Detecting goals of care conversations in clinical notes with active learning.

OBJECTIVE: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence. METHODS: To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning. RESULTS: Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification. CONCLUSION: When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

Barriers and Facilitators of Surrogates Providing Consent for Critically Ill Patients in Clinical Trials: A Qualitative Study.

BACKGROUND: Enrollment into critical care clinical trials is often hampered by the need to rely on surrogate decision-makers. To identify potential interventions facilitating enrollment into critical care clinical trials, a better understanding of surrogate decision-making for critical care clinical trial enrollment is needed. RESEARCH QUESTION: What are the barriers and facilitators of critical care trial enrollment? What are surrogate decision-makers' perspectives on proposed interventions to facilitate trial enrollment? STUDY DESIGN AND METHODS: We conducted semistructured interviews with 20 surrogate decision-makers of critically ill patients receiving mechanical ventilation. The interviews were recorded and transcribed verbatim, and analyzed for themes using an inductive approach. RESULTS: Twenty surrogate decision-makers of critically ill patients participated in the semistructured interviews. Thematic analysis confirmed previous research showing that trust in the system, assessing the risks and benefits of trial participation, the desire to help others, and building medical knowledge as important motivating factors for trial enrollment. Two previously undescribed concerns among surrogate decision-makers of critically ill patients were identified, including the potential to interfere with clinical treatment decisions and negative sentiment about placebos. Surrogates viewed public recognition and charitable donations for participation as favorable potential interventions to encourage trial enrollment. However, participants viewed direct financial incentives and prioritizing research participants during medical rounds negatively. INTERPRETATION: This study confirms and extends previous findings that health system trust, study risks and benefits, altruism, knowledge generation, interference with clinical care, and placebos are key concerns and barriers for surrogate decision-makers to enroll patients in critical care trials. Future studies are needed to evaluate if charitable giving on the patient's behalf and public recognition are effective strategies to promote enrollment into critical care trials.

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.

Defining the Time-limited Trial for Patients with Critical Illness: An Official American Thoracic Society Workshop Report.

In critical care, the specific, structured approach to patient care known as a "time-limited trial" has been promoted in the literature to help patients, surrogate decision makers, and clinicians navigate consequential decisions about life-sustaining therapy in the face of uncertainty. Despite promotion of the time-limited trial approach, a lack of consensus about its definition and essential elements prevents optimal clinical use and rigorous evaluation of its impact. The objectives of this American Thoracic Society Workshop Committee were to establish a consensus definition of a time-limited trial in critical care, identify the essential elements for conducting a time-limited trial, and prioritize directions for future work. We achieved these objectives through a structured search of the literature, a modified Delphi process with 100 interdisciplinary and interprofessional stakeholders, and iterative committee discussions. We conclude that a time-limited trial for patients with critical illness is a collaborative plan among clinicians and a patient and/or their surrogate decision makers to use life-sustaining therapy for a defined duration, after which the patient's response to therapy informs the decision to continue care directed toward recovery, transition to care focused exclusively on comfort, or extend the trial's duration. The plan's 16 essential elements follow four sequential phases: consider, plan, support, and reassess. We acknowledge considerable gaps in evidence about the impact of time-limited trials and highlight a concern that if inadequately implemented, time-limited trials may perpetuate unintended harm. Future work is needed to better implement this defined, specific approach to care in practice through a person-centered equity lens and to evaluate its impact on patients, surrogates, and clinicians.

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.

Using the Hospital Frailty Risk Score to assess mortality risk in older medical patients admitted to the intensive care unit.

