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Exposure to nature may reduce stress in low-income parents. This prospective randomized trial compares the effect of a physician's counseling about nature with or without facilitated group outings on stress and other outcomes among low-income parents. Parents of patients aged 4-18 years at a clinic serving low-income families were randomized to a supported park prescription versus independent park prescription in a 2:1 ratio. Parents in both groups received physician counseling about nature, maps of local parks, a journal, and pedometer. The supported group received additional phone and text reminders to attend three weekly family nature outings with free transportation, food, and programming. Outcomes measured in parents at baseline, one month and three months post-enrollment included: stress (using the 40-point Perceived Stress Scale [PSS10]); park visits per week (self-report and journaling); loneliness (modified UCLA-Loneliness Scale); physical activity (self-report, journaling, pedometry); physiologic stress (salivary cortisol); and nature affinity (validated scale). We enrolled 78 parents, 50 in the supported and 28 in the independent group. One-month follow-up was available for 60 (77%) participants and three-month follow up for 65 (83%). Overall stress decreased by 1.71 points (95% CI, -3.15, -0.26). The improvement in stress did not differ significantly by group assignment, although the independent group had more park visits per week (mean difference 1.75; 95% CI [0.46, 3.04], p = 0.0085). In multivariable analysis, each unit increase in park visits per week was associated with a significant and incremental decrease in stress (change in PSS10-0.53; 95% CI [-0.89, -0.16]; p = 0.005) at three months. While we were unable to demonstrate the additional benefit of group park visits, we observed an overall decrease in parental stress both overall and as a function of numbers of park visits per week. Paradoxically the park prescription without group park visits led to a greater increase in weekly park visits than the group visits. To understand the benefits of this intervention, larger trials are needed. ClinicalTrials.gov NCT02623855.

ObjectivesEstimating mortality risk in hospitalised SARS-CoV-2+ patients may help with choosing level of care and discussions with patients. The Coronavirus Clinical Characterisation Consortium Mortality Score (4C Score) is a promising COVID-19 mortality risk model. We examined the association of risk factors with 30-day mortality in hospitalised, full-code SARS-CoV-2+ patients and investigated the discrimination and calibration of the 4C Score. This was a retrospective cohort study of SARS-CoV-2+ hospitalised patients within the RECOVER (REgistry of suspected COVID-19 in EmeRgency care) network.Setting99 emergency departments (EDs) across the USA.ParticipantsPatients ≥18 years old, positive for SARS-CoV-2 in the ED, and hospitalised.Primary outcomeDeath within 30 days of the index visit. We performed logistic regression analysis, reporting multivariable risk ratios (MVRRs) and calculated the area under the ROC curve (AUROC) and mean prediction error for the original 4C Score and after dropping the C reactive protein (CRP) component.ResultsOf 6802 hospitalised patients with COVID-19, 1149 (16.9%) died within 30 days. The 30-day mortality was increased with age 80+ years (MVRR=5.79, 95% CI 4.23 to 7.34); male sex (MVRR=1.17, 1.05 to 1.28); and nursing home/assisted living facility residence (MVRR=1.29, 1.1 to 1.48). The 4C Score had comparable discrimination in the RECOVER dataset compared with the original 4C validation dataset (AUROC: RECOVER 0.786 (95% CI 0.773 to 0.799), 4C validation 0.763 (95% CI 0.757 to 0.769). Score-specific mortalities in our sample were lower than in the 4C validation sample (mean prediction error 6.0%). Dropping the CRP component from the 4C Score did not substantially affect discrimination and 4C risk estimates were now close (mean prediction error 0.7%).ConclusionsWe independently validated 4C Score as predicting risk of 30-day mortality in hospitalised SARS-CoV-2+ patients. We recommend dropping the CRP component of the score and using our recalibrated mortality risk estimates.

Ileocolic intussusception can be challenging to diagnose due to vague complaints, but rapid diagnosis and treatment can help prevent morbidity and mortality. Prior research has focused on radiologic ultrasound, with more recent studies focusing on point-of-care ultrasonography (POCUS). This systematic review and meta-analysis assesses the diagnostic accuracy of POCUS for children with suspected ileocolic intussusception. PubMed, Embase, CINAHL, LILACS, the Cochrane databases, Google Scholar, conference abstracts, and bibliographies of selected articles were searched for studies evaluating the accuracy of POCUS for the diagnosis of intussusception in children. Data were dual extracted into a predefined worksheet, and quality analysis was performed with the QUADAS-2 tool. Data were summarized, and a meta-analysis was performed. Eleven studies (n = 2400 children) met our inclusion criteria. Overall, 14.4% of children had intussusception. POCUS was 95.1% (95% CI: 90.3% to 97.2%) sensitive and 98.1% (95% CI: 95.8% to 99.2%) specific with a positive likelihood ratio of 50 (95% CI: 23 to 113) and a negative likelihood ratio of 0.05 (95% CI: 0.03 to 0.09). POCUS has excellent diagnostic accuracy for intussusception in children presenting to the emergency department.

