Association Between Nurse Copatient Illness Severity and Mortality in the ICU.

OBJECTIVES: In the context of traditional nurse-to-patient ratios, ICU patients are typically paired with one or more copatients, creating interdependencies that may affect clinical outcomes. We aimed to examine the effect of copatient illness severity on ICU mortality. DESIGN: We conducted a retrospective cohort study using electronic health records from a multihospital health system from 2018 to 2020. We identified nurse-to-patient assignments for each 12-hour shift using a validated algorithm. We defined copatient illness severity as whether the index patient's copatient received mechanical ventilation or vasoactive support during the shift. We used proportional hazards regression with time-varying covariates to assess the relationship between copatient illness severity and 28-day ICU mortality. SETTING: Twenty-four ICUs in eight hospitals. PATIENTS: Patients hospitalized in the ICU between January 1, 2018, and August 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main analysis included 20,650 patients and 84,544 patient-shifts. Regression analyses showed a patient's risk of death increased when their copatient received both mechanical ventilation and vasoactive support (hazard ratio [HR]: 1.30; 95% CI, 1.05-1.61; p = 0.02) or vasoactive support alone (HR: 1.82; 95% CI, 1.39-2.38; p < 0.001), compared with situations in which the copatient received neither treatment. However, if the copatient was solely on mechanical ventilation, there was no significant increase in the risk of death (HR: 1.03; 95% CI, 0.86-1.23; p = 0.78). Sensitivity analyses conducted on cohorts with varying numbers of copatients consistently showed an increased risk of death when a copatient received vasoactive support. CONCLUSIONS: Our findings suggest that considering copatient illness severity, alongside the existing practice of considering individual patient conditions, during the nurse-to-patient assignment process may be an opportunity to improve ICU outcomes.

Development and Evaluation of Pediatric Acute Care Hospital Referral Regions in Eight States.

OBJECTIVE: To develop a set of pediatric acute care hospital referral regions for use in studying pediatric acute care delivery and test their utility relative to other regional systems. STUDY DESIGN: We used state-level administrative databases capturing all pediatric acute care in 8 states to construct novel referral regions. We first constructed pediatric hospital service areas (PHSAs) based on 5 837 464 pediatric emergency department encounters. We then aggregated these PHSAs to pediatric hospital referral regions (PHRRs) based on 344 440 pediatric hospitalizations. Finally, we used 3 measures of spatial accuracy (localization index, market share index, and net patient flow) to compare this novel region system with the Dartmouth Atlas, designed originally to study adult specialty care, and the Pittsburgh Atlas, designed originally to study adult acute care. RESULTS: The development procedure resulted in 717 novel PHSAs, which were then aggregated to 55 PHRRs across the included states. Relative to hospital referral regions in the Dartmouth and Pittsburgh Atlases, PHRRs were fewer in number and larger in area and population. PHRRs more accurately captured patterns of pediatric hospitalizations, (eg, mean localization index: 69.1 out of 100, compared with a mean of 58.1 for the Dartmouth Atlas and 62.4 for the Pittsburgh Atlas). CONCLUSIONS: The use of regional definitions designed specifically to study pediatric acute care better captures contemporary pediatric acute care delivery than the use of existing regional definitions. Future work should extend these definitions to all US states to enable national analyses of pediatric acute care delivery.

Association Between Medicare's Sepsis Reporting Policy (SEP-1) and the Documentation of a Sepsis Diagnosis in the Clinical Record.

