COVID-19 Across Pandemic Variant Periods: The Severe Acute Respiratory Infection-Preparedness (SARI-PREP) Study.

IMPORTANCE: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has evolved through multiple phases in the United States, with significant differences in patient centered outcomes with improvements in hospital strain, medical countermeasures, and overall understanding of the disease. We describe how patient characteristics changed and care progressed over the various pandemic phases; we also emphasize the need for an ongoing clinical network to improve the understanding of known and novel respiratory viral diseases. OBJECTIVES: To describe how patient characteristics and care evolved across the various COVID-19 pandemic periods in those hospitalized with viral severe acute respiratory infection (SARI). DESIGN: Severe Acute Respiratory Infection-Preparedness (SARI-PREP) is a Centers for Disease Control and Prevention Foundation-funded, Society of Critical Care Medicine Discovery-housed, longitudinal multicenter cohort study of viral pneumonia. We defined SARI patients as those hospitalized with laboratory-confirmed respiratory viral infection and an acute syndrome of fever, cough, and radiographic infiltrates or hypoxemia. We collected patient-level data including demographic characteristics, comorbidities, acute physiologic measures, serum and respiratory specimens, therapeutics, and outcomes. Outcomes were described across four pandemic variant periods based on a SARS-CoV-2 sequenced subsample: pre-Delta, Delta, Omicron BA.1, and Omicron post-BA.1. SETTING: Multicenter cohort of adult patients admitted to an acute care ward or ICU from seven hospitals representing diverse geographic regions across the United States. PARTICIPANTS: Patients with SARI caused by infection with respiratory viruses. MAIN OUTCOMES AND RESULTS: Eight hundred seventy-four adult patients with SARI were enrolled at seven study hospitals between March 2020 and April 2023. Most patients (780, 89%) had SARS-CoV-2 infection. Across the COVID-19 cohort, median age was 60 years (interquartile range, 48.0-71.0 yr) and 66% were male. Almost half (430, 49%) of the study population belonged to underserved communities. Most patients (76.5%) were admitted to the ICU, 52.5% received mechanical ventilation, and observed hospital mortality was 25.5%. As the pandemic progressed, we observed decreases in ICU utilization (94% to 58%), hospital length of stay (median, 26.0 to 8.5 d), and hospital mortality (32% to 12%), while the number of comorbid conditions increased. CONCLUSIONS AND RELEVANCE: We describe increasing comorbidities but improved outcomes across pandemic variant periods, in the setting of multiple factors, including evolving care delivery, countermeasures, and viral variants. An understanding of patient-level factors may inform treatment options for subsequent variants and future novel pathogens.

Opioid-related xylazine toxicity manifesting as myonecrosis, rhabdomyolysis, multifocal ischemic cerebral infarcts, and cerebral edema.

This article provides a report of a case of organ dysfunction, myonecrosis, rhabdomyolysis, multifocal ischemic cerebral infarcts, and cerebral edema after a patient's use of xylazine and fentanyl. Within the US opioid epidemic, xylazine is emerging as a troubling national sub-story. The prevalence of xylazine within illicitly manufactured opioids and the proportion of opioid-involved overdose deaths with detected xylazine are rising dramatically, the latter increasing 276% between 2019 and 2022. A 27-year-old woman with opioid use disorder, active intravenous drug use, and prior bacteremia presented to our institution's emergency department (ED) with left lower extremity pain and associated weakness, new acute bilateral hearing loss, multiple electrolyte derangements, and cerebral infarcts followed by cerebral edema, leading to an emergent sub-occipital decompressive craniectomy and placement of an external ventricular drain. A definitive mechanism was not determined; however, we hypothesized that xylazine toxicity played a role in her clinical presentation, which could have future clinical implications, including the possibility to incorporate xylazine as part of toxicology screens.

The Penn Medicine COVID-19 Therapeutics Committee-Reflections on a Model for Rapid Evidence Review and Dynamic Practice Recommendations During a Public Health Emergency.

