Considerations for prenatal and postpartum management of a female patient with ornithine transcarbamylase deficiency.

We report on pregnancy management and outcomes in a 27-year-old female patient with ornithine transcarbamylase (OTC) deficiency, the most common inherited enzyme deficiency in the urea cycle. Our patient was diagnosed during childhood after hyperammonemia associated with surgery and steroid treatment and was well-controlled with nitrogen scavenger treatment, low-protein diet, and L-citrulline supplementation. OTC gene sequencing identified a variant of unknown significance that has more recently been classified as likely pathogenic. Women with OTC deficiency are at increased risk of hyperammonemia during pregnancy and the postpartum period, therefore monthly follow up and laboratory assessments are critical in management decision making. Our patient was maintained on glycerol phenylbutyrate, L-citrulline and essential amino acid supplements, along with restricted protein intake during pregnancy. A multidisciplinary approach with the obstetrics, prenatal genetics, high risk obstetric, and anesthesia teams was also necessary for optimal management during pregnancy, throughout labor and delivery, and during the postpartum period. After successful childbirth and discharge, our patient experienced a hyperammonemic crisis related to poor enteral nutrition, and acute management protocols were implemented to stabilize her. For her newborn son, acute hyperammonemia protocols were on standby, and newborn screening and laboratory testing were expedited to assess his risk. He was healthy and did not experience symptoms of concern. In this case report, we emphasize the importance of close collaboration with maternal-fetal medicine team members during and immediately after pregnancy to ensure successful management of a female patient with OTC deficiency and her newborn.

Recommendations for developing clinical care protocols during pandemics: From theory and practice.

In 2019, a novel coronavirus called the severe acute respiratory syndrome coronavirus 2 led to the outbreak of the coronavirus disease 2019, which was deemed a pandemic by the World Health Organization in March 2020. Owing to the accelerated rate of mortality and utilization of hospital resources, health care systems had to adapt to these major changes. This affected patient care across all disciplines and specifically within the perioperative services. In this review, we discuss the strategies and pitfalls of how perioperative services in a large academic medical center responded to the initial onset of a pandemic, adjustments made to airway management and anesthesia specialty services - including critical care medicine, obstetric anesthesiology, and cardiac anesthesiology - and strategies for reopening surgical caseload during the pandemic.

Fatal Chest Injury Caused by Windblown Beach Umbrella.

We present the case of a windblown beach umbrella inflicting fatal penetrating blunt force to the chest of a 55-year-old female beachgoer. A postmortem examination and detailed case history review were performed which revealed left ventricular trauma, determined to be the cause of death. Using recorded wind speeds from the date of the incident and the weight of the umbrella, we were able to calculate the pressure with which the umbrella struck the victim to be 16,000 PSI. Witness reports, medical records, scene details, and examination of a rental beach umbrella allowed the forensic pathologist to conclude that the object causing this woman's death was a windblown beach umbrella.

Antidepressant Medication Prescribing Practices for Treatment of Major Depressive Disorder.

OBJECTIVE: The purpose of this study was to describe the prescribing practices of clinicians for patients with major depressive disorder (MDD). METHODS: This population-based, descriptive study of insured patients (N=54,107) identified people who were 18 years or older, had a claim for MDD, had at least one prescription for an antidepressant medication in 2013, and had continuous insurance coverage during the study period. Prescription claims were evaluated to determine the most commonly prescribed antidepressant medication and most common dose. RESULTS: The three most commonly prescribed antidepressant medications were citalopram (N=11,995, 22.2%), sertraline (N=10,791, 19.9%), and trazodone (N=9,501, 17.6%). The most common daily doses were 20 mg citalopram (N=6,304, 52.6%), 50 mg sertraline (N=4,173, 38.7%), and 100 mg trazodone (N=3,220, 33.9%). CONCLUSIONS: This is the first report of its kind that provides drug- and dosage-level details to demonstrate that antidepressant prescribing in clinical practice is largely within recommended guidelines.

