Development and Validation of a High-Quality Composite Real-World Mortality Endpoint

Abstract

Objective: To create a high-quality electronic health record (EHR)-derived mortality dataset for retrospective and prospective real-world evidence generation.

Data sources/study setting: Oncology EHR data, supplemented with external commercial and US Social Security Death Index data, benchmarked to the National Death Index (NDI).

Study design: We developed a recent, linkable, high-quality mortality variable amalgamated from multiple data sources to supplement EHR data, benchmarked against the highest completeness U.S. mortality data, the NDI. Data quality of the mortality variable version 2.0 is reported here.

Principal findings: For advanced non-small-cell lung cancer, sensitivity of mortality information improved from 66 percent in EHR structured data to 91 percent in the composite dataset, with high date agreement compared to the NDI. For advanced melanoma, metastatic colorectal cancer, and metastatic breast cancer, sensitivity of the final variable was 85 to 88 percent. Kaplan-Meier survival analyses showed that improving mortality data completeness minimized overestimation of survival relative to NDI-based estimates.

Conclusions: For EHR-derived data to yield reliable real-world evidence, it needs to be of known and sufficiently high quality. Considering the impact of mortality data completeness on survival endpoints, we highlight the importance of data quality assessment and advocate benchmarking to the NDI.

Keywords: Mortality data; data quality; electronic health records; external validation; oncology.

Figure 1

Overall Survival for Advanced NSCLC Determined Using Indicated Mortality Data

1. Notes. NDI data were used as the benchmark in this study and were assumed to have 100 percent completeness. Patients were excluded from this analysis if their death date fell before the advanced diagnosis date.

Figure 2

Sensitivity of advNSCLC Data by Practice

1. Notes. Data were restricted to practices with ≥100 patients. Boxplots show the median sensitivity, with lower and upper hinges of the boxes corresponding to the 25 and 75 percent interquartile range (IQR); lower and upper whiskers indicate sensitivity within 1.5 IQR of the lower and upper quantiles, respectively; and points outside of the whiskers show the rest of the data.