Classification of Documented Goals of Care Among Hospitalized Patients with High Mortality Risk: a Mixed-Methods Feasibility Study.

BACKGROUND: The ability to classify patients' goals of care (GOC) from clinical documentation would facilitate serious illness communication quality improvement efforts and pragmatic measurement of goal-concordant care. Feasibility of this approach remains unknown. OBJECTIVE: To evaluate the feasibility of classifying patients' GOC from clinical documentation in the electronic health record (EHR), describe the frequency and patterns of changes in patients' goals over time, and identify barriers to reliable goal classification. DESIGN: Retrospective, mixed-methods chart review study. PARTICIPANTS: Adults with high (50-74%) and very high (≥ 75%) 6-month mortality risk admitted to three urban hospitals. MAIN MEASURES: Two physician coders independently reviewed EHR notes from 6 months before through 6 months after admission to identify documented GOC discussions and classify GOC. GOC were classified into one of four prespecified categories: (1) comfort-focused, (2) maintain or improve function, (3) life extension, or (4) unclear. Coder interrater reliability was assessed using kappa statistics. Barriers to classifying GOC were assessed using qualitative content analysis. KEY RESULTS: Among 85 of 109 (78%) patients, 338 GOC discussions were documented. Inter-rater reliability was substantial (75% interrater agreement; Cohen's kappa = 0.67; 95% CI, 0.60-0.73). Patients' initial documented goal was most frequently "life extension" (N = 37, 44%), followed by "maintain or improve function" (N = 28, 33%), "unclear" (N = 17, 20%), and "comfort-focused" (N = 3, 4%). Among the 66 patients whose goals' classification changed over time, most changed to "comfort-focused" goals (N = 49, 74%). Primary reasons for unclear goals were the observation of concurrently held or conditional goals, patient and family uncertainty, and limited documentation. CONCLUSIONS: Clinical notes in the EHR can be used to reliably classify patients' GOC into discrete, clinically germane categories. This work motivates future research to use natural language models to promote scalability of the approach in clinical care and serious illness research.

Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.

Measuring Quality-weighted Hospital-Free-Days in Acute Respiratory Failure: A Modified Delphi Study.

RATIONALE: Hospital-free days (HFDs), a measure of the number of days alive spent outside the hospital, is increasingly used as an endpoint in studies of patients with acute respiratory failure (ARF) or other critical and serious illnesses. Current approaches to measuring HFDs do not account for decrements in functional status or quality of life that ARF survivors and family members value. OBJECTIVES: To develop an acceptable approach to measure quality-weighted HFDs using patient-reported outcomes. METHODS: We conducted a 4-round modified Delphi among ARF experts - those with lived or professional experience. Experts rated survivorship domains, instrument and data collection characteristics, and methods to translate responses into quality-weighted HFDs. The consensus threshold was that >70% of respondents rated an item "Totally Acceptable" or "Acceptable" and <15% of respondents rated the item "Totally Unacceptable", "Unacceptable", or "Slightly Unacceptable." RESULTS: Fifty-seven experts participated in Round 1. Response rates were 82-93% for subsequent rounds. Priority survivorship domains were physical function and health-related quality of life. Participants reached consensus that data collection during ARF recovery should take fewer than 15 minutes per assessment, allow for surrogate completion when patients are unable, and continue for at least 24 months of follow-up. Using the EuroQol-5 Dimensions (EQ-5D) to quality-weight HFDs met consensus criteria for acceptability. A majority of panelists preferred quality-weighted HFDs to unweighted HFDs or survival for use in future ARF studies. CONCLUSIONS: Quality-weighting HFDs using patient and/or surrogate responses to the EQ-5D captured stakeholder priorities and was acceptable to this Delphi panel.

How do surrogates make treatment decisions for patients with dementia: An experimental survey study.

