The Effect of a Post-Bronchodilator FEV1/FVC < 0.7 on COPD Diagnosis and Treatment: A Regression Discontinuity Design.

Background: Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines recommend the diagnosis of chronic obstructive pulmonary disease (COPD) only in patients with a post-bronchodilator forced expiratory volume in 1 second to forced vital capacity ratio (FEV1/FVC) less than 0.7. However the impact of this recommendation on clinical practice is unknown. Research Question: What is the effect of a documented post-bronchodilator FEV1/FVC < 0.7 on the diagnosis and treatment of COPD? Study Design and Methods: We used a national electronic health record database to identify clinical encounters between 2007 to 2022 with patients 18 years of age and older in which a post-bronchodilator FEV1/FVC value was documented. An encounter was associated with a COPD diagnosis if a diagnostic code for COPD was assigned, and was associated with COPD treatment if a prescription for a medication commonly used to treat COPD was filled within 90 days. We used a regression discontinuity design to measure the effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment. Results: Among 27 817 clinical encounters, involving 18 991 patients, a post-bronchodilator FEV1/FVC < 0.7 was present in 14 876 (53.4%). The presence of a documented post-bronchodilator FEV1/FVC < 0.7 had a small effect on the probability of a COPD diagnosis, increasing by 6.0% (95% confidence interval [CI] 1.1% to 10.9%) from 38.0% just above the 0.7 cutoff to 44.0% just below this cutoff. The presence of a documented post-bronchodilator FEV1/FVC had no effect on the probability of COPD treatment (-2.1%, 95% CI -7.2% to 3.0%). Interpretation: The presence of a documented post-bronchodilator FEV1/FVC < 0.7 has only a small effect on the probability that a clinician will make a guideline-concordant diagnosis of COPD and has no effect on corresponding treatment decisions.

"What choice do we have?" Reactive and proactive decision-making for aging in place with dementia.

BACKGROUND: Promoting options for aging in place (AIP) has broad appeal to policymakers and professionals providing services to persons living with dementia (PWD). However, the benefits or burdens of AIP likely vary among individuals and families. We sought to describe factors influencing decision-making to age in place versus seek a higher level of residential care for PWD. METHODS: A qualitative study was undertaken as part of a larger mixed-methods study utilizing semi-structured interviews with PWD, family care partners, and dementia clinicians. Interview transcripts were analyzed using qualitative content analysis with constant comparison. Sample size was determined by thematic saturation within subgroups. RESULTS: We conducted 74 interviews among 14 PWD, 36 care partners, and 24 clinicians. Preferences for AIP were driven by (1) desire to preserve independence, (2) a sense that the "best care" is delivered by loved ones and in a familiar environment, (3) distrust and fear of care facilities, and (4) caregiver guilt. PWD and care partners frequently considered moving from home as a "last resort" and wanted to avoid planning for future care needs. Many decisions to move were reactive and triggered by patient safety events, physical dependency, or the loss of caregiver. Proactive decision-making was facilitated by (1) prior experience witnessing the challenges of caring for a person with advanced dementia in the home; and (2) having substantial financial resources such that participants could seek major home adaptations or avoid "lower quality" institutions. CONCLUSIONS: Decisions regarding care setting for PWD frequently do not feel like a choice and are made under imperfect conditions. Programs using AIP as an outcome measure should recognize the various patient-centered and non-patient-centered factors that influence such choices, and interventions should be designed to promote more informed and equitable decision-making for care setting in dementia.

Willingness to trade-off years of life for an HIV cure - an experimental exploration of affective forecasting.

BACKGROUND: In the US, 1.2 million people live with HIV (PWH). Despite having near-normal life expectancies due to antiretroviral therapy (ART), many PWH seek an HIV cure, even if it means risking their lives. This willingness to take risks for a cure raises questions about "affective forecasting biases," where people tend to overestimate the positive impact of future events on their well-being. We conducted a study to test two interventions to mitigate affective forecasting in the decisions of PWH about taking HIV cure medication. METHODS: We recruited PWH to complete a 30-minute survey about their current quality of life (QoL) and the QoL they anticipate after being cured of HIV, and assigned them to either no additional intervention, to one of two interventions intended to reduce affective forecasting bias, or to both interventions: (1) a defocusing intervention designed to broaden the number of life domains people consider when imagining life changes associated with new circumstances (e.g. HIV cure); and (2) an adaptation intervention to help them gauge fading of strong emotions over time. The study design included a 2 × 2 design: defocusing (yes/no) x adaptation (yes/no) intervention. We assessed PWH's willingness to take hypothetical HIV sterilizing cure medication using the Time Trade-Off (TTO) and their quality of life predictions with WHOQOL-HIV. RESULTS: 296 PWH participated. Counter to what we had hypothesized, neither intervention significantly reduced PWH's willingness to trade time for a cure. Instead, the defocusing intervention increased their willingness to trade time (IRR 1.77, p = 0.03). Exploratory analysis revealed that PWH with lower current quality of life who received the defocusing intervention were more willing to trade time for a cure. CONCLUSION: These negative findings suggest that either these biases are difficult to overcome in the settings of HIV curative medication or other factors beyond affective forecasting biases influence willingness to participate in HIV curative studies, such as respondents' current quality of life.

Gender Differences in the Diagnosis of Chronic Obstructive Pulmonary Disease after Spirometry.