The Penn Medicine COVID-19 Therapeutics Committee-an interspecialty, clinician-pharmacist, and specialist-front line primary care collaboration-has served as a forum for rapid evidence review and the production of dynamic practice recommendations during the 3-year coronavirus disease 2019 public health emergency. We describe the process by which the committee went about its work and how it navigated specific challenging scenarios. Our target audiences are clinicians, hospital leaders, public health officials, and researchers invested in preparedness for inevitable future threats. Our objectives are to discuss the logistics and challenges of forming an effective committee, undertaking a rapid evidence review process, aligning evidence-based guidelines with operational realities, and iteratively revising recommendations in response to changing pandemic data. We specifically discuss the arc of evidence for corticosteroids; the noble beginnings and dangerous misinformation end of hydroxychloroquine and ivermectin; monoclonal antibodies and emerging viral variants; and patient screening and safety processes for tocilizumab, baricitinib, and nirmatrelvir-ritonavir.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Frequency and Trends of Pre-Pandemic Surge Periods in U.S. Emergency Departments, 2006-2019.

OBJECTIVES: To quantify the frequency, outside of the pandemic setting, with which individual healthcare facilities faced surge periods due to severe increases in demand for emergency department (ED) care. DESIGN: Retrospective cohort study. SETTING: U.S. EDs. PATIENTS: All ED encounters in the all-payer, nationally representative Nationwide Emergency Department Sample from the Healthcare Cost and Utilization Project, 2006-2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Frequency of surge periods defined as ED months in which an individual facility ED saw a greater than 50% increase in ED visits per month above facility-/calendar month-specific medians. During 2006-2019, 3,317 U.S. EDs reported 354,534,229 ED visits across 142,035 ED months. Fifty-seven thousand four hundred ninety-five ED months (40.5%) during the study period had a 0% to 50% increase in ED visits that month above facility-specific medians and 1,952 ED months (1.4%) qualified as surge periods and had a greater than 50% increase in ED visits that month above facility-specific medians. These surge months were experienced by 397 unique facility EDs (12.0%). Compared with 2006, the most proximal pre-pandemic period of 2016-2019 had a notably elevated likelihood of ED-month surge periods (odds ratios [ORs], 2.36-2.84; all p < 0.0005). Compared with the calendar month of January, the winter ED months in December through March have similar likelihood of an ED-month qualifying as a surge period (ORs, 0.84-1.03; all p > 0.05), while the nonwinter ED months in April through November have a lower likelihood of an ED-month qualifying as a surge period (ORs, 0.65-0.81; all p < 0.05). CONCLUSIONS: Understanding the frequency of surges in demand for ED care-which appear to have increased in frequency even before the COVID-19 pandemic and are concentrated in winter months-is necessary to better understand the burden of potential and realized acute surge events and to inform cost-effectiveness preparedness strategies.

Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2.

BACKGROUND: Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes. OBJECTIVE: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. RESEARCH DESIGN: Retrospective cohort study. PATIENTS: ICU patients in 5 hospitals from October 2017 through September 2019. MEASURES: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using 4 hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c -statistics, and calibration plots. RESULTS: The cohort included 13,993 patients and 107,699 ICU days. Across validation hospitals, patient-day-level models including daily LAPS2 (SBS: 0.119-0.235; c -statistic: 0.772-0.878) consistently outperformed models with admission LAPS2 alone in patient-level (SBS: 0.109-0.175; c -statistic: 0.768-0.867) and patient-day-level (SBS: 0.064-0.153; c -statistic: 0.714-0.861) models. Across all predicted mortalities, daily models were better calibrated than models with admission LAPS2 alone. CONCLUSIONS: Patient-day-level models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population performs as well or better than models incorporating modified admission LAPS2 alone. The use of daily LAPS2 may offer an improved tool for clinical prognostication and risk adjustment in research in this population.

Racial Disparities in Length of Stay Among Severely Ill Patients Presenting With Sepsis and Acute Respiratory Failure.

