Freundlich, Robert E.

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

BACKGROUND: After hospital discharge, patients who had sepsis have increased mortality. We sought to estimate factors associated with postdischarge mortality and how they vary with time after discharge. METHODS: This was a retrospective study of hospital survivors of sepsis using time-varying Cox proportional hazard models, which produce a baseline hazard ratio (HR) and a second number (δHR) that reflects the amount by which the baseline HR changes with time. RESULTS: Of the 32,244 patients who survived sepsis at hospital discharge, 13,565 patients (42%) died (mean ± standard deviation: 1.41 ± 1.87 years) after discharge from the index hospitalization, while 18,679 patients were still alive at follow-up (4.98 ± 2.86 years). The mortality rate decreased with time after discharge: approximately 8.7% of patients died during the first month after discharge, 1.1% of patients died during the 12th month after discharge, and 0.3%% died during the 60th month; after Kaplan-Meier analysis, survival was 91% (95% confidence interval [CI], 91%–92%) at 1 month, 76% (95% CI, 76%–77%) at 1 year, 57% (95% CI, 56%–58%) at 5 years, and 48% (95% CI, 47%–48%) at 10 years after discharge. Organ dysfunction at discharge was associated with worse survival. In particular, elevated urea nitrogen at discharge (HR, 1.10 per 10 mg/dL, 95% CI, 1.08–1.12, P < .001) was associated with increased mortality, but the HR decreased with time from discharge (δHR, 0.98 per 10 mg/dL per year, 95% CI, 0.98–0.99, P < .001). Higher hemoglobin levels were associated with lower mortality (HR, 0.92 per g/dL, 95% CI, 0.91–0.93, P < .001), but this association increased with increasing time after discharge (δHR, 1.02 per g/dL per year, 95% CI, 1.01–1.02, P < .001). Older age was associated with an increased risk of mortality (HR, 1.29 per decade of age, 95% CI, 1.27–1.31, P < .001) that grew with increasing time after discharge (δHR, 1.01 per year of follow-up per decade of age, 95% CI, 1.00–1.02, P < .001). Compared to private insurances Medicaid as primary insurance was associated with an increased risk of mortality (HR, 1.17, 95% CI, 1.10–1.25, P < .001) that did not change with time after discharge. In contrast, Medicare status was initially associated with a similar risk of mortality as private insurance at discharge (HR, 1), but was associated with greater risk as time after discharge increased (δHR, 1.04 per year of follow-up, 95% CI, 1.03–1.05, P < .001). CONCLUSIONS: Acute physiologic derangements and organ dysfunction were associated with postdischarge mortality with the associations decreasing over time.

Objectives: Recently, the definition of sepsis has changed from a physiologic derangement (Sepsis-1 and -2) to organ dysfunction (Sepsis-3) based. We sought to determine the concordance between the different sepsis phenotypes and how that affected mortality. Design: Retrospective, multicenter study. Setting: Three academic medical centers. Patients: 29,459 patients who had suspected infection, defined as obtaining blood cultures and receiving antibiotics: 18,183 (62%) had either Sepsis-2 or Sepsis-3. Measurements and Main Results: Kappa was used to show agreement between phenotypes. Conditional logistic regression was used to create models of associations between factors and phenotypes and between factors and mortality. About 12,981 patients had Sepsis-2; 12,043 had Sepsis-3; and 6,841 patients had both Sepsis-2 and Sepsis-3. Fifty-three percent of Sepsis-2 patients also had Sepsis-3, whereas 57% of Sepsis-3 patients also had Sepsis-2. Agreement between the two phenotypes was poor: kappa = 0.213 ± 0.006. Mortality was 6% in patients with only Sepsis-2, 10% with only Sepsis-3, and 18% in patients who had both phenotypes. Combining the variables in Sepsis-2 and Sepsis-3 improved the discrimination (C-statistic = 0.742 ± 0.005, p < 0.001) of mortality. Conclusions: We found that Sepsis-2 and Sepsis-3-based sepsis diagnoses represent separate phenotypes with poor agreement. Patients who have both phenotypes are at increased risk of mortality compared with having either phenotype alone. Inclusion of both systemic inflammatory response syndrome and Sequential Organ Failure Assessment criteria in the same model improves the discrimination of mortality.