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ImportanceDiscussions about goals of care are important for high-quality palliative care yet are often lacking for hospitalized older patients with serious illness.ObjectiveTo evaluate a communication-priming intervention to promote goals-of-care discussions between clinicians and hospitalized older patients with serious illness.Design, Setting, and ParticipantsA pragmatic, randomized clinical trial of a clinician-facing communication-priming intervention vs usual care was conducted at 3 US hospitals within 1 health care system, including a university, county, and community hospital. Eligible hospitalized patients were aged 55 years or older with any of the chronic illnesses used by the Dartmouth Atlas project to study end-of-life care or were aged 80 years or older. Patients with documented goals-of-care discussions or a palliative care consultation between hospital admission and eligibility screening were excluded. Randomization occurred between April 2020 and March 2021 and was stratified by study site and history of dementia.InterventionPhysicians and advance practice clinicians who were treating the patients randomized to the intervention received a 1-page, patient-specific intervention (Jumpstart Guide) to prompt and guide goals-of-care discussions.Main Outcomes and MeasuresThe primary outcome was the proportion of patients with electronic health record–documented goals-of-care discussions within 30 days. There was also an evaluation of whether the effect of the intervention varied by age, sex, history of dementia, minoritized race or ethnicity, or study site.ResultsOf 3918 patients screened, 2512 were enrolled (mean age, 71.7 [SD, 10.8] years and 42% were women) and randomized (1255 to the intervention group and 1257 to the usual care group). The patients were American Indian or Alaska Native (1.8%), Asian (12%), Black (13%), Hispanic (6%), Native Hawaiian or Pacific Islander (0.5%), non-Hispanic (93%), and White (70%). The proportion of patients with electronic health record–documented goals-of-care discussions within 30 days was 34.5% (433 of 1255 patients) in the intervention group vs 30.4% (382 of 1257 patients) in the usual care group (hospital- and dementia-adjusted difference, 4.1% [95% CI, 0.4% to 7.8%]). The analyses of the treatment effect modifiers suggested that the intervention had a larger effect size among patients with minoritized race or ethnicity. Among 803 patients with minoritized race or ethnicity, the hospital- and dementia-adjusted proportion with goals-of-care discussions was 10.2% (95% CI, 4.0% to 16.5%) higher in the intervention group than in the usual care group. Among 1641 non-Hispanic White patients, the adjusted proportion with goals-of-care discussions was 1.6% (95% CI, −3.0% to 6.2%) higher in the intervention group than in the usual care group. There was no evidence of differential treatment effects of the intervention on the primary outcome by age, sex, history of dementia, or study site.Conclusions and RelevanceAmong hospitalized older adults with serious illness, a pragmatic clinician-facing communication-priming intervention significantly improved documentation of goals-of-care discussions in the electronic health record, with a greater effect size in racially or ethnically minoritized patients.Trial RegistrationClinicalTrials.gov Identifier: NCT04281784

CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising approach for identifying goals-of-care discussions in the electronic health record (EHR). OBJECTIVES: To compare three NLP modeling approaches for identifying EHR documentation of goals-of-care discussions and generate hypotheses about differences in performance. METHODS: We conducted a mixed-methods study to evaluate performance and misclassification for three NLP featurization approaches modeled with regularized logistic regression: bag-of-words (BOW), rule-based, and a hybrid approach. From a prospective cohort of 150 patients hospitalized with serious illness over 2018–2020, we collected 4,391 inpatient EHR notes; 99 (2.3%) contained documented goals-of-care discussions. We used leave-one-out cross-validation to estimate performance by comparing pooled NLP predictions to human abstractors with receiver-operating-characteristic (ROC) and precision-recall (PR) analyses. We qualitatively examined a purposive sample of 70 NLP-misclassified notes using content analysis to identify linguistic features that allowed us to generate hypotheses underpinning misclassification. RESULTS: All three modeling approaches discriminated between notes with and without goals-of-care discussions (AUC(ROC): BOW, 0.907; rule-based, 0.948; hybrid, 0.965). Precision and recall were only moderate (precision at 70% recall: BOW, 16.2%; rule-based, 50.4%; hybrid, 49.3%; AUC(PR): BOW, 0.505; rule-based, 0.579; hybrid, 0.599). Qualitative analysis revealed patterns underlying performance differences between BOW and rule-based approaches. CONCLUSION: NLP holds promise for identifying EHR-documented goals-of-care discussions. However, the rarity of goals-of-care content in EHR data limits performance. Our findings highlight opportunities to optimize NLP modeling approaches, and support further exploration of different NLP approaches to identify goals-of-care discussions.

