Evaluation of the accuracy of algorithms to identify soft tissue sarcoma (STS) in administrative claims

Nicole Princic,Donna Mcmorrow,Philip Chan,Lisa Hess

doi:10.1186/s13569-020-00130-y

Nicole Princic, Donna Mcmorrow + Show 2 more

Open Access

https://doi.org/10.1186/s13569-020-00130-y

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Abstract

BackgroundLack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims. The objective of this study to evaluate published algorithms to identify patients with soft tissue sarcoma (STS) in administrative claims and to evaluate new algorithms to improved performance.MethodsTwo cancer populations including STS cases and non-STS controls were selected from the MarketScan Explorys Linked Claims-Electronic Medical Record (EMR) Database between January 1, 2000 and July 31, 2018. Eligible cases had a diagnosis on a clinical record for STS in the EMR while controls had no evidence of STS on any EMR records. Both cases and controls were enrolled in administrative claims during a period of observation and were aged ≥ 18 years. A split sample was used to test and validate algorithms using data from administrative claims. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated for 14 algorithms. Prior literature validating algorithms in administrative claims across other cancer types report both sensitivity and specificity ranging from as low as 73% to as high as 95%. This was used as a benchmark for defining algorithm success.ResultsThere were 784 STS cases and 249,062 non-STS cancer controls eligible for analysis. Requiring at least two claims with an ICD-CM diagnosis code for STS achieved a sensitivity of 67% but had a specificity of 72%. Algorithms that required NCCN-recommended systemic treatment for STS improved the specificity to over 90% but dropped the sensitivity to below 20%. Other combinations of diagnostic tests, symptoms, and procedures did not improve performance.ConclusionsThe algorithms tested in this study sample did not achieve sufficient performance and suggest the ability to accurately identify the STS population in administrative data is problematic. Difficulties are likely due to the origin of STS in a variety of locations, the non-specific symptoms of STS, and the common diagnostic tests recommended to diagnose the disease. Future research applying machine learning to examine timing and patterns of variables that comprise the diagnostic process may further investigate the ability to accurately identify STS cases in claims databases.

Highlights

Lack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims
No evidence of Gastrointestinal stromal tumors (GIST), Osteosarcoma, Kaposi's sarcoma in the Electronic Medical Record (EMR) N = 875
No evidence of GIST, Osteosarcoma, Kaposi's sarcoma in the EMR N = 2,610,859

Summary

Introduction

Lack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims. The objective of this study to evaluate published algorithms to identify patients with soft tissue sarcoma (STS) in administrative claims and to evaluate new algorithms to improved performance. Using ICD-CM diagnosis codes for health outcomes research is challenging as the codes often lack specificity needed to accurately identify patients [9, 10]. One study developed an algorithm to identify multiple myeloma patients using administrative claims and found that requiring two diagnosis at least 30 days apart resulted in a sensitivity of 95% and a specificity of 73%. A second study tested an administrative claims-based algorithm using a combination of diagnosis codes, procedures codes and treatments to identify patients with breast, colorectal, and lung cancer, the sensitivity of the algorithm was 77%, 72%, and 81% for each cancer type respectively [12]

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