Structured Mobilization for Critically Ill Patients: A Pragmatic Cluster-randomized Trial.

Rationale: Small trials and professional recommendations support mobilization interventions to improve recovery among critically ill patients, but their real-world effectiveness is unknown. Objective: To evaluate a low-cost, multifaceted mobilization intervention. Methods: We conducted a stepped-wedge cluster-randomized trial across 12 ICUs with diverse case mixes. The primary and secondary samples included patients mechanically ventilated for ⩾48 hours who were ambulatory before admission, and all patients with ICU stays ⩾48 hours, respectively. The mobilization intervention included 1) designation and posting of daily mobilization goals; 2) interprofessional closed-loop communication coordinated by each ICU's facilitator; and 3) performance feedback. Measurements and Main Results: From March 4, 2019 through March 15, 2020, 848 and 1,069 patients were enrolled in the usual care and intervention phases in the primary sample, respectively. The intervention did not increase the primary outcome, patient's maximal Intensive Care Mobility Scale (range, 0-10) score within 48 hours before ICU discharge (estimated mean difference, 0.16; 95% confidence interval, -0.31 to 0.63; P = 0.51). More patients in the intervention (37.2%) than usual care (30.7%) groups achieved the prespecified secondary outcome of ability to stand before ICU discharge (odds ratio, 1.48; 95% confidence interval, 1.02 to 2.15; P = 0.04). Similar results were observed among the 7,115 patients in the secondary sample. The percentage of days on which patients received physical therapy mediated 90.1% of the intervention effect on standing. ICU mortality (31.5% vs. 29.0%), falls (0.7% vs. 0.4%), and unplanned extubations (2.0% vs. 1.8%) were similar between groups (all P > 0.3). Conclusions: A low-cost, multifaceted mobilization intervention did not improve overall mobility but improved patients' odds of standing and was safe. Clinical trial registered with www.clinicaltrials.gov (NCT03863470).

Default Palliative Care Consultation for Seriously Ill Hospitalized Patients: A Pragmatic Cluster Randomized Trial.

Importance: Increasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking. Objective: To determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes. Design, Setting, and Participants: A pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019. Intervention: Ordering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care. Main Outcomes and Measures: The primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality. Results: Of 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P < .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, -0.53% [95% CI, -3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08]). Conclusions and Relevance: Default palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes. Trial Registration: ClinicalTrials.gov Identifier: NCT02505035.

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.

Adaptation of the Acute Organ Failure Score for Use in a Medicare Population.

OBJECTIVES: Without widely available physiologic data, a need exists for ICU risk adjustment methods that can be applied to administrative data. We sought to expand the generalizability of the Acute Organ Failure Score by adapting it to a commonly used administrative database. DESIGN: Retrospective cohort study. SETTING: One hundred fifty-one hospitals in Pennsylvania. PATIENTS: A total of 90,733 ICU admissions among 77,040 unique patients between January 1, 2009, and December 1, 2009, in the Medicare Provider Analysis and Review database. MEASUREMENTS AND MAIN RESULTS: We used multivariable logistic regression on a random split cohort to predict 30-day mortality, and to examine the impact of using different comorbidity measures in the model and adding historical claims data. Overall 30-day mortality was 17.6%. In the validation cohort, using the original Acute Organ Failure Score model's ß coefficients resulted in poor discrimination (C-statistic, 0.644; 95% CI, 0.639-0.649). The model's C-statistic improved to 0.721 (95% CI, 0.711-0.730) when the Medicare cohort was used to recalibrate the ß coefficients. Model discrimination improved further when comorbidity was expressed as the COmorbidity Point Score 2 (C-statistic, 0.737; 95% CI, 0.728-0.747; p < 0.001) or the Elixhauser index (C-statistic, 0.748; 95% CI, 0.739-0.757) instead of the Charlson index. Adding historical claims data increased the number of comorbidities identified, but did not enhance model performance. CONCLUSIONS: Modification of the Acute Organ Failure Score resulted in good model discrimination among a diverse population regardless of comorbidity measure used. This study expands the use of the Acute Organ Failure Score for risk adjustment in ICU research and outcomes reporting using standard administrative data.