Despite the growing need for surrogate decision-making for older adults, little is known about how surrogates make decisions and whether advance directives would change decision-making. We conducted a nationally representative experimental survey that cross-randomized cognitive impairment, gender, and characteristics of advance care planning among hospitalized older adults through a series of vignettes. Our study yielded three main findings: first, respondents were much less likely to recommend life-sustaining treatments for patients with dementia, especially after personal exposure. Second, respondents were more likely to ignore patient preferences for life-extending treatment when the patient had dementia, and choose unwanted life-extending treatments for patients without dementia. Third, in scenarios where the patient's wishes were unclear, respondents were more likely to choose treatments that matched their own preferences. These findings underscore the need for improved communication and decision-making processes for patients with cognitive impairment and highlight the importance of choosing a surrogate decision-maker with similar treatment preferences.

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.

A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials.

In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.

Integrating Cost into Shared Decision-Making for Heart Failure with Reduced Ejection Fraction (POCKET-COST-HF): A Trial Providing Out-of-Pocket Costs for Heart Failure Medications during Clinical Encounters.

BACKGROUND: Evidence-based medical therapy for heart failure with reduced ejection fraction (HFrEF) often entails substantial out-of-pocket costs that can vary appreciably between patients. This has raised concerns regarding financial toxicity, equity, and adherence to medical therapy. In spite of these concerns, cost discussions in the HFrEF population appear to be rare, partly because out-of-pocket costs are generally unavailable during clinical encounters. In this trial, out-of-pocket cost information is given to patients and clinicians during outpatient encounters with the aim to assess the impact of providing this information on medication discussions and decisions. HYPOTHESIS: Cost-informed decision-making will be facilitated by providing access to patient-specific out-of-pocket cost estimates at the time of clinical encounter. DESIGN: Integrating Cost into Shared Decision-Making for Heart Failure with Reduced Ejection Fraction (POCKET-COST-HF) is a multicenter trial based at Emory Healthcare and University of Colorado Health. Adapting an existing patient activation tool from the EPIC-HF trial, patients and clinicians are presented a checklist with medications approved for treatment of HFrEF with or without patient-specific out-of-pocket costs (obtained from a financial navigation firm). Clinical encounters are audio-recorded, and patients are surveyed about their experience. The trial utilizes a stepped-wedge cluster randomized design, allowing for each site to enroll control and intervention group patients while minimizing contamination of the control arm. DISCUSSION: This trial will elucidate the potential impact of robust cost disclosure efforts and key information regarding patient and clinician perspectives related to cost and cost communication. It also will reveal important challenges associated with providing out-of-pocket costs for medications during clinical encounters. Acquiring medication costs for this trial requires an involved process and outsourcing of work. In addition, costs may change throughout the year, raising questions regarding what specific information is most valuable. These data will represent an important step towards understanding the role of integrating cost discussions into heart failure care. GOV IDENTIFIER: NCT04793880.

Time-Limited Trials for Patients With Critical Illness: A Review of the Literature.

TOPIC IMPORTANCE: Since the 1990s, time-limited trials have been described as an approach to navigate uncertain benefits and limits of life-sustaining therapies in patients with critical illness. In this review, we aim to synthesize the evidence on time-limited trials in critical care, establish what is known, and highlight important knowledge gaps. REVIEW FINDINGS: We identified 18 empirical studies and 15 ethical analyses about time-limited trials in patients with critical illness. Observational studies suggest time-limited trials are part of current practice in ICUs in the United States, but their use varies according to unit and physician factors. Some ICU physicians are familiar with, endorse, and have participated in time-limited trials, and some older adults appear to favor time-limited trial strategies over indefinite life-sustaining therapy or care immediately focused on comfort. When time-limited trials are used, they are often implemented incompletely and challenged by systematic barriers (eg, continually rotating ICU staff). Predictive modeling studies support prevailing clinical wisdom that prognostic uncertainty decreases over time in the ICU for some patients. One study prospectively comparing usual ICU care with an intervention designed to support time-limited trials yielded promising preliminary results. Ethical analyses describe time-limited trials as a pragmatic approach within the longstanding discussion about withholding and withdrawing life-sustaining therapies. SUMMARY: Time-limited trials are endorsed by physicians, align with the priorities of some older adults, and are part of current practice. Substantial efforts are needed to test their impact on patient-centered outcomes, improve their implementation, and maximize their potential benefit.