Ileocolic intussusception is a common cause of pediatric bowel obstruction in young children but can be difficult to diagnose clinically due to vague abdominal complaints. If left untreated, it may cause significant morbidity. Point-of-care ultrasound (POCUS) is a rapid, bedside method of assessment that may potentially aid in the diagnosis of intussusception. The purpose of this systematic review and meta-analysis was to determine the diagnostic accuracy of POCUS for children with suspected ileocolic intussusception by emergency physicians (EP). We conducted a systematic search on PubMed, Embase, CINAHL, LILACS, the Cochrane databases, Google Scholar, as well as conference abstracts, and assessed bibliographies of selected articles for all studies evaluating the accuracy of POCUS for the diagnosis of intussusception in children. We dual extracted data into a predefined worksheet and performed quality analysis with the QUADAS-2 tool. Data were summarized and a meta-analysis was performed. Six studies (n = 1303 children) met our inclusion criteria. Overall, 11.9% of children had intussusception. POCUS was 94.9% (95% confidence interval [CI], 89.9% to 97.5%) sensitive and 99.1% (95% CI, 94.7% to 99.8%) specific with a likelihood ratio (LR)+ of 105 (95% CI, 18 to 625) and a LR- of 0.05 (95% CI, 0.03 to 0.10). POCUS by EPs is highly sensitive and specific for the identification of intussusception for children presenting to the emergency department.

Partial verification bias occurs in studies assessing the accuracy of existing diagnostic tests when a positive index test makes application of the reference standard more likely. This article first describes alternative sampling frames for a diagnostic test accuracy study then discusses partial verification bias. One approach to avoiding partial verification bias is to apply the reference standard to all individuals who are positive on the index test and a random sample of those who are negative. Estimating sensitivity and specificity then requires adjusting for the sampling fraction in the test-negative group. If a finding was already used to determine who should get the definitive test, one can't look at those who got the definitive test to determine the usefulness of the finding.

Correspondence to Dr Michael A Kohn, Epidemiology and Biostatistics, UCSF, San Francisco, CA 94143, USA; Michael.Kohn@ucsf.edu The prototypical study of diagnostic test accuracy compares the results of one or more index tests to an independent ‘reference-standard’ determination of true disease status: ‘disease positive’ (D+) or ‘disease negative’ (D−). The false negative rate of RT-PCR (obtained by NP swab) on the day before symptom onset has been estimated at 35%.2 But this was on a single specimen, not three specimens obtained simultaneously, so let’s assume a false negative rate of only 10%. Accuracy is not the only consideration in choosing among tests. Since the patients reported less discomfort with the OPS and it seemed to be less costly, the authors’ conclusion in favour of this oropharyngeal sampling over nasopharyngeal sampling seems justified.

Advanced Trauma Life Support field triage utilizes the Glasgow Coma Scale (GCS) to assess the level of consciousness. However, prehospital care providers in low- and middle-income countries (LMICs) often use the Alert, Verbal, Pain, and Unresponsive (AVPU) scale to assess the level of consciousness. This study aimed to determine whether prehospital AVPU categorization correlates with mortality rates in trauma victims, similarly to GCS. In this cross-sectional study conducted between November 2015 and January 2016, we enrolled a convenience sample of prehospital trauma-related field activations. The primary outcome measure was the probability of death within 48 h for each category of AVPU. In a convenience sample of 4514 activations, 1606 (35.6%) met exclusion criteria, four did not have AVPU, and four did not have GCS, leaving 2900 (64.2%) trauma activations with both AVPU and GCS available for analysis. Forty-eight-hour follow-up data were available for 2184 (75.3%) activations out of these 2900. The 48-h mortality rates for each category of AVPU were 1.1% (Alert), 4.3% (Verbal), 17.9% (Pain), 53.2% (Unresponsive); and, for each GCS-based injury severity category, they were 0.9% (Mild, GCS 13–15), 8.1% (Moderate, GCS 9–12), 43.5% (Severe, GCS ≤ 8). Overall, there was a statistically significant difference in GCS for each category of AVPU (p < 0.001) except between patients responding to verbal commands and those responding to pain (p = 0.18). The discriminative ability of AVPU (AUC 79.7% (95% CI 73.4–86.1)) and GCS (AUC 81.5% (95% CI 74.8–88.2)) for death within 48-h following hospital drop-off were comparable. EMT assessments of AVPU and GCS relate to each other, and AVPU predicts mortality at 48 h. Future studies using AVPU to assess the level of consciousness in prehospital trauma protocols may simplify their global application without impacting the overall quality of care.