BACKGROUND: Despite its widespread implementation, it is unclear whether Physician Orders for Life-Sustaining Treatment (POLST) are safe and improve the delivery of care that patients desire. We sought to systematically review the influence of POLST on treatment intensity among patients with serious illness and/or frailty. METHODS: We performed a systematic review of POLST and similar programs using MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database for Systematic Reviews, and PsycINFO, from inception through February 28, 2020. We included adults with serious illness and/or frailty with life expectancy

Goals-of-care discussions are important for patient-centered care among hospitalized patients with serious illness. However, there are little data on the occurrence, predictors, and timing of these discussions.To examine the occurrence, predictors, and timing of electronic health record (EHR)-documented goals-of-care discussions for hospitalized patients.This retrospective cohort study used natural language processing (NLP) to examine EHR-documented goals-of-care discussions for adults with chronic life-limiting illness or age ≥80 hospitalized 2015-2019. The primary outcome was NLP-identified documentation of a goals-of-care discussion during the index hospitalization. We used multivariable logistic regression to evaluate associations with baseline characteristics.Of 16,262 consecutive, eligible patients without missing data, 5,918 (36.4%) had a documented goals-of-care discussion during hospitalization; approximately 57% of these discussions occurred within 24 hours of admission. In multivariable analysis, documented goals-of-care discussions were more common for women (OR=1.26, 95%CI 1.18-1.36), older patients (OR=1.04 per year, 95%CI 1.03-1.04), and patients with more comorbidities (OR=1.11 per Deyo-Charlson point, 95%CI 1.10-1.13), cancer (OR=1.88, 95%CI 1.72-2.06), dementia (OR=2.60, 95%CI 2.29-2.94), higher acute illness severity (OR=1.12 per National Early Warning Score point, 95%CI 1.11-1.14), or prior advance care planning documents (OR=1.18, 95%CI 1.08-1.30). Documentation of these discussions was less common for racially or ethnically minoritized patients (OR=0.823, 95%CI 0.75-0.90).Among hospitalized patients with serious illness, documented goals-of-care discussions identified by NLP were more common among patients with older age and increased burden of acute or chronic illness, and less common among racially or ethnically minoritized patients. This suggests important disparities in goals-of-care discussions.

ImportanceMany clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes efficiently, but ignoring NLP-related misclassification may lead to underpowered studies.ObjectiveTo evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a communication intervention.Design, Setting, and ParticipantsThis diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened human abstraction (manual verification of NLP-positive records), and (3) conventional manual abstraction. The study included hospitalized patients aged 55 years or older with serious illness enrolled between April 23, 2020, and March 26, 2021, in a pragmatic randomized clinical trial of a communication intervention in a multihospital US academic health system.Main Outcomes and MeasuresMain outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-of-care discussions. Performance of NLP was evaluated with receiver operating characteristic (ROC) curves and precision-recall (PR) analyses and examined the effects of misclassification on power using mathematical substitution and Monte Carlo simulation.ResultsA total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learning NLP trained on a separate training data set identified patients with documented goals-of-care discussions with moderate accuracy (maximal F1 score, 0.82; area under the ROC curve, 0.924; area under the PR curve, 0.879). Manual abstraction of the outcome from the trial data set would require an estimated 2000 abstractor-hours and would power the trial to detect a risk difference of 5.4% (assuming 33.5% control-arm prevalence, 80% power, and 2-sided α = .05). Measuring the outcome by NLP alone would power the trial to detect a risk difference of 7.6%. Measuring the outcome by NLP-screened human abstraction would require 34.3 abstractor-hours to achieve estimated sensitivity of 92.6% and would power the trial to detect a risk difference of 5.7%. Monte Carlo simulations corroborated misclassification-adjusted power calculations.Conclusions and RelevanceIn this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified power loss from NLP-related misclassification, suggesting that incorporation of this approach into the design of studies using NLP would be beneficial.

