Effects of accelerometry-derived physical activity energy expenditure on urinary C-peptide levels in a wild primate (Papio ursinus).

Animals have finite energy reserves for growth, survival, and reproduction and must maintain a stable energy balance. Measuring energy balance in the wild, however, is beset with methodological challenges. Quantification of urinary C-peptide (uCP), a proxy for insulin secretion, has enabled researchers to non-invasively estimate energy balance, and positive relationships between uCP levels and energy intake have been documented in numerous non-human primates. Comparatively few studies show that, consistent with insulin physiology, energy expenditure also alters levels of uCP. The timescale and extent of this relationship, however, remains unclear given the reliance on crude measures of activity and inferred energy expenditure. Here, for the first time, we test for effects of accelerometer-derived Vectorial Dynamic Body Acceleration (VeDBA) - a continuous measure of physical activity energy expenditure - on urinary C-peptide (uCP) levels in n = 12 wild chacma baboons (Papio ursinus). Applying a model selection approach, we show that VeDBA summed over short timescales (30 min to 1 h) prior to urine collection was negatively associated with uCP levels. Using the acceleration-based time individuals spent 'non-stationary' (i.e. locomoting) prior to urine collection as a predictor - instead of summed VeDBA - revealed similar but less clear results. Overall, the negative relationship between VeDBA and uCP levels highlights the importance of quantifying physical activity energy expenditure when using uCP measures to estimate energy balance and has potential implications for the field of energetics accelerometry.

Measuring Performance on the ABCDEF Bundle During Interprofessional Rounds via a Nurse-Based Assessment Tool.

BACKGROUND: Nurse-led rounding checklists are a common strategy for facilitating evidence-based practice in the intensive care unit (ICU). To streamline checklist workflow, some ICUs have the nurse or another individual listen to the conversation and customize the checklist for each patient. Such customizations assume that individuals can reliably assess whether checklist items have been addressed. OBJECTIVE: To evaluate whether 1 critical care nurse can reliably assess checklist items on rounds. METHODS: Two nurses performed in-person observation of multidisciplinary ICU rounds. Using a standardized paper-based assessment tool, each nurse indicated whether 17 items related to the ABCDEF bundle were discussed during rounds. For each item, generalizability coefficients were used as a measure of reliability, with a single-rater value of 0.70 or greater considered sufficient to support its assessment by 1 nurse. RESULTS: The nurse observers assessed 118 patient discussions across 15 observation days. For 11 of 17 items (65%), the generalizability coefficient for a single rater met or exceeded the 0.70 threshold. The generalizability coefficients (95% CIs) of a single rater for key items were as follows: pain, 0.86 (0.74-0.97); delirium score, 0.74 (0.64-0.83); agitation score, 0.72 (0.33-1.00); spontaneous awakening trial, 0.67 (0.49-0.83); spontaneous breathing trial, 0.80 (0.70-0.89); mobility, 0.79 (0.69-0.87); and family (future/past) engagement, 0.82 (0.73-0.90). CONCLUSION: Using a paper-based assessment tool, a single trained critical care nurse can reliably assess the discussion of elements of the ABCDEF bundle during multidisciplinary rounds.

Stressful Life Events and Risk of Homelessness After Active Duty: An Assessment of Risk and Resilience Among Servicemembers.

OBJECTIVES: The transition from military to civilian life may present increased exposure to various stressful life events (SLEs) that can increase the risk of homelessness (eg, loss of employment, dissolution of romantic relationships). We assessed the extent to which exposure to SLEs occurring proximal to US Army soldier transitions out of active duty was associated with risk of homelessness. METHODS: A total of 16 589 respondents who were no longer on active duty but participated while on active duty during 2011-2014 baseline surveys completed follow-up surveys during 2016-2018 and 2018-2019. The follow-up surveys assessed SLEs and homelessness occurring in the past 12 months. We used modified Poisson regression models to evaluate how much differential SLE exposure and effects explained the aggregate association of a risk index with homelessness among a sample of 6837 respondents, weighted to represent the full sample. RESULTS: More than half (n = 3510, 52.8%) of respondents reported experiencing any SLEs in the past 12 months. Most (60.5%) of the difference in prevalence of homelessness among respondents defined as being at high risk of homelessness (vs lower risk) was explained by differential exposure to, and/or effects of, these SLEs. Personal betrayal by a loved one and economic problems played the largest roles in adjusted risk differences (0.045 and 0.074, respectively). CONCLUSIONS: Homelessness might be reduced by gearing interventions toward soldiers at high risk of homelessness who are transitioning out of active duty to reduce exposure to and effects of modifiable SLEs on experiencing homelessness.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

