Development of a large outpatient psychological dataset of Marines and Navy personnel.

To test the hypothesis that children and young people with neurological conditions who missed outpatient appointments have more emergency inpatient admissions and Accident and Emergency centre (A&E) visits than those who missed none. Retrospective cohort of individuals aged up to 19 years with neurological conditions, identified from routine hospital data in England, UK from April 1st, 2003 to March 31st, 2015 using an International Statistical Classification of Diseases and Related Health Problems, coding framework. Counts of emergency inpatient admissions and A&E visits per person per year were modelled (random intercept negative binomial regression) with outpatient attendance the independent variable of interest. The cohort numbered 524 613 individuals. Those who missed outpatient appointments had 19 per cent (95% confidence interval [CI] 18-19) more emergency inpatient admissions and 16 per cent (95% CI 15-17) more A&E visits per year than those who missed none. 'Did not attends' had a larger increase in unplanned health care than patient or provider cancellations. If no appointments were missed, the models predict there would have been 107 000 fewer A&E visits from 2007/2008 to 2014/2015 and 104 000 fewer emergency inpatient admissions from 2003/2004 to 2014/2015. Missed outpatient appointments were associated with increased unplanned health care. Improving outpatient attendance may have the potential to reduce emergency inpatient admissions and A&E visits. Missed outpatient appointments by children and young people with neurological conditions are associated with increased unplanned health care. Both emergency inpatient admissions and Accident and Emergency centre visits are increased. 'Did not attends' are more strongly associated with unplanned health care than cancellations.

Aim The association between particulate air pollution and increased risk of type 2 diabetes (T2DM) is well established. Air pollution, a major public health concern worldwide, affects several noncommunicable diseases, including T2DM. Italy faces significant challenges in relation to both particulate air pollution and T2DM. There are no studies in Italy assessing the association between particulate matter and T2DM in a large dataset of patients with clinically diagnosed T2DM. Subject and methods This study aims to assess the association between particulate matter (PM2.5 and PM10) and T2DM prevalence and incidence rates in Lombardy compared with the rest of Italy from 2006 to 2019. The association with years lived with disability (YLDs) was assessed using data from 2006 to 2016. Data were obtained from the AMD dataset, a comprehensive outpatient longitudinal dataset, while particulate matter data were obtained from the European Environment Agency and ARPA Lombardy, the regional environmental protection agency for Lombardy. The association was assessed using mixed-effects models. Results The mixed-effects model showed a significant positive association between particulate matter and T2DM incidence in Italy, with notable variations over time and between regions (Lombardy vs. the rest of Italy). While no significant effect of particulate matter was observed with respect to prevalence and YLD rates, a significant positive association was found between particulate matter and incidence rates for Italy, with the effect increasing over time. The opposite tendency was observed for Lombardy, with a negative association between particulate matter and incidence, and a decreases in the effect over time. Conclusion Particulate matter pollution, specifically PM2.5 and PM10, appears to be significantly associated with T2DM incidence rates in Italy. However, the effect varies between regions, with Lombardy showing a complex relationship influenced by socioeconomic factors. This study highlights the importance of addressing air pollution as a public health priority, particularly in regions with high levels of pollution such as Lombardy, in order to reduce the risk of T2DM and its associated burden. Graphical Abstract

ABSTRACTAntibiotic-resistant E. coli infections represent a major cause of morbidity and mortality and pose a challenge to antibiotic stewardship. We analyzed a large outpatient data set of E. coli urinary isolates to determine whether resistance patterns vary between types of outpatient practices. Using deidentified data from a clinical reference laboratory over 5 years and logistic regression, we examined the association of antibiotic resistance with outpatient practice type, controlling for testing year, patient sex, and patient age. The odds of antibiotic resistance were significantly higher in urology/nephrology practices for ampicillin (odds ratio [OR] 1.36; 95% CI, 1.10 to 1.69), ciprofloxacin (OR 2.29; 95% CI, 1.77 to 2.94), trimethoprim-sulfamethoxazole (OR 1.52; 95% CI, 1.18 to 1.94), and gentamicin (OR 1.72; 95% CI, 1.16 to 2.46). Odds of resistance were also higher for ciprofloxacin in oncology practices (OR 1.54; 95% CI, 1.08 to 2.15) and “all other specialties” (OR 1.33; 95% CI, 1.13 to 1.56). In contrast, specimens from obstetrics and gynecology practices had lower odds of having resistance to ampicillin (OR 0.90; 95% CI, 0.82 to 0.99) and trimethoprim-sulfa (OR 0.83; 95% CI, 0.73 to 0.93) but higher odds of having resistance to nitrofurantoin (OR 1.33; 95% CI, 1.03 to 1.70). Other findings included lower odds of having resistance to trimethoprim-sulfa in pediatric practices (OR 0.78; 95% CI, 0.64 to 0.94) and lower odds of having resistance to gentamicin in isolates from internal medicine practices (OR 0.66; 95% CI, 0.51 to 0.84) (all P < 0.05).IMPORTANCE Patterns of antibiotic resistance in E. coli urinary isolates can vary between outpatient specialties. The use of clinical data to create practice and specialty-specific antibiograms in outpatient settings may improve antibiotic stewardship.

