Hospital Policy Variation in Addressing Decisions to Withhold and Withdraw Life-Sustaining Treatment.

BACKGROUND: Sociodemographic disparities in physician decisions to withhold and withdraw life-sustaining treatment exist. Little is known about the content of hospital policies that guide physicians involved in these decisions. RESEARCH QUESTION: What is the prevalence of US hospitals with policies that address withholding and withdrawing life-sustaining treatment; how do these policies approach ethically controversial scenarios; and how do these policies address sociodemographic disparities in decisions to withhold and withdraw life-sustaining treatment? STUDY DESIGN AND METHODS: This national cross-sectional survey assessed the content of hospital policies addressing decisions to withhold or withdraw life-sustaining treatment. We distributed the survey electronically to American Society for Bioethics and Humanities members between July and August 2023 and descriptively analyzed responses. RESULTS: Among 93 respondents from hospitals or hospital systems representing all 50 US states, Puerto Rico, and Washington, DC, 92% had policies addressing decisions to withhold or withdraw life-sustaining treatment. Hospitals varied in their stated guidance, permitting life-sustaining treatment to be withheld or withdrawn in cases of patient or surrogate request (82%), physiologic futility (81%), and potentially inappropriate treatment (64%). Of the 8% of hospitals with policies that addressed patient sociodemographic disparities in decisions to withhold or withdraw life-sustaining treatment, these policies provided opposing recommendations to either exclude sociodemographic factors in decision-making or actively acknowledge and incorporate these factors in decision-making. Only 3% of hospitals had policies that recommended collecting and maintaining information about patients for whom life-sustaining treatment was withheld or withdrawn that could be used to identify disparities in decision-making. INTERPRETATION: Although most surveyed US hospital policies addressed withholding or withdrawing life-sustaining treatment, these policies varied widely in criteria and processes. Surveyed policies also rarely addressed sociodemographic disparities in these decisions.

Top Ten Tips Palliative Care Clinicians Should Know About Leveraging the Electronic Health Record for Data Collection and Quality Improvement.

As palliative care (PC) programs rapidly grow and expand across settings, the need to measure, improve, and standardize high-quality PC has also grown. The electronic health record (EHR) is a key component of these efforts as a central hub of care delivery and a repository of patient and system data. Deliberate efforts to leverage the EHR for PC quality improvement (QI) can help PC programs and health systems improve care for patients with serious illnesses. This article, written by clinicians with experience in QI, informatics, and clinical program development, provides practical tips and guidance on EHR strategies and tools for QI and quality measurement.

The Timing of Family Meetings in the Medical Intensive Care Unit.

PURPOSE: Family meetings in the medical intensive care unit can improve outcomes. Little is known about when meetings occur in practice. We aimed to determine the time from admission to family meetings in the medical intensive care unit and assess the relationship of meetings with mortality. METHODS: We performed a prospective cohort study of critically ill adult patients admitted to the medical intensive care unit at an urban academic medical center. Using manual chart review, the primary outcome was any attempt at holding a family meeting within 72 hours of admission. Competing risk models estimated the time from admission to family meeting and to patient death or discharge. RESULTS: Of the 131 patients who met inclusion criteria in the 12-month study period, the median time from admission to family meeting was 4 days. Fewer than half of patients had a documented family meeting within 72 hours of admission (n = 60/131, 46%), with substantial interphysician variability in meeting rates ranging from 28% to 63%. Patients with family meetings within 72 hours were 30 times more likely to die within 72 hours (32% vs 1%, P < .001). Of the 55 patients who died in the intensive care unit, 27 (49%) had their first family meeting within 1 day of death. CONCLUSIONS: Family meetings occur considerably later than 72 hours and are often held in close proximity to a patient's death. This suggests for some physicians, family meetings may primarily be used to negotiate withdrawal of life support rather than to support the patient and family.

A Rules-Based Algorithm to Prioritize Poor Prognosis Cancer Patients in Need of Advance Care Planning.

