Ingraham, Nicholas E.

Introduction: Life-support limits (i.e., code status), the first step in the continuum of palliation and end-of-life (EoL) care, stem from decisions made by patients and their providers to align care near the EoL with patients’ wishes. The pandemic created health care strain that may have influenced life-support limitation practices among patients with serious COVID-19. In this study, we examine variations in life-support limitations (do-not-resuscitate, DNR; do-not-intubate, DNI) depending on the hospital and period of hospitalization. Methods: We included all adults admitted to a hospital in the United States with COVID-19 from January 2020 to December 2021 using the VIRUS registry. Our outcome was any life-support limitation on admission (DNR, DNI, or both) using regression modeling. Main exposures were hospital and period of hospitalization (early vs. late pandemic). Covariates included age, sex, race/ethnicity, comorbidities, and hospitalization diagnoses. Results: There were 42,383 patients from 75 hospitals in the US. The median age was 63 (interquartile range: 50–75), 46.5% were female, and 37.5% were non-Hispanic White. The life-support limitation rate was 7.9%, and the palliative care consultation rate was 2.3%. The odds of life-support limitation were 1.86 (95% CI: 1.6–2.1) depending on the hospital, but the period of hospitalization was not associated with odds of life-support limitation. Older age was associated with the greatest increased odds (4.1 per standard deviation ∼ 17.9 years, 95% CI: 3.9–4.4), followed by comorbidities including paralysis (2.4, 95% CI: 1.8–3.0) and dementia (2.3, 95% CI: 2.0–2.6). Conclusions: After adjusting for patient-level factors, we report significant inter-hospital but no temporal variation in life-support limitation upon admission with COVID-19. Future studies should investigate specific practices that led to health care resilience to temporal practice variations in life-support limitation during the pandemic.

AbstractObjectiveWith COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.Materials and MethodsPerformance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.ResultsThis rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.DiscussionOpportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.ConclusionThis rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.