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BACKGROUND: Palliative decompressive gastrostomy tubes are intended to relieve the severe physical symptoms of malignant small bowel obstruction (SBO) near the end of life. The objective of this study was to assess the impact of palliative decompressive gastrostomy tube on patient and caregiver well-being. PATIENTS AND METHODS: We prospectively enrolled patients with a malignant SBO and their caregivers at the time of informed consent for decompressive gastrostomy tube placement. We collected the Edmonton Symptom Assessment Scale (ESAS) and the Functional Assessment of Chronic Illness Therapy-Palliative (FACIT-Pal-14) surveys from patients at baseline and at 2-week post-procedure follow-up. The Caregiver Burden Scale survey was administered to caregivers at baseline. Survey scores were compared using paired t-tests. We also conducted semistructured interviews with patients and their caregivers at two-week follow-up until thematic saturation was reached. Content analysis was used to identify themes with two independent coders. RESULTS: We enrolled 15 patient-caregiver dyads. Preprocedure, the median caregiver burden scale score was 37.5 (significant burden ≥ 21). Eight patients (53%) survived to 2 weeks; among these patients, median ESAS scores (51 versus 43.5, p < 0.001) and median FACIT-Pal-14 scores (22 versus 32, p = 0.015) were significantly improved at 2-week follow-up. Interviews revealed three major themes: improved symptom management, new stressors, and opportunities for better education and resources. CONCLUSIONS: Decompressive gastrostomy tubes effectively alleviated symptoms in patients with inoperable malignant SBOs. This palliative intervention may provide greater benefit if performed earlier, and caregivers and patients need improved resources and education for tube management to minimize added stressors.
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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.
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Registros Eletrônicos de Saúde , Estudos de Viabilidade , Planejamento de Assistência ao Paciente , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Estudos Retrospectivos , Idoso de 80 Anos ou mais , Documentação/normas , Documentação/métodos , Hospitalização/estatística & dados numéricos , Adulto , Mortalidade Hospitalar/tendênciasRESUMO
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 34â¯239 patients enrolled, 24â¯065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10â¯313 in the default order group and 13â¯752 in the usual care group; 13â¯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.
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Estado Terminal , Cuidados Paliativos , Encaminhamento e Consulta , Idoso , Feminino , Humanos , Masculino , Hospitais para Doentes Terminais , Mortalidade Hospitalar , Estado Terminal/terapia , Hospitalização , Doença Pulmonar Obstrutiva Crônica/terapia , Demência/terapia , Insuficiência Renal/terapiaRESUMO
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.
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Planejamento Antecipado de Cuidados , Cuidados Paliativos , Continuidade da Assistência ao Paciente , Humanos , Políticas , Sociedades Médicas , Estados UnidosRESUMO
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.
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Cuidados Críticos , Tomada de Decisões , Adulto , Humanos , Unidades de Terapia Intensiva , Pessoa de Meia-Idade , Pesquisa Qualitativa , Respiração ArtificialRESUMO
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.
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COVID-19/mortalidade , COVID-19/terapia , Estado Terminal/mortalidade , Estado Terminal/terapia , Pneumonia Viral/mortalidade , Pneumonia Viral/terapia , Choque/mortalidade , Choque/terapia , APACHE , Centros Médicos Acadêmicos , Idoso , Feminino , Mortalidade Hospitalar , Humanos , Unidades de Terapia Intensiva , Tempo de Internação/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Pandemias , Readmissão do Paciente/estatística & dados numéricos , Pennsylvania/epidemiologia , Pneumonia Viral/virologia , Respiração Artificial/estatística & dados numéricos , Estudos Retrospectivos , SARS-CoV-2 , Choque/virologia , Taxa de SobrevidaRESUMO
PURPOSE OF REVIEW: This review describes the learning healthcare system paradigm, recent examples, and future directions. Patients, clinicians, and health systems frequently encounter decisions between available treatments, technologies, and healthcare delivery methods with little or no evidence about the comparative effectiveness and safety of the available options. Learning healthcare systems endeavor to recognize such knowledge gaps, integrate comparative effectiveness research - including clinical trials - into clinical care to address the knowledge gaps, and seamlessly implement the results into practice to improve care and patient outcomes. RECENT FINDINGS: Recent studies comparing the effectiveness of diagnostic tests and treatments, using information technology to identify patients likely to experience an outcome or benefit from an intervention, and evaluating models of healthcare delivery have demonstrated how a learning healthcare system approach can reduce arbitrary variation in care, decrease cost, and improve patient outcomes. SUMMARY: Learning healthcare systems have the potential to answer questions of importance to patients, clinicians, and health system leaders, improve efficiency of healthcare delivery, and improve patient outcomes. Achieving this goal will require realignment of the culture around clinical care, institutional and federal investment, expanded stakeholder engagement, tailored ethical and regulatory guidance, and methodologic advances in information technology and biostatistics.