STUDY DESIGN: Interrupted time series analysis of a retrospective, electronic health record cohort. OBJECTIVE: To determine the association between the implementation of Medicare's sepsis reporting measure (SEP-1) and sepsis diagnosis rates as assessed in clinical documentation. BACKGROUND: The role of health policy in the effort to improve sepsis diagnosis remains unclear. PATIENTS AND METHODS: Adult patients hospitalized with suspected infection and organ dysfunction within 6 hours of presentation to the emergency department, admitted to one of 11 hospitals in a multi-hospital health system from January 2013 to December 2017. Clinician-diagnosed sepsis, as reflected by the inclusion of the terms "sepsis" or "septic" in the text of clinical notes in the first two calendar days following presentation. RESULTS: Among 44,074 adult patients with sepsis admitted to 11 hospitals over 5 years, the proportion with sepsis documentation was 32.2% just before the implementation of SEP-1 in the third quarter of 2015 and increased to 37.3% by the fourth quarter of 2017. Of the 9 post-SEP-1 quarters, 8 had odds ratios for a sepsis diagnosis >1 (overall range: 0.98-1.26; P value for a joint test of statistical significance = 0.005). The effects were clinically modest, with a maximum effect of an absolute increase of 4.2% (95% CI: 0.9-7.8) at the end of the study period. The effect was greater in patients who did not require vasopressors compared with patients who required vasopressors ( P value for test of interaction = 0.02). CONCLUSIONS: SEP-1 implementation was associated with modest increases in sepsis diagnosis rates, primarily among patients who did not require vasoactive medications.

Treatment Patterns and Clinical Outcomes After the Introduction of the Medicare Sepsis Performance Measure (SEP-1).

BACKGROUND: Medicare requires that hospitals report on their adherence to the Severe Sepsis and Septic Shock Early Management Bundle (SEP-1). OBJECTIVE: To evaluate the effect of SEP-1 on treatment patterns and patient outcomes. DESIGN: Longitudinal study of hospitals using repeated cross-sectional cohorts of patients. SETTING: 11 hospitals within an integrated health system. PATIENTS: 54 225 encounters between January 2013 and December 2017 for adults with sepsis who were hospitalized through the emergency department. INTERVENTION: Onset of the SEP-1 reporting requirement in October 2015. MEASUREMENTS: Changes in SEP-1-targeted processes, including antibiotic administration, lactate measurement, and fluid administration at 3 hours from sepsis onset; repeated lactate and vasopressor administration for hypotension within 6 hours of sepsis onset; and sepsis outcomes, including risk-adjusted intensive care unit (ICU) admission, in-hospital mortality, and home discharge among survivors. RESULTS: Two years after its implementation, SEP-1 was associated with variable changes in process measures, with the greatest effect being an increase in lactate measurement within 3 hours of sepsis onset (absolute increase, 23.7 percentage points [95% CI, 20.7 to 26.7 percentage points]; P < 0.001). There were small increases in antibiotic administration (absolute increase, 4.7 percentage points [CI, 1.9 to 7.6 percentage points]; P = 0.001) and fluid administration of 30 mL/kg of body weight within 3 hours of sepsis onset (absolute increase, 3.4 percentage points [CI, 1.5 to 5.2 percentage points]; P < 0.001). There was no change in vasopressor administration. There was a small increase in ICU admissions (absolute increase, 2.0 percentage points [CI, 0 to 4.0 percentage points]; P = 0.055) and no changes in mortality (absolute change, 0.1 percentage points [CI, -0.9 to 1.1 percentage points]; P = 0.87) or discharge to home. LIMITATION: Data are from a single health system. CONCLUSION: Implementation of the SEP-1 mandatory reporting program was associated with variable changes in process measures, without improvements in clinical outcomes. Revising the measure may optimize its future effect. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Advanced Practice Provider-inclusive Staffing Models and Patient Outcomes in Pediatric Critical Care.