How do we use electronic clinical decision support and feedback to promote good transfusion practice.

This report describes the evolution of the electronic clinical decision support system (CDSS) and feedback methods at our center and the challenges and lessons learned. The electronic blood product order with integrated CDSS ensures collection of data regarding the patient's clinical condition and the justification for the blood product order. An alert is generated in real time if the order is placed outside agreed guidelines. We have provided feedback in several ways. We began with monthly review meetings with the junior hematology clinicians responsible for ordering blood. This was successful in reducing unjustified transfusions in this setting. We expanded the feedback to the rest of our hospitals in two ways. First, a dashboard was developed allowing visualization of ordering data by clinicians. Second, these data were summarized on a quarterly basis into a report circulated to the senior clinical staff by e-mail. Finally, a daily report collates all orders placed for blood products that have triggered a CDSS alert from the previous day. A multidisciplinary team reviews these daily. If an order appears unjustified the specialist transfusion clinician contacts the prescribing clinician to ask for further information and, if necessary, provides education. The CDSS and feedback, allied with other patient blood management measures, have reduced total blood product costs for our hospitals by 26% over 6 years. The description of how we have developed and implemented CDSS and feedback to influence transfusion practice may be of particular value to others developing their own systems.

Assessment of a Risk Index for Suicide Attempts Among US Army Soldiers With Suicide Ideation: Analysis of Data From the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

Importance: The Department of Veterans Affairs recently began requiring annual suicide ideation (SI) screening of all patients and additional structured questions for patients reporting SI. Related changes are under consideration at the Department of Defense. These changes will presumably lead to higher SI detection, which will require hiring additional clinical staff and/or developing a clinical decision support system to focus in-depth suicide risk assessments on patients considered high risk. Objective: To carry out a proof-of-concept study for whether a brief structured question battery from a survey of US Army soldiers can help target in-depth suicide risk assessments by identifying soldiers with self-reported lifetime SI who are at highest risk of subsequent administratively recorded nonfatal suicide attempts (SAs). Design, Setting, and Participants: Cohort study with prospective observational design. Data were collected from May 2011 to February 2013. Participants were followed up through December 2014. Analyses were conducted from March to November 2018. A logistic regression model was used to assess risk for subsequent administratively recorded nonfatal SAs. A total of 3649 Regular Army soldiers in 3 Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) surveys who reported lifetime SI were followed up for 18 to 45 months from baseline to assess administratively reported nonfatal SAs. Main Outcomes and Measures: Outcome was administratively recorded nonfatal SAs between survey response and December 2014. Predictors were survey variables. Results: The 3649 survey respondents were 80.5% male and had a median (interquartile range) age of 29 (25-36) years (range, 18-55 years); 69.4% were white non-Hispanic, 14.6% were black, 9.0% were Hispanic, 7.0% were another racial/ethnic group. Sixty-five respondents had administratively recorded nonfatal SAs between survey response and December 2014. One additional respondent died by suicide without making a nonfatal SA but was excluded from analysis based on previous evidence that predictors are different for suicide death and nonfatal SAs. Significant risk factors were SI recency (odds ratio [OR], 7.2; 95% CI, 2.9-18.0) and persistence (OR, 2.6; 95% CI, 1.0-6.8), positive screens for mental disorders (OR, 26.2; 95% CI, 6.1-112.0), and Army career characteristics (OR for junior enlisted rank, 30.0; 95% CI, 3.3-272.5 and OR for senior enlisted rank, 6.7; 95% CI, 0.8-54.9). Cross-validated area under the curve was 0.78. The 10% of respondents with highest estimated risk accounted for 39.2% of subsequent SAs. Conclusions and Relevance: Results suggest the feasibility of developing a clinically useful risk index for SA among soldiers with SI using a small number of self-report questions. If implemented, a continuous quality improvement approach should be taken to refine the structured question series.

