectively) and high specificity (above 90%) with a few having moderate sensitivity (60% to 100%). The prediction model-based BCR classification had acceptable performance (AUC > 0.7); however, claims-based BCR had moderate performance statistics with sensitivity in the range of 3%-19% and specificity in the range of 83%-98%. One claims-based algorithm for metastatic CRPC had high sensitivity (77%) and specificity (100%). Studies for mHSPC were based on clinical reasoning without assessing their diagnostic accuracy. Claims-based algorithms for performance status had at least 75% sensitivity and relatively high specificity.
CONCLUSIONS: Our SR highlights the acceptable accuracy of computable phenotypes for PC, including (bone) metastasis, BCR, and performance status within RWD. Further validation studies are needed for RWD-based phenotypes to account for changes in therapeutic options in PC.
OBJECTIVES: To conduct a systematic review (SR) to summarize validation statistics on PC-specific phenotypes, including metastasis, biochemical recurrence (BCR), castration-resistant prostate cancer (CRPC), hormone-sensitive prostate cancer (HSPC), progression-free survival, and performance status.
METHODS: We conducted a SR in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies guidelines. We systematically searched PubMed/Medline and EMBASE for studies reporting algorithms and prediction models for PC phenotypes based on structured RWD published between 2012 and 2024. A summary of algorithms and prediction models, along with their respective estimates of diagnostic accuracy compared to reference standards and/or measures of uncertainty, was provided. An area under the curve (AUC) > 0.7 was considered an acceptable phenotype.
RESULTS: Out of 7427 retrieved citations, 29 unique retrospective studies (31 citations) were included. Both claims-based codes and prediction model-based classification for any metastasis and bone metastases had an acceptable performance with high AUC (0.88 and > 0.7, respectively) and high specificity (above 90%) with a few having moderate sensitivity (60% to 100%). The prediction model-based BCR classification had acceptable performance (AUC > 0.7); however, claims-based BCR had moderate performance statistics with sensitivity in the range of 3%-19% and specificity in the range of 83%-98%. One claims-based algorithm for metastatic CRPC had high sensitivity (77%) and specificity (100%). Studies for mHSPC were based on clinical reasoning without assessing their diagnostic accuracy. Claims-based algorithms for performance status had at least 75% sensitivity and relatively high specificity.
CONCLUSIONS: Our SR highlights the acceptable accuracy of computable phenotypes for PC, including (bone) metastasis, BCR, and performance status within RWD. Further validation studies are needed for RWD-based phenotypes to account for changes in therapeutic options in PC.