Management of REM sleep behavior disorder: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment.

This systematic review provides supporting evidence for a clinical practice guideline for the management of rapid eye movement (REM) sleep behavior disorder in adults and children. The American Academy of Sleep Medicine commissioned a task force of 7 experts in sleep medicine. A systematic review was conducted to identify randomized controlled trials and observational studies that addressed interventions for the management of REM sleep behavior disorder in adults and children. Statistical analyses were performed to determine the clinical significance of critical and important outcomes. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence for making recommendations. The literature search identified 4,690 studies; 148 studies provided data suitable for statistical analyses; evidence for 45 interventions is presented. The task force provided a detailed summary of the evidence assessing the certainty of evidence, the balance of benefits and harms, patient values and preferences, and resource use considerations. CITATION: Howell M, Avidan AY, Foldvary-Schaefer N, et al. Management of REM sleep behavior disorder: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment. J Clin Sleep Med. 2023;19(4):769-810.

Ensuring Fairness in Machine Learning to Advance Health Equity.

Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and structural biases in the past-called protected groups-are vulnerable to harm by incorrect predictions or withholding of resources. This article describes how model design, biases in data, and the interactions of model predictions with clinicians and patients may exacerbate health care disparities. Rather than simply guarding against these harms passively, machine-learning systems should be used proactively to advance health equity. For that goal to be achieved, principles of distributive justice must be incorporated into model design, deployment, and evaluation. The article describes several technical implementations of distributive justice-specifically those that ensure equality in patient outcomes, performance, and resource allocation-and guides clinicians as to when they should prioritize each principle. Machine learning is providing increasingly sophisticated decision support and population-level monitoring, and it should encode principles of justice to ensure that models benefit all patients.

Comparison of Hospital Resource Use and Outcomes Among Hospitalists, Primary Care Physicians, and Other Generalists.

Importance: A physician's prior experience caring for a patient may be associated with patient outcomes and care patterns during and after hospitalization. Objective: To examine differences in the use of health care resources and outcomes among hospitalized patients cared for by hospitalists, their own primary care physicians (PCPs), or other generalists. Design, Setting, and participants: This retrospective study analyzed admissions for the 20 most common medical diagnoses among elderly fee-for-service Medicare patients from January 1 through December 31, 2013. Patients had at least 1 previous encounter with an outpatient clinician within the 365 days before admission, and diagnoses were restricted to the 20 most common diagnosis related groups. Data were collected from Medicare Parts A and B claims data, and outcomes were analyzed from January 1, 2013, through January 31, 2014. Exposures: Physician types included hospitalists, PCPs (ie, the physicians who provided a plurality of ambulatory visits in the year preceding admission), or generalists (not the patients' PCPs). Main Outcomes and Measures: Number of in-hospital specialist consultations, length of stay, discharge site, all-cause 7- and 30-day readmission rates, and 30-day mortality. Results: A total of 560 651 admissions were analyzed (41.9% men and 59.1% women; mean [SD] age, 80 [8] years). Patients' physicians were hospitalists in 59.7% of admissions; PCPs, in 14.2%; and other generalists, in 26.1%. Primary care physicians used consultations 3% more (relative risk, 1.03; 95% CI, 1.02-1.05) and other generalists used consultations 6% more (relative risk, 1.06; 95% CI, 1.05-1.07) than hospitalists. Lengths of stay were 12% longer among patients cared for by PCPs (adjusted incidence rate ratio, 1.12; 95% CI, 1.11-1.13) and 6% longer among those cared for by other generalists (adjusted incidence rate ratio, 1.06; 95% CI, 1.05-1.07) compared with patients cared for by hospitalists. However, PCPs were more likely to discharge patients home (adjusted odds ratio [AOR], 1.14; 95% CI, 1.11-1.17), whereas other generalists were less likely to do so (AOR, 0.94; 95% CI, 0.92-0.96). Relative to hospitalists, patients cared for by PCPs had similar readmission rates at 7 days (AOR, 0.98; 95% CI, 0.96-1.01) and 30 days (AOR, 1.02; 95% CI, 0.99-1.04), whereas other generalists' readmission rates were greater than hospitalists' rates at 7 (AOR, 1.05; 95% CI, 1.02-1.07) and 30 (AOR, 1.04; 95% CI, 1.03-1.06) days. Patients cared for by PCPs had lower 30-day mortality than patients of hospitalists (AOR, 0.94; 95% CI, 0.91-0.97), whereas the mortality rate of patients of other generalists was higher (AOR, 1.09; 95% CI, 1.07-1.12). Conclusions and Relevance: A PCP's prior experience with a patient may be associated with inpatient use of resources and patient outcomes. Patients cared for by their own PCP had slightly longer lengths of stay and were more likely to be discharged home but also were less likely to die within 30 days compared with those cared for by hospitalists or other generalists.

Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit.

RATIONALE: The 2016 definitions of sepsis included the quick Sepsis-related Organ Failure Assessment (qSOFA) score to identify high-risk patients outside the intensive care unit (ICU). OBJECTIVES: We sought to compare qSOFA with other commonly used early warning scores. METHODS: All admitted patients who first met the criteria for suspicion of infection in the emergency department (ED) or hospital wards from November 2008 until January 2016 were included. The qSOFA, Systemic Inflammatory Response Syndrome (SIRS), Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS) were compared for predicting death and ICU transfer. MEASUREMENTS AND MAIN RESULTS: Of the 30,677 included patients, 1,649 (5.4%) died and 7,385 (24%) experienced the composite outcome (death or ICU transfer). Sixty percent (n = 18,523) first met the suspicion criteria in the ED. Discrimination for in-hospital mortality was highest for NEWS (area under the curve [AUC], 0.77; 95% confidence interval [CI], 0.76-0.79), followed by MEWS (AUC, 0.73; 95% CI, 0.71-0.74), qSOFA (AUC, 0.69; 95% CI, 0.67-0.70), and SIRS (AUC, 0.65; 95% CI, 0.63-0.66) (P < 0.01 for all pairwise comparisons). Using the highest non-ICU score of patients, ≥2 SIRS had a sensitivity of 91% and specificity of 13% for the composite outcome compared with 54% and 67% for qSOFA ≥2, 59% and 70% for MEWS ≥5, and 67% and 66% for NEWS ≥8, respectively. Most patients met ≥2 SIRS criteria 17 hours before the combined outcome compared with 5 hours for ≥2 and 17 hours for ≥1 qSOFA criteria. CONCLUSIONS: Commonly used early warning scores are more accurate than the qSOFA score for predicting death and ICU transfer in non-ICU patients. These results suggest that the qSOFA score should not replace general early warning scores when risk-stratifying patients with suspected infection.

Differences between early and late readmissions among patients: a cohort study.

BACKGROUND: Early and late readmissions may have different causal factors, requiring different prevention strategies. OBJECTIVE: To determine whether predictors of readmission change within 30 days after discharge. DESIGN: Retrospective cohort study. SETTING: Academic medical center. PARTICIPANTS: Patients admitted between 1 January 2009 and 31 December 2010. MEASUREMENTS: Factors related to the index hospitalization (acute illness burden, inpatient care process factors, and clinical indicators of instability at discharge) and unrelated factors (chronic illness burden and social determinants of health) and how they affect early readmissions (0 to 7 days after discharge) and late readmissions (8 to 30 days after discharge). RESULTS: 13 334 admissions, representing 8078 patients, were included in the analysis. Early readmissions were associated with markers of acute illness burden, including length of hospital stay (odds ratio [OR], 1.02 [95% CI, 1.00 to 1.03]) and whether a rapid response team was called for assessment (OR, 1.48 [CI, 1.15 to 1.89]); markers of chronic illness burden, including receiving a medication indicating organ failure (OR, 1.19 [CI, 1.02 to 1.40]); and social determinants of health, including barriers to learning (OR, 1.18 [CI, 1.01 to 1.38]). Early readmissions were less likely if a patient was discharged between 8:00 a.m. and 12:59 p.m. (OR, 0.76 [CI, 0.58 to 0.99]). Late readmissions were associated with markers of chronic illness burden, including receiving a medication indicating organ failure (OR, 1.24 [CI, 1.08 to 1.41]) or hemodialysis (OR, 1.61 [CI, 1.12 to 2.17]), and social determinants of health, including barriers to learning (OR, 1.24 [CI, 1.09 to 1.42]) and having unsupplemented Medicare or Medicaid (OR, 1.16 [CI, 1.01 to 1.33]). LIMITATION: Readmissions were ascertained at 1 institution. CONCLUSION: The time frame of 30 days after hospital discharge may not be homogeneous. Causal factors and readmission prevention strategies may differ for the early versus late periods. PRIMARY FUNDING SOURCE: Health Resources and Services Administration, National Institute on Aging, National Institutes of Health, Harvard Catalyst, and Harvard University.

Determining the Ideal Strategy for Ventilator-associated Pneumonia Prevention. Cost-Benefit Analysis.