BACKGROUND: Pediatric intensive care units (PICUs) are increasingly staffed with advanced practice providers (APPs), supplementing traditional physician staffing models. OBJECTIVES: We evaluate the effect of APP-inclusive staffing models on clinical outcomes and resource utilization in US PICUs. RESEARCH DESIGN: Retrospective cohort study of children admitted to PICUs in 9 states in 2016 using the Healthcare Cost and Utilization Project's State Inpatient Databases. PICU staffing models were assessed using a contemporaneous staffing survey. We used multivariate regression to examine associations between staffing models with and without APPs and outcomes. MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes included odds of hospital acquired conditions and ICU and hospital lengths of stay. RESULTS: The sample included 38,788 children in 40 PICUs. Patients admitted to PICUs with APP-inclusive staffing were younger (6.1±5.9 vs. 7.1±6.2 y) and more likely to have complex chronic conditions (64% vs. 43%) and organ failure on admission (25% vs. 22%), compared with patients in PICUs with physician-only staffing. There was no difference in mortality between PICU types [adjusted odds ratio (AOR): 1.23, 95% confidence interval (CI): 0.83-1.81, P=0.30]. Patients in PICUs with APP-inclusive staffing had lower odds of central line-associated blood stream infections (AOR: 0.76, 95% CI: 0.59-0.98, P=0.03) and catheter-associated urinary tract infections (AOR: 0.73, 95% CI: 0.61-0.86, P<0.001). There were no differences in lengths of stay. CONCLUSIONS: Despite being younger and sicker, children admitted to PICUs with APP-inclusive staffing had no increased odds of mortality and lower odds of some hospital acquired conditions compared with those in PICUs with physician-only staffing. Further research can inform APP integration strategies which optimize outcomes.

Economic Analysis of Mandated Protocolized Sepsis Care in New York Hospitals.

OBJECTIVES: Initial evidence suggests that state-level regulatory mandates for sepsis quality improvement are associated with decreased sepsis mortality. However, sepsis mandates require financial investments on the part of hospitals and may lead to increased spending. We evaluated the effects of the 2013 New York State sepsis regulations on the costs of care for patients hospitalized with sepsis. DESIGN: Retrospective cohort study using state discharge data from the U.S. Healthcare Costs and Utilization Project and a comparative interrupted time series analytic approach. Costs were calculated from admission-level charge data using hospital-specific cost-to-charge ratios. SETTING: General, short stay, acute care hospitals in New York, and four control states: Florida, Massachusetts, Maryland, and New Jersey. PATIENTS: All patients hospitalized with sepsis between January 1, 2011, and September 30, 2015. INTERVENTIONS: The 2013 New York mandate that all hospitals develop and implement protocols for sepsis identification and treatment, educate staff, and report performance data to the state. MEASUREMENTS AND MAIN RESULTS: The analysis included 1,026,664 admissions in 520 hospitals. Mean unadjusted costs per hospitalization in New York State were $42,036 ± $60,940 in the pre-regulation period and $39,719 ± $59,063 in the post-regulation period, compared with $34,642 ± $52,403 pre-regulation and $31,414 ± $48,155 post-regulation in control states. In the comparative interrupted time series analysis, the regulations were not associated with a significant difference in risk-adjusted mean cost per hospitalization (p = 0.12) or risk-adjusted mean cost per hospital day (p = 0.44). For example, in the 10th quarter after implementation of the regulations, risk-adjusted mean cost per hospitalization was $3,627 (95% CI, -$681 to $7,934) more than expected in New York State relative to control states. CONCLUSIONS: Mandated protocolized sepsis care was not associated with significant changes in hospital costs in patients hospitalized with sepsis in New York State.

An administrative model for benchmarking hospitals on their 30-day sepsis mortality.

BACKGROUND: Given the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis. METHODS: We conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission. RESULTS: In 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings. CONCLUSIONS: A novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.

Association Between State-Mandated Protocolized Sepsis Care and In-hospital Mortality Among Adults With Sepsis.