Importance: Although racial and ethnic minority patients with sepsis and acute respiratory failure (ARF) experience worse outcomes, how patient presentation characteristics, processes of care, and hospital resource delivery are associated with outcomes is not well understood. Objective: To measure disparities in hospital length of stay (LOS) among patients at high risk of adverse outcomes who present with sepsis and/or ARF and do not immediately require life support and to quantify associations with patient- and hospital-level factors. Design, Setting, and Participants: This matched retrospective cohort study used electronic health record data from 27 acute care teaching and community hospitals across the Philadelphia metropolitan and northern California areas between January 1, 2013, and December 31, 2018. Matching analyses were performed between June 1 and July 31, 2022. The study included 102 362 adult patients who met clinical criteria for sepsis (n = 84 685) or ARF (n = 42 008) with a high risk of death at the time of presentation to the emergency department but without an immediate requirement for invasive life support. Exposures: Racial or ethnic minority self-identification. Main Outcomes and Measures: Hospital LOS, defined as the time from hospital admission to the time of discharge or inpatient death. Matches were stratified by racial and ethnic minority patient identity, comparing Asian and Pacific Islander patients, Black patients, Hispanic patients, and multiracial patients with White patients in stratified analyses. Results: Among 102 362 patients, the median (IQR) age was 76 (65-85) years; 51.5% were male. A total of 10.2% of patients self-identified as Asian American or Pacific Islander, 13.7% as Black, 9.7% as Hispanic, 60.7% as White, and 5.7% as multiracial. After matching racial and ethnic minority patients to White patients on clinical presentation characteristics, hospital capacity strain, initial intensive care unit admission, and the occurrence of inpatient death, Black patients experienced longer LOS relative to White patients in fully adjusted matches (sepsis: 1.26 [95% CI, 0.68-1.84] days; ARF: 0.97 [95% CI, 0.05-1.89] days). Length of stay was shorter among Asian American and Pacific Islander patients with ARF (-0.61 [95% CI, -0.88 to -0.34] days) and Hispanic patients with sepsis (-0.22 [95% CI, -0.39 to -0.05] days) or ARF (-0.47 [-0.73 to -0.20] days). Conclusions and Relevance: In this cohort study, Black patients with severe illness who presented with sepsis and/or ARF experienced longer LOS than White patients. Hispanic patients with sepsis and Asian American and Pacific Islander and Hispanic patients with ARF both experienced shorter LOS. Because matched differences were independent of commonly implicated clinical presentation-related factors associated with disparities, identification of additional mechanisms that underlie these disparities is warranted.

Association of Time of Day with Delays in Antimicrobial Initiation among Ward Patients with Hospital-Onset Sepsis.

Rationale: Although the mainstay of sepsis treatment is timely initiation of broad-spectrum antimicrobials, treatment delays are common, especially among patients who develop hospital-onset sepsis. The time of day has been associated with suboptimal clinical care in several contexts, but its association with treatment initiation among patients with hospital-onset sepsis is unknown. Objectives: Assess the association of time of day with antimicrobial initiation among ward patients with hospital-onset sepsis. Methods: This retrospective cohort study included ward patients who developed hospital-onset sepsis while admitted to five acute care hospitals in a single health system from July 2017 through December 2019. Hospital-onset sepsis was defined by the Centers for Disease Control and Prevention Adult Sepsis Event criteria. We estimated the association between the hour of day and antimicrobial initiation among patients with hospital-onset sepsis using a discrete-time time-to-event model, accounting for time elapsed from sepsis onset. In a secondary analysis, we fit a quantile regression model to estimate the association between the hour of day of sepsis onset and time to antimicrobial initiation. Results: Among 1,672 patients with hospital-onset sepsis, the probability of antimicrobial initiation at any given hour varied nearly fivefold throughout the day, ranging from 3.0% (95% confidence interval [CI], 1.8-4.1%) at 7 a.m. to 13.9% (95% CI, 11.3-16.5%) at 6 p.m., with nadirs at 7 a.m. and 7 p.m. and progressive decline throughout the night shift (13.4% [95% CI, 10.7-16.0%] at 9 p.m. to 3.2% [95% CI, 2.0-4.0] at 6 a.m.). The standardized predicted median time to antimicrobial initiation was 3.2 hours (interquartile range [IQR], 2.5-3.8 h) for sepsis onset during the day shift (7 a.m.-7 p.m.) and 12.9 hours (IQR, 10.9-14.9 h) during the night shift (7 p.m.-7 a.m.). Conclusions: The probability of antimicrobial initiation among patients with new hospital-onset sepsis declined at shift changes and overnight. Time to antimicrobial initiation for patients with sepsis onset overnight was four times longer than for patients with onset during the day. These findings indicate that time of day is associated with important care processes for ward patients with hospital-onset sepsis. Future work should validate these findings in other settings and elucidate underlying mechanisms to inform quality-enhancing interventions.