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

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

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

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

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

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

Comparing Smoking Cessation Interventions among Underserved Patients Referred for Lung Cancer Screening: A Pragmatic Trial Protocol.

Smoking burdens are greatest among underserved patients. Lung cancer screening (LCS) reduces mortality among individuals at risk for smoking-associated lung cancer. Although LCS programs must offer smoking cessation support, the interventions that best promote cessation among underserved patients in this setting are unknown. This stakeholder-engaged, pragmatic randomized clinical trial will compare the effectiveness of four interventions promoting smoking cessation among underserved patients referred for LCS. By using an additive study design, all four arms provide standard "ask-advise-refer" care. Arm 2 adds free or subsidized pharmacologic cessation aids, arm 3 adds financial incentives up to $600 for cessation, and arm 4 adds a mobile device-delivered episodic future thinking tool to promote attention to long-term health goals. We hypothesize that smoking abstinence rates will be higher with the addition of each intervention when compared with arm 1. We will enroll 3,200 adults with LCS orders at four U.S. health systems. Eligible patients include those who smoke at least one cigarette daily and self-identify as a member of an underserved group (i.e., is Black or Latinx, is a rural resident, completed a high school education or less, and/or has a household income <200% of the federal poverty line). The primary outcome is biochemically confirmed smoking abstinence sustained through 6 months. Secondary outcomes include abstinence sustained through 12 months, other smoking-related clinical outcomes, and patient-reported outcomes. This pragmatic randomized clinical trial will identify the most effective smoking cessation strategies that LCS programs can implement to reduce smoking burdens affecting underserved populations. Clinical trial registered with clinicaltrials.gov (NCT04798664). Date of registration: March 12, 2021. Date of trial launch: May 17, 2021.

Structural dynamics in floppy systems: ultrafast conformeric motions in Rydberg-excited triethylamine.

Rotations about its three carbon-nitrogen bonds give triethylamine a complex, 3-dimensional potential energy landscape of conformeric structures. Electronic excitation to Rydberg states prepares the molecule in a high-energy, nonequilibrium distribution of such conformers, initiating ultrafast transitions between them. Time-resolved Rydberg electron binding energy spectra, observed using photoionization-photoelectron spectroscopy with ultrashort laser pulses, reveal these time-evolving structures. The time-dependent structural fingerprint spectra are assigned with the aid of a computational analysis of the potential energy landscape. Upon 209 nm electronic excitation to the 3p Rydberg state, triethylamine decays to 3s with a 200 fs time constant. The initially prepared conformer reacts to a mixture of structures with a time constant of 232 fs and settles into a final geometry distribution on a further subpicosecond time scale. The binding energy of the Rydberg electron is found to be an important determinant of the conformeric energy landscape.

Influence of bedspacing on outcomes of hospitalised medicine service patients: a retrospective cohort study.

BACKGROUND: Specialty wards cohort hospitalised patients to improve outcomes and lower costs. When demand exceeds capacity, patients overflow and are "bedspaced" to alternate wards. Some studies have demonstrated that bedspacing among medicine service patients is associated with adverse patient-centred outcomes, however, results have been inconsistent and have primarily been performed within national health systems. The objective of this study was to assess the association of bedspacing with patient-centred outcomes among United States patients admitted to general medicine services. METHODS: We performed a retrospective cohort study of internal medicine, family medicine and geriatric service patients who were bedspaced vs cohorted for the entirety of their hospital stay within three large, urban United States hospitals (quaternary referral centre, tertiary referral centre and community hospital, with different patient demographics and case-mixes) in 2014 and 2015. We performed quantile regression to determine differences in length of stay (LOS) between bedspaced vs cohorted patients and logistic regression for in-hospital mortality and discharge to home. RESULTS: Among 18 802 patients in 33 wards, 6119 (33%) patients were bedspaced. Bedspaced patients had significantly longer LOS compared with cohorted patients at the 25th (0.1 days, 95% CI: 0.05 to 0.2, p=0.001), 50th (0.2 days, 95% CI: 0.1 to 0.3, p=0.003) and 75th (0.3 days, 95% CI: 0.2 to 0.5, p<0.001) percentiles; and no statistically significant differences in odds of mortality (OR=0.9, 95% CI: 0.6 to 1.3, p=0.5) or discharge to home (OR=0.9, 95% CI: 0.9 to 1.0, p=0.06) in adjusted analyses. CONCLUSION: Bedspacing is associated with adverse patient-centred outcomes. Future work is needed to confirm these findings, understand mechanisms contributing to adverse outcomes and identify factors that mitigate these adverse effects in order to provide high-value, patient-centred care to hospitalised patients.