Application of multivariate probabilistic (Bayesian) networks to substance use disorder risk stratification and cost estimation.

INTRODUCTION: This paper explores the use of machine learning and Bayesian classification models to develop broadly applicable risk stratification models to guide disease management of health plan enrollees with substance use disorder (SUD). While the high costs and morbidities associated with SUD are understood by payers, who manage it through utilization review, acute interventions, coverage and cost limitations, and disease management, the literature shows mixed results for these modalities in improving patient outcomes and controlling cost. Our objective is to evaluate the potential of data mining methods to identify novel risk factors for chronic disease and stratification of enrollee utilization, which can be used to develop new methods for targeting disease management services to maximize benefits to both enrollees and payers. METHODS: For our evaluation, we used DecisionQ machine learning algorithms to build Bayesian network models of a representative sample of data licensed from Thomson-Reuters' MarketScan consisting of 185,322 enrollees with three full-year claim records. Data sets were prepared, and a stepwise learning process was used to train a series of Bayesian belief networks (BBNs). The BBNs were validated using a 10 percent holdout set. RESULTS: The networks were highly predictive, with the risk-stratification BBNs producing area under the curve (AUC) for SUD positive of 0.948 (95 percent confidence interval [CI], 0.944-0.951) and 0.736 (95 percent CI, 0.721-0.752), respectively, and SUD negative of 0.951 (95 percent CI, 0.947-0.954) and 0.738 (95 percent CI, 0.727-0.750), respectively. The cost estimation models produced area under the curve ranging from 0.72 (95 percent CI, 0.708-0.731) to 0.961 (95 percent CI, 0.95-0.971). CONCLUSION: We were able to successfully model a large, heterogeneous population of commercial enrollees, applying state-of-the-art machine learning technology to develop complex and accurate multivariate models that support near-real-time scoring of novel payer populations based on historic claims and diagnostic data. Initial validation results indicate that we can stratify enrollees with SUD diagnoses into different cost categories with a high degree of sensitivity and specificity, and the most challenging issue becomes one of policy. Due to the social stigma associated with the disease and ethical issues pertaining to access to care and individual versus societal benefit, a thoughtful dialogue needs to occur about the appropriate way to implement these technologies.

Anti-intellectualism and political ideology in a sample of undergraduate and graduate students.

To estimate correlations for scores on a student anti-intellectualism scale with scores on a measure of political conservatism, 235 students were given a survey containing a student anti-intellectualism scale, a political conservatism scale, and a demographics questionnaire identifying the participants' sex, college classification, ethnicity, political party affiliation, and self-described political ideology. The political conservatism scale contained two factors, Religiosity and Economic Conservatism, both of which were scored separately in addition to an overall Conservatism score. Students' Anti-intellectualism scores were correlated with Political Conservatism scores (r = .37, p < .01), with Religiosity scores (r = .42, p < .01), and with Economic Conservatism scores (r = .17, p < .05). An analysis of variance indicated a significant difference in students' Anti-intellectualism scores based on college classification (F4,233 = 2.27, p < .04). Specifically, freshman had significantly higher scores than graduate students.

Positive and Negative Incentive Contrast Effects with Rats as a Function of Sex.

Experiment 1 attempted to determine if sex was a controlling factor in the production of negative contrast. The Ss were 60 male and female albino rats 68-104 days of age. In an operant conditioning chamber a saccharine solution was reduced in concentration from 1.2% to. 10% for both males and females. Only the males displayed the downward incentive effect. Experiment 2 attempted to discover if sex was a determiner of positive contrast in 60 male and female albino rats 68-104 days of age. Saccharine concentration was increased from .01% to 1.0% in an operant box for both sexed groups. Again, only the males exhibited contrast. The conclusion from the two above investigations is that the factor of sex is one controller of both the downward and upward effects.