Hospital Strain and Variation in Sepsis ICU Admission Practices and Associated Outcomes.

To understand how strain-process-outcome relationships in patients with sepsis may vary among hospitals. DESIGN: Retrospective cohort study using a validated hospital capacity strain index as a within-hospital instrumental variable governing ICU versus ward admission, stratified by hospital. SETTING: Twenty-seven U.S. hospitals from 2013 to 2018. PATIENTS: High-acuity emergency department patients with sepsis who do not require life support therapies. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The mean predicted probability of ICU admission across strain deciles ranged from 4.9% (lowest ICU-utilizing hospital for sepsis without life support) to 61.2% (highest ICU-utilizing hospital for sepsis without life support). The difference in the predicted probabilities of ICU admission between the lowest and highest strain deciles ranged from 9.0% (least strain-sensitive hospital) to 45.2% (most strain-sensitive hospital). In pooled analyses, emergency department patients with sepsis (n = 90,150) experienced a 1.3-day longer median hospital length of stay (LOS) if admitted initially to the ICU compared with the ward, but across the 27 study hospitals (n = 517-6,564), this effect varied from 9.0 days shorter (95% CI, -10.8 to -7.2; p < 0.001) to 19.0 days longer (95% CI, 16.7-21.3; p < 0.001). Corresponding ranges for inhospital mortality with ICU compared with ward admission revealed odds ratios (ORs) from 0.16 (95% CI, 0.03-0.99; p = 0.04) to 4.62 (95% CI, 1.16-18.22; p = 0.02) among patients with sepsis (pooled OR = 1.48). CONCLUSIONS: There is significant among-hospital variation in ICU admission rates for patients with sepsis not requiring life support therapies, how sensitive those ICU admission decisions are to hospital capacity strain, and the association of ICU admission with hospital LOS and hospital mortality. Hospital-level heterogeneity should be considered alongside patient-level heterogeneity in critical and acute care study design and interpretation.

Perceived Hospital Stress, Severe Acute Respiratory Syndrome Coronavirus 2 Activity, and Care Process Temporal Variance During the COVID-19 Pandemic.

OBJECTIVES: The COVID-19 pandemic threatened standard hospital operations. We sought to understand how this stress was perceived and manifested within individual hospitals and in relation to local viral activity. DESIGN: Prospective weekly hospital stress survey, November 2020-June 2022. SETTING: Society of Critical Care Medicine's Discovery Severe Acute Respiratory Infection-Preparedness multicenter cohort study. SUBJECTS: Thirteen hospitals across seven U.S. health systems. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 839 hospital-weeks of data over 85 pandemic weeks and five viral surges. Perceived overall hospital, ICU, and emergency department (ED) stress due to severe acute respiratory infection patients during the pandemic were reported by a mean of 43% ( sd , 36%), 32% (30%), and 14% (22%) of hospitals per week, respectively, and perceived care deviations in a mean of 36% (33%). Overall hospital stress was highly correlated with ICU stress (ρ = 0.82; p < 0.0001) but only moderately correlated with ED stress (ρ = 0.52; p < 0.0001). A county increase in 10 severe acute respiratory syndrome coronavirus 2 cases per 100,000 residents was associated with an increase in the odds of overall hospital, ICU, and ED stress by 9% (95% CI, 5-12%), 7% (3-10%), and 4% (2-6%), respectively. During the Delta variant surge, overall hospital stress persisted for a median of 11.5 weeks (interquartile range, 9-14 wk) after local case peak. ICU stress had a similar pattern of resolution (median 11 wk [6-14 wk] after local case peak; p = 0.59) while the resolution of ED stress (median 6 wk [5-6 wk] after local case peak; p = 0.003) was earlier. There was a similar but attenuated pattern during the Omicron BA.1 subvariant surge. CONCLUSIONS: During the COVID-19 pandemic, perceived care deviations were common and potentially avoidable patient harm was rare. Perceived hospital stress persisted for weeks after surges peaked.

Angiopoietin-Like4 Is a Novel Marker of COVID-19 Severity.