Effectiveness and Ethics of Incentives for Research Participation: 2 Randomized Clinical Trials.

Importance: Incentivizing research participation is controversial and variably regulated because of uncertainty regarding whether financial incentives serve as undue inducements by diminishing peoples' sensitivity to research risks or unjust inducements by preferentially increasing enrollment among underserved individuals. Objective: To determine whether incentives improve enrollment in real randomized clinical trials (RCTs) or serve as undue or unjust inducements. Design, Setting, and Participants: Two RCTs of incentives that were embedded in 2 parent RCTs, 1 comparing smoking cessation interventions (conducted at smoking cessation clinics in 2 health systems) and 1 evaluating an ambulation intervention (conducted across wards of the Hospital of the University of Pennsylvania) included all persons eligible for the parent trials who did not have prior knowledge of the incentives trials. Recruitment occurred from September 2017 to August 2019 for the smoking trial and January 2018 through May 2019 for the ambulation trial; data were analyzed from January 2020 to July 2020. Interventions: Patients were randomly assigned to incentives of $0, $200, or $500 for participating in the smoking cessation trial and $0, $100, or $300 for the ambulation trial. Main Outcomes and Measures: The primary outcome of each incentive trial was the proportion of people assigned to each recruitment strategy that consented to participate. Each trial was powered to test the hypotheses that incentives served neither as undue inducements (based on the interaction between incentive size and perceived research risk, as measured using a 10-point scale, on the primary outcome), nor unjust inducements (based on the interaction between incentive size and participants' self-reported income). Noninferiority methods were used to test whether the data were compatible with these 2 effects of incentives and superiority methods to compare the primary and other secondary outcomes. Results: There were a total of 654 participants (327 women [50.0%]; mean [SD] age, 50.6 [12.1] years; 394 Black/African American [60.2%], 214 White [32.7%], and 24 multiracial individuals [3.7%]) in the smoking trial, and 642 participants (364 women [56.7%]; mean [SD] age, 46.7 [15.6] years; 224 Black/African American [34.9%], 335 White [52.2%], and 5 multiracial individuals [0.8%]) in the ambulation trial. Incentives significantly increased consent rates among those in the smoking trial in 47 of 216 (21.8%), 78 of 217 (35.9%), and 104 of 221 (47.1%) in the $0, $200, and $500 groups, respectively (adjusted odds ratio [aOR] for each increase in incentive, 1.70; 95% CI, 1.34-2.17; P < .001). Incentives did not increase consent among those in the ambulation trial: 98 of 216 (45.4%), 102 of 212 (48.1%), and 92 of 214 (43.0%) in the $0, $100, and $300 groups, respectively (aOR, 0.88; 95% CI, 0.64-1.22; P = .45). In neither trial was there evidence of undue or unjust inducement (upper confidence limits of ORs for undue inducement, 1.15 and 0.99; P < .001 showing noninferiority; upper confidence limits of ORs for unjust inducement, 1.21 and 1.26; P = .01 and P < .001, respectively). There were no significant effects of incentive size on the secondary outcomes in either trial, including time spent reviewing the risk sections of consent forms, perceived research risks, trial understanding, perceived coercion, or therapeutic misconceptions. Conclusions and Relevance: In these 2 randomized clinical trials, financial incentives increased trial enrollment in 1 of 2 trials and did not produce undue or unjust inducement or other unintended consequences in either trial. Trial Registration: ClinicalTrials.gov Identifier: NCT02697799.

Prognosticating Outcomes and Nudging Decisions with Electronic Records in the Intensive Care Unit Trial Protocol.