High-quality goals-of-care communication is critical to delivering goal-concordant, patient-centered care to hospitalized patients with chronic life-limiting illness. However, implementation and documentation of goals-of-care discussions remain important shortcomings in many health systems.To evaluate the efficacy, feasibility, and acceptability of a patient-facing and clinician-facing communication-priming intervention to promote goals-of-care communication for patients hospitalized with serious illness.This randomized clinical trial enrolled patients from November 6, 2018, to February 18, 2020. The setting was 2 hospitals in an academic health care system in Seattle, Washington. Participants included hospitalized adults with chronic life-limiting illness, aged 65 years or older and with markers of frailty, or aged 80 years or older. Data analysis was performed from August 2020 to August 2021.Patients were randomized to usual care with baseline questionnaires (control) vs the Jumpstart communication-priming intervention. Patients or surrogates in the intervention group and their clinicians received patient-specific Jumpstart Guides populated with data from questionnaires and the electronic health records (EHRs) that were designed to prompt and guide a goals-of-care discussion.The primary outcome was EHR documentation of a goals-of-care discussion between randomization and hospital discharge. Additional outcomes included patient-reported or surrogate-reported goals-of-care discussions, patient-reported or surrogate-reported quality of communication, and intervention feasibility and acceptability.Of 428 eligible patients, this study enrolled 150 patients (35% enrollment rate; mean [SD] age, 59.2 [13.6] years; 66 women [44%]; 132 [88%] by patient consent and 18 [12%] by surrogate consent). Seventy-five patients each were randomized to the intervention and control groups. Compared with the control group, the cumulative incidence of EHR-documented goals-of-care discussions between randomization and hospital discharge was higher in the intervention group (16 of 75 patients [21%] vs 6 of 75 patients [8%]; risk difference, 13% [95% CI, 2%-24%]; risk ratio, 2.67 [95% CI, 1.10-6.44]; P = .04). Patient-reported or surrogate-reported goals-of-care discussions did not differ significantly between groups (30 of 66 patients [45%] vs 36 of 66 patients [55%]), although the intrarater consistency of patient and surrogate reports was poor. Patient-rated or surrogate-rated quality of communication did not differ significantly between groups. The intervention was feasible and acceptable to patients, surrogates, and clinicians.In this randomized clinical trial, a patient-facing and clinician-facing communication priming intervention for seriously ill, hospitalized patients promoted EHR-documented goals-of-care discussions before discharge with good feasibility and acceptability. Communication-priming interventions should be reexamined in a larger randomized clinical trial to better understand their effectiveness in the inpatient setting.ClinicalTrials.gov Identifier: NCT03746392.

OBJECTIVES The acute respiratory distress syndrome is common in critically ill patients. Recognition is crucial because acute respiratory distress syndrome is associated with a high mortality rate, and low tidal volume ventilation improves mortality. However, acute respiratory distress syndrome often goes unrecognized. Risk factors for under-recognition and trends over time have not been fully described. DESIGN Retrospective chart review of patients with acute respiratory distress syndrome from a prospective cohort study of critically ill patients. For each patient's ICU stay, we searched the chart for terms that indicated that acute respiratory distress syndrome was diagnosed, in the differential diagnosis, or treated with low tidal volume ventilation. SETTING ICUs at a tertiary hospital at the University of California, San Francisco between 2008 and 2016. PATIENTS Critically ill patients with acute respiratory distress syndrome. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Acute respiratory distress syndrome was recognized in 70% of patients, and recognition increased from 60% in 2008-2009 to 92% in 2016 (p = 0.004). Use of tidal volumes less than 6.5 mL/kg also increased (p < 0.001) from 20% to 92%. Increased acute respiratory distress syndrome severity (p = 0.01) and vasopressor use (p = 0.04) were associated with greater recognition. Clinician diagnosis of acute respiratory distress syndrome and inclusion of acute respiratory distress syndrome in the differential diagnosis were associated with tidal volumes less than 6.5 mL/kg (51% use of tidal volume ≤ 6.5 mL/kg if acute respiratory distress syndrome recognized vs 15% if not recognized; p = 0.002). Diagnosing acute respiratory distress syndrome was associated with lower tidal volume in multivariate analysis. CONCLUSIONS Although acute respiratory distress syndrome recognition and low tidal volume ventilation use have increased over time, they remain less than universal. Clinician recognition of acute respiratory distress syndrome is associated with both systemic and respiratory severity of illness and is also associated with use of low tidal volume ventilation.