BACKGROUND: Accurate understanding of the prognosis of an advanced cancer patient can lead to decreased aggressive care at the end of life and earlier hospice enrollment. OBJECTIVE: Our goal was to determine the association between high-risk clinical events identified by a simple, rules-based algorithm and decreased overall survival, to target poor prognosis cancer patients who would urgently benefit from advanced care planning. DESIGN: A retrospective analysis was performed on outpatient oncology patients with an index visit from April 1, 2015, through June 30, 2015. We examined a three-month window for "high-risk events," defined as (1) change in chemotherapy, (2) emergency department (ED) visit, and (3) hospitalization. Patients were followed until January 31, 2017. SETTING/SUBJECTS: A total of 219 patients receiving palliative chemotherapy at the University of Chicago Medicine with a prognosis of ≤12 months were included. MEASUREMENTS: The main outcome was overall survival, and each "high-risk event" was treated as a time-varying covariate in a Cox proportional hazards regression model to calculate a hazard ratio (HR) of death. RESULTS: A change in chemotherapy regimen, ED visit, hospitalization, and at least one high-risk event occurred in 54% (118/219), 10% (22/219), 26% (57/219), and 67% (146/219) of patients, respectively. The adjusted HR of death for patients with a high-risk event was 1.72 (95% confidence interval [CI] 1.19-2.46, p = 0.003), with hospitalization reaching significance (HR 2.74, 95% CI 1.84-4.09, p < 0.001). CONCLUSIONS: The rules-based algorithm identified those with the greatest risk of death among a poor prognosis patient group. Implementation of this algorithm in the electronic health record can identify patients with increased urgency to address goals of care.

Relationship between ICU bed availability, ICU readmission, and cardiac arrest in the general wards.

OBJECTIVE: The decision to admit a patient to the ICU is complex, reflecting patient factors and available resources. Previous work has shown that ICU census does not impact mortality of patients admitted to the ICU. However, the effect of ICU bed availability on patients outside the ICU is unknown. We sought to determine the association between ICU bed availability, ICU readmissions, and ward cardiac arrests. DESIGN: In this observational study using data collected between 2009 and 2011, rates of ICU readmission and ward cardiac arrest were determined per 12-hour shift. The relationship between these rates and the number of available ICU beds at the start of each shift (accounting for census and nursing capacity) was investigated. Grouped logistic regression was used to adjust for potential confounders. SETTING: Five specialized adult ICUs comprising 63 adult ICU beds in an academic medical center. PATIENTS: Any patient admitted to a non-ICU inpatient unit was counted in the ward census and considered at risk for ward cardiac arrest. Patients discharged from an ICU were considered at risk for ICU readmission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data were available for 2,086 of 2,190 shifts. The odds of ICU readmission increased with each decrease in the overall number of available ICU beds (odds ratio = 1.06; 95% CI, 1.00-1.12; p = 0.03), with a similar but not statistically significant association demonstrated in ward cardiac arrest rate (odds ratio = 1.06; 95% CI, 0.98-1.14; p = 0.16). In subgroup analysis, the odds of ward cardiac arrest increased with each decrease in the number of medical ICU beds available (odds ratio = 1.26; 95% CI, 1.06-1.49; p = 0.01). CONCLUSIONS: Reduced ICU bed availability is associated with increased rates of ICU readmission and ward cardiac arrest. This suggests that systemic factors are associated with patient outcomes, and flexible critical care resources may be needed when demand is high.

Predicting cardiac arrest on the wards: a nested case-control study.

BACKGROUND: Current rapid response team activation criteria were not statistically derived using ward vital signs, and the best vital sign predictors of cardiac arrest (CA) have not been determined. In addition, it is unknown when vital signs begin to accurately detect this event prior to CA. METHODS: We conducted a nested case-control study of 88 patients experiencing CA on the wards of a university hospital between November 2008 and January 2011, matched 1:4 to 352 control subjects residing on the same ward at the same time as the case CA. Vital signs and Modified Early Warning Scores (MEWS) were compared on admission and during the 48 h preceding CA. RESULTS: Case patients were older (64 ± 16 years vs 58 ± 18 years; P = .002) and more likely to have had a prior ICU admission than control subjects (41% vs 24%; P = .001), but had similar admission MEWS (2.2 ± 1.3 vs 2.0 ± 1.3; P = .28). In the 48 h preceding CA, maximum MEWS was the best predictor (area under the receiver operating characteristic curve [AUC] 0.77; 95% CI, 0.71-0.82), followed by maximum respiratory rate (AUC 0.72; 95% CI, 0.65-0.78), maximum heart rate (AUC 0.68; 95% CI, 0.61-0.74), maximum pulse pressure index (AUC 0.61; 95% CI, 0.54-0.68), and minimum diastolic BP (AUC 0.60; 95% CI, 0.53-0.67). By 48 h prior to CA, the MEWS was higher in cases (P = .005), with increasing disparity leading up to the event. CONCLUSIONS: The MEWS was significantly different between patients experiencing CA and control patients by 48 h prior to the event, but includes poor predictors of CA such as temperature and omits significant predictors such as diastolic BP and pulse pressure index.