Expert recommendations to discuss prognosis and offer palliative options for critically ill patients at high risk of death are variably heeded by intensive care unit (ICU) clinicians. How to best promote such communication to avoid potentially unwanted aggressive care is unknown. The PONDER-ICU (Prognosticating Outcomes and Nudging Decisions with Electronic Records in the ICU) study is a 33-month pragmatic, stepped-wedge cluster randomized trial testing the effectiveness of two electronic health record (EHR) interventions designed to increase ICU clinicians' engagement of critically ill patients at high risk of death and their caregivers in discussions about all treatment options, including care focused on comfort. We hypothesize that the quality of care and patient-centered outcomes can be improved by requiring ICU clinicians to document a functional prognostic estimate (intervention A) and/or to provide justification if they have not offered patients the option of comfort-focused care (intervention B). The trial enrolls all adult patients admitted to 17 ICUs in 10 hospitals in North Carolina with a preexisting life-limiting illness and acute respiratory failure requiring continuous mechanical ventilation for at least 48 hours. Eligibility is determined using a validated algorithm in the EHR. The sequence in which hospitals transition from usual care (control), to intervention A or B and then to combined interventions A + B, is randomly assigned. The primary outcome is hospital length of stay. Secondary outcomes include other clinical outcomes, palliative care process measures, and nurse-assessed quality of dying and death.Clinical trial registered with clinicaltrials.gov (NCT03139838).

Association of an Emergency Department-embedded Critical Care Unit with Hospital Outcomes and Intensive Care Unit Use.

Rationale: A small but growing number of hospitals are experimenting with emergency department-embedded critical care units (CCUs) in an effort to improve the quality of care for critically ill patients with sepsis and acute respiratory failure (ARF).Objectives: To evaluate the potential impact of an emergency department-embedded CCU at the Hospital of the University of Pennsylvania among patients with sepsis and ARF admitted from the emergency department to a medical ward or intensive care unit (ICU) from January 2016 to December 2017.Methods: The exposure was eligibility for admission to the emergency department-embedded CCU, which was defined as meeting a clinical definition for sepsis or ARF and admission to the emergency department during the intervention period on a weekday. The primary outcome was hospital length of stay (LOS); secondary outcomes included total emergency department plus ICU LOS, hospital survival, direct admission to the ICU, and unplanned ICU admission. Primary interrupted time series analyses were performed using ordinary least squares regression comparing monthly means. Secondary retrospective cohort and before-after analyses used multivariable Cox proportional hazard and logistic regression.Results: In the baseline and intervention periods, 3,897 patients met the inclusion criteria for sepsis and 1,865 patients met the criteria for ARF. Among patients admitted with sepsis, opening of the emergency department-embedded CCU was not associated with hospital LOS (ß = -1.82 d; 95% confidence interval [CI], -4.50 to 0.87; P = 0.17 for the first month after emergency department-embedded CCU opening compared with baseline; ß = -0.26 d; 95% CI, -0.58 to 0.06; P = 0.10 for subsequent months). Among patients admitted with ARF, the emergency department-embedded CCU was not associated with a significant change in hospital LOS for the first month after emergency department-embedded CCU opening (ß = -3.25 d; 95% CI, -7.86 to 1.36; P = 0.15) but was associated with a 0.64 d/mo shorter hospital LOS for subsequent months (ß = -0.64 d; 95% CI, -1.12 to -0.17; P = 0.01). This result persisted among higher acuity patients requiring ventilatory support but was not supported by alternative analytic approaches. Among patients admitted with sepsis who did not require mechanical ventilation or vasopressors in the emergency department, the emergency department-embedded CCU was associated with an initial 9.9% reduction in direct ICU admissions in the first month (ß = -0.099; 95% CI, -0.153 to -0.044; P = 0.002), followed by a 1.1% per month increase back toward baseline in subsequent months (ß = 0.011; 95% CI, 0.003-0.019; P = 0.009). This relationship was supported by alternative analytic approaches and was not seen in ARF. No associations with emergency department plus ICU LOS, hospital survival, or unplanned ICU admission were observed among patients with sepsis or ARF.Conclusions: The emergency department-embedded CCU was not associated with clinical outcomes among patients admitted with sepsis or ARF. Among less sick patients with sepsis, the emergency department-embedded CCU was initially associated with reduced rates of direct ICU admission from the emergency department. Additional research is necessary to further evaluate the impact and utility of the emergency department-embedded CCU model.

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units.