Accuracy of Expected Symptoms and Subsequent Quality of Life Measures Among Adults With COPD.

Importance: Patients' expectations for future health guide their decisions and enable them to prepare, adapt, and cope. However, little is known about how inaccurate expectations may affect patients' illness outcomes. Objective: To assess the association between patients' expectation inaccuracies and health-related quality of life. Design, Setting, and Participants: This cohort study of patients with severe chronic obstructive pulmonary disease (COPD) was conducted from 2017 to 2021, which included a 24-month follow-up period. Eligible participants received outpatient primary care at pulmonary clinics of a single large US health system. Data were analyzed between 2021 and 2023. Exposure: Expectation accuracy, measured by comparing patients' self-reported expectations of their symptom burden with their actual physical and emotional symptoms 3, 12, and 24 months in the future. Main Outcome and Measure: Health-related quality of life, measured by the St George's Respiratory Questionnaire-COPD at 3, 12, and 24 months. Results: A total of 207 participants were included (median age, 65.5 years [range, 42.0-86.0 years]; 120 women [58.0%]; 118 Black [57.0%], 79 White [38.2%]). The consent rate among approached patients was 80.0%. Most patients reported no or only limited discussions of future health and symptom burdens with their clinicians. Across physical and emotional symptoms and all 3 time points, patients' expectations were more optimistic than their experiences. There were no consistent patterns of measured demographic or behavioral characteristics associated with expectation accuracy. Regression models revealed that overoptimistic expectations of future burdens of dyspnea (linear regression estimate, 4.68; 95% CI, 2.68 to 6.68) and negative emotions (linear regression estimate, -3.04; 95% CI, -4.78 to 1.29) were associated with lower health-related quality of life at 3 months after adjustment for baseline health-related quality of life, forced expiratory volume over 1 second, and interval clinical events (P < .001 for both). Similar patterns were observed at 12 months (dyspnea: linear regression estimate, 2.41; 95% CI, 0.45 to 4.37) and 24 months (negative emotions: linear regression estimate, -2.39; 95% CI, -4.67 to 0.12; dyspnea: linear regression estimate, 3.21; 95% CI, 0.82 to 5.60), although there was no statistically significant association between expectation of negative emotions and quality of life at 12 months. Conclusions and Relevance: In this cohort study of patients with COPD, we found that patients are overoptimistic in their expectations about future negative symptom burdens, and such inaccuracies were independently associated with worse well-being over time. Developing and implementing strategies to improve patients' symptom expectations may improve patient-centered outcomes.

A vignette-based evaluation of ChatGPT's ability to provide appropriate and equitable medical advice across care contexts.

ChatGPT is a large language model trained on text corpora and reinforced with human supervision. Because ChatGPT can provide human-like responses to complex questions, it could become an easily accessible source of medical advice for patients. However, its ability to answer medical questions appropriately and equitably remains unknown. We presented ChatGPT with 96 advice-seeking vignettes that varied across clinical contexts, medical histories, and social characteristics. We analyzed responses for clinical appropriateness by concordance with guidelines, recommendation type, and consideration of social factors. Ninety-three (97%) responses were appropriate and did not explicitly violate clinical guidelines. Recommendations in response to advice-seeking questions were completely absent (N = 34, 35%), general (N = 18, 18%), or specific (N = 44, 46%). 53 (55%) explicitly considered social factors like race or insurance status, which in some cases changed clinical recommendations. ChatGPT consistently provided background information in response to medical questions but did not reliably offer appropriate and personalized medical advice.

Decision Making for Patients With Severe Dementia Versus Normal Cognition Near the End of Life.