BACKGROUND: Prognostic information at the time of hospital discharge can help guide goals-of-care discussions for future care. We sought to assess the association between the Hospital Frailty Risk Score (HFRS), which may highlight patients' risk of adverse outcomes at the time of hospital discharge, and in-hospital death among patients admitted to the intensive care unit (ICU) within 12 months of a previous hospital discharge. METHODS: We conducted a multicentre retrospective cohort study that included patients aged 75 years or older admitted at least twice over a 12-month period to the general medicine service at 7 academic centres and large community-based teaching hospitals in Toronto and Mississauga, Ontario, Canada, from Apr. 1, 2010, to Dec. 31, 2019. The HFRS (categorized as low, moderate or high frailty risk) was calculated at the time of discharge from the first hospital admission. Outcomes included ICU admission and death during the second hospital admission. RESULTS: The cohort included 22 178 patients, of whom 1767 (8.0%) were categorized as having high frailty risk, 9464 (42.7%) as having moderate frailty risk, and 10 947 (49.4%) as having low frailty risk. One hundred patients (5.7%) with high frailty risk were admitted to the ICU, compared to 566 (6.0%) of those with moderate risk and 790 (7.2%) of those with low risk. After adjustment for age, sex, hospital, day of admission, time of admission and Laboratory-based Acute Physiology Score, the odds of ICU admission were not significantly different for patients with high (adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.78 to 1.23) or moderate (adjusted OR 0.97, 95% CI 0.86 to 1.09) frailty risk compared to those with low frailty risk. Among patients admitted to the ICU, 75 (75.0%) of those with high frailty risk died, compared to 317 (56.0%) of those with moderate risk and 416 (52.7%) of those with low risk. After multivariable adjustment, the risk of death after ICU admission was higher for patients with high frailty risk than for those with low frailty risk (adjusted OR 2.86, 95% CI 1.77 to 4.77). INTERPRETATION: Among patients readmitted to hospital within 12 months, patients with high frailty risk were similarly likely as those with lower frailty risk to be admitted to the ICU but were more likely to die if admitted to ICU. The HFRS at hospital discharge can inform prognosis, which can help guide discussions for preferences for ICU care during future hospital stays.

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.

"It's that invisible illness": Patient and clinician perspectives on outcomes in pulmonary arterial hypertension treatment.

Knowledge of what outcomes are most meaningful to pulmonary arterial hypertension (PAH) stakeholders is limited. In this qualitative study, patients and clinicians endorsed personalized physical activity, symptoms, and psychosocial well-being as key outcomes to assess PAH treatment response, yet few are routinely measured in PAH clinical trials.

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.

Telehealth Palliative Care in Nursing Homes: A Scoping Review.

OBJECTIVES: Many adults older than 65 spend time in a nursing home (NH) at the end of life where specialist palliative care is limited. However, telehealth may improve access to palliative care services. A review of the literature was conducted to synthesize the evidence for telehealth palliative care in NHs to provide recommendations for practice, research, and policy. DESIGN: Joanna Briggs Institute guidance for scoping reviews, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews frameworks were used to guide this literature review. SETTINGS AND PARTICIPANTS: Reviewed articles focused on residents in NHs with telehealth palliative care interventionists operating remotely. Participants included NH residents, care partner(s), and NH staff/clinicians. METHODS: We searched Medline (Ovid), Embase (Elsevier), Cochrane Library (WileyOnline), Scopus (Elsevier), CINHAL (EBSCOhost), Trip PRO, and Dissertations & Theses Global (ProQuest) in June 2021, with an update in January 2022. We included observational and qualitative studies, clinical trials, quality improvement projects, and case and clinical reports that self-identified as telehealth palliative care for NH residents. RESULTS: The review yielded 11 eligible articles published in the United States and internationally from 2008 to 2020. Articles described live video as the preferred telehealth delivery modality with goals of care and physical aspects of care being most commonly addressed. Findings in the articles focused on 5 patient and family-centered outcomes: symptom management, quality of life, advance care planning, health care use, and evaluation of care. Consistent benefits of telehealth palliative care included increased documentation of goals of care and decrease in acute care use. Disadvantages included technological difficulties and increased NH financial burden. CONCLUSIONS AND IMPLICATIONS: Although limited in scope and quality, the current evidence for telehealth palliative care interventions shows promise for improving quality and outcomes of serious illness care in NHs. Future empirical studies should focus on intervention effectiveness, implementation outcomes (eg, managing technology), stakeholders' experience, and costs.

Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.

Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period.

Palliative Care Early in the Care Continuum among Patients with Serious Respiratory Illness: An Official ATS/AAHPM/HPNA/SWHPN Policy Statement.

Background: Patients with serious respiratory illness and their caregivers suffer considerable burdens, and palliative care is a fundamental right for anyone who needs it. However, the overwhelming majority of patients do not receive timely palliative care before the end of life, despite robust evidence for improved outcomes. Goals: This policy statement by the American Thoracic Society (ATS) and partnering societies advocates for improved integration of high-quality palliative care early in the care continuum for patients with serious respiratory illness and their caregivers and provides clinicians and policymakers with a framework to accomplish this. Methods: An international and interprofessional expert committee, including patients and caregivers, achieved consensus across a diverse working group representing pulmonary-critical care, palliative care, bioethics, health law and policy, geriatrics, nursing, physiotherapy, social work, pharmacy, patient advocacy, psychology, and sociology. Results: The committee developed fundamental values, principles, and policy recommendations for integrating palliative care in serious respiratory illness care across seven domains: 1) delivery models, 2) comprehensive symptom assessment and management, 3) advance care planning and goals of care discussions, 4) caregiver support, 5) health disparities, 6) mass casualty events and emergency preparedness, and 7) research priorities. The recommendations encourage timely integration of palliative care, promote innovative primary and secondary or specialist palliative care delivery models, and advocate for research and policy initiatives to improve the availability and quality of palliative care for patients and their caregivers. Conclusions: This multisociety policy statement establishes a framework for early palliative care in serious respiratory illness and provides guidance for pulmonary-critical care clinicians and policymakers for its proactive integration.

Performance Drift in a Mortality Prediction Algorithm during the SARS-CoV-2 Pandemic.

Research Objective: Health systems use clinical predictive algorithms to allocate resources to high-risk patients. Such algorithms are trained using historical data and are later implemented in clinical settings. During this implementation period, predictive algorithms are prone to performance changes ("drift") due to exogenous shocks in utilization or shifts in patient characteristics. Our objective was to examine the impact of sudden utilization shifts during the SARS-CoV-2 pandemic on the performance of an electronic health record (EHR)-based prognostic algorithm. Study Design: We studied changes in the performance of Conversation Connect, a validated machine learning algorithm that predicts 180-day mortality among outpatients with cancer receiving care at medical oncology practices within a large academic cancer center. Conversation Connect generates mortality risk predictions before each encounter using data from 159 EHR variables collected in the six months before the encounter. Since January 2019, Conversation Connect has been used as part of a behavioral intervention to prompt clinicians to consider early advance care planning conversations among patients with ≥10% mortality risk. First, we descriptively compared encounter-level characteristics in the following periods: January 2019-February 2020 ("pre-pandemic"), March-May 2020 ("early-pandemic"), and June-December 2020 ("later-pandemic"). Second, we quantified changes in high-risk patient encounters using interrupted time series analyses that controlled for pre-pandemic trends and demographic, clinical, and practice covariates. Our primary metric of performance drift was false negative rate (FNR). Third, we assessed contributors to performance drift by comparing distributions of key EHR inputs across periods and predicting later pandemic utilization using pre-pandemic inputs. Population Studied: 237,336 in-person and telemedicine medical oncology encounters. Principal Findings: Age, race, average patient encounters per month, insurance type, comorbidity counts, laboratory values, and overall mortality were similar among encounters in the pre-, early-, and later-pandemic periods. Relative to the pre-pandemic period, the later-pandemic period was characterized by a 6.5-percentage-point decrease (28.2% vs. 34.7%) in high-risk encounters (p<0.001). FNR increased from 41.0% (95% CI 38.0-44.1%) in the pre-pandemic period to 57.5% (95% CI 51.9-63.0%) in the later pandemic period. Compared to the pre-pandemic period, the early and later pandemic periods had higher proportions of telemedicine encounters (0.01% pre-pandemic vs. 20.0% early-pandemic vs. 26.4% later-pandemic) and encounters with no preceding laboratory draws (17.7% pre-pandemic vs. 19.8% early-pandemic vs. 24.1% later-pandemic). In the later pandemic period, observed laboratory utilization was lower than predicted (76.0% vs 81.2%, p<0.001). In the later-pandemic period, mean 180-day mortality risk scores were lower for telemedicine encounters vs. in-person encounters (10.3% vs 11.2%, p<0.001) and encounters with no vs. any preceding laboratory draws (1.5% vs. 14.0%, p<0.001). Conclusions: During the SARS-CoV-2 pandemic period, the performance of a machine learning prognostic algorithm used to prompt advance care planning declined substantially. Increases in telemedicine and declines in laboratory utilization contributed to lower performance. Implications for Policy or Practice: This is the first study to show algorithm performance drift due to SARS-CoV-2 pandemic-related shifts in telemedicine and laboratory utilization. These mechanisms of performance drift could apply to other EHR clinical predictive algorithms. Pandemic-related decreases in care utilization may negatively impact the performance of clinical predictive algorithms and warrant assessment and possible retraining of such algorithms.

