Bartlettmclamb5625

Prone positioning is recommended for moderate-to-severe acute respiratory distress syndrome patients on invasive ventilation. Awake prone position has been frequently applied in COVID-19 patients one randomized trial showed improved oxygenation and lower intubation rate in patients receiving 6-h sessions of awake prone positioning, as compared to conventional management.

Noninvasive respiratory support and awake prone position are tools possibly capable of averting endotracheal intubation in COVID-19 patients; carefully monitoring during any treatment is warranted to avoid delays in endotracheal intubation, especially in patients with PaO2/FiO2 < 200 mmHg.

Noninvasive respiratory support and awake prone position are tools possibly capable of averting endotracheal intubation in COVID-19 patients; carefully monitoring during any treatment is warranted to avoid delays in endotracheal intubation, especially in patients with PaO2/FiO2  less then  200 mmHg.

We present here the case of an 83 y.o. male with intestinal perforation from pneumatosis cystoides intestinalis and consequent sepsis.

The patient underwent urgency intestinal resection in our institute, with complete restitution ad integrum Discussion Pneumatosis cystoides intestinalis is a rare affection, which can be categorized as primary (15%) or idiopathic( 85%). The clinical appearance can be very variable from patient to patient, since it can be completely asymptomatic or start with life-threatening clinical presentation of bowel perforation and sepsis. There are various theories about the formation of the gas bubbles trough the intestinal wall. The mechanical theory assumes that the gas, tearing trough the intestinal wall seeps trough it. The bacterial theory assumes that antibiotic treatment, such as with metronidazole, allows the creation of gas by microbiological elements like Clostridium Perfringens or Clostridium Difficile. The pulmonary theory, instead, assumes that air released from ruptured alveoli gets into the mediastinum and retro peritoneum, reaching the intestinal tract. The treatment is conservative most of the times, except for the cases of intestinal perforation and sepsis.

Despite of the long history of the disease, with the first description in 1783, little is known nowadays about PCI, due to the rarity of symptomatic disease. Further studies are needed to better evaluate the aetiology of the condition, and the prognostic criteria, which may be very important for clinical decisions about conservative or surgical treatment.

Diagnosis, Pneumatosis cystoides intestinalis, Peritonitis, Therapy.

Diagnosis, Pneumatosis cystoides intestinalis, Peritonitis, Therapy.

The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process.

This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening.

The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and vidor other disorders, particularly neurodevelopmental disorders.

Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.

Disclosure of cancer statistics (eg, survival or incidence rates) based on a representative group of patients can help increase cancer survivors' understanding of their own diagnostic and prognostic situation, and care planning. More recently, there has been an increasing interest in the use of cancer registry data for disclosing and communicating personalized cancer statistics (tailored toward personal and clinical characteristics) to cancer survivors and relatives.

The aim of this study was to explore breast cancer (BCa) and prostate cancer (PCa) survivor needs and preferences for disclosing (what) and presenting (how) personalized statistics from a large Dutch population-based data set, the Netherlands Cancer Registry (NCR).

To elicit survivor needs and preferences for communicating personalized NCR statistics, we created different (non)interactive tools visualizing hypothetical scenarios and adopted a qualitative multimethod study design. We first conducted 2 focus groups (study 1; n=13) for collectcancer registries to address this unmet need, but also for those who are developing or implementing personalized data-driven information tools for patients and relatives.

The majority of our sample of cancer survivors wanted to receive personalized statistics from the NCR. Given the variation in patient needs and preferences for presenting personalized statistics, designers of similar information tools may consider potential tailoring strategies on multiple levels, as well as effective ways for providing supporting information to make sure that the personalized statistics are properly understood. This is encouraging for cancer registries to address this unmet need, but also for those who are developing or implementing personalized data-driven information tools for patients and relatives.

Many people with psychosis experience difficulties in everyday social situations. Anxiety can make life challenging, leading to withdrawal. Cognitive therapy, using active in vivo learning, enables people to overcome fears. click here These treatments are not readily available to people with psychosis. Automated virtual reality (VR) therapy is a potential route to increase accessibility. The gameChange automated VR cognitive therapy is designed to help people overcome anxious avoidance and build confidence in everyday social situations. A virtual coach guides the person through the treatment. Understanding user experience is key to facilitating future implementation. Peer research methods, in which people with lived experience of the issues being studied are involved in collecting and analyzing data, may be useful in developing this understanding. This encourages researchers to draw on their lived experience to explore participant perspectives and co-create knowledge.

The primary objective is to use a peer research s.

DERR1-10.2196/31742.

DERR1-10.2196/31742.

As-needed (PRN) opioid orders with duplicate indications can lead to medication errors and opioid-related adverse drug events.

The objective of our study was to build and validate real-time alerts that detect duplicate PRN opioid orders and assist clinicians in optimizing the safety of opioid orders.

This single-center, prospective study used an iterative, 3-step process to refine alert performance by advancing from small sample evaluations of positive predictive values (PPVs) (step 1) through intensive evaluations of accuracy (step 2) to evaluations of clinical impact (step 3). Validation cohorts were randomly sampled from eligible patients for each step.

During step 1, the PPV was 100% (one-sided, 97.5% CI 70%-100%) for moderate and severe pain alerts. During step 2, duplication of 1 or more PRN opioid orders was identified for 17% (34/201; 95% CI, 12%-23%) of patients during chart review. This bundle of alerts showed 94% sensitivity (95% CI 80%-99%) and 96% specificity (95% CI 92%-98%) for identifying patients who had duplicate PRN opioid orders. During step 3, at least 1 intervention was made to the medication profile for 77% (46/60; 95% CI 64%-87%) of patients, and at least 1 inappropriate duplicate PRN opioid order was discontinued for 53% (32/60; 95% CI 40%-66%) of patients.

The bundle of alerts developed in this study was validated against chart review by a pharmacist and identified patients who benefited from medication safety interventions to optimize PRN opioid orders.

The bundle of alerts developed in this study was validated against chart review by a pharmacist and identified patients who benefited from medication safety interventions to optimize PRN opioid orders.

Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.

This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.

Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depr CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.

Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.

ClinicalTrials.gov NCT04061642; http//clinicaltrials.gov/ct2/show/NCT04061642.

ClinicalTrials.gov NCT04061642; http//clinicaltrials.gov/ct2/show/NCT04061642.

Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening.

The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population.

This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations bend are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.