Importance: Beginning in 2013, New York State implemented regulations mandating that hospitals implement evidence-based protocols for sepsis management, as well as report data on protocol adherence and clinical outcomes to the state government. The association between these mandates and sepsis outcomes is unknown. Objective: To evaluate the association between New York State sepsis regulations and the outcomes of patients hospitalized with sepsis. Design, Setting, and Participants: Retrospective cohort study of adult patients hospitalized with sepsis in New York State and in 4 control states (Florida, Maryland, Massachusetts, and New Jersey) using all-payer hospital discharge data (January 1, 2011-September 30, 2015) and a comparative interrupted time series analytic approach. Exposures: Hospitalization for sepsis before (January 1, 2011-March 31, 2013) vs after (April 1, 2013-September 30, 2015) implementation of the 2013 New York State sepsis regulations. Main Outcomes and Measures: The primary outcome was 30-day in-hospital mortality. Secondary outcomes were intensive care unit admission rates, central venous catheter use, Clostridium difficile infection rates, and hospital length of stay. Results: The final analysis included 1 012 410 sepsis admissions to 509 hospitals. The mean age was 69.5 years (SD, 16.4 years) and 47.9% were female. In New York State and in the control states, 139 019 and 289 225 patients, respectively, were admitted before implementation of the sepsis regulations and 186 767 and 397 399 patients, respectively, were admitted after implementation of the sepsis regulations. Unadjusted 30-day in-hospital mortality was 26.3% in New York State and 22.0% in the control states before the regulations, and was 22.0% in New York State and 19.1% in the control states after the regulations. Adjusting for patient and hospital characteristics as well as preregulation temporal trends and season, mortality after implementation of the regulations decreased significantly in New York State relative to the control states (P = .02 for the joint test of the comparative interrupted time series estimates). For example, by the 10th quarter after implementation of the regulations, adjusted absolute mortality was 3.2% (95% CI, 1.0% to 5.4%) lower than expected in New York State relative to the control states (P = .004). The regulations were associated with no significant differences in intensive care unit admission rates (P = .09) (10th quarter adjusted difference, 2.8% [95% CI, -1.7% to 7.2%], P = .22), a significant relative decrease in hospital length of stay (P = .04) (10th quarter adjusted difference, 0.50 days [95% CI, -0.47 to 1.47 days], P = .31), a significant relative decrease in the C difficile infection rate (P < .001) (10th quarter adjusted difference, -1.8% [95% CI, -2.6% to -1.0%], P < .001), and a significant relative increase in central venous catheter use (P = .02) (10th quarter adjusted difference, 4.8% [95% CI, 2.3% to 7.4%], P < .001). Conclusions and Relevance: In New York State, mandated protocolized sepsis care was associated with a greater decrease in sepsis mortality compared with sepsis mortality in control states that did not implement sepsis regulations. Because baseline mortality rates differed between New York and comparison states, it is uncertain whether these findings are generalizable to other states.

Hospital Variation in Risk-Adjusted Pediatric Sepsis Mortality.

OBJECTIVES: With continued attention to pediatric sepsis at both the clinical and policy levels, it is important to understand the quality of hospitals in terms of their pediatric sepsis mortality. We sought to develop a method to evaluate hospital pediatric sepsis performance using 30-day risk-adjusted mortality and to assess hospital variation in risk-adjusted sepsis mortality in a large state-wide sample. DESIGN: Retrospective cohort study using administrative claims data. SETTINGS: Acute care hospitals in the state of Pennsylvania from 2011 to 2013. PATIENTS: Patients between the ages of 0-19 years admitted to a hospital with sepsis defined using validated International Classification of Diseases, Ninth revision, Clinical Modification, diagnosis and procedure codes. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: During the study period, there were 9,013 pediatric sepsis encounters in 153 hospitals. After excluding repeat visits and hospitals with annual patient volumes too small to reliably assess hospital performance, there were 6,468 unique encounters in 24 hospitals. The overall unadjusted mortality rate was 6.5% (range across all hospitals: 1.5-11.9%). The median number of pediatric sepsis cases per hospital was 67 (range across all hospitals: 30-1,858). A hierarchical logistic regression model for 30-day risk-adjusted mortality controlling for patient age, gender, emergency department admission, infection source, presence of organ dysfunction at admission, and presence of chronic complex conditions showed good discrimination (C-statistic = 0.80) and calibration (slope and intercept of calibration plot: 0.95 and -0.01, respectively). The hospital-specific risk-adjusted mortality rates calculated from this model varied minimally, ranging from 6.0% to 7.4%. CONCLUSIONS: Although a risk-adjustment model for 30-day pediatric sepsis mortality had good performance characteristics, the use of risk-adjusted mortality rates as a hospital quality measure in pediatric sepsis is not useful due to the low volume of cases at most hospitals. Novel metrics to evaluate the quality of pediatric sepsis care are needed.

