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.

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 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.

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.

Associations of Race With Sedation Depth Among Mechanically Ventilated Adults: A Retrospective Cohort Study.

OBJECTIVES: To evaluate the association of race with proportion of time in deep sedation among mechanically ventilated adults. DESIGN: Retrospective cohort study from October 2017 to December 2019. SETTING: Five hospitals within a single health system. PATIENTS: Adult patients who identified race as Black or White who were mechanically ventilated for greater than or equal to 24 hours in one of 12 medical, surgical, cardiovascular, cardiothoracic, or mixed ICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The exposure was White compared with Black race. The primary outcome was the proportion of time in deep sedation during the first 48 hours of mechanical ventilation, defined as Richmond Agitation-Sedation Scale values of -3 to -5. For the primary analysis, we performed mixed-effects linear regression models including ICU as a random effect, and adjusting for age, sex, English as preferred language, body mass index, Elixhauser comorbidity index, Laboratory-based Acute Physiology Score, Version 2, ICU admission source, admission for a major surgical procedure, and the presence of septic shock. Of the 3337 included patients, 1242 (37%) identified as Black, 1367 (41%) were female, and 1002 (30%) were admitted to a medical ICU. Black patients spent 48% of the first 48 hours of mechanical ventilation in deep sedation, compared with 43% among White patients in unadjusted analysis. After risk adjustment, Black race was significantly associated with more time in early deep sedation (mean difference, 5%; 95% CI, 2-7%; p < 0.01). CONCLUSIONS: There are disparities in sedation during the first 48 hours of mechanical ventilation between Black and White patients across a diverse set of ICUs. Future work is needed to determine the clinical significance of these findings, given the known poorer outcomes for patients who experience early deep sedation.

Wellness and Coping of Physicians Who Worked in ICUs During the Pandemic: A Multicenter Cross-Sectional North American Survey.

OBJECTIVES: Few surveys have focused on physician moral distress, burnout, and professional fulfilment. We assessed physician wellness and coping during the COVID-19 pandemic. DESIGN: Cross-sectional survey using four validated instruments. SETTING: Sixty-two sites in Canada and the United States. SUBJECTS: Attending physicians (adult, pediatric; intensivist, nonintensivist) who worked in North American ICUs. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We analysed 431 questionnaires (43.3% response rate) from 25 states and eight provinces. Respondents were predominantly male (229 [55.6%]) and in practice for 11.8 ± 9.8 years. Compared with prepandemic, respondents reported significant intrapandemic increases in days worked/mo, ICU bed occupancy, and self-reported moral distress (240 [56.9%]) and burnout (259 [63.8%]). Of the 10 top-ranked items that incited moral distress, most pertained to regulatory/organizational ( n = 6) or local/institutional ( n = 2) issues or both ( n = 2). Average moral distress (95.6 ± 66.9), professional fulfilment (6.5 ± 2.1), and burnout scores (3.6 ± 2.0) were moderate with 227 physicians (54.6%) meeting burnout criteria. A significant dose-response existed between COVID-19 patient volume and moral distress scores. Physicians who worked more days/mo and more scheduled in-house nightshifts, especially combined with more unscheduled in-house nightshifts, experienced significantly more moral distress. One in five physicians used at least one maladaptive coping strategy. We identified four coping profiles (active/social, avoidant, mixed/ambivalent, infrequent) that were associated with significant differences across all wellness measures. CONCLUSIONS: Despite moderate intrapandemic moral distress and burnout, physicians experienced moderate professional fulfilment. However, one in five physicians used at least one maladaptive coping strategy. We highlight potentially modifiable factors at individual, institutional, and regulatory levels to enhance physician wellness.

Well-Being Among Respiratory Therapists in an Academic Medical Center During the COVID-19 Pandemic.

BACKGROUND: Recent studies have revealed high rates of burnout among respiratory therapists (RTs), which has implications for patient care and outcomes as well as for the health care workforce. We sought to better understand RT well-being during the COVID-19 pandemic. The purpose of this study was to determine rates and identify determinants of well-being, including burnout and professional fulfillment, among RTs in ICUs. METHODS: We conducted a mixed-methods study comprised of a survey administered quarterly from July 2020-May 2021 to critical-care health care professionals and semi-structured interviews from April-May 2021 with 10 ICU RTs within a single health center. We performed multivariable analyses to compare RT well-being to other professional groups and to evaluate changes in well-being over time. We analyzed qualitative interview data using thematic analysis, followed by mapping themes to the Maslow needs hierarchy. RESULTS: One hundred eight RTs responded to at least one quarterly survey. Eighty-two (75%) experienced burnout; 39 (36%) experienced professional fulfillment, and 62 (58%) reported symptoms of depression. Compared to clinicians of other professions in multivariable analyses, RTs were significantly more likely to experience burnout (odds ratio 2.32 [95% CI 1.41-3.81]) and depression (odds ratio 2.73 [95% CI 1.65-4.51]) and less likely to experience fulfillment (odds ratio 0.51 [95% CI 0.31-0.85]). We found that staffing challenges, safety concerns, workplace conflict, and lack of work-life balance led to burnout. Patient care, use of specialized skills, appreciation and a sense of community at work, and purpose fostered professional fulfillment. Themes identified were mapped to Maslow's hierarchy of needs; met needs led to professional fulfillment, and unmet needs led to burnout. CONCLUSIONS: ICU RTs experienced burnout during the pandemic at rates higher than other professions. To address RT needs, institutions should design and implement strategies to reduce burnout across all levels.