Background: Women are more likely than men to report delays in the diagnosis of chronic obstructive pulmonary disease (COPD), though the etiology of these delays is unknown. We sought to test whether delays in COPD diagnosis persist after the performance of spirometry. Methods: We used the Optum Labs Data Warehouse to identify patients 18 years of age and older without a prior diagnosis of COPD, with a post-bronchodilator forced expiratory volume in 1 second (FEV1) to forced vital capacity (FVC) ratio of less than 0.7 on spirometry. We used a Cox proportional hazards model to compare the time to diagnosis after spirometry in men and women, adjusting for age, race, ethnicity, tobacco use, and post-bronchodilator FEV1/FVC. Results: The probability of receiving a COPD diagnosis after the performance of spirometry was lower among women than men (adjusted hazard ratio [aHR] 0.66, 95% confidence interval [CI] 0.50 to 0.88). Conclusion: In this retrospective cohort study of patients with spirometric evidence of obstruction, the time to diagnosis of COPD was greater among women than men. While previous vignette-based studies have found that gender differences in the diagnosis of COPD resolve with the performance of spirometry, we found that gender differences persist after spirometry has been performed. Clinicians were less likely to diagnose COPD in women even when spirometry supported this diagnosis.

A randomized trial of behavioral interventions yielding sustained reductions in distracted driving.

Distracted driving is responsible for nearly 1 million crashes each year in the United States alone, and a major source of driver distraction is handheld phone use. We conducted a randomized, controlled trial to compare the effectiveness of interventions designed to create sustained reductions in handheld use while driving (NCT04587609). Participants were 1,653 consenting Progressive® Snapshot® usage-based auto insurance customers ages 18 to 77 who averaged at least 2 min/h of handheld use while driving in the month prior to study invitation. They were randomly assigned to one of five arms for a 10-wk intervention period. Arm 1 (control) got education about the risks of handheld phone use, as did the other arms. Arm 2 got a free phone mount to facilitate hands-free use. Arm 3 got the mount plus a commitment exercise and tips for hands-free use. Arm 4 got the mount, commitment, and tips plus weekly goal gamification and social competition. Arm 5 was the same as Arm 4, plus offered behaviorally designed financial incentives. Postintervention, participants were monitored until the end of their insurance rating period, 25 to 65 d more. Outcome differences were measured using fractional logistic regression. Arm 4 participants, who received gamification and competition, reduced their handheld use by 20.5% relative to control (P < 0.001); Arm 5 participants, who additionally received financial incentives, reduced their use by 27.6% (P < 0.001). Both groups sustained these reductions through the end of their insurance rating period.

Feedback and Financial Incentives for Reducing Cell Phone Use While Driving: A Randomized Clinical Trial.

Importance: Handheld phone use while driving is a major factor in vehicle crashes. Scalable interventions are needed to encourage drivers not to use their phones. Objective: To test whether interventions involving social comparison feedback and/or financial incentives can reduce drivers' handheld phone use. Design, Setting, and Participants: In a randomized clinical trial, interventions were administered nationwide in the US via a mobile application in the context of a usage-based insurance program (Snapshot Mobile application). Customers were eligible to be invited to participate in the study if enrolled in the usage-based insurance program for 30 to 70 days. The study was conducted from May 13 to June 30, 2019. Analysis was completed December 22, 2023. Interventions: Participants were randomly assigned to 1 of 6 trial arms for a 7-week intervention period: (1) control; (2) feedback, with weekly push notification about their handheld phone use compared with that of similar others; (3) standard incentive, with a maximum $50 award at the end of the intervention based on how their handheld phone use compared with similar others; (4) standard incentive plus feedback, combining interventions of arms 2 and 3; (5) reframed incentive plus feedback, with a maximum $7.15 award each week, framed as participant's to lose; and (6) doubled reframed incentive plus feedback, a maximum $14.29 weekly loss-framed award. Main Outcome and Measure: Proportion of drive time engaged in handheld phone use in seconds per hour (s/h) of driving. Analyses were conducted with the intention-to-treat approach. Results: Of 17 663 customers invited by email to participate, 2109 opted in and were randomized. A total of 2020 drivers finished the intervention period (68.0% female; median age, 30 [IQR, 25-39] years). Median baseline handheld phone use was 216 (IQR, 72-480) s/h. Relative to control, feedback and standard incentive participants did not reduce their handheld phone use. Standard incentive plus feedback participants reduced their use by -38 (95% CI, -69 to -8) s/h (P = .045); reframed incentive plus feedback participants reduced their use by -56 (95% CI, -87 to -26) s/h (P < .001); and doubled reframed incentive plus feedback participants reduced their use by -42 s/h (95% CI, -72 to -13 s/h; P = .007). The 5 active treatment arms did not differ significantly from each other. Conclusions and Relevance: In this randomized clinical trial, providing social comparison feedback plus incentives reduced handheld phone use while individuals were driving. Trial Registration: ClinicalTrials.gov Identifier: NCT03833219.

Demystifying estimands in cluster-randomised trials.

Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned with the study's objectives. Cluster-randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster-specific, and whether they are participant- or cluster-average. In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster-level summaries. Then, through a re-analysis of a published cluster-randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 (p = 0.17) to 1.83 (p = 0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, is essential to ensuring that cluster-randomised trials address the right question.

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 four-round modified Delphi process 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 a consensus that data collection during ARF recovery should take less than 15 minutes per assessment, allow surrogate completion when patients are unable, and continue for at least 24 months of follow-up. Using the EuroQol-5 Dimensions (EQ-5D) questionnaire 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.