Travel Distances for Interhospital Transfers of Critically Ill Children: A Geospatial Analysis.

IMPORTANCE: The U.S. pediatric acute care system has become more centralized, placing increasing importance on interhospital transfers. OBJECTIVES: We conducted a geospatial analysis of critically ill children undergoing interfacility transfer with a specific focus on understanding travel distances between the patient's residence and the hospitals in which they receive care. DESIGN, SETTING, AND PARTICIPANTS: Retrospective geospatial analysis using five U.S. state-level administrative databases; four states observed from 2016 to 2019 and one state from 2018 to 2019. Participants included 10,665 children who experienced 11,713 episodes of critical illness involving transfer between two hospitals. MAIN OUTCOMES AND MEASURES: Travel distances and the incidence of "potentially suboptimal triage," in which patients were transferred to a second hospital less than five miles further from their residence than the first hospital. RESULTS: Patients typically present to hospitals near their residence (median distance from residence to first hospital, 4.2 miles; interquartile range [IQR], 1.8-9.6 miles). Transfer distances are relatively large (median distance between hospitals, 28.9 miles; IQR, 11.2-53.2 miles), taking patients relatively far away from their residences (median distance from residence to second hospital, 30.1 miles; IQR, 12.2-54.9 miles). Potentially suboptimal triage was frequent: 24.2 percent of patients were transferred to a hospital less than five miles further away from their residence than the first hospital. Potentially suboptimal triage was most common in children living in urban counties, and became less common with increasing medical complexity. CONCLUSIONS AND RELEVANCE: The current pediatric critical care system is organized in a hub-and-spoke model, which requires large travel distances for some patients. Some transfers might be prevented by more efficient prehospital triage. Current transfer patterns suggest the choice of initial hospital is influenced by geography as well as by attempts to match hospital resources with perceived patient needs.

Intensivist physician-to-patient ratios and mortality in the intensive care unit.

PURPOSE: A high daily census may hinder the ability of physicians to deliver quality care in the intensive care unit (ICU). We sought to determine the relationship between intensivist-to-patient ratios and mortality among ICU patients. METHODS: We performed a retrospective cohort study of intensivist-to-patient ratios in 29 ICUs in 10 hospitals in the United States from 2018 to 2020. We used meta-data from progress notes in the electronic health record to determine an intensivist-specific caseload for each ICU day. We then fit a multivariable proportional hazards model with time-varying covariates to estimate the relationship between the daily intensivist-to-patient ratio and ICU mortality at 28 days. RESULTS: The final analysis included 51,656 patients, 210,698 patient days, and 248 intensivist physicians. The average caseload per day was 11.8 (standard deviation: 5.7). There was no association between the intensivist-to-patient ratio and mortality (hazard ratio for each additional patient: 0.987, 95% confidence interval: 0.968-1.007, p = 0.2). This relationship persisted when we defined the ratio as caseload over the sample-wide average (hazard ratio: 0.907, 95% confidence interval: 0.763-1.077, p = 0.26) and cumulative days with a caseload over the sample-wide average (hazard ratio: 0.991, 95% confidence interval: 0.966-1.018, p = 0.52). The relationship was not modified by the presence of physicians-in-training, nurse practitioners, and physician assistants (p value for interaction term: 0.14). CONCLUSIONS: Mortality for ICU patients appears resistant to high intensivist caseloads. These results may not generalize to ICUs organized differently than those in this sample, such as ICUs outside the United States.