RATIONALE: Ventilator-associated pneumonia (VAP) is a common healthcare-associated infection with high associated cost and poor patient outcomes. Many strategies for VAP reduction have been evaluated. However, the combination of strategies with the optimal cost-benefit ratio remains unknown. OBJECTIVES: To determine the preferred VAP prevention strategy, both from the hospital and societal perspectives. METHODS: A cost-benefit decision model with a Markov model was constructed. Baseline probability of VAP, death, reintubation, and discharge from the intensive care unit (ICU) alive were ascertained from clinical trial data. Model inputs were obtained from the medical literature and the U.S. Department of Labor; a device cost was obtained from the manufacturer. Sensitivity analyses were completed to test the robustness of model results. MEASUREMENTS AND MAIN RESULTS: Overall least expensive strategy and the strategy with the best cost-benefit ratio, up to a willingness to pay threshold of $50,000-100,000 per case of VAP averted was sought. We examined a total of 120 unique combinations of VAP prevention strategies. The preferred strategy from the hospital perspective included subglottic suction endotracheal tubes, probiotics, and the Institute for Healthcare Improvement VAP Prevention Bundle. The preferred strategy from the point of view of society also included additional prevention measures (oral care with chlorhexidine and selective oral decontamination). No preferred strategies included silver endotracheal tubes or selective gut decontamination. CONCLUSIONS: Despite their infrequent use, current data suggest that the use of prophylactic probiotics and subglottic endotracheal tubes are cost-effective for preventing VAP from the societal and hospital perspectives.

Variation in Inpatient Consultation Among Older Adults in the United States.

BACKGROUND: Differences among hospitals in the use of inpatient consultation may contribute to variation in outcomes and costs for hospitalized patients, but basic epidemiologic data on consultations nationally are lacking. OBJECTIVE: The purpose of the study was to identify physician, hospital, and geographic factors that explain variation in rates of inpatient consultation. DESIGN: This was a retrospective observational study. SETTING AND PARTICIPANTS: This work included 3,118,080 admissions of Medicare patients to 4,501 U.S. hospitals in 2009 and 2010. MAIN MEASURES: The primary outcome measured was number of consultations conducted during the hospitalization, summarized at the hospital level as the number of consultations per 1,000 Medicare admissions, or "consultation density." KEY RESULTS: Consultations occurred 2.6 times per admission on average. Among non-critical access hospitals, use of consultation varied 3.6-fold across quintiles of hospitals (933 versus 3,390 consultations per 1,000 admissions, lowest versus highest quintiles, p < 0.001). Sicker patients received greater intensity of consultation (rate ratio [RR] 1.18, 95% CI 1.17-1.18 for patients admitted to ICU; and RR 1.19, 95% CI 1.18-1.20 for patients who died). However, even after controlling for patient-level factors, hospital characteristics also predicted differences in rates of consultation. For example, hospital size (large versus small, RR 1.31, 95% CI 1.25-1.37), rural location (rural versus urban, RR 0.78, CI 95% 0.76-0.80), ownership status (public versus not-for-profit, RR 0.94, 95% CI 0.91-0.97), and geographic quadrant (Northeast versus West, RR 1.17, 95% CI 1.12-1.21) all influenced the intensity of consultation use. CONCLUSIONS: Hospitals exhibit marked variation in the number of consultations per admission in ways not fully explained by patient characteristics. Hospital "consultation density" may constitute an important focus for monitoring resource use for hospitals or health systems.

Intensivist/patient ratios in closed ICUs: a statement from the Society of Critical Care Medicine Taskforce on ICU Staffing.

OBJECTIVES: Increases in the number, size, and occupancy rates of ICUs have not been accompanied by a commensurate growth in the number of critical care physicians leading to a workforce shortage. Due to concern that understaffing may exist, the Society of Critical Care Medicine created a taskforce to generate guidelines on maximum intensivists/patient ratios. DATA SOURCES: A multidisciplinary taskforce conducted a review of published literature on intensivist staffing and related topics, a survey of pulmonary/Critical Care physicians, and held an expert roundtable conference. DATA EXTRACTION: A statement was generated and revised by the taskforce members using an iterative consensus process and submitted for review to the leadership council of the Society of Critical Care Medicine. For the purposes of this statement, the taskforce limited its recommendations to ICUs that use a "closed" model where the intensivists control triage and patient care. DATA SYNTHESIS AND CONCLUSIONS: The taskforce concluded that while advocating a specific maximum number of patients cared for is unrealistic, an approach that uses the following principles is essential: 1) proper staffing impacts patient care; 2) large caseloads should not preclude rounding in a timely fashion; 3) staffing decisions should factor surge capacity and nondirect patient care activities; 4) institutions should regularly reassess their staffing; 5) high staff turnover or decreases in quality-of-care indicators in an ICU may be markers of overload; 6) telemedicine, advanced practice professionals, or nonintensivist medical staff may be useful to alleviate overburdening the intensivist, but should be evaluated using rigorous methods; 7) in teaching institutions, feedback from faculty and trainees should be sought to understand the implications of potential understaffing on medical education; and 8) in academic medical ICUs, there is evidence that intensivist/patient ratios less favorable than 1:14 negatively impact education, staff well-being, and patient care.

