Circadian Profile of an Emergency Medicine Department: Scheduling Practices and Their Effects on Sleep and Performance.

BACKGROUND: Shiftwork causes circadian disruption and is the primary reason for attrition from Emergency Medicine. OBJECTIVES: We aimed to develop concrete recommendations to mitigate negative effects of shiftwork based on measures of work, sleep, alertness, and performance in emergency physicians. METHODS: Thirty-one Emergency Medicine residents were surveyed retrospectively about sleep and alertness on different shifts. Additionally, the sleep, performance, and alertness of 22 Emergency Medicine resident and attending physicians was tracked continuously over 4 weeks via sleep logs, actigraphy, real-time reported sleepiness, and performance on a vigilance task. Schedules were analyzed for circadian disruption. Physicians also predicted their sleep schedules, which were compared with actual schedules; participants tracked extensions of shifts, schedule changes, and shifts in other hospitals. RESULTS: Daily rhythms were apparent in real-time performance and alertness data, with peaks at around 4 pm. Sleep difficulty was highest, sleep shortest, and alertness and performance lowest for night shifts. Emergency Medicine residents tended to cluster multiple night shifts in a row, despite evidence of accumulating sleep debt over consecutive shifts. There were many shifts that caused high circadian disruption, which could be avoided by simple amendments to scheduling practices. CONCLUSIONS: Circadian principles should be applied as suggested by the American College of Emergency Physicians. Chronotype should be considered in scheduling. Night shifts, particularly, should not be extended. Clustering all night shifts in a row should probably be discouraged. The additional vulnerabilities for night shift could be mitigated by adopting napping mid- or post night shift and by providing pay differentials.

Performance of Machine Learning Suicide Risk Models in an American Indian Population.

Importance: Few suicide risk identification tools have been developed specifically for American Indian and Alaska Native populations, even though these populations face the starkest suicide-related inequities. Objective: To examine the accuracy of existing machine learning models in a majority American Indian population. Design, Setting, and Participants: This prognostic study used secondary data analysis of electronic health record data collected from January 1, 2017, to December 31, 2021. Existing models from the Mental Health Research Network (MHRN) and Vanderbilt University (VU) were fitted. Models were compared with an augmented screening indicator that included any previous attempt, recent suicidal ideation, or a recent positive suicide risk screen result. The comparison was based on the area under the receiver operating characteristic curve (AUROC). The study was performed in partnership with a tribe and local Indian Health Service (IHS) in the Southwest. All patients were 18 years or older with at least 1 encounter with the IHS unit during the study period. Data were analyzed between October 6, 2022, and July 29, 2024. Exposures: Suicide attempts or deaths within 90 days. Main Outcomes and Measures: Model performance was compared based on the ability to distinguish between those with a suicide attempt or death within 90 days of their last IHS visit with those without this outcome. Results: Of 16 835 patients (mean [SD] age, 40.0 [17.5] years; 8660 [51.4%] female; 14 251 [84.7%] American Indian), 324 patients (1.9%) had at least 1 suicide attempt, and 37 patients (0.2%) died by suicide. The MHRN model had an AUROC value of 0.81 (95% CI, 0.77-0.85) for 90-day suicide attempts, whereas the VU model had an AUROC value of 0.68 (95% CI, 0.64-0.72), and the augmented screening indicator had an AUROC value of 0.66 (95% CI, 0.63-0.70). Calibration was poor for both models but improved after recalibration. Conclusion and Relevance: This prognostic study found that existing risk identification models for suicide prevention held promise when applied to new contexts and performed better than relying on a combined indictor of a positive suicide risk screen result, history of attempt, and recent suicidal ideation.

Developing a suicide risk model for use in the Indian Health Service.

We developed and evaluated an electronic health record (EHR)-based model for suicide risk specific to an American Indian patient population. Using EHR data for all patients over 18 with a visit between 1/1/2017 and 10/2/2021, we developed a model for the risk of a suicide attempt or death in the 90 days following a visit. Features included demographics, medications, diagnoses, and scores from relevant screening tools. We compared the predictive performance of logistic regression and random forest models against existing suicide screening, which was augmented to include the history of previous attempts or ideation. During the study, 16,835 patients had 331,588 visits, with 490 attempts and 37 deaths by suicide. The logistic regression and random forest models (area under the ROC (AUROC) 0.83 [0.80-0.86]; both models) performed better than enhanced screening (AUROC 0.64 [0.61-0.67]). These results suggest that an EHR-based suicide risk model can add value to existing practices at Indian Health Service clinics.

Titration of mean airway pressure and FiO2 during high frequency oscillatory ventilation in a porcine model of acute lung injury.

BACKGROUND: High frequency oscillatory ventilation (HFOV) is frequently utilized for patients with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). However, precise criteria to titrate mean airway pressure (mPaw) and FiO(2) as the patient's condition improves are lacking. We hypothesized that reducing mPaw and FiO(2) too quickly after reaching target arterial oxygen saturation levels would promote ventilator induced lung injury (VILI). MATERIALS AND METHODS: ALI was induced by instilling 3% Tween 20. Pigs were placed supine and received 30 min of nonprotective ventilation. Pigs were separated into two groups: HFOV constant (HFOVC, n = 3) = constant mPaw and FiO(2) for the duration; HFOV titrated (HFOVT, n = 4) = FiO(2) and/or mPaw were reduced every 30 min if the oxygen saturation remained between 88%-95%. Hemodynamic and pulmonary measurements were made at baseline, after lung injury, and every 30 min during the 6-h study. Lung histopathology was determined by quantifying alveolar hyperdistension, fibrin, congestion, atelectasis, and polymorphonuclear leukocyte (PMN) infiltration. RESULTS: Oxygenation was significantly lower in the HFOVT group compared to the HFOVC group after 6 h. Lung histopathology was significantly increased in the HFOVT group in the following categories: PMN infiltration, alveolar hyperdistension, congestion, and fibrin deposition. CONCLUSIONS: Rapid reduction of mPaw and FiO(2) in our ALI model significantly reduced oxygenation, but, more importantly, caused VILI as evidenced by increased lung inflammation and alveolar hyperdistension. Specific criteria for titration of mPaw and inspired oxygen are needed to maximize the lung protective effects of HFOV while maintaining adequate gas exchange.