ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015
Measuring Mortality Information in Clinical Data Warehouses.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Comparative Merits of Available Mortality Data Sources for Clinical Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning.JAMIA open · 2021Article
- Real-World Treatment Patterns, Healthcare Resource Utilization, and Costs for Patients with Newly Diagnosed Systolic versus Diastolic Heart Failure.American health & drug benefits · 2020Article
- Hypoxemia Within the First 3 Postoperative Days Is Associated With Increased 1-Year Postoperative Mortality After Adjusting for Perioperative Opioids and Other Confounders.Anesthesia and analgesia · 2020Observational
- Opioid Prescriptions at Hospital Discharge Are Associated With More Postdischarge Healthcare Utilization.Journal of the American Heart Association · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to track and report long-term outcomes, especially mortality, is essential for advancing clinical research. The purpose of this study was to present a framework for assessing the quality of mortality information in clinical research databases. Using the clinical data warehouse (CDW) at Columbia University Medical Center as a case study, we measured: 1) agreement in vital status between our institution's patient registration system and the U.S. Social Security Administration's Death Master File (DMF), 2) the proportion of patients marked as deceased according to the DMF records who had subsequent visits to our institution, and 3) the proportion of patients still living according to Columbia's CDW who were over 100 and 120 years of age. Of 33,295 deaths recorded in our institution's patient registration system, 13,167 (39.5%) did not exist in the DMF. Of 315,037 patients in our CDW who marked as deceased according to the DMF, 2.1% had a subsequent clinical encounter at our institution. The proportion of patients still living according to Columbia's CDW who were over 100 and 120 years of age was 43.6% and 43.1%, respectively. These measures may be useful to other clinical research investigators seeking to assess the quality of mortality data (1-4).
Identifiers
26306284PMC4525266What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.