Study objective Physician Orders for Life-Sustaining Treatment (POLST) forms are intended to help prevent the provision of unwanted medical interventions among patients with advanced illness or frailty who are approaching the end of life. We seek to evaluate how POLST form completion, treatment limitations, or both influence intensity of treatment among patients who present to the emergency department (ED). Methods This was a retrospective cohort study of adults who presented to the ED at an academic medical center in Oregon between April 2015 and October 2016. POLST form completion and treatment limitations were the main exposures. Primary outcome was hospital admission; secondary outcomes included ICU admission and a composite measure of aggressive treatment. Results A total of 26,128 patients were included; 1,769 (6.8%) had completed POLST forms. Among patients with POLST, 52.1% had full treatment orders, and 6.4% had their forms accessed before admission. POLST form completion was not associated with hospital admission (adjusted odds ratio [aOR]=0.97; 95% confidence interval [CI] 0.84 to 1.12), ICU admission (aOR=0.82; 95% CI 0.55 to 1.22), or aggressive treatment (aOR=1.06; 95% CI 0.75 to 1.51). Compared with POLST forms with full treatment orders, those with treatment limitations were not associated with hospital admission (aOR=1.12; 95% CI 0.92 to 1.37) or aggressive treatment (aOR=0.87; 95% CI 0.5 to 1.52), but were associated with lower odds of ICU admission (aOR=0.31; 95% CI 0.16 to 0.61). Conclusion Among patients presenting to the ED with POLST, the majority of POLST forms had orders for full treatment and were not accessed by emergency providers. These findings may partially explain why we found no association of POLST with treatment intensity. However, treatment limitations on POLST forms were associated with reduced odds of ICU admission. Implementation and accessibility of POLST forms are crucial when considering their effect on the provision of treatment consistent with patients’ preferences.

Although goals-of-care discussions are important for high-quality palliative care, this communication is often lacking for hospitalized older patients with serious illness. Electronic health records (EHR) provide an opportunity to identify patients who might benefit from these discussions and promote their occurrence, yet prior interventions using the EHR for this purpose are limited. We designed two complementary yet independent randomized trials to examine effectiveness of a communication-priming intervention (Jumpstart) for hospitalized older adults with serious illness.We report the protocol for these 2 randomized trials. Trial 1 has two arms, usual care and a clinician-facing Jumpstart, and is a pragmatic trial assessing outcomes with the EHR only (n = 2000). Trial 2 has three arms: usual care, clinician-facing Jumpstart, and clinician- and patient-facing (bi-directional) Jumpstart (n = 600). We hypothesize the clinician-facing Jumpstart will improve outcomes over usual care and the bi-directional Jumpstart will improve outcomes over the clinician-facing Jumpstart and usual care. We use a hybrid effectiveness-implementation design to examine implementation barriers and facilitators.For both trials, the primary outcome is EHR documentation of a goals-of-care discussion within 30 days of randomization; additional outcomes include intensity of end-of-life care. Trial 2 also examines patient- or family-reported outcomes assessed by surveys targeting 3-5 days and 4-8 weeks after randomization including quality of goals-of-care communication, receipt of goal-concordant care, and psychological symptoms.This novel study incorporates two complementary randomized trials and a hybrid effectiveness-implementation approach to improve the quality and value of care for hospitalized older adults with serious illness.STUDY00007031-A and STUDY00007031-B.

Summary For patients with chronic, life-limiting illnesses, admission to the intensive care unit (ICU) near the end of life might not improve patient outcomes or be consistent with patient and family values, goals, and preferences. In this context, advance care planning and palliative care interventions designed to clarify patients' values, goals, and preferences have the potential to reduce provision of high-intensity interventions that are unwanted or non-beneficial. In this Series paper, we have assessed interventions that are effective at helping patients with chronic, life-limiting illnesses to avoid an unwanted ICU admission. The evidence found was largely from observational studies, with considerable heterogeneity in populations, methods, and types of interventions. Results from randomised trials of interventions to improve communication about goals of care are scarce, of variable quality, and mixed. Although observational studies show that advance care planning and palliative care interventions are associated with a reduced number of ICU admissions at the end of life, causality has not been well established. Using the available evidence we suggest recommendations to help to avoid ICU admission when it does not align with patient and family values, goals, and preferences and conclude with future directions for research.

