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BACKGROUND: Social determinants of health (SDOH) have been linked to neurocritical care outcomes. We sought to examine the extent to which SDOH explain differences in decisions regarding life-sustaining therapy, a key outcome determinant. We specifically investigated the association of a patient's home geography, individual-level SDOH, and neighborhood-level SDOH with subsequent early limitation of life-sustaining therapy (eLLST) and early withdrawal of life-sustaining therapy (eWLST), adjusting for admission severity. METHODS: We developed unique methods within the Bridge to Artificial Intelligence for Clinical Care (Bridge2AI for Clinical Care) Collaborative Hospital Repository Uniting Standards for Equitable Artificial Intelligence (CHoRUS) program to extract individual-level SDOH from electronic health records and neighborhood-level SDOH from privacy-preserving geomapping. We piloted these methods to a 7 years retrospective cohort of consecutive neuroscience intensive care unit admissions (2016-2022) at two large academic medical centers within an eastern Massachusetts health care system, examining associations between home census tract and subsequent occurrence of eLLST and eWLST. We matched contextual neighborhood-level SDOH information to each census tract using public data sets, quantifying Social Vulnerability Index overall scores and subscores. We examined the association of individual-level SDOH and neighborhood-level SDOH with subsequent eLLST and eWLST through geographic, logistic, and machine learning models, adjusting for admission severity using admission Glasgow Coma Scale scores and disorders of consciousness grades. RESULTS: Among 20,660 neuroscience intensive care unit admissions (18,780 unique patients), eLLST and eWLST varied geographically and were independently associated with individual-level SDOH and neighborhood-level SDOH across diagnoses. Individual-level SDOH factors (age, marital status, and race) were strongly associated with eLLST, predicting eLLST more strongly than admission severity. Individual-level SDOH were more strongly predictive of eLLST than neighborhood-level SDOH. CONCLUSIONS: Across diagnoses, eLLST varied by home geography and was predicted by individual-level SDOH and neighborhood-level SDOH more so than by admission severity. Structured shared decision-making tools may therefore represent tools for health equity. Additionally, these findings provide a major warning: prognostic and artificial intelligence models seeking to predict outcomes such as mortality or emergence from disorders of consciousness may be encoded with self-fulfilling biases of geography and demographics.
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Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29-2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48-3.78; P < 0.001), delta (OR, 3.03; CI, 2.94-3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90-3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35-9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.
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BACKGROUND: Management of hypertension, dyslipidemia, diabetes and other modifiable factors may mitigate the cardiovascular disease (CVD) risk in people with human immunodeficiency virus (HIV, PWH) compared with people without HIV (PWoH). METHODS: This was a retrospective cohort study of 8285 PWH and 170 517 PWoH from an integrated health system. Risk factor control was measured using a novel disease management index (DMI) accounting for amount/duration above treatment goals (0% to 100% [perfect control]), including 2 DMIs for hypertension (diastolic and systolic blood pressure), 3 for dyslipidemia (low-density lipoprotein, total cholesterol, triglycerides), and 1 for diabetes (HbA1c). CVD risk by HIV status was evaluated overall and in subgroups defined by DMIs, smoking, alcohol use, and overweight/obesity in adjusted Cox proportional hazards models. RESULTS: PWH and PWoH had similar DMIs (80%-100%) except for triglycerides (worse for PWH) and HbA1c (better for PWH). In adjusted models, PWH had an elevated risk of CVD compared with PWoH (hazard ratio [HR], 1.18; 95% confidence interval [CI], 1.07-1.31). This association was attenuated in subgroups with controlled dyslipidemia and diabetes but remained elevated for PWH with controlled hypertension or higher total cholesterol. The strongest HIV status association with CVD was seen in the subgroup with frequent unhealthy alcohol use (HR, 2.13; 95% CI, 1.04-4.34). CONCLUSIONS: Control of dyslipidemia and diabetes, but not hypertension, attenuated the HIV status association with CVD. The strong association of HIV and CVD with frequent unhealthy alcohol use suggests enhanced screening and treatment of alcohol problems in PWH is warranted.