COVID-19 Critical Illness: A Data-Driven Review.

The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges in critical care medicine, including extreme demand for intensive care unit (ICU) resources and rapidly evolving understanding of a novel disease. Up to one-third of hospitalized patients with COVID-19 experience critical illness. The most common form of organ failure in COVID-19 critical illness is acute hypoxemic respiratory failure, which clinically presents as acute respiratory distress syndrome (ARDS) in three-quarters of ICU patients. Noninvasive respiratory support modalities are being used with increasing frequency given their potential to reduce the need for intubation. Determining optimal patient selection for and timing of intubation remains a challenge. Management of mechanically ventilated patients with COVID-19 largely mirrors that of non-COVID-19 ARDS. Organ failure is common and portends a poor prognosis. Mortality rates have improved over the course of the pandemic, likely owing to increasing disease familiarity, data-driven pharmacologics, and improved adherence to evidence-based critical care.

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.

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.

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure.

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

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.

Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models.

Rationale: Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. Objectives: To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white patients. Methods: We included Black and white patients admitted for sepsis or acute respiratory failure at 27 hospitals. We calculated the discrimination and calibration for in-hospital mortality of SOFA, LAPS2, and modified versions of each, including categorical SOFA groups recommended in a popular CSC and a SOFA score without creatinine to reduce the influence of race. Measurements and Main Results: Of 113,158 patients, 27,644 (24.4%) identified as Black. The LAPS2 demonstrated higher discrimination (area under the receiver operating characteristic curve [AUC], 0.76; 95% confidence interval [CI], 0.76-0.77) than the SOFA score (AUC, 0.68; 95% CI, 0.68-0.69). The LAPS2 was also better calibrated than the SOFA score, but both underestimated in-hospital mortality for white patients and overestimated in-hospital mortality for Black patients. Thus, in a simulation using observed mortality, 81.6% of Black patients were included in lower-priority CSC categories, and 9.4% of all Black patients were erroneously excluded from receiving the highest prioritization. The SOFA score without creatinine reduced racial miscalibration. Conclusions: Using SOFA in CSCs may lead to racial disparities in resource allocation. More equitable mortality prediction scores are needed.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage.

PURPOSE OF REVIEW: Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic. RECENT FINDINGS: Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic. SUMMARY: The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.

National Psoriasis Foundation COVID-19 Task Force guidance for management of psoriatic disease during the pandemic: Version 2-Advances in psoriatic disease management, COVID-19 vaccines, and COVID-19 treatments.

OBJECTIVE: To update guidance regarding the management of psoriatic disease during the COVID-19 pandemic. STUDY DESIGN: The task force (TF) includes 18 physician voting members with expertise in dermatology, rheumatology, epidemiology, infectious diseases, and critical care. The TF was supplemented by nonvoting members, which included fellows and National Psoriasis Foundation staff. Clinical questions relevant to the psoriatic disease community were informed by inquiries received by the National Psoriasis Foundation. A Delphi process was conducted. RESULTS: The TF updated evidence for the original 22 statements and added 5 new recommendations. The average of the votes was within the category of agreement for all statements, 13 with high consensus and 14 with moderate consensus. LIMITATIONS: The evidence behind many guidance statements is variable in quality and/or quantity. CONCLUSIONS: These statements provide guidance for the treatment of patients with psoriatic disease on topics including how the disease and its treatments affect COVID-19 risk, how medical care can be optimized during the pandemic, what patients should do to lower their risk of getting infected with severe acute respiratory syndrome coronavirus 2 (including novel vaccination), and what they should do if they develop COVID-19. The guidance is a living document that is continuously updated by the TF as data emerge.

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. OBJECTIVE: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. DESIGN: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle. SETTING: 3 hospitals in an academic health system. PATIENTS: All people living in the greater Philadelphia region. MEASUREMENTS: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators. RESULTS: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators. LIMITATIONS: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. CONCLUSION: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic. PRIMARY FUNDING SOURCE: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

National Psoriasis Foundation COVID-19 Task Force Guidance for Management of Psoriatic Disease During the Pandemic: Version 1.