Natural Language Processing (NLP) is a key enabling technology for re-use of information in free-text clinical notes. However, a barrier to deployment is the availability of labeled corpora for supervised machine learning, which are expensive to acquire as they must be annotated by experienced clinicians. Where corpora are available, they may be opportunistically collected and thus vulnerable to bias. Here we evaluate an approach for accounting for dataset bias in the context of identifying specific patient-provider interactions. In this context, bias is the result of a phenomenon being over or under-represented in a particular type of clinical note as a result of the way a dataset was curated. Using a clinical dataset which represents a great deal of variation in terms of author and setting, we control for confounding variables using a backdoor adjustment approach [1, 2], which to our knowledge has not been previously applied the clinical domain. This approach improves precision by up to 5% and the adjusted models’ scores for false positives are generally lower, resulting in a more generalizable model with the potential to enhance the downstream utility of models trained using opportunistically collected clinical corpora.

This case series examines outcomes of in-hospital cardiac arrest among patients with COVID-19.

Context Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health record (EHR) efficiently. Objectives To develop, train, and test an automated approach to identifying goals-of-care discussions in the EHR, using natural language processing (NLP) and machine learning (ML). Methods From the electronic health records of an academic health system, we collected a purposive sample of 3183 EHR notes (1435 inpatient notes and 1748 outpatient notes) from 1426 patients with serious illness over 2008–2016, and manually reviewed each note for documentation of goals-of-care discussions. Separately, we developed a program to identify notes containing documentation of goals-of-care discussions using NLP and supervised ML. We estimated the performance characteristics of the NLP/ML program across 100 pairs of randomly partitioned training and test sets. We repeated these methods for inpatient-only and outpatient-only subsets. Results Of 3183 notes, 689 contained documentation of goals-of-care discussions. The mean sensitivity of the NLP/ML program was 82.3% (SD 3.2%), and the mean specificity was 97.4% (SD 0.7%). NLP/ML results had a median positive likelihood ratio of 32.2 (IQR 27.5–39.2) and a median negative likelihood ratio of 0.18 (IQR 0.16–0.20). Performance was better in inpatient-only samples than outpatient-only samples. Conclusion Using NLP and ML techniques, we developed a novel approach to identifying goals-of-care discussions in the EHR. NLP and ML represent a potential approach toward measuring goals-of-care discussions as a research outcome and quality metric.

BACKGROUND/OBJECTIVES: Many older persons with chronic illness use Physician Orders for Life-Sustaining Treatment (POLST) to document portable medical orders for emergency care. However, some POLSTs contain combinations of orders that do not translate into a cohesive care plan (eg, cardiopulmonary resuscitation [CPR] without intensive care, or intensive care without antibiotics). This study characterizes the prevalence and predictors of POLSTs with conflicting orders. DESIGN: Retrospective cohort study. SETTING: Large academic health system. PARTICIPANTS: A total of 3,123 POLST users with chronic life-limiting illness who died between 2010 and 2015 (mean age = 69.7 years). MEASUREMENTS: In a retrospective review of all POLSTs in participants’ electronic health records, we describe the prevalence of POLSTs with conflicting orders for cardiac arrest and medical interventions, and use clustered logistic regression to evaluate potential predictors of conflicting orders. We also examine the prevalence of conflicts between POLST orders for antibiotics and artificial nutrition with orders for cardiac arrest or medical interventions. RESULTS: Among 3,924 complete POLSTs belonging to 3,123 decedents, 209 (5.3%) POLSTs contained orders to “attempt CPR” paired with orders for “limited interventions” or “comfort measures only”; 745/3169 (23.5%) POLSTs paired orders to restrict antibiotics with orders to deliver non-comfort-only care; and, 170/3098 (5.5%) POLSTs paired orders to withhold artificial nutrition with orders to deliver CPR or intensive care. Among POLSTs with orders to avoid intensive care, orders to attempt CPR were more likely to be present in POLSTs completed earlier in the patient’s illness course (adjusted odds ratio = 1.27 per twofold increase in days from POLST to death; 95% confidence interval = 1.18–1.36; P < .001). CONCLUSION: Although most POLSTs are actionable by clinicians, 5% had conflicting orders for cardiac arrest and medical interventions, and 24% had one or more conflicts between orders for cardiac arrest, medical interventions, antibiotics, and artificial nutrition. These conflicting orders make implementation of POLST challenging for clinicians in acute care settings.