Vascular dysfunction and capillary leak are common in critically ill COVID-19 patients, but identification of endothelial pathways involved in COVID-19 pathogenesis has been limited. Angiopoietin-like 4 (ANGPTL4) is a protein secreted in response to hypoxic and nutrient-poor conditions that has a variety of biological effects including vascular injury and capillary leak. OBJECTIVES: To assess the role of ANGPTL4 in COVID-19-related outcomes. DESIGN SETTING AND PARTICIPANTS: Two hundred twenty-five COVID-19 ICU patients were enrolled from April 2020 to May 2021 in a prospective, multicenter cohort study from three different medical centers, University of Washington, University of Southern California and New York University. MAIN OUTCOMES AND MEASURES: Plasma ANGPTL4 was measured on days 1, 7, and 14 after ICU admission. We used previously published tissue proteomic data and lung single nucleus RNA (snRNA) sequencing data from specimens collected from COVID-19 patients to determine the tissues and cells that produce ANGPTL4. RESULTS: Higher plasma ANGPTL4 concentrations were significantly associated with worse hospital mortality (adjusted odds ratio per log2 increase, 1.53; 95% CI, 1.17-2.00; p = 0.002). Higher ANGPTL4 concentrations were also associated with higher proportions of venous thromboembolism and acute respiratory distress syndrome. Longitudinal ANGPTL4 concentrations were significantly different during the first 2 weeks of hospitalization in patients who subsequently died compared with survivors (p for interaction = 8.1 × 10-5). Proteomics analysis demonstrated abundance of ANGPTL4 in lung tissue compared with other organs in COVID-19. ANGPTL4 single-nuclear RNA gene expression was significantly increased in pulmonary alveolar type 2 epithelial cells and fibroblasts in COVID-19 lung tissue compared with controls. CONCLUSIONS AND RELEVANCE: ANGPTL4 is expressed in pulmonary epithelial cells and fibroblasts and is associated with clinical prognosis in critically ill COVID-19 patients.

Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2).

Background: Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes. Objectives: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. Research design: Retrospective cohort study. Subjects: All ICU patients in five hospitals from October 2017 through September 2019. Measures: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots. Results: The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone. Conclusions: Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

Among-Hospital Variation in Intensive Care Unit Admission Practices and Associated Outcomes for Patients with Acute Respiratory Failure.

Rationale: We have previously shown that hospital strain is associated with intensive care unit (ICU) admission and that ICU admission, compared with ward admission, may benefit certain patients with acute respiratory failure (ARF). Objectives: To understand how strain-process-outcomes relationships in patients with ARF may vary among hospitals and what hospital practice differences may account for such variation. Methods: We examined high-acuity patients with ARF who did not require mechanical ventilation or vasopressors in the emergency department (ED) and were admitted to 27 U.S. hospitals from 2013 to 2018. Stratifying by hospital, we compared hospital strain-ICU admission relationships and hospital length of stay (LOS) and mortality among patients initially admitted to the ICU versus the ward using hospital strain as a previously validated instrumental variable. We also surveyed hospital practices and, in exploratory analyses, evaluated their associations with the above processes and outcomes. Results: There was significant among-hospital variation in ICU admission rates, in hospital strain-ICU admission relationships, and in the association of ICU admission with hospital LOS and hospital mortality. Overall, ED patients with ARF (n = 45,339) experienced a 0.82-day shorter median hospital LOS if admitted initially to the ICU compared with the ward, but among the 27 hospitals (n = 224-3,324), this effect varied from 5.85 days shorter (95% confidence interval [CI], -8.84 to -2.86; P < 0.001) to 4.38 days longer (95% CI, 1.86-6.90; P = 0.001). Corresponding ranges for in-hospital mortality with ICU compared with ward admission revealed odds ratios from 0.08 (95% CI, 0.01-0.56; P < 0.007) to 8.89 (95% CI, 1.60-79.85; P = 0.016) among patients with ARF (pooled odds ratio, 0.75). In exploratory analyses, only a small number of measured hospital practices-the presence of a sepsis ED disposition guideline and maximum ED patient capacity-were potentially associated with hospital strain-ICU admission relationships. Conclusions: Hospitals vary considerably in ICU admission rates, the sensitivity of those rates to hospital capacity strain, and the benefits of ICU admission for patients with ARF not requiring life support therapies in the ED. Future work is needed to more fully identify hospital-level factors contributing to these relationships.