Background and Objectives: The clinical progression of severe dementia frequently leads to situations where surrogate decision makers must quickly make choices about potentially burdensome treatments that offer limited clinical benefit. We examined whether the number of decision makers and their access to advance directives were related to treatment choice for patients with severe dementia in comparison to those with normal cognition. Research Design and Methods: We retrospectively linked survey responses about end-of-life treatment decisions to Medicare claims for Health and Retirement Study respondents dying between 2002 and 2015 whose next-of-kin reported a need for surrogate decision making. We estimated multivariable logistic regression models to study measures of aggressive care in the last 6 months of life; in-hospital death, burdensome transfers, and burdensome treatments. Results: Compared to patients who were cognitively normal near the end of life (n = 1 198), patients with severe dementia (n = 722) were less likely to experience burdensome treatments (18% [95% confidence interval {CI} 14-21] vs 32% [95% CI 29-35]), burdensome transfers (20% [95% CI 17-24] vs 30% [95% CI 27-33]), and in-hospital death (24% [95% CI 20-28] vs 30% [95% CI 26-33]) when surrogates were involved. Rates of burdensome treatments, transfers, or in-hospital death for decedents with severe dementia did not vary with single versus multiple decision makers or when decision makers were informed by advance directives. However, among decedents with normal cognition, a single decision maker informed by an advance directive was associated with the lowest rates of burdensome treatments and in-hospital death. Discussion and Implications: Surrogate decision makers made similar choices around end-of-life care for patients with severe dementia regardless of the number of decision makers and availability of advance directives. However, both advance directives and single decision makers were associated with less aggressive care for cognitively normal decedents.

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.

Global, Race-Neutral Reference Equations and Pulmonary Function Test Interpretation.

Importance: Race and ethnicity are routinely used to inform pulmonary function test (PFT) interpretation. However, there is no biological justification for such use, and it may reinforce health disparities. Objective: To compare the PFT interpretations produced with race-neutral and race-specific equations. Design, Setting, and Participants: In this cross-sectional study, race-neutral reference equations recently developed by the Global Lung Function Initiative (GLI) were used to interpret PFTs performed at an academic medical center between January 2010 and December 2020. The interpretations produced with these race-neutral reference equations were compared with those produced using the race and ethnicity-specific reference equations produced by GLI in 2012. The analysis was conducted from April to October 2022. Main Outcomes and Measures: The primary outcomes were differences in the percentage of obstructive, restrictive, mixed, and nonspecific lung function impairments identified using the 2 sets of reference equations. Secondary outcomes were differences in severity of these impairments. Results: PFTs were interpreted from 2722 Black (686 men [25.4%]; mean [SD] age, 51.8 [13.9] years) and 5709 White (2654 men [46.5%]; mean [SD] age, 56.4 [14.3] years) individuals. Among Black individuals, replacing the race-specific reference equations with the race-neutral reference equations was associated with an increase in the prevalence of restriction from 26.8% (95% CI, 25.2%-28.5%) to 37.5% (95% CI, 35.7%-39.3%) and of a nonspecific pattern of impairment from 3.2% (95% CI, 2.5%- 3.8%) to 6.5% (95% CI, 5.6%-7.4%) and no significant change in the prevalence of obstruction (19.9% [95% CI, 18.4%-21.4%] vs 19.5% [95% CI, 18.0%-21.0%]). Among White individuals, replacing the race-specific reference equations with the race-neutral reference equations was associated with a decrease in the prevalence of restriction from 22.6% (95% CI, 21.5%-23.6%) to 18.0% (95% CI, 17.0%-19.0%), a decrease in the prevalence of a nonspecific pattern of impairment from 8.7% (95% CI, 7.9%-9.4%) to 4.0% (95% CI, 3.5%-4.5%), and no significant change in the percentage with obstruction from 23.9% (95% CI, 22.8%-25.1%) to 25.1% (95% CI, 23.9%- 26.2%). The race-neutral reference equations were associated with an increase in severity in 22.8% (95% CI, 21.2%-24.4%) of Black individuals and a decrease in severity in 19.3% (95% CI, 18.2%-20.3%) of White individuals vs the race-specific reference equations. Conclusions and Relevance: In this cross-sectional study, the use of race-neutral reference equations to interpret PFTs resulted in a significant increase in the number of Black individuals with respiratory impairments along with a significant increase in the severity of the identified impairments. More work is needed to quantify the effect these reference equations would have on diagnosis, referral, and treatment patterns.

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

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

Stakeholder Perspectives on Minimum Clinically Important Difference and Noninferiority Margin for Hospital-Free Days to Assess Interventions.

