Dataset Information

A method to predict breast cancer stage using Medicare claims.

ABSTRACT:

Background

In epidemiologic studies, cancer stage is an important predictor of outcomes. However, cancer stage is typically unavailable in medical insurance claims datasets, thus limiting the usefulness of such data for epidemiologic studies. Therefore, we sought to develop an algorithm to predict cancer stage based on covariates available from claims-based data.

Methods

We identified a cohort of 77,306 women age >/= 66 years with stage I-IV breast cancer, using the Surveillence Epidemiology and End Results (SEER)-Medicare database. We formulated an algorithm to predict cancer stage using covariates (demographic, tumor, and treatment characteristics) obtained from claims. Logistic regression models derived prediction equations in a training set, and equations' test characteristics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value [NPV]) were calculated in a validation set.

Results

Of the entire sample of women diagnosed with invasive breast cancer, 51% had stage I; 26% stage II; 11% stage III; and 4% stage IV disease. The equation predicting stage IV disease achieved sensitivity of 81%, specificity 89%, positive predictive value (PPV) 24%, and negative predictive value (NPV) 99%, while the equation distinguishing stage I/II from stage III disease achieved sensitivity 83%, specificity 78%, PPV 98%, and NPV 31%. Combined, the equations most accurately identified early stage disease and ascertained a sample in which 98% of patients were stage I or II.

Conclusions

A claims-based algorithm was utilized to predict breast cancer stage, and was particularly successful when used to identify early stage disease. These prediction equations may be applied in future studies of breast cancer patients, substantially improving the utility of claims-based studies in this group. This method may similarly be employed to develop algorithms permitting claims-based epidemiologic studies of patients with other cancers.

SUBMITTER: Smith GL

PROVIDER: S-EPMC2818641 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Publications

A method to predict breast cancer stage using Medicare claims.

Smith Grace L GL Shih Ya-Chen T YC Giordano Sharon H SH Smith Benjamin D BD Buchholz Thomas A TA

Epidemiologic perspectives & innovations : EP+I 20100115

<h4>Background</h4>In epidemiologic studies, cancer stage is an important predictor of outcomes. However, cancer stage is typically unavailable in medical insurance claims datasets, thus limiting the usefulness of such data for epidemiologic studies. Therefore, we sought to develop an algorithm to predict cancer stage based on covariates available from claims-based data.<h4>Methods</h4>We identified a cohort of 77,306 women age >/= 66 years with stage I-IV breast cancer, using the Surveillence E ...[more]

PMID: 20145734

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Project description:ImportancePredicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models.ObjectiveTo determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.Design, setting, and participantsThis comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019.Main outcomes and measuresThe models' goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2.ResultsAmong the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions.Conclusions and relevanceLeveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.

Dataset Information

A method to predict breast cancer stage using Medicare claims.

Background

Methods

Results

Conclusions

Publications

A method to predict breast cancer stage using Medicare claims.

Similar Datasets

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