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

Effect of Default Options in Advance Directives on Hospital-Free Days and Care Choices Among Seriously Ill Patients: A Randomized Clinical Trial.

Importance: There is limited evidence regarding how patients make choices in advance directives (ADs) or whether these choices influence subsequent care. Objective: To examine whether default options in ADs influence care choices and clinical outcomes. Design, Setting, and Participants: This randomized clinical trial included 515 patients who met criteria for having serious illness and agreed to participate. Patients were enrolled at 20 outpatient clinics affiliated with the University of Pennsylvania Health System and the University of Pittsburgh Medical Center from February 2014 to April 2016 and had a median follow-up of 18 months. Data analysis was conducted from November 2018 to April 2019. Interventions: Patients were randomly assigned to complete 1 of the 3 following ADs: (1) a comfort-promoting plan of care and nonreceipt of potentially life-sustaining therapies were selected by default (comfort AD), (2) a life-extending plan of care and receipt of potentially life-sustaining therapies were selected by default (life-extending AD), or (3) no choices were preselected (standard AD). Main Outcomes and Measures: This trial was powered to rule out a reduction in hospital-free days in the intervention groups. Secondary outcomes included choices in ADs for an overall comfort-oriented approach to care, choices to forgo 4 forms of life support, patients' quality of life, decision conflict, place of death, admissions to hospitals and intensive care units, and costs of inpatient care. Results: Among 515 patients randomized, 10 withdrew consent and 13 were later found to be ineligible, leaving 492 (95.5%) in the modified intention-to-treat (mITT) sample (median [interquartile range] age, 63 [56-70] years; 279 [56.7%] men; 122 [24.8%] black; 363 [73.8%] with cancer). Of these, 264 (53.7%) returned legally valid ADs and were debriefed about their assigned intervention. Among these, patients completing comfort ADs were more likely to choose comfort care (54 of 85 [63.5%]) than those returning standard ADs (45 of 91 [49.5%]) or life-extending ADs (33 of 88 [37.5%]) (P = .001). Among 492 patients in the mITT sample, 57 of 168 patients [33.9%] who completed the comfort AD, 47 of 165 patients [28.5%] who completed the standard AD, and 35 of 159 patients [22.0%] who completed the life-extending AD chose comfort care (P = .02), with patients not returning ADs coded as not selecting comfort care. In mITT analyses, median (interquartile range) hospital-free days among 168 patients assigned to comfort ADs and 159 patients assigned to life-extending default ADs were each noninferior to those among 165 patients assigned to standard ADs (standard AD: 486 [306-717] days; comfort AD: 554 [296-833] days; rate ratio, 1.05; 95% CI, 0.90-1.23; P < .001; life-extending AD: 550 [325-783] days; rate ratio, 1.03; 95% CI, 0.88-1.20; P < .001). There were no differences among groups in other secondary outcomes. Conclusions and Relevance: In this randomized clinical trial, default options in ADs altered the choices seriously ill patients made regarding their future care without changing clinical outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT02017548.

Randomized evaluation of trial acceptability by INcentive (RETAIN): Study protocol for two embedded randomized controlled trials.

INTRODUCTION: The most common and conceptually sound ethical concerns with financial incentives for research participation are that they may (1) represent undue inducements by blunting peoples' perceptions of research risks, thereby preventing fully informed consent; or (2) represent unjust inducements by encouraging enrollment preferentially among the poor. Neither of these concerns has been shown to manifest in studies testing the effects of incentives on decisions to participate in hypothetical randomized clinical trials (RCTs), but neither has been assessed in real RCTs. METHODS AND ANALYSES: We are conducting randomized trials of real incentives embedded within two parent RCTs. In each of two trials conducted in parallel, we are randomizing 576 participants to one of three incentive groups. Following preliminary determination of patients' eligibility in the parent RCT, we assess patients' research attitudes, demographic characteristics, perceived research risks, time spent reviewing consent documents, ability to distinguish research from patient care, and comprehension of key trial features. These quantitative assessments will be supplemented by semi-structured interviews for a selected group of participants that more deeply explore patients' motivations for trial participation. The trials are each designed to have adequate power to rule out undue and unjust inducement. We are also exploring potential benefits of incentives, including possible increased attention to research risks and cost-effectiveness.