Association of Unit Census with Delays in Antimicrobial Initiation among Ward Patients with Hospital-acquired Sepsis.

Rationale: Patients with hospital-acquired sepsis (HAS) experience higher mortality and delayed care compared with those with community-acquired sepsis. Capacity strain, the extent to which demand for hospital resources exceeds availability, thus impacting patient care, is a possible mechanism underlying antimicrobial delays for HAS but has not been studied. Objectives: Assess the association of ward census with the timing of antimicrobial initiation among ward patients with HAS. Methods: This retrospective cohort study included adult patients hospitalized at five acute care hospitals between July 2017 and December 2019 who developed ward-onset HAS, distinguished from community-acquired sepsis by onset after 48 hours of hospitalization. The primary exposure was ward census, measured as the number of patients present in each ward at each hour, standardized by quarter and year. The primary outcome was time from sepsis onset to antimicrobial initiation. We used quantile regression to assess the association between ward census at sepsis onset and time to antimicrobial initiation among patients with HAS defined by Centers for Disease Control and Prevention Adult Sepsis Event criteria. We adjusted for hospital, year, quarter, age, sex, race, ethnicity, severity of illness, admission diagnosis, and service type. Results: A total of 1,672 hospitalizations included at least one ward-onset HAS episode. Median time to antimicrobial initiation after HAS onset was 4.1 hours (interquartile range, 0.4-22.3). Marginal adjusted time to antimicrobial initiation ranged from 3.6 hours (95% confidence interval [CI], 2.4-4.8 h) to 6.8 hours (95% CI, 5.3-8.4 h) at census levels 2 standard deviations (SDs) below and above the ward-specific mean, respectively. Each 1-SD increase in ward census at sepsis onset, representing a median of 2.4 patients, was associated with an increase in time to antimicrobial initiation of 0.80 hours (95% CI, 0.32-1.29 h). In sensitivity analyses, results were consistent across severity of illness and electronic health record-based sepsis definitions. Conclusions: Time to antimicrobial initiation increased with increasing census among ward patients with HAS. These findings suggest that delays in care for HAS may be related to ward capacity strain as measured by census. Additional work is needed to validate these findings and identify potential mechanisms operating through clinician behavior and care delivery processes.

Adherence to Lung Protective Ventilation in Patients With Coronavirus Disease 2019.

Prior studies have demonstrated suboptimal adherence to lung protective ventilation among patients with acute respiratory distress syndrome. A common barrier to providing this evidence-based practice is diagnostic uncertainty. We sought to test the hypothesis that patients with acute respiratory distress syndrome due to coronavirus disease 2019, in whom acute respiratory distress syndrome is easily recognized, would be more likely to receive low tidal volume ventilation than concurrently admitted acute respiratory distress syndrome patients without coronavirus disease 2019. DESIGN: Retrospective cohort study. SETTING: Five hospitals of a single health system. PATIENTS: Mechanically ventilated patients with coronavirus disease 2019 or noncoronavirus disease 2019 acute respiratory distress syndrome as identified by an automated, electronic acute respiratory distress syndrome finder in clinical use at study hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 333 coronavirus disease 2019 patients and 234 noncoronavirus disease 2019 acute respiratory distress syndrome patients, the average initial tidal volume was 6.4 cc/kg predicted body weight and 6.8 cc/kg predicted body weight, respectively. Patients had tidal volumes less than or equal to 6.5 cc/kg predicted body weight for a mean of 70% of the first 72 hours of mechanical ventilation in the coronavirus disease 2019 cohort, compared with 52% in the noncoronavirus disease 2019 cohort (unadjusted p < 0.001). After adjusting for height, gender, admitting hospital, and whether or not the patient was admitted to a medical specialty ICU, coronavirus disease 2019 diagnosis was associated with a 21% higher percentage of time receiving tidal volumes less than or equal to 6.5 cc/kg predicted body weight within the first 72 hours of mechanical ventilation (95% CI, 14-28%; p < 0.001). CONCLUSIONS: Adherence to low tidal volume ventilation during the first 72 hours of mechanical ventilation is higher in patients with coronavirus disease 2019 than with acute respiratory distress syndrome without coronavirus disease 2019. This population may present an opportunity to understand facilitators of implementation of this life-saving evidence-based practice.

