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.
Evidence gathered in conventional clinical trials used to assess safety and efficacy of new therapies is not necessarily generalizable to real-world patients receiving these drugs following regulatory approval. Real-world evidence derived from electronic health record data can yield complementary evidence to enable optimal clinical decisions. Examined here is a cohort of programmed cell death protein 1 inhibitor-treated metastatic non-small cell lung cancer patients in the first year following regulatory approval of these therapies in this indication. The analysis revealed how the real-world cohort differed from the clinical trial cohorts, which will inform which patients are underrepresented and warrant additional studies.
IntroductionReal-world evidence derived from electronic health records (EHRs) is increasingly recognized as a supplement to evidence generated from traditional clinical trials. In oncology, tumor-based Response Evaluation Criteria in Solid Tumors (RECIST) endpoints are standard clinical trial metrics. The best approach for collecting similar endpoints from EHRs remains unknown. We evaluated the feasibility of a RECIST-based methodology to assess EHR-derived real-world progression (rwP) and explored non-RECIST-based approaches.MethodsIn this retrospective study, cohorts were randomly selected from Flatiron Health’s database of de-identified patient-level EHR data in advanced non-small cell lung cancer. A RECIST-based approach tested for feasibility (N = 26). Three non-RECIST approaches were tested for feasibility, reliability, and validity (N = 200): (1) radiology-anchored, (2) clinician-anchored, and (3) combined. Qualitative and quantitative methods were used.ResultsA RECIST-based approach was not feasible: cancer progression could be ascertained for 23% (6/26 patients). Radiology- and clinician-anchored approaches identified at least one rwP event for 87% (173/200 patients). rwP dates matched 90% of the time. In 72% of patients (124/173), the first clinician-anchored rwP event was accompanied by a downstream event (e.g., treatment change); the association was slightly lower for the radiology-anchored approach (67%; 121/180). Median overall survival (OS) was 17 months [95% confidence interval (CI) 14, 19]. Median real-world progression-free survival (rwPFS) was 5.5 months (95% CI 4.6, 6.3) and 4.9 months (95% CI 4.2, 5.6) for clinician-anchored and radiology-anchored approaches, respectively. Correlations between rwPFS and OS were similar across approaches (Spearman’s rho 0.65–0.66). Abstractors preferred the clinician-anchored approach as it provided more comprehensive context.ConclusionsRECIST cannot adequately assess cancer progression in EHR-derived data because of missing data and lack of clarity in radiology reports. We found a clinician-anchored approach supported by radiology report data to be the optimal, and most practical, method for characterizing tumor-based endpoints from EHR-sourced data.FundingFlatiron Health Inc., which is an independent subsidiary of the Roche group.Electronic supplementary materialThe online version of this article (10.1007/s12325-019-00970-1) contains supplementary material, which is available to authorized users.
Background Evidence from cancer clinical trials has strong internal validity but can be difficult to generalize to real‐world patient populations. Here we analyzed real‐world outcomes of patients with metastatic non‐small cell lung cancer (mNSCLC) treated with programmed cell death protein 1 (PD‐1) inhibitors in the first year following U.S. regulatory approval. Materials and Methods This retrospective study leveraged electronic health record (EHR) data collected during routine patient care in community cancer care clinics. The cohort included patients with mNSCLC who had received nivolumab or pembrolizumab for metastatic disease (n = 1,344) with >1 EHR‐documented visit from January 1, 2011, to March 31, 2016. Patients with a > 90‐day gap between advanced disease diagnosis and first EHR structured data entry were excluded. Results Estimated median overall survival (OS) was 8.0 months (95% confidence interval 7.4–9.0 months). Estimated median OS was 4.7 months (3.4–6.6) for patients with anaplastic lymphoma kinase rearrangement‐ and epidermal growth factor receptor mutation‐positive tumors, and 8.6 months (7.7–10.6) for patients without such mutations. Age at PD‐1 inhibitor initiation or line of therapy did not impact OS. Conclusion This analysis suggests OS in real‐world patients may be shorter than in conventional clinical trial patient cohorts, potentially due to narrow trial eligibility criteria. The lack of difference in OS by line of therapy or age at immunotherapy initiation suggests sustained benefit of PD‐1 inhibitors in multitreated patients with mNSCLC and that age is not a predictor of outcome. Further studies are underway in patients with comorbidities, organ dysfunction, and multiple prior therapies. Implications for Practice This study evaluated data derived from electronic health records of patients with metastatic non‐small cell lung cancer treated with programmed cell death protein 1 (PD‐1) inhibitors in the year following regulatory approval. This real‐world cohort had shorter overall survival (OS) indexed to PD‐1 inhibitor initiation than reported in clinical trials. Late‐line treatment did not influence OS, and patients aged >75 at immunotherapy initiation did not have worse outcomes than younger patients. As new therapies enter clinical practice, real‐world data can complement clinical trial evidence providing information on generalizability and helping inform clinical treatment decisions.
Purpose The aim of this study was to assess the impact of missing death data on survival analyses conducted in an oncology EHR‐derived database. Methods The study was conducted using the Flatiron Health oncology database and the National Death Index (NDI) as a gold standard. Three analytic frameworks were evaluated in advanced non‐small cell lung cancer (aNSCLC) patients: median overall survival [mOS]), relative risk estimates conducted within the EHR‐derived database, and “external control arm” analyses comparing an experimental group augmented with mortality data from the gold standard to a control group from the EHR‐derived database only. The hazard ratios (HRs) obtained within the EHR‐derived database (91% sensitivity) and the external control arm analyses, were compared with results when both groups were augmented with mortality data from the gold standard. The above analyses were repeated using simulated lower mortality sensitivities to understand the impact of more extreme levels of missing deaths. Results Bias in mOS ranged from modest (0.6–0.9 mos.) in the EHR‐derived cohort with (91% sensitivity) to substantial when lower sensitivities were generated through simulation (3.3–9.7 mos.). Overall, small differences were observed in the HRs for the EHR‐derived cohort across comparative analyses when compared with HRs obtained using the gold standard data source. When only one treatment arm was subject to estimation bias, the bias was slightly more pronounced, but increased substantially when lower sensitivities were simulated. Conclusions The impact on survival analysis is minimal with high mortality sensitivity with only modest impact associated within external control arm applications.
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