Machine learning-based prediction of low-value care for hospitalized patients.

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period; and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system.

The Use of Electronic Health Record Metadata to Identify Nurse-Patient Assignments in the Intensive Care Unit: Algorithm Development and Validation.

BACKGROUND: Nursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse's unique skills are matched to each patient's needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale. OBJECTIVE: This study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU. METHODS: We used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts. RESULTS: The analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions. CONCLUSIONS: Metadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

Characterizing intensive care unit rounding teams using meta-data from the electronic health record.

PURPOSE: Teamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams. METHODS: We analyzed EHR data from 27 ICUs over one year. We linked intensivist physicians, nurses, and respiratory therapists to individual patients based on selected EHR meta-data. We then characterized ICU teams by their members' overall past experience and shared past experience; and used network analysis to characterize ICUs by their network's density and centralization. RESULTS: We identified 2327 unique providers and 30,892 unique care teams. Teams varied based on their average team member experience (median and total range: 262.2 shifts, 9.0-706.3) and average shared experience (median and total range: 13.2 shared shifts, 1.0-99.3). ICUs varied based on their network's density (median and total range: 0.12, 0.07-0.23), degree centralization (0.50, 0.35-0.65) and closeness centralization (0.45, 0.11-0.60). In a regression analysis, this variation was only partially explained by readily observable ICU characteristics. CONCLUSIONS: EHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.

Adoption and Deadoption of Medications to Treat Hospitalized Patients With COVID-19.

OBJECTIVES: The COVID-19 pandemic was characterized by rapidly evolving evidence regarding the efficacy of different therapies, as well as rapidly evolving health policies in response to that evidence. Data on adoption and deadoption are essential as we learn from this pandemic and prepare for future public health emergencies. DESIGN: We conducted an observational cohort study in which we determined patterns in the use of multiple medications to treat COVID-19: remdesivir, hydroxychloroquine, IV corticosteroids, tocilizumab, heparin-based anticoagulants, and ivermectin. We analyzed changes both overall and within subgroups of critically ill versus Noncritically ill patients. SETTING: Data from Optum's deidentified Claims-Clinical Dataset, which contains multicenter electronic health record data from U.S. hospitals. PATIENTS: Adults hospitalized with COVID-19 from January 2020 to June 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 141,533 eligible patients, 34,515 (24.4%) required admission to an ICU, 14,754 (10.4%) required mechanical ventilation, and 18,998 (13.4%) died during their hospitalization. Averaged over the entire time period, corticosteroid use was most common (47.0%), followed by remdesivir (33.2%), anticoagulants (19.3%), hydroxychloroquine (7.3%), and tocilizumab (3.4%). Usage patterns varied substantially across treatments. For example, hydroxychloroquine use peaked in March 2020 and leveled off to near zero by June 2020, whereas the use of remdesivir, corticosteroids, and tocilizumab all increased following press releases announcing positive results of large international trials. Ivermectin use increased slightly over the study period but was extremely rare overall (0.4%). CONCLUSIONS: During the COVID-19 pandemic, medication treatment patterns evolved reliably in response to emerging evidence and changes in policy. These findings may inform efforts to promote optimal adoption and deadoption of treatments for acute care conditions.

Willingness to Treat with Therapies of Unknown Effectiveness in Severe COVID-19: A Survey of Intensivist Physicians.