Implementing nudges to promote utilization of low tidal volume ventilation (INPUT): a stepped-wedge, hybrid type III trial of strategies to improve evidence-based mechanical ventilation management.

BACKGROUND: Behavioral economic insights have yielded strategies to overcome implementation barriers. For example, default strategies and accountable justification strategies have improved adherence to best practices in clinical settings. Embedding such strategies in the electronic health record (EHR) holds promise for simple and scalable approaches to facilitating implementation. A proven-effective but under-utilized treatment for patients who undergo mechanical ventilation involves prescribing low tidal volumes, which protects the lungs from injury. We will evaluate EHR-based implementation strategies grounded in behavioral economic theory to improve evidence-based management of mechanical ventilation. METHODS: The Implementing Nudges to Promote Utilization of low Tidal volume ventilation (INPUT) study is a pragmatic, stepped-wedge, hybrid type III effectiveness implementation trial of three strategies to improve adherence to low tidal volume ventilation. The strategies target clinicians who enter electronic orders and respiratory therapists who manage the mechanical ventilator, two key stakeholder groups. INPUT has five study arms: usual care, a default strategy within the mechanical ventilation order, an accountable justification strategy within the mechanical ventilation order, and each of the order strategies combined with an accountable justification strategy within flowsheet documentation. We will create six matched pairs of twelve intensive care units (ICUs) in five hospitals in one large health system to balance patient volume and baseline adherence to low tidal volume ventilation. We will randomly assign ICUs within each matched pair to one of the order panels, and each pair to one of six wedges, which will determine date of adoption of the order panel strategy. All ICUs will adopt the flowsheet documentation strategy 6 months afterwards. The primary outcome will be fidelity to low tidal volume ventilation. The secondary effectiveness outcomes will include in-hospital mortality, duration of mechanical ventilation, ICU and hospital length of stay, and occurrence of potential adverse events. DISCUSSION: This stepped-wedge, hybrid type III trial will provide evidence regarding the role of EHR-based behavioral economic strategies to improve adherence to evidence-based practices among patients who undergo mechanical ventilation in ICUs, thereby advancing the field of implementation science, as well as testing the effectiveness of low tidal volume ventilation among broad patient populations. TRIAL REGISTRATION: ClinicalTrials.gov , NCT04663802 . Registered 11 December 2020.

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.

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. METHODS: CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. FINDINGS: In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89-0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88-0·96), sensitivity 83·0% (95% CI 74·0-91·1), and specificity 88·3% (95% CI 83·1-92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85-0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92-0·95). INTERPRETATION: A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. FUNDING: National Institutes of Health, Department of Defense, and Department of Veterans Affairs.

Actions Taken by US Hospitals to Prepare for Increased Demand for Intensive Care During the First Wave of COVID-19: A National Survey.

BACKGROUND: The COVID-19 pandemic placed considerable strain on critical care resources. How US hospitals responded to this crisis is unknown. RESEARCH QUESTION: What actions did US hospitals take to prepare for a potential surge in demand for critical care services in the context of the COVID-19 pandemic? STUDY DESIGN AND METHODS: From September to November 2020, the chief nursing officers of a representative sample of US hospitals were surveyed regarding organizational actions taken to increase or maintain critical care capacity during the COVID-19 pandemic. Weighted proportions of hospitals for each potential action were calculated to create estimates across the entire population of US hospitals, accounting for both the sampling strategy and nonresponse. Also examined was whether the types of actions taken varied according to the cumulative regional incidence of COVID-19 cases. RESULTS: Responses were received from 169 of 540 surveyed US hospitals (response rate, 31.3%). Almost all hospitals canceled or postponed elective surgeries (96.7%) and nonsurgical procedures (94.8%). Few hospitals created new medical units in areas not typically dedicated to health care (12.9%), and almost none adopted triage protocols (5.6%) or protocols to connect multiple patients to a single ventilator (4.8%). Actions to increase or preserve ICU staff, including use of ICU telemedicine, were highly variable, without any single dominant strategy. Hospitals experiencing a higher incidence of COVID-19 did not consistently take different actions compared with hospitals facing lower incidence. INTERPRETATION: Responses of hospitals to the mass need for critical care services due to the COVID-19 pandemic were highly variable. Most hospitals canceled procedures to preserve ICU capacity and scaled up ICU capacity using existing clinical space and staffing. Future research linking hospital response to patient outcomes can inform planning for additional surges of this pandemic or other events in the future.