Context Recent analyses of Medicare data show decreases over time in intensity of end-of-life care. Few studies exist regarding trends in intensity of end-of-life care for those under 65 years of age. Objectives To examine recent temporal trends in place of death, and both hospital and intensive care unit (ICU) utilization, for age-stratified decedents with chronic, life-limiting diagnoses ( Methods Retrospective cohort using death certificates and electronic health records for 22,068 patients with chronic illnesses who died between 2010 and 2015. We examined utilization overall and stratified by age using multiple regression. Results The proportion of deaths at home did not change, but hospital admissions in the last 30 days of life decreased significantly from 2010 to 2015 (hospital b = −0.026; CI = −0.041, −0.012). ICU admissions in the last 30 days also declined over time for the full sample and for patients aged 65 years or older (overall b = −0.023; CI = −0.039, −0.007), but was not significant for younger decedents. Length of stay (LOS) did not decrease for those using the hospital or ICU. Conclusion From 2010 to 2015, we observed a decrease in hospital admissions for all age groups and in ICU admissions for those over 65 years. As there were no changes in the proportion of patients with chronic illness who died at home nor in hospital or ICU LOS in the last 30 days, hospital and ICU admissions in the last 30 days may be a more responsive quality metric than site of death or LOS for palliative care interventions.

CONTEXT. Advance care planning (ACP) is difficult in the setting of a life-threatening trauma but may be equally important in this context, especially with increasing numbers of trauma victims being elderly or having multimorbidity. OBJECTIVES. Identify predictors of absent ACP documentation in the electronic health records of patients with underlying chronic illness who died of traumatic injury. METHODS. We used death records and electronic health records to identify decedents with chronic life-limiting illness who died of traumatic injury between 2010 and 2015 and to evaluate factors associated with documentation of living wills, durable powers of attorney, or physician orders for life-sustaining treatment. RESULTS. Only 22% of decedents had ACP documentation at time of injury. Among those without preinjury ACP documentation, 4% completed ACP documentation after injury. In multipredictor analyses, patients were less likely to have ACP documentation at the time of injury if they were younger (P < 0.001), had fewer chronic illnesses (P = 0.002), and had fewer nonsurgical hospitalizations (P = 0.042) in the year before injury. Among patients without ACP documentation before injury, those with fewer postinjury nonsurgical hospitalizations were less likely to complete ACP documentation after injury (P = 0.019). CONCLUSIONS. Our findings suggest that patient characteristics play an important role in the completion of ACP among patients with chronic life-limiting illness and who died from sudden severe injury. Interventions to improve ACP completion by patients with serious chronic conditions have the potential for increasing goal-concordant care in the event of traumatic injury.

Family members of ICU survivors report long-term psychologic symptoms of posttraumatic stress disorder. We describe patient- and family-member risk factors for posttraumatic stress disorder symptoms among family members of survivors of acute respiratory distress syndrome.Prospective cohort study of family members of acute respiratory distress syndrome survivors.Single tertiary care center in Seattle, Washington.From 2010 to 2015, we assembled an inception cohort of adult acute respiratory distress syndrome survivors who identified family members involved in ICU and post-ICU care. One-hundred sixty-two family members enrolled in the study, corresponding to 120 patients.None.Family members were assessed for self-reported psychologic symptoms 6 months after patient discharge using the Posttraumatic Stress Disorder Checklist-Civilian Version, the Patient Health Questionnaire 9-item depression scale, and the Generalized Anxiety Disorder 7-item scale. The primary outcome was posttraumatic stress disorder symptoms, and secondary outcomes were symptoms of depression and anxiety. We used clustered multivariable logistic regression to identify patient- and family-member risk factors for psychologic symptoms. Posttraumatic stress disorder symptoms were present in 31% (95% CI, 24-39%) of family participants. Family member risk factors for posttraumatic stress disorder symptoms included preexisting mental health disorders (adjusted odds ratio, 3.22; 95% CI, 1.42-7.31), recent personal experience of serious physical illness (adjusted odds ratio, 3.07; 95% CI, 1.40-6.75), and female gender (adjusted odds ratio, 5.18; 95% CI, 1.74-15.4). Family members of previously healthy patients (Charlson index of zero) had higher frequency of posttraumatic stress disorder symptoms (adjusted odds ratio, 2.25; 95% CI, 1.06-4.77). Markers of patient illness severity were not associated with family posttraumatic stress disorder symptoms.The prevalence of long-term posttraumatic stress disorder symptoms among family members of acute respiratory distress syndrome survivors is high. Family members with preexisting mental health disorders, recent experiences of serious physical illness, and family members of previously healthy patients are at increased risk for posttraumatic stress disorder symptoms.