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Sistema de Aprendizagem em Saúde , Atenção à Saúde , HumanosRESUMO
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.
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Participação do Paciente , Ensaios Clínicos Controlados Aleatórios como Assunto , Síndrome do Desconforto Respiratório , Sepse , Humanos , Participação do Paciente/estatística & dados numéricos , Publicações Periódicas como Assunto , Síndrome do Desconforto Respiratório/terapia , Sepse/terapiaRESUMO
BACKGROUND: Little is known about the palliative care needs of patients awaiting lung transplantation. The aim of this study was to describe these needs in patients undergoing evaluation for or awaiting lung transplantation. METHODS: Cross-sectional survey using an adapted version of the Needs at the End-of-life Screening Tool (NEST-13) at a US-based transplant program. RESULTS: Among the 111 participants, 83.5% were White, 60.0% were female, and almost three-quarters had either restrictive or obstructive lung disease. The greatest palliative care needs included difficulty being physically active (mean: 7.9/10; SD: 2.6; median: 9.0), physical symptoms (mean: 7.4/10; SD: 2.6; median: 8.0), missing work due to illness (mean: 6.2/10; SD: 4.0; median: 8.0), and concerns that life might end (mean: 5.1/10; SD: 3.6; median: 5.0). Participants reported that religious/spiritual beliefs contribute to their sense of purpose (mean: 4.1/10; SD: 3.9) but had few unmet needs in this area (mean: 0.9/10; median: 0.0). Only 6.4% reported seeing a palliative care specialist, and 48.2% were unsure what a palliative care specialist is. CONCLUSION: There are substantial palliative care needs among lung transplant candidates, particularly physical symptoms and end-of-life concerns. These findings support integrating palliative care and end-of-life discussions in the management of lung transplant candidates.
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Transplante de Pulmão , Cuidados Paliativos , Estudos Transversais , Feminino , Humanos , Masculino , ReligiãoRESUMO
BACKGROUND: Development of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated. OBJECTIVE: To assess the clinical impact of triggering palliative care using an EHR prediction model. DESIGN: Pilot prospective before-after study on the general medical wards at an urban academic medical center. PARTICIPANTS: Adults with a predicted probability of 6-month mortality of ≥ 0.3. INTERVENTION: Triggered (with opt-out) palliative care consult on hospital day 2. MAIN MEASURES: Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS). KEY RESULTS: The control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P < .001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P = .02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P = .03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P = .01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P = .13), code status changes (42 [30] vs 39 [29]; P = .81), or consult requests for lower risk (< 0.3) patients (48/1004 [5] vs 33/798 [4]; P = .48). CONCLUSIONS: Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.