Rationale: Little is known about how physicians develop their beliefs about new treatments or update their beliefs in the face of new clinical evidence. These issues are particularly salient in the context of the coronavirus disease (COVID-19) pandemic, which created rapid demand for novel therapies in the absence of robust evidence. Objectives: To identify psychological traits associated with physicians' willingness to treat with unproven therapies and willingness to update their treatment preferences in the setting of new evidence in the context of COVID-19. Methods: We administered a longitudinal e-mail survey to United States physicians board certified in intensive care medicine in April and May 2020 (phase one) and October and November 2020 (phase two). We assessed five psychological traits potentially related to evidence uptake: need for cognition, evidence skepticism, need for closure, risk tolerance, and research engagement. We then examined the relationship between these traits and physician preferences for pharmacological treatment for a hypothetical patient with severe COVID-19 pneumonia. Results: There were 592 responses to the phase one survey, conducted prior to publication of trial data. At this time physicians were most willing to treat with macrolide antibiotics (50.5%), followed by antimalaria agents (36.1%), corticosteroids (24.5%), antiretroviral agents (22.6%), and angiotensin inhibitors (4.4%). Greater evidence skepticism (relative risk [RR], 1.40; 95% confidence interval [CI], 1.30-1.52; P < 0.001), greater need for closure (RR, 1.19; 95% CI, 1.06-1.34; P = 0.003), and greater risk tolerance (RR, 1.17; 95% CI, 1.08-1.26; P < 0.001) were associated with an increased willingness to treat, whereas greater need for cognition (RR, 0.85; 95% CI, 0.75-0.96, P = 0.010) and greater research engagement (RR, 0.91; 95% CI, 0.88-0.95; P < 0.0001) were associated with decreased willingness to treat. In phase two, most physicians updated their beliefs after publication of trial data about antimalarial agents and corticosteroids. Physicians with greater evidence skepticism were more likely to persist in their beliefs. Conclusions: Psychological traits associated with clinical decisions in the setting of uncertain evidence may provide insight into strategies to better align clinical practice with published evidence.

Factors associated with potentially avoidable interhospital transfers in emergency general surgery-A call for quality improvement efforts.

BACKGROUND: Emergency general surgery conditions are common, require urgent surgical evaluation, and are associated with high mortality and costs. Although appropriate interhospital transfers are critical to successful emergency general surgery care, the performance of emergency general surgery transfer systems remains unclear. We aimed to describe emergency general surgery transfer patterns and identify factors associated with potentially avoidable transfers. METHODS: We performed a retrospective cohort study of emergency general surgery episodes in 8 US states using the 2016 Healthcare Cost and Utilization Project State Inpatient and Emergency Department Databases and the American Hospital Association Annual Surveys. We identified Emergency Department-to-Inpatient and Inpatient-to-Inpatient interhospital emergency general surgery transfers. Potentially avoidable transfers were defined as discharge within 72 hours after transfer without undergoing any procedure or operation at the destination hospital. We examined transfer incidence and characteristics. We performed multilevel regression examining patient-level and hospital-level factors associated with potentially avoidable transfers. RESULTS: Of 514,410 adult emergency general surgery episodes, 26,281 (5.1%) involved interhospital transfers (Emergency Department-to-Inpatient: 65.0%, Inpatient-to-Inpatient: 35.1%). Over 1 in 4 transfers were potentially avoidable (7,188, 27.4%), with the majority occurring from the emergency department. Factors associated with increased odds of potentially avoidable transfers included self-pay (versus government insurance, odds ratio: 1.26, 95% confidence interval: 1.09-1.45, P = .002), level 1 trauma centers (versus non-trauma centers, odds ratio: 1.24, 95% confidence interval: 1.05-1.47, P = .01), and critical access hospitals (versus non-critical access, odds ratio: 1.30, 95% confidence interval: 1.15-1.47, P < .001). Hospital-level factors (size, trauma center, ownership, critical access, location) accounted for 36.1% of potentially avoidable transfers variability. CONCLUSION: Over 1 in 4 emergency general surgery transfers are potentially avoidable. Understanding factors associated with potentially avoidable transfers can guide research, quality improvement, and infrastructure development to optimize emergency general surgery care.

Assessment of Hospital Characteristics and Interhospital Transfer Patterns of Adults With Emergency General Surgery Conditions.