CONTEXT: Prioritizing amongst potentially conflicting end-of-life values may help patients discriminate amongst treatments and allow clinicians to align treatments with values. OBJECTIVES: To investigate end-of-life values that patients prioritize when facing explicit trade-offs and identify predictors of patients whose values and treatment preferences seem inconsistent. METHODS: Analysis of surveys from a multi-center cluster-randomized trial of patients with serious illness. Respondents prioritized end-of-life values and identified cardiopulmonary resuscitation (CPR) preferences in two health states. RESULTS: Of 535 patients, 60% prioritized relief of discomfort over extending life, 17% prioritized extending life over relief of discomfort, and 23% were unsure. Patients prioritizing extending life were most likely to prefer CPR, with 93% preferring CPR in current health and 67% preferring CPR if dependent on others, compared to 69% and 21% respectively for patients prioritizing relief of discomfort, and 78% and 33% respectively for patients unsure of their prioritized value (p

Importance Patients with chronic illness frequently use Physician Orders for Life-Sustaining Treatment (POLST) to document treatment limitations. Objectives To evaluate the association between POLST order for medical interventions and intensive care unit (ICU) admission for patients hospitalized near the end of life. Design, Setting, and Participants Retrospective cohort study of patients with POLSTs and with chronic illness who died between January 1, 2010, and December 31, 2017, and were hospitalized 6 months or less before death in a 2-hospital academic health care system. Exposures POLST order for medical interventions (“comfort measures only” vs “limited additional interventions” vs “full treatment”), age, race/ethnicity, education, days from POLST completion to admission, histories of cancer or dementia, and admission for traumatic injury. Main Outcomes and Measures The primary outcome was the association between POLST order and ICU admission during the last hospitalization of life; the secondary outcome was receipt of a composite of 4 life-sustaining treatments: mechanical ventilation, vasopressors, dialysis, and cardiopulmonary resuscitation. For evaluating factors associated with POLST-discordant care, the outcome was ICU admission contrary to POLST order for medical interventions during the last hospitalization of life. Results Among 1818 decedents (mean age, 70.8 [SD, 14.7] years; 41% women), 401 (22%) had POLST orders for comfort measures only, 761 (42%) had orders for limited additional interventions, and 656 (36%) had orders for full treatment. ICU admissions occurred in 31% (95% CI, 26%-35%) of patients with comfort-only orders, 46% (95% CI, 42%-49%) with limited-interventions orders, and 62% (95% CI, 58%-66%) with full-treatment orders. One or more life-sustaining treatments were delivered to 14% (95% CI, 11%-17%) of patients with comfort-only orders and to 20% (95% CI, 17%-23%) of patients with limited-interventions orders. Compared with patients with full-treatment POLSTs, those with comfort-only and limited-interventions orders were significantly less likely to receive ICU admission (comfort only: 123/401 [31%] vs 406/656 [62%], aRR, 0.53 [95% CI, 0.45-0.62]; limited interventions: 349/761 [46%] vs 406/656 [62%], aRR, 0.79 [95% CI, 0.71-0.87]). Across patients with comfort-only and limited-interventions POLSTs, 38% (95% CI, 35%-40%) received POLST-discordant care. Patients with cancer were significantly less likely to receive POLST-discordant care than those without cancer (comfort only: 41/181 [23%] vs 80/220 [36%], aRR, 0.60 [95% CI, 0.43-0.85]; limited interventions: 100/321 [31%] vs 215/440 [49%], aRR, 0.63 [95% CI, 0.51-0.78]). Patients with dementia and comfort-only orders were significantly less likely to receive POLST-discordant care than those without dementia (23/111 [21%] vs 98/290 [34%], aRR, 0.44 [95% CI, 0.29-0.67]). Patients admitted for traumatic injury were significantly more likely to receive POLST-discordant care (comfort only: 29/64 [45%] vs 92/337 [27%], aRR, 1.52 [95% CI, 1.08-2.14]; limited interventions: 51/91 [56%] vs 264/670 [39%], aRR, 1.36 [95% CI, 1.09-1.68]). In patients with limited-interventions orders, older age was significantly associated with less POLST-discordant care (aRR, 0.93 per 10 years [95% CI, 0.88-1.00]). Conclusions and Relevance Among patients with POLSTs and with chronic life-limiting illness who were hospitalized within 6 months of death, treatment-limiting POLSTs were significantly associated with lower rates of ICU admission compared with full-treatment POLSTs. However, 38% of patients with treatment-limiting POLSTs received intensive care that was potentially discordant with their POLST.