10570 Background: Application of ML-based risk prediction models to lung cancer screening cohorts have been shown to increase screening efficiency. However, ML-based models may be vulnerable to sexual and racial bias arising from historical bias in health care access as well as biased training data. Demonstrating fairness in the predictions of ML-based models is a prerequisite to their acceptance by clinicians and patients. We assessed the clinical performance of LungFlag based on sex and race, two key demographic subgroups at risk for disparate outcomes due to bias. Methods: The LungFlag machine learning model is a retrospectively validated ML model intended to identify individuals who are at elevated risk for lung cancer and should be counseled regarding lung cancer screening. LungFlag uses existing routine outpatient lab measurements, smoking history, comorbidities, and demographic data to flag high-risk individuals. We compare performance of LungFlag between sexes and between races and calculate the sensitivity at the overall positivity rate of 3%. We chose a case-control design based on a large US-based community and outpatient dataset including 39,135 case patients with NSCLC and 212,454 contemporaneous NSCLC-free controls. We included ever-smokers, ages 45-80, with available lab measurements from 3-12 months before diagnosis, and minimal follow-up of 24 months. Sub-populations with less than 1% representation from the total population were excluded. Results: The comparison between the sub-populations presented by sensitivity and specificity indexes is detailed in the table. No statistically significant difference was demonstrated in the sensitivity of the model to flag individuals that were diagnosed with NSCLC on multiple sub-populations. Conclusions: The LungFlag model demonstrated fairness with respect to sex and race based on similar clinical sensitivity in a large, community-based retrospective dataset. Further assessment in prospective studies and in additional racial sub-populations is recommended to support this conclusion. [Table: see text]

24-hour urine collections are used to assess excretion of various analytes. Although concomitant measurement of creatinine excretion adjusted for body weight (BW) is utilized to determine adequacy of collection, no gold standard exists for determining completeness of a collection. Use of current reference ranges for daily creatinine excretion/BW of 15–20 mg creatinine/kg BW/day in women and 20–25 mg creatinine/kg/d in men established prior to the rising prevalence of obesity has resulted in a large proportion of contemporary individuals appearing to have “incomplete” urine collections.Our objective was to evaluate the range of creatinine excretion in accurately collected urine specimen from adults with a wide spread of age and BW, and to generate an equation that accurately predicts 24-hour urine creatinine excretion rate from readily available clinical parameters to aid in assessing adequacy of a 24-hour urine collection.We analyzed data from participants who completed two consecutive 24-hour urine collections while consuming a fixed metabolic diet during inpatient research admissions. Results from participants with 24-hour urine creatinine excretion rate differing by >10% between the two consecutive collections were excluded. In the initial 115 pairs of inpatient 24-hour urine collections (50 female, 65 male) participants, creatinine excretion/BW fell outside the currently accepted reference ranges in >50% of collections. The proportion below the reference range increased with higher BMI. In this derivation dataset, linear regression models were then constructed to predict 24-hr urine creatinine excretion from race, sex, age, weight and height. Reliable prediction of observed 24-hr urine creatinine excretion was confirmed in a validation dataset that included 50 pairs of 24-hour urine samples similarly collected in an inpatient research setting.This new prediction model performed significantly better than the currently used reference ranges in a large outpatient dataset including 1,399 pairs of 24-hour urine collections. In women, actual creatinine excretion fell within the 95% prediction intervals for our derived equation in 90% of cases using the new interval vs 46% using the current reference range. The corresponding values were 90% and 33% in men. In both genders, the superiority of the new prediction over the current reference range was more pronounced at higher BMI.We therefore propose revision of currently used criteria to define adequacy of 24-hour urine collection to account for the impact of obesity. The proposed equation incorporates readily available demographic parameters to predict urine creatinine excretion. These findings have wide implications on patient care and research studies, and future studies should test this equation in different settings, diets, and populations.