A Qualitative Analysis of Factors Influencing Critical Care Trial Enrollment Among Surrogates.

BACKGROUND: We sought to identify factors that influence surrogate decision makers' decisions to enroll patients into a critical care randomized controlled trial. METHODS: We conducted a qualitative study embedded within a randomized controlled trial testing the effect of a behavioral nudge intervention for surrogate decision makers on enrollment rate in a sham ventilatory weaning trial among patients with acute respiratory failure. Participants were adult surrogate decision makers of patients receiving mechanical ventilation for acute respiratory failure. The study was conducted in 10 ICUs across 2 urban hospitals within an academic medical center in Philadelphia, Pennsylvanaia, United States. Immediately following their trial enrollment decision, surrogate decision makers were asked to enter free-text responses about the factors that influenced their decision. Responses were analyzed using content analysis. RESULTS: Of the 90 (49%) participants who provided free-text responses, the mean age was 54.9 years (SD 14.3), 69 (79%) were Caucasian, and 48 (53%) were the spouse of the eligible patient. We identified 5 themes influencing enrollment decisions: (i) trial characteristics, (ii) patient clinical condition, (iii) decision making processes, (iv) altruism, and (v) enrollment attempt. Among surrogates who enrolled the patient in the trial (n = 40), the most commonly cited factors were helping future patients (n = 24, 60%) and following the patient's wishes (n = 11, 28%). In contrast, those who declined enrollment (n = 50) most commonly reported that the patient was too sick (n = 27, 54%) and that they feared complicating the patient's condition (n = 11, 22%). CONCLUSIONS: Surrogates who enroll patients into trials most often cite altruistic motivations, while those who decline enrollment are most often concerned with the severity of the patients' condition.

Opportunities to Improve Palliative Care Delivery in Trauma Critical Illness.