As our population ages and the burden of chronic illness rises, there is increasing need to implement quality metrics that measure and benchmark care of the seriously ill, including the delivery of both primary care and specialty palliative care. Such metrics can be used to drive quality improvement, value-based payment, and accountability for population-based outcomes.In this article, we examine use of the electronic health record (EHR) as a tool to assess quality of serious illness care through narrative review and description of a palliative care quality metrics program in a large healthcare system.In the search for feasible, reliable, and valid palliative care quality metrics, the EHR is an attractive option for collecting quality data on large numbers of seriously ill patients. However, important challenges to using EHR data for quality improvement and accountability exist, including understanding the validity, reliability, and completeness of the data, as well as acknowledging the difference between care documented and care delivered. Challenges also include developing achievable metrics that are clearly linked to patient and family outcomes and addressing data interoperability across sites as well as EHR platforms and vendors. This article summarizes the strengths and weakness of the EHR as a data source for accountability of community- and population-based programs for serious illness, describes the implementation of EHR data in the palliative care quality metrics program at the University of Washington, and, based on that experience, discusses opportunities and challenges. Our palliative care metrics program was designed to serve as a resource for other healthcare systems.Although the EHR offers great promise for enhancing quality of care provided for the seriously ill, significant challenges remain to operationalizing this promise on a national scale and using EHR data for population-based quality and accountability.

Molecular brushes, with side chains consisting of two copolymers: 2-(dimethylamino)ethyl methacrylate with methyl methacrylate, and N,N-dimethylacrylamide with butyl acrylate were prepared by grafting-from via atom transfer radical polymerization (ATRP). Poly(2-(2-bromoisobutyryloxy)ethyl methacrylate) and poly(2-(2-bromopropionyloxy)ethyl methacrylate) were used as macroinitiators. Dynamic light scattering (DLS) studies were performed for aqueous solutions of molecular brushes below and above the lower critical solution temperature (LCST), and an unusual concentration-dependent LCST was observed. Due to the compact structure of molecular brushes, intramolecular collapse can occur when the average distance between molecules is much larger than the hydrodynamic dimensions of the individual macromolecules. However, if the concentration of the solution of molecular brushes is increased to the level in which the separation distance is comparable with the brush hydrodynamic dimensions, intermolecular aggregation occurs, as typically observed for solutions of linear polymers.

To prepare polymers with pendant functionality capable of participating in highly efficient CuI-catalyzed 1,3-dipolar cycloaddition of azide and alkynes, monomers with acetylene or azido groups were polymerized via controlled radical polymerization. Atom transfer radical polymerization (ATRP) of propargyl methacrylate (PgMA) resulted in high polydispersities (Mw/Mn > 3), multimodal molecular weight distributions, and cross-linked networks at moderate to high conversion. The poor results obtained with this monomer were presumably due to addition of the propagating radicals to the acetylene group, transfer reactions, and/or interference with the catalyst. A novel monomer, 3-azidopropyl methacrylate (AzPMA), was polymerized via ATRP with good control of the polymer molecular weight distribution and retention of chain functionality. Poly(3-azidopropyl methacrylate) was coupled with propargyl alcohol, propargyl triphenylphosphonium bromide, propargyl 2-bromoisobutyrate, and 4-pentynoic acid via a highly effici...