OBJECTIVE: To provide guidance about management of psoriatic disease during the coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: A task force (TF) of 18 physician voting members with expertise in dermatology, rheumatology, epidemiology, infectious diseases, and critical care was convened. The TF was supplemented by nonvoting members, which included fellows and National Psoriasis Foundation (NPF) staff. Clinical questions relevant to the psoriatic disease community were informed by questions received by the NPF. A Delphi process was conducted. RESULTS: The TF approved 22 guidance statements. The average of the votes was within the category of agreement for all statements. All guidance statements proposed were recommended, 9 with high consensus and 13 with moderate consensus. LIMITATIONS: The evidence behind many guidance statements is limited in quality. CONCLUSION: These statements provide guidance for the management of patients with psoriatic disease on topics ranging from how the disease and its treatments impact COVID-19 risk and outcome, how medical care can be optimized during the pandemic, what patients should do to lower their risk of getting infected with severe acute respiratory syndrome coronavirus 2 and what they should do if they develop COVID-19. The guidance is intended to be a living document that will be updated by the TF as data emerge.

Intensive Care Unit Capacity Strain and Outcomes of Critical Illness in a Resource-Limited Setting: A 2-Hospital Study in South Africa.

OBJECTIVE: To measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting. METHODS: We performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country's first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression. RESULTS: No measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; P = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality. CONCLUSIONS: In a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.

Validation of an Administrative Definition of ICU Admission Using Revenue Center Codes.

OBJECTIVES: Describe the operating characteristics of a proposed set of revenue center codes to correctly identify ICU stays among hospitalized patients. DESIGN: Retrospective cohort study. We report the operating characteristics of all ICU-related revenue center codes for intensive and coronary care, excluding nursery, intermediate, and incremental care, to identify ICU stays. We use a classification and regression tree model to further refine identification of ICU stays using administrative data. The gold standard for classifying ICU admission was an electronic patient location tracking system. SETTING: The University of Pennsylvania Health System in Philadelphia, PA, United States. PATIENTS: All adult inpatient hospital admissions between July 1, 2013, and June 30, 2015. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 127,680 hospital admissions, the proposed combination of revenue center codes had 94.6% sensitivity (95% CI, 94.3-94.9%) and 96.1% specificity (95% CI, 96.0-96.3%) for correctly identifying hospital admissions with an ICU stay. The classification and regression tree algorithm had 92.3% sensitivity (95% CI, 91.6-93.1%) and 97.4% specificity (95% CI, 97.2-97.6%), with an overall improved accuracy (χ = 398; p < 0.001). CONCLUSIONS: Use of the proposed combination of revenue center codes has excellent sensitivity and specificity for identifying true ICU admission. A classification and regression tree algorithm with additional administrative variables offers further improvements to accuracy.

Time to selected quit date and subsequent rates of sustained smoking abstinence.

In efforts to combat tobacco dependence, most smoking cessation programs offer individuals who smoke the choice of a target quit date. However, it is uncertain whether the time to the selected quit date is associated with participants' chances of achieving sustained abstinence. In a pre-specified secondary analysis of a randomized clinical trial of four financial-incentive programs or usual care to encourage smoking cessation (Halpern et al. in N Engl J Med 372(22):2108-2117, doi: 10.1056/NEJMoa1414293 , 2015), study participants were instructed to select a quit date between 0 and 90 days from enrollment. Among those who selected a quit date and provided complete baseline data (n = 1848), we used multivariable logistic regression to evaluate the association of the time to the selected quit date with 6- and 12-month biochemically-confirmed abstinence rates. In the fully adjusted model, the probability of being abstinent at 6 months if the participant selected a quit date in weeks 1, 5, 10, and 13 were 39.6, 22.6, 10.9, and 4.3%, respectively.