Many clinical diagnostic tests, such as the joint fluid white blood cell count, produce results on a continuous scale, rather than a mere positive or negative. The accuracy of such tests is often reported as a positive and negative likelihood ratio at each of several potential cutoff points (e.g., ≥25,000/μL vs. not, ≥50,000/μL vs. not; ≥100,000/μL vs. not).  This Key Concepts article reviews the definition of a likelihood ratio and explains why the practice of dichotomizing the test is problematic. Instead, it proposes that such continuous scales be divided into multiple intervals (e.g., 0–25,000, >25,000–50,000, >50,000–100,000, >100,000) and each interval be given its own likelihood ratio.  This practice not only aligns with clinical common sense and practice but also enables a more accurate estimate of the updated risk of disease, given a pre-test risk.

The Mortality Probability Model (MPM) is used in research and quality improvement to adjust for severity of illness and can also inform triage decisions. However, a limitation for its automated use or application is that it includes the variable “intracranial mass effect” (IME), which requires human engagement with the electronic health record (EHR). We developed and tested a natural language processing (NLP) algorithm to identify IME from CT head reports. We obtained initial CT head reports from adult patients who were admitted to the ICU from our ED between 10/2013 and 9/2016. Each head CT head report was labeled yes/no IME by at least two of five independent labelers. The reports were then randomly divided 80/20 into training and test sets. All reports were preprocessed to remove linguistic and style variability, and a dictionary was created to map similar common terms. We tested three vectorization strategies: Term Frequency-Inverse Document frequency (TF-IDF), Word2Vec, and Universal Sentence Encoder to convert the report text to a numerical vector. This vector served as the input to a classification-tree-based ensemble machine learning algorithm (XGBoost). After training, model performance was assessed in the test set using the area under the receiver operating characteristic curve (AUROC). We also divided the continuous range of scores into positive/inconclusive/negative categories for IME. Of the 1202 CT reports in the training set, 308 (25.6%) reports were manually labeled as “yes” for IME. Of the 355 reports in the test set, 108 (30.4%) were labeled as “yes” for IME. The TF-IDF vectorization strategy as an input for the XGBoost model had the best AUROC:-- 0.9625 (95% CI 0.9443–0.9807). TF-IDF score categories were defined and had the following likelihood ratios: “positive” (TF-IDF score > 0.5) LR = 24.59; “inconclusive” (TF-IDF 0.05–0.5) LR = 0.99; and “negative” (TF-IDF < 0.05) LR = 0.05. 82% of reports were classified as either “positive” or “negative”. In the test set, only 4 of 199 (2.0%) reports with a “negative” classification were false negatives and only 8 of 93 (8.6%) reports classified as “positive” were false positives. NLP can accurately identify IME from free-text reports of head CTs in approximately 80% of records, adequate to allow automatic calculation of MPM based on EHR data for many applications.

Adequate glucose control is an important, yet a challenging task, in the early postoperative care after liver transplantation (LTx). Potential of continuous glucose monitoring (CGM) in this context had not been fully explored because of concerns about sensors' precision in critical care. We initiated a trial to assess feasibility, accuracy, and benefit of CGM used in addition to standard blood glucose (BG) management. Prospective randomized trial. Patients undergoing LTx were included and randomized to wearing (1) unblinded (\"active\") CGM which could help guiding insulin therapy, (2) blinded CGM serving as controls. Dexcom G6 was applied immediately after surgery, arterial BG values measured with blood gas analyzer served as reference and for calibration. We evaluated mean absolute relative difference (MARD), Diabetes Technology Society (DTS) Error Grid zones, bias, 15/15 agreement rate, evolvement of accuracy over time, and possible interfering factors. Fisher test, Mann-Whitney test, DTS software, and linear mixed model were used for statistical analysis. We included 155 LTx recipients, 13 were excluded (1 died during surgery, 12 experienced sensor failure-6 from each group), data from 142 patients (80 in active and 62 in blinded CGM group) were analyzed. Overall, MARD was 9.1% in active and 10.7% in the blinded group (  < 0.0001). DTS error grid in active group had shown 90% in zone A versus 85.3% in the blinded group. These results were consistent for reference glucose ranges 3.9-10 mmol/L and above 10 mmol/L, with less precision in the low ranges (these values were however rare). Sensor accuracy peaked on days 2-3 and deteriorated over time with significant worsening from day 7 on in both groups. Our study demonstrates feasibility and acceptable accuracy of CGM comparable to reports from the outpatient care. We believe this could be attributed to sensor calibration, reflected by more favorable outcomes in the active CGM group. In addition, we demonstrate decline in accuracy over time, suggesting their use in critical care could be limited to less days to secure sufficient reliability. This study is part of an ongoing prospective trial registered on ClinicalTrials.gov (NCT05585801) and was registered on October 12, 2022.