Importance: Although patients with emergency general surgery (EGS) conditions frequently undergo interhospital transfers, the transfer patterns and associated factors are not well understood. Objective: To examine whether patients with EGS conditions are consistently directed to hospitals with more resources and better outcomes. Design, Setting, and Participants: This cohort study performed a network analysis of interhospital transfers among adults with EGS conditions from January 1 to December 31, 2016. The analysis used all-payer claims data from the 2016 Healthcare Cost and Utilization Project state inpatient and emergency department databases in 8 states. A total of 728 hospitals involving 85 415 transfers of 80 307 patients were included. Patients were eligible for inclusion if they were 18 years or older and had an acute care hospital encounter with a diagnosis of an EGS condition as defined by the American Association for the Surgery of Trauma. Data were analyzed from January 1, 2020, to June 17, 2021. Exposures: Hospital-level measures of size (total bed capacity), resources (intensive care unit [ICU] bed capacity, teaching status, trauma center designation, and presence of trauma and/or surgical critical care fellowships), EGS volume (annual EGS encounters), and EGS outcomes (risk-adjusted failure to rescue and in-hospital mortality). Main Outcomes and Measures: The main outcome was hospital-level centrality ratio, defined as the normalized number of incoming transfers divided by the number of outgoing transfers. A higher centrality ratio indicated more incoming transfers per outgoing transfer. Multivariable regression analysis was used to test the hypothesis that a higher hospital centrality ratio would be associated with more resources, higher volume, and better outcomes. Results: Among 80 307 total patients, the median age was 63 years (interquartile range [IQR], 50-75 years); 52.1% of patients were male and 78.8% were White. The median number of outgoing and incoming transfers per hospital were 106 (IQR, 61-157) and 36 (IQR, 8-137), respectively. A higher log-transformed centrality ratio was associated with more resources, such as higher ICU capacity (eg, >25 beds vs 0-10 beds: ß = 1.67 [95% CI, 1.16-2.17]; P < .001), and higher EGS volume (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = 0.78 [95% CI, 0-1.57]; P = .01). However, a higher log-transformed centrality ratio was not associated with better outcomes, such as lower in-hospital mortality (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = 0.30 [95% CI, -0.09 to 0.68]; P = .83) and lower failure to rescue (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = -0.50 [95% CI, -1.13 to 0.12]; P = .27). Conclusions and Relevance: In this study, EGS transfers were directed to high-volume hospitals with more resources but were not necessarily directed to hospitals with better clinical outcomes. Optimizing transfer destination in the interhospital transfer network has the potential to improve EGS outcomes.

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.

Psychological Safety in Intensive Care Unit Rounding Teams.

Rationale: Psychological safety is the condition by which members of an organization feel safe to voice concerns and take risks. Although psychological safety is an important determinant of team performance, little is known about its role in the intensive care unit (ICU). Objectives: To identify the factors associated with psychological safety and the potential influence of psychological safety on team performance in critical care. Methods: We performed daily surveys of healthcare providers in 12 ICUs within an integrated health system over a 2-week period. Survey domains included psychological safety, leader familiarity, leader inclusiveness, role clarity, job strain, and teamwork. These data were linked to daily performance on lung-protective ventilation and spontaneous breathing trials. We used regression models to examine the antecedents of psychological safety as well as the influence of psychological safety on both perceived teamwork and actual performance. Results: We received 553 responses from 270 unique providers. At the individual provider level, higher leader inclusiveness (adjusted ß = 0.32; 95% confidence interval [CI], 0.24 to 0.41) and lower job strain (adjusted ß = -0.07, 95% CI, -0.13 to -0.02) were independently associated with greater psychological safety. Higher psychological safety was independently associated with greater perception of teamwork (adjusted ß = 0.30; 95% CI, 0.25 to 0.36). There was no association between team psychological safety and performance on either spontaneous breathing trials (incident rate ratio for each 1-unit change in team psychological safety, 0.85; 95% CI, 0.81 to 1.10) or lung-protective ventilation (incident rate ratio, 0.77; 95% CI, 0.57 to 1.04). Conclusions: Psychological safety is associated with several modifiable factors in the ICU but is not associated with actual use of evidence-based practices.