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Técnicas de Apoio para a Decisão , Cuidados Paliativos/organização & administração , Aceitação pelo Paciente de Cuidados de Saúde/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Registros Eletrônicos de Saúde , Feminino , Hospitalização/estatística & dados numéricos , Humanos , Masculino , Pessoa de Meia-Idade , Ensaios Clínicos Controlados não Aleatórios como Assunto , Projetos Piloto , Estudos Prospectivos , Encaminhamento e Consulta/organização & administração , Encaminhamento e Consulta/estatística & dados numéricosRESUMO
Sentiment analysis may offer insights into patient outcomes through the subjective expressions made by clinicians in the text of encounter notes. We analyzed the predictive, concurrent, convergent, and content validity of six sentiment methods in a sample of 793,725 multidisciplinary clinical notes among 41,283 hospitalizations associated with an intensive care unit stay. None of these approaches improved early prediction of in-hospital mortality using logistic regression models, but did improve both discrimination and calibration when using random forests. Additionally, positive sentiment measured by the CoreNLP (OR 0.04, 95% CI 0.002-0.55), Pattern (OR 0.09, 95% CI 0.04-0.17), sentimentr (OR 0.37, 95% CI 0.25-0.63), and Opinion (OR 0.25, 95% CI 0.07-0.89) methods were inversely associated with death on the concurrent day after adjustment for demographic characteristics and illness severity. Median daily lexical coverage ranged from 5.4% to 20.1%. While sentiment between all methods was positively correlated, their agreement was weak. Sentiment analysis holds promise for clinical applications but will require a novel domain-specific method applicable to clinical text.
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Estado Terminal , Prontuários Médicos , Atitude , Mortalidade Hospitalar , Humanos , Unidades de Terapia Intensiva , IdiomaRESUMO
OBJECTIVES: Without widely available physiologic data, a need exists for ICU risk adjustment methods that can be applied to administrative data. We sought to expand the generalizability of the Acute Organ Failure Score by adapting it to a commonly used administrative database. DESIGN: Retrospective cohort study. SETTING: One hundred fifty-one hospitals in Pennsylvania. PATIENTS: A total of 90,733 ICU admissions among 77,040 unique patients between January 1, 2009, and December 1, 2009, in the Medicare Provider Analysis and Review database. MEASUREMENTS AND MAIN RESULTS: We used multivariable logistic regression on a random split cohort to predict 30-day mortality, and to examine the impact of using different comorbidity measures in the model and adding historical claims data. Overall 30-day mortality was 17.6%. In the validation cohort, using the original Acute Organ Failure Score model's ß coefficients resulted in poor discrimination (C-statistic, 0.644; 95% CI, 0.639-0.649). The model's C-statistic improved to 0.721 (95% CI, 0.711-0.730) when the Medicare cohort was used to recalibrate the ß coefficients. Model discrimination improved further when comorbidity was expressed as the COmorbidity Point Score 2 (C-statistic, 0.737; 95% CI, 0.728-0.747; p < 0.001) or the Elixhauser index (C-statistic, 0.748; 95% CI, 0.739-0.757) instead of the Charlson index. Adding historical claims data increased the number of comorbidities identified, but did not enhance model performance. CONCLUSIONS: Modification of the Acute Organ Failure Score resulted in good model discrimination among a diverse population regardless of comorbidity measure used. This study expands the use of the Acute Organ Failure Score for risk adjustment in ICU research and outcomes reporting using standard administrative data.
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Medicare/estatística & dados numéricos , Escores de Disfunção Orgânica , Risco Ajustado/métodos , Idoso , Idoso de 80 Anos ou mais , Comorbidade , Feminino , Mortalidade Hospitalar , Humanos , Modelos Logísticos , Masculino , Modelos Estatísticos , Estudos Retrospectivos , Estados UnidosAssuntos
Ensaios Clínicos como Assunto/métodos , Cuidados Críticos/normas , Estado Terminal/terapia , Hospitalização/estatística & dados numéricos , Avaliação de Resultados em Cuidados de Saúde/métodos , Assistência Centrada no Paciente/normas , Indicadores de Qualidade em Assistência à Saúde , HumanosRESUMO
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: 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.
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Ensaios Clínicos como Assunto , Cuidados Críticos , Estado Terminal , Tomada de Decisões , Pesquisa Qualitativa , Humanos , Estado Terminal/terapia , Estado Terminal/psicologia , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Idoso , Confiança , Consentimento Livre e Esclarecido , Consentimento do Representante Legal , Seleção de Pacientes , Respiração Artificial , Motivação , Entrevistas como AssuntoRESUMO
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.
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Estado Terminal , Tomada de Decisões , Humanos , Estados Unidos , Estado Terminal/terapia , Cuidados Críticos , Consenso , PacientesRESUMO
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.
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Cobertura do Seguro , Idioma , Humanos , Feminino , Fatores Sociais , ÚteroRESUMO
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.
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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.