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Related Experiment Video

Updated: Jul 7, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

A high positive predictive value algorithm using hospital administrative data identified incident cancer cases.

Ileana Baldi¹, Piera Vicari, Daniela Di Cuonzo

¹Cancer Epidemiology Unit, University of Turin and C.P.O. Piemonte, San Giovanni Battista Hospital, Via Santena 7, 10126 Turin, Italy. ileana.baldi@cpo.it

Journal of Clinical Epidemiology

|March 4, 2008

Summary

A new algorithm using hospital discharge abstracts can identify incident breast, colorectal, and lung cancer cases. While not replacing cancer registries, it aids surveillance and monitoring of cancer care.

Related Experiment Videos

Last Updated: Jul 7, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Area of Science:

Oncology
Public Health
Health Informatics

Background:

Accurate cancer case ascertainment is crucial for surveillance and research.
Hospital discharge abstracts offer a potential data source for identifying cancer cases.

Purpose of the Study:

To develop and validate an algorithm for identifying incident breast, colorectal, and lung cancer cases using Piedmont hospital discharge abstracts.
To assess the algorithm's performance in terms of sensitivity and positive predictive value.

Main Methods:

Algorithm development using 2000 data and validation using 2001 data.
Individual-level validation by linking algorithm-identified cases with the Piedmont Cancer Registry.
Evaluation of sensitivity and positive predictive values for lung, colorectal, and breast cancers.

Main Results:

The algorithm demonstrated higher sensitivity for lung cancer (80.8%) compared to breast (76.7%) and colorectal (72.4%) cancers.
Positive predictive values were 78.7% (lung), 87.9% (colorectal), and 92.6% (breast).
High positive predictive values for colorectal and breast cancers were attributed to distinguishing prevalent from incident cases and accurate surgery claims.

Conclusions:

The algorithm is a valuable tool for cancer surveillance, complementing rather than replacing traditional cancer registration.
It provides a valid basis for monitoring cancer care practices, outcomes, geographic and temporal variations, and costs.