Intensivist time allocation: economic and ethical issues surrounding how intensivists use their time.

Intensivists' time is a fundamentally constrained resource. Many factors can put intensivists under conditions in which demands for their time outstrip the amount of time available. The importance of intensivist time strain is increasing both because demand for intensivists exceeds supply and because the roles that intensivists are being asked to take on are constantly expanding. There is strong evidence that time strain affects the decisions that intensivists make; evidence about whether it impacts patient outcomes is mixed. In deciding how to allocate their time, intensivists face many challenges. This article highlights two of these challenges: (1) How should intensivists approach two common scheduling-related issues (24/7 intensive care unit coverage and long blocks of service time that promote continuity but sacrifice weekends off) and balance these issues with the very real workforce concern of accelerated professional burnout? (2) What are the hidden financial impacts of intensivist participation in quality improvement programs, given current reimbursement systems?

The costs and cost-effectiveness of an integrated sepsis treatment protocol.

CONTEXT: Sepsis is associated with high mortality and treatment costs. International guidelines recommend the implementation of integrated sepsis protocols; however, the true cost and cost-effectiveness of these are unknown. OBJECTIVE: To assess the cost-effectiveness of an integrated sepsis protocol, as compared with conventional care. DESIGN: Prospective cohort study of consecutive patients presenting with septic shock and enrolled in the institution's integrated sepsis protocol. Clinical and economic outcomes were compared with a historical control cohort. SETTING: Beth Israel Deaconess Medical Center. PATIENTS: Overall, 79 patients presenting to the emergency department with septic shock in the treatment cohort and 51 patients in the control group. INTERVENTIONS: An integrated sepsis treatment protocol incorporating empirical antibiotics, early goal-directed therapy, intensive insulin therapy, lung-protective ventilation, and consideration for drotrecogin alfa and steroid therapy. MAIN OUTCOME MEASURES: In-hospital treatment costs were collected using the hospital's detailed accounting system. The cost-effectiveness analysis was performed from the perspective of the healthcare system using a lifetime horizon. The primary end point for the cost-effectiveness analysis was the incremental cost per quality-adjusted life year gained. RESULTS: Mortality in the treatment group was 20.3% vs. 29.4% in the control group (p = .23). Implementing an integrated sepsis protocol resulted in a mean increase in cost of approximately $8,800 per patient, largely driven by increased intensive care unit length of stay. Life expectancy and quality-adjusted life years were higher in the treatment group; 0.78 and 0.54, respectively. The protocol was associated with an incremental cost of $11,274 per life-year saved and a cost of $16,309 per quality-adjusted life year gained. CONCLUSIONS: In patients with septic shock, an integrated sepsis protocol, although not cost-saving, appears to be cost-effective and compares very favorably to other commonly delivered acute care interventions.

Simple triage scoring system predicting death and the need for critical care resources for use during epidemics.

OBJECTIVES: In the event of pandemic influenza, the number of critically ill victims will likely overwhelm critical care capacity. To date, no standardized method for allocating scarce resources when the number of patients in need far exceeds capacity exists. We sought to derive and validate such a triage scheme. DESIGN: : Retrospective analysis of prospectively collected data. SETTING: Emergency departments of two urban tertiary care hospitals. PATIENTS: Three separate cohorts of emergency department patients with suspected infection, comprising a total of 5,133 patients. INTERVENTIONS: None. MEASUREMENTS: A triage decision rule for use in an epidemic was developed using only those vital signs and patient characteristics that were readily available at initial presentation to the emergency department. The triage schema was derived from a cohort at center 1, validated on a second cohort from center 1, and then validated on a third cohort of patients from center 2. The primary outcome for the analysis was in-hospital mortality. Secondary outcomes were intensive care unit admission and use of mechanical ventilation. MAIN RESULTS: Multiple logistic regression demonstrated the following as independent predictors of death: a) age of >65 yrs, b) altered mental status, c) respiratory rate of >30 breaths/min, d) low oxygen saturation, and e) shock index of >1 (heart rate > blood pressure). This model had an area under the receiver operating characteristic curve of 0.80 in the derivation set and 0.74 and 0.76 in the validation sets. When converted to a simple rule assigning 1 point per covariate, the discrimination of the model remained essentially unchanged. The model was equally effective at predicting need for intensive care unit admission and mechanical ventilation. CONCLUSIONS: If, as expected, patient demand far exceeds the capability to provide critical care services in an epidemic, a fair and just system to allocate limited resources will be essential. The triage rule we have developed can serve as an initial guide for such a process.