Objective We expanded the previous assessment of a mortality variable suited for real‐world evidence‐focused oncology research. Data source We used a nationwide electronic health record (EHR)‐derived de‐identified database. Data collection We included patients with at least 1 of 18 cancer types between January 1, 2011 and December 31, 2017. Patient‐level structured data (EHRs, obituaries, and Social Security Death Index) and unstructured EHR data (abstracted) were linked to generate a composite mortality variable. Study design We benchmarked sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ±15‐day agreement against the National Death Index (NDI). Real‐world overall survival (rwOS) was estimated using the Kaplan‐Meier method. We performed sensitivity analyses using a smaller patient cohort that underwent next‐generation sequencing testing. Principal findings Compared with the NDI across 18 cancer types (overall N = 160 436): sensitivity, 83.9%‐91.5% (17/18 cancer types had sensitivity ≥85.0%); specificity, 93.5%‐99.7%; PPV, 96.3%‐98.3%; NPV, 75.0%‐98.7%; ±15‐day agreement, 95.6%‐97.6%; and median rwOS estimates ranging from 2.8% to 12.7% greater. Sensitivity analysis results (n = 17 540) were consistent with the main analysis. Conclusions Across all cancer types analyzed, this composite mortality variable showed high sensitivity, specificity, PPV, NPV, and ±15‐day agreement, and yielded median rwOS values modestly overestimated when compared to NDI‐based results.
Objective: In oncology research, mortality, as a variable, and overall survival (OS), as an efficacy endpoint, are critical. As real-world data gains use in supporting regulatory decisions, assessing the validity of mortality data is important. Mortality data derived solely from electronic health records (EHRs) has known gaps. We developed a mortality variable based on multiple sources of death data and benchmarked it against the National Death Index (NDI) data as a gold standard within 4 cancer types (Curtis et al, 2018). We refreshed and expanded validity assessment to 18 cancer types. Methods: Patients diagnosed with at least 1 of 18 cancer types between 1/1/2011 and 12/31/2017 were included from the Flatiron Health EHR-derived de-identified US database. To develop a composite mortality variable, we amalgamated multiple sources of real-world death data, linking patient-level structured (EHR, commercial, Social Security Death Index) and unstructured data curated via technology-enabled abstraction. We calculated validity metrics (sensitivity, specificity, ±15-day accuracy) by benchmarking against the NDI data in each cancer type, as a composite variable, as well as by individual and combinations of death data sources. OS was estimated using the Kaplan-Meier method. Results: There were 160,436 patients included in the study cohort. In the 18 cancer types, sensitivity ranged from 83.9 - 91.5% (17 out of 18 had sensitivity ≥85%), specificity ranged from 93.5 - 99.7%, and ±15-day accuracy ranged from 95.6 - 97.6%, compared to the NDI (Table). Median OS estimates for the composite mortality variable when compared to that from the NDI ranged from 3 - 13% greater across all cancer types. Cancer Type Note: some patients could have >1 cancer typeNSensitivity (%)Specificity (%)+/- 15-day Accuracy (%)Solid TumorBreast Cancer (Early)166983.9 (77.4, 90.3)99.7 (99.5, 100)96.3 (92.7, 99.9)Breast Cancer (Metastatic)1647388.3 (87.6, 88.9)97.7 (97.4, 98.0)96.9 (96.6, 97.3)Colorectal Cancer (Metastatic)1723286.4 (85.7, 87.1)97.1 (96.8, 97.5)96.3 (95.9, 96.8)Gastro-Esophageal Cancer (Advanced)716988.5 (87.6, 89.4)94.3 (93.3, 95.3)97.1 (96.6, 97.6)Head and Neck Cancer (Advanced)527189.3 (88.2, 90.3)95.9 (95.0, 96.8)96.9 (96.3, 97.5)Hepatocellular Carcinoma278485.0 (83.3, 86.7)95.7 (94.5, 96.9)95.6 (94.5, 96.6)Malignant Pleural Mesothelioma170091.0 (89.4, 92.6)95.6 (93.7, 97.4)97.6 (96.7, 98.5)Melanoma (Advanced)703189.5 (88.4, 90.6)98.7 (98.4, 99.1)97.4 (96.8, 98)Non-small Cell Lung Cancer (Advanced)4507090.4 (90.1, 90.7)95.1 (94.8, 95.5)97.2 (97.0, 97.4)Ovarian Cancer496486.8 (85.2, 88.3)98.7 (98.3, 99.1)96.8 (95.9, 97.6)Pancreatic Cancer (Metastatic)545890.2 (89.3, 91.1)93.5 (92.2, 94.8)97.2 (96.7, 97.7)Prostate Cancer (Metastatic)849589.0 (88.0, 90)98.7 (98.4, 99.0)97.1 (96.5, 97.7)Renal Cell Carcinoma (Metastatic)577088.5 (87.4, 89.6)97.7 (97.1, 98.3)97.2 (96.6, 97.8)Small Cell Lung Cancer472490.4 (89.4, 91.4)95.8 (94.7, 96.8)97.5 (97.0, 98.1)Urothelial Cancer (Advanced)629390.2 (89.3, 91.1)96.6 (95.8, 97.4)97.5 (97.0, 98.0)Liquid TumorChronic Lymphocytic Leukemia903591.5 (90.4, 92.7)99.3 (99.1, 99.5)96.8 (96.1, 97.6)Diffuse Large B-cell Lymphoma434489.1 (87.4, 90.8)99 (98.7, 99.4)96.1 (95.0, 97.3)Multiple Myeloma780388.5 (87.3, 89.7)99.0 (98.8, 99.3)97.0 (96.3, 97.7) Conclusions: We observed high sensitivity, specificity, and date accuracy of our composite mortality variable across all cancer types. Future research will investigate the validity of our mortality variable stratified by demographic and clinical characteristics to understand performance in subpopulations. Citation Format: Qianyi Zhang, Anala Gossai, Shirley Monroe, Nathan C. Nussbaum, Christina M. Parrinello. Validation analysis of a composite real-world mortality endpoint for US cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5772.
The aim of the study was to compare the sensitivity of self-collected with clinician-collected human papillomavirus (HPV) tests and cytology for cervical cancer. A total of 250 non-pregnant, 25 -60-year-old women from Leon, Nicaragua, self-collected vaginal specimens for HPV and received a pelvic examination for cytology and refl ex HPV. All participants underwent colposcopy and completed questionnaires regarding demographic and medical information. The sensitivities of self-collected brushes, self-collected swabs and cliniciancollected HPV tests were 25%, 16.7%, and 16.7%, respectively, with colposcopy as the gold standard and 30%, 22.2% and 40% when cytology was the gold standard. Agreement between self-collection methods was signifi cant ( k ؍ 0.84, p < 0.001). Although utilisation of colposcopy in every participant resulted in lower sensitivities, the self-collected tests surpassed cytology and signifi cantly agreed with the clinician-collected results. Further clarifi cation of the sensitivity will be required to employ self-collection for cervical cancer screening in low resource areas like rural Nicaragua.
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