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Short Report
31 August 2026

Validation of an all-source composite mortality endpoint in the US population

Authors: Yanina Natanzon [email protected], Lukas Slipski, and Mark S WalkerAuthor Info & Affiliations
Volume 15, Number 10
https://doi.org/10.57264/cer-2025-0198
PDFFormats

Abstract

Aim: To validate ConcertAI’s All Source Composite Mortality Endpoint (ASCME), which combines information from electronic health records, obituary, government and administrative claims. To compare overall survival (OS) estimates using ASCME versus a National Death Index (NDI) dataset across clinical cohorts. Materials & methods: Retrospective study of oncology real-world data, reporting sensitivity, specificity, positive predictive value and negative predictive value compared with the NDI standard, plus 5, 7 and 15-day concordance on date of death. Additional comparisons included Kaplan–Meier OS measured by ASCME versus NDI. Data sources included ConcertAI’s Patient360™ dataset, and a 2022 annual finalized NDI dataset. The sample included cancer patients prevalent from 1 April 2014 to 31 December 2022 in any of 10 of ConcertAI’s solid tumor-specific datasets. Results: Of 32,358 study patients, 14,241 (44.0%) were deceased as defined by an NDI true match. Sensitivity was 95.0% overall (an incremental 5.2% due to claims) and ranged from 92.7% to 97.8% across clinical cohorts. Overall specificity was 96.5%, with positive predictive value and negative predictive value of 95.8% each. ASCME's 5-day concordance was 97.9%, with 7-day and 15-day concordance of 98.2% and 99.1%, respectively. ASCME and NDI-based median OS estimates differed by 12.2 days among non-metastatic cohorts, and 4.5 days among metastatic cohorts. Conclusion: Results show ConcertAI’s ASCME death indicator to provide high completeness and accuracy, producing OS estimates largely indistinguishable from NDI-based estimates. Findings show the importance of including claims in composite mortality indicators and demonstrate the value of real-world data in assessing OS outcomes in metastatic and non-metastatic cancer patient populations.

Plain language summary: Checking the accuracy of a multi-source method for tracking deaths in the US

What is this article about?

This study checked the accuracy of a method for tracking deaths in cancer patients who are treated in community oncology practices. The method combines information about deaths from patient health records, obituaries, billing for medical care and government records. This matters because studies of cancer patients often track how long patients live, and this requires complete and accurate death information.

How was the study done?

The study used a dataset from ConcertAI that included information from more than 30,000 patients, including information about whether patients had died, and when. The study compared that dataset to information from another dataset that is considered a gold standard. The study reported how often patients shown as deceased by the gold standard appeared deceased in the dataset, how often the dataset accurately showed patients to still be living, and how closely dates of death in the two sources matched.

What were the results? What do they mean?

The results showed that of every 100 patients shown as deceased in the gold standard, 95 appeared deceased in the dataset. Of every 100 who were living according to the gold standard, 96 appeared living in the dataset. Also, among patients shown as deceased in the dataset and the gold standard, more than 95 of 100 had dates of death that matched within 5 days. These findings show that including billing for medical care improves the accuracy of death information, that the overall accuracy of death information in this dataset is very good, and that it is suitable for research with cancer patients treated in community practices.

Shareable abstract

Electronic health record data linked with government, obituary and claims-based death records provide robust coverage for real-world assessment of mortality-related endpoint.

Supplementary Material

File (supplementary materials.docx)
Download, DOCX (4.90 MB)

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