ICU Mortality Across Prepandemic and Pandemic Cohorts in a Resource-Limited Setting: A Critical Care Resiliency Analysis From South Africa.

BACKGROUND: Hospital adaptation and resiliency, required during public health emergencies to optimize outcomes, are understudied especially in resource-limited settings. RESEARCH QUESTION: What are the prepandemic and pandemic critical illness outcomes in a resource-limited setting and in the context of capacity strain? STUDY DESIGN AND METHODS: We performed a retrospective cohort study among patients admitted to ICUs at two public hospitals in the KwaZulu-Natal Department of Health in South Africa preceding and during the COVID-19 pandemic (2017-2022). We used multivariate logistic regression to analyze the association between three patient cohorts (prepandemic non-COVID-19, pandemic non-COVID-19, and pandemic COVID-19) and ICU capacity strain and the primary outcome of ICU mortality. RESULTS: Three thousand two hundred twenty-one patients were admitted to the ICU during the prepandemic period and 2,539 patients were admitted to the ICU during the pandemic period (n = 375 [14.8%] with COVID-19 and n = 2,164 [85.2%] without COVID-19). The prepandemic and pandemic non-COVID-19 cohorts were similar. Compared with the non-COVID-19 cohorts, the pandemic COVID-19 cohort showed older age, higher rates of chronic cardiovascular disease and diabetes, less extrapulmonary organ dysfunction, and longer ICU length of stay. Compared with the prepandemic non-COVID-19 cohort, the pandemic non-COVID-19 cohort showed similar odds of ICU mortality (OR, 1.06; 95% CI, 0.90-1.25; P = .50) whereas the pandemic COVID-19 cohort showed significantly increased odds of ICU mortality (OR, 3.91; 95% CI, 3.03-5.05 P < .0005). ICU occupancy was not associated with ICU mortality in either the COVID-19 cohort (OR, 1.05 per 10% change in ICU occupancy; 95% CI, 0.96-1.14; P = .27) or the pooled non-COVID-19 cohort (OR, 1.01 per 10% change in ICU occupancy; 95% CI, 0.98-1.03; P = .52). INTERPRETATION: Patients admitted to the ICU before and during the pandemic without COVID-19 were broadly similar in clinical characteristics and outcomes, suggesting critical care resiliency, whereas patients admitted to the ICU with COVID-19 showed important clinical differences and significantly higher mortality.

Severe Acute Respiratory Infection-Preparedness: Protocol for a Multicenter Prospective Cohort Study of Viral Respiratory Infections.

Respiratory virus infections cause significant morbidity and mortality ranging from mild uncomplicated acute respiratory illness to severe complications, such as acute respiratory distress syndrome, multiple organ failure, and death during epidemics and pandemics. We present a protocol to systematically study patients with severe acute respiratory infection (SARI), including severe acute respiratory syndrome coronavirus 2, due to respiratory viral pathogens to evaluate the natural history, prognostic biomarkers, and characteristics, including hospital stress, associated with clinical outcomes and severity. DESIGN: Prospective cohort study. SETTING: Multicenter cohort of patients admitted to an acute care ward or ICU from at least 15 hospitals representing diverse geographic regions across the United States. PATIENTS: Patients with SARI caused by infection with respiratory viruses that can cause outbreaks, epidemics, and pandemics. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Measurements include patient demographics, signs, symptoms, and medications; microbiology, imaging, and associated tests; mechanical ventilation, hospital procedures, and other interventions; and clinical outcomes and hospital stress, with specimens collected on days 0, 3, and 7-14 after enrollment and at discharge. The primary outcome measure is the number of consecutive days alive and free of mechanical ventilation (VFD) in the first 30 days after hospital admission. Important secondary outcomes include organ failure-free days before acute kidney injury, shock, hepatic failure, disseminated intravascular coagulation, 28-day mortality, adaptive immunity, as well as immunologic and microbiologic outcomes. CONCLUSIONS: SARI-Preparedness is a multicenter study under the collaboration of the Society of Critical Care Medicine Discovery, Resilience Intelligence Network, and National Emerging Special Pathogen Training and Education Center, which seeks to improve understanding of prognostic factors associated with worse outcomes and increased resource utilization. This can lead to interventions to mitigate the clinical impact of respiratory virus infections associated with SARI.