BACKGROUND: Despite recommendations to integrate palliative care (PC) into care for critically ill trauma patients, little is known about current PC practices in trauma care to inform opportunities for improvement. OBJECTIVE: Describe patterns of PC delivery among a large, critically ill trauma cohort. SETTING/SUBJECTS: Retrospective cohort study of adult (≥18 years) trauma patients admitted to an intensive care unit (ICU) at an urban, level one trauma center in the United States from March 1, 2017 to March 1, 2019. METHODS: We linked the electronic medical record with the institutional trauma registry. PC process measures included a PC consult order, advance care planning (ACP) note, and hospice use. Unadjusted results are reported for the total population, decedents, and subgroups at risk for poor outcomes (age ≥55 years, Black race ≥1 pre-existing comorbidity, and severe injury) after trauma. RESULTS: Among 1309 eligible admissions, 902 (68.9%) were male, 640 (48.9%) were Black, and 654 (50.0%) were ≥55 years old. Eighty-one (6.2%) patients received a PC consult order, 66 (5.0%) had an ACP note, and 13 (1.1%) were discharged to hospice. Among decedents (N = 91; 7%), 28 (30.8%) received a PC consult order and 36 (39.6%) had an ACP note. For high-risk subgroups, PC consult orders and ACP note rates ranged from 4.5-12.8% and 4.5-11.8%, respectively. CONCLUSION: PC delivery was rare among this cohort, including those at high risk for poor outcomes. Urgent efforts are needed to identify barriers to and develop targeted interventions for high quality PC delivery in trauma ICU care.

Deficits in Identification of Goals and Goal-Concordant Care After Sepsis Hospitalization.

In a recent study, identifying and supporting patients' care goals was named the highest priority in hospital medicine. Although sepsis is one of the leading causes of death and postdischarge morbidity among hospitalized patients, little is known about how frequently care goals are assessed prior to discharge and adhered to in the 90 days after sepsis hospitalization. Evaluating a cohort of 679 high-risk sepsis survivors enrolled in a clinical trial, we found that care goals were documented explicitly in a standardized tool in 130 patients; an additional 139 patients were identified using all available clinical documentation, resulting in only 269 (40%) patients with goals that could be ascertained from the electronic health record (EHR). Among those categorized, goals were classified as prioritizing longevity (35%), function (52%), and comfort (12%). Based on expert review of the care provided during the 90 days subsequent to discharge, goal-concordant care was identified in 184 (68%) cases for which goals were specified. Documentation of goals in a standardized EHR tool was associated with increased likelihood of receiving goal-concordant care (odds ratio, 3.6; 95% CI, 2.4-5.5). Hospitalization and peridischarge time points represent important opportunities to address deficits in the documentation of goals and provision of goal-concordant care for sepsis survivors.

A systematic review and meta-analysis of enrollment into ARDS and sepsis trials published between 2009 and 2019 in major journals.

BACKGROUND: Enrollment problems are common among randomized controlled trials conducted in the ICU. However, little is known about actual trial enrollment rates and influential factors. We set out to determine the overall enrollment rate in recent randomized controlled trials (RCTs) of patients with acute respiratory distress syndrome (ARDS), acute lung injury (ALI), or sepsis, and which factors influenced enrollment rate. METHODS: We conducted a systematic review by searching Pubmed using predefined terms for ARDS/ALI and sepsis to identify individually RCTs published among the seven highest impact general medicine and seven highest impact critical care journals between 2009 and 2019. Cluster randomized trials were excluded. Data were extracted by two independent reviewers using an electronic database management system. We conducted a random-effects meta-analysis of the eligible trials for the primary outcome of enrollment rate by time and site. RESULTS: Out of 457 articles identified, 94 trials met inclusion criteria. Trials most commonly evaluated pharmaceutical interventions (53%), were non-industry funded (78%), and required prospective informed consent (81%). The overall mean enrollment rate was 0.83 (95% confidence interval: 0.57-1.21) participants per month per site. Enrollment in ARDS/ALI and sepsis trials were 0.48 (95% CI 0.32-0.70) and 0.98 (95% CI 0.62-1.56) respectively. The enrollment rate was significantly higher for single-center trials (4.86; 95% CI 2.49-9.51) than multicenter trials (0.52; 95% CI 0.41-0.66). Of the 36 trials that enrolled < 95% of the target sample size, 8 (22%) reported slow enrollment as the reason. CONCLUSIONS: In this systematic review and meta-analysis, recent ARDS/ALI and sepsis clinical trials had an overall enrollment rate of less than 1 participant per site per month. Novel approaches to improve critical care trial enrollment efficiency are needed to facilitate the translation of best evidence into practice.