Lifetime Suicidal Behaviors and Career Characteristics Among U.S. Army Soldiers: Results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The current report presents data on lifetime prevalence of suicide ideation and nonfatal attempts as reported by the large representative sample of U.S. Army soldiers who participated in the Consolidated All-Army Survey of the Army Study to Assess Risk and Resilience in Servicemembers (N = 29,982). We also examine associations of key Army career characteristics with these outcomes. Prevalence estimates for lifetime suicide ideation are 12.7% among men and 20.1% among women, and for lifetime suicide attempts are 2.5% and 5.1%, respectively. Retrospective age-of-onset reports suggest that 53.4%-70% of these outcomes had preenlistment onsets. Results revealed that, for both men and women, being in the Regular Army, compared with being in the National Guard or Army Reserve, and being in an enlisted rank, compared with being an officer, is associated with increased risk of suicidal behaviors and that this elevated risk is present both before and after joining the Army.

Emergency Care of Children with Ambulatory Care Sensitive Conditions in the United States.

BACKGROUND: Ambulatory care sensitive (ACS) conditions are health problems that could be prevented or ameliorated with adequate access to primary care services. OBJECTIVES: To determine the extent to which ACS conditions account for care received by children in U.S. emergency departments (EDs) and the patient charges for this care. METHODS: A retrospective, cross-sectional analysis of the 2010 Nationwide Emergency Department Sample was performed. Patients 0-19 years of age were included and visits for ACS conditions were identified. Main outcome measures were the percentage of visits for ACS conditions, regression models predicting presentation for ACS conditions based on patient demographic characteristics, and ED charges for ACS ED visits. RESULTS: Of almost 30 million pediatric ED visits in the United States in 2010, 13.2% were for exclusively ACS conditions. Patients with public or no insurance were 1.2 times more likely than privately insured patients to present for an ACS condition. Lower household income (adjusted odds ratio [aOR] 1.49; 95% confidence interval [CI] 1.33-1.66) and younger patient age (aOR = 2.55; 95% CI 2.41-2.69) were also predictive of an ACS ED visit. The total of charges for ACS visits was almost $3 billion, of which publicly insured patients accounted for $1.5 billion. CONCLUSIONS: Almost one in seven U.S. pediatric ED visits may be preventable by quality primary care. Patients with public insurance and lower income are more likely than other groups to present with ACS conditions. Better access to and use of primary care services could reduce health care costs and relieve ED overcrowding.

Maltreatment-Related Emergency Department Visits Among Children 0 to 3 Years Old in the United States.

The emergency department (ED) is a vital entry point in the health care system for children who experience maltreatment. This study fills a gap in the maltreatment literature by presenting systematic, national estimates of maltreatment-related ED visits in the United States by children ≤3 years old, from 2006 to 2011, using the Nationwide Emergency Department Sample (NEDS). Children who experienced and likely experienced maltreatment were identified via International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes. Maltreatment was classified as physical or sexual abuse, neglect, or poly-victimization. The clinical and demographic profiles of children who experienced maltreatment were described. Approximately 10,095 children who experienced maltreatment (0.1% of total ED visits) and 129,807 children who likely experienced maltreatment (1.2% of total ED visits) were documented each year. Maltreatment was associated with significantly greater risk of injury, hospitalization, and death in the ED setting. Physical abuse was the most common explicit maltreatment diagnosis (33 ED visits per 100,000 children ≤3 years old) and neglect was the most common likely maltreatment diagnosis (436 ED visits per 100,000 children ≤3 years old). This study established the NEDS as a valuable complement to existing surveillance efforts of child maltreatment from a public health perspective.