This survey study examined perceptions of patients, caregivers and health care professionals on the number of hospital-free days required for detection of a minimum clinically important difference or noninferiority margin of new interventions.

The Conditional Effects of Multimorbidity on Operative Versus Nonoperative Management of Emergency General Surgery Conditions: A Retrospective Observational Study Using an Instrumental Variable Analysis.

OBJECTIVE: To understand how multimorbidity impacts operative versus nonoperative management of emergency general surgery (EGS) conditions. BACKGROUND: EGS is a heterogenous field, encompassing operative and nonoperative treatment options. Decision-making is particularly complex for older patients with multimorbidity. METHODS: Using an instrumental variable approach with near-far matching, this national, retrospective observational cohort study of Medicare beneficiaries examines the conditional effects of multimorbidity, defined using qualifying comorbidity sets, on operative versus nonoperative management of EGS conditions. RESULTS: Of 507,667 patients with EGS conditions, 155,493 (30.6%) received an operation. Overall, 278,836 (54.9%) were multimorbid. After adjustment, multimorbidity significantly increased the risk of in-hospital mortality associated with operative management for general abdominal patients (+9.8%; P = 0.002) and upper gastrointestinal patients (+19.9%, P < 0.001) and the risk of 30-day mortality (+27.7%, P < 0.001) and nonroutine discharge (+21.8%, P = 0.007) associated with operative management for upper gastrointestinal patients. Regardless of multimorbidity status, operative management was associated with a higher risk of in-hospital mortality among colorectal patients (multimorbid: + 12%, P < 0.001; nonmultimorbid: +4%, P = 0.003), higher risk of nonroutine discharge among colorectal (multimorbid: +42.3%, P < 0.001; nonmultimorbid: +55.1%, P < 0.001) and intestinal obstruction patients (multimorbid: +14.6%, P = 0.001; nonmultimorbid: +14.8%, P = 0.001), and lower risk of nonroutine discharge (multimorbid: -11.5%, P < 0.001; nonmultimorbid: -11.9%, P < 0.001) and 30-day readmissions (multimorbid: -8.2%, P = 0.002; nonmultimorbid: -9.7%, P < 0.001) among hepatobiliary patients. CONCLUSIONS: The effects of multimorbidity on operative versus nonoperative management varied by EGS condition category. Physicians and patients should have honest conversations about the expected risks and benefits of treatment options, and future investigations should aim to understand the optimal management of multimorbid EGS patients.

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).

Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis.

OBJECTIVE: Evaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socioeconomic status (SES), which may vary due to social forces (eg, racism) that shape health, healthcare and health data. DESIGN: Retrospective evaluation of prediction model. SETTING: Three urban hospitals within a single health system. PARTICIPANTS: All patients ≥18 years admitted between 1 January and 31 December 2017, excluding observation, obstetric, rehabilitation and hospice (n=58 464 encounters, 41 327 patients). MAIN OUTCOME MEASURES: General performance metrics (c-statistic, integrated calibration index (ICI), Brier Score) and additional measures relevant to health equity (accuracy, false positive rate (FPR), false negative rate (FNR)). RESULTS: For black versus non-Hispanic white patients, the model's accuracy was higher (0.051, 95% CI 0.044 to 0.059), FPR lower (-0.060, 95% CI -0.067 to -0.052) and FNR higher (0.049, 95% CI 0.023 to 0.078). A similar pattern was observed among patients who were Hispanic, younger, with Medicaid/missing insurance, or living in low SES zip codes. No consistent differences emerged in c-statistic, ICI or Brier Score. Younger age had the second-largest effect size in the mortality prediction model, and there were large standardised group differences in age (eg, 0.32 for non-Hispanic white versus black patients), suggesting age may contribute to systematic differences in the predicted probabilities between groups. CONCLUSIONS: An EHR-based mortality risk model was less likely to identify some marginalised patients as potentially benefiting from palliative care, with younger age pinpointed as a possible mechanism. Evaluating predictive performance is a critical preliminary step in addressing algorithmic inequities in healthcare, which must also include evaluating clinical impact, and governance and regulatory structures for oversight, monitoring and accountability.