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Development And Optimization Of A Bladder Cancer Algorithm Using Seer-medicare Claims Data

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Development And Optimization Of A Bladder Cancer Algorithm Using Seer-medicare Claims Data

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Development and Optimization of a Bladder Cancer Algorithm Using SEER-Medicare Claims Data

John L Gore¹, Phoebe Wright², Vanessa Shih²

¹University of Washington, Seattle, WA.

JCO Clinical Cancer Informatics

|September 19, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new algorithm accurately categorizes bladder cancer (BC) patients into non-muscle-invasive (NMIBC), muscle-invasive (MIBC), and advanced stages using healthcare claims data. This tool aids researchers in staging BC for administrative claims-based studies.

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Area of Science:

Oncology
Health Informatics
Biostatistics

Background:

Disease staging is crucial for cancer research, particularly in administrative claims-based studies.
Claims databases often lack comprehensive disease stage information, necessitating algorithmic approaches.
No prior algorithm has been developed to categorize bladder cancer (BC) by stage in US healthcare claims data.

Purpose of the Study:

To develop and validate a claims-based algorithm for staging bladder cancer (BC) patients.
To categorize BC into non-muscle-invasive BC (NMIBC), muscle-invasive BC (MIBC), and locally advanced/metastatic urothelial carcinoma (la/mUC).

Main Methods:

A claims-based algorithm was developed using SEER-Medicare linked data.
The algorithm was validated against the SEER registry, with iterative parameter refinement.

Performance was assessed using agreement, Cohen's kappa (κ), specificity, positive predictive value (PPV), and negative predictive value (NPV).

Main Results:

The study included 15,484 BC patients: 71.0% NMIBC, 23.5% MIBC, and 5.5% la/mUC.
The optimized algorithm achieved 82.5% agreement with SEER data (κ = 0.58).
High PPVs were observed for NMIBC (87.0%) and MIBC (76.8%), with an excellent NPV for la/mUC (98.0%).

Conclusions:

The developed claims-based algorithm is a viable tool for staging bladder cancer patients in research.
This algorithm can facilitate more accurate administrative claims-based studies on bladder cancer.