Do-not-attempt-resuscitation decision making: physicians' recommendations differ from the GO-FAR score predictions.

BACKGROUND AND AIM: In-hospital cardiac arrest (IHCA) is a major cause of mortality globally, and over 50% of the survivors will require institutional care as a result of poor neurological outcome. It is important that physicians discuss the likely outcome of resuscitation with patients and families during end-of-life discussions to help them with decisions about cardiopulmonary resuscitation. We aim to compare three consultants' do-not-resuscitate (DNR) decisions with the GO-FAR score predictions of the probability of survival with good neurological outcomes following in-hospital cardiac arrest (IHCA). METHODS: This is a retrospective study of all patients 18 years or older placed on a DNR order by a consensus of three consultants in a tertiary institution in the United Arab Emirates over 12 months. Patients' socio-demographics and the GO-FAR variables were abstracted from the electronic medical records. We applied the GO-FAR score and the probability of survival with good neurological outcomes for each patient. RESULTS: A total of 788 patients received a DNR order, with a median age of 71 years and a majority being males and expatriates. The GO-FAR model categorized 441 (56%) of the patients as having a low or very low probability of survival and 347 (44%) as average or above. There were 219 patients with a primary diagnosis of cancer, of whom 148 (67.6%) were in the average and above-average probability groups. There were more In-hospital deaths among patients in the average and above-average probability of survival group compared with those with very low and low probability (243 (70%) versus 249 (56.5%) (P < 0.0001)). The DNR patients with an average or above average chance of survival by GO-FAR score were more likely to be expatriates, oncology patients, and did not have sepsis. CONCLUSIONS: The GO-FAR score provides a guide for joint decision-making on the possible outcomes of CPR in the event of IHCA. The physicians' recommendation and the ultimate patient's resuscitation choice may differ due to more complex contextual medico-social factors.

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.

Adverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities. To address these limitations, we propose a node embedding-based graph outlier detection algorithm to predict adverse pregnancy outcomes. We developed a knowledge graph using a well-curated representative dataset of the Emirati population and two node embedding algorithms. The graph autoencoder (GAE) was trained by applying a combination of original risk factors and node embedding features. Samples that were difficult to reconstruct at the output of GAE were identified as outliers considered representing PTB and LBW samples. Our experiments using LBW, PTB, and very PTB datasets demonstrated that incorporating node embedding considerably improved performance, achieving a 12% higher AUC-ROC compared to traditional GAE. Our study demonstrates the effectiveness of node embedding and graph outlier detection in improving the prediction performance of adverse pregnancy outcomes in well-curated population datasets.

Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy.

Objective: To assess the cost-effectiveness of artificial intelligence (AI) for supporting clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology. Importance: AI has been referred to as a facilitator for more precise, personalized, and safer health care, and AI algorithms have been reported to have diagnostic accuracies at or above the average physician in dermatology, dentistry, and ophthalmology. Design, Setting, and Participants: This economic evaluation analyzed data from 3 Markov models used in previous cost-effectiveness studies that were adapted to compare AI vs standard of care to detect melanoma on skin photographs, dental caries on radiographs, and diabetic retinopathy on retina fundus imaging. The general US and German population aged 50 and 12 years, respectively, as well as individuals with diabetes in Brazil aged 40 years were modeled over their lifetime. Monte Carlo microsimulations and sensitivity analyses were used to capture lifetime efficacy and costs. An annual cycle length was chosen. Data were analyzed between February 2021 and August 2021. Exposure: AI vs standard of care. Main Outcomes and Measures: Association of AI with tooth retention-years for dentistry and quality-adjusted life-years (QALYs) for individuals in dermatology and ophthalmology; diagnostic costs. Results: In 1000 microsimulations with 1000 random samples, AI as a diagnostic-support system showed limited cost-savings and gains in tooth retention-years and QALYs. In dermatology, AI showed mean costs of $750 (95% CI, $608-$970) and was associated with 86.5 QALYs (95% CI, 84.9-87.9 QALYs), while the control showed higher costs $759 (95% CI, $618-$970) with similar QALY outcome. In dentistry, AI accumulated costs of €320 (95% CI, €299-€341) (purchasing power parity [PPP] conversion, $429 [95% CI, $400-$458]) with 62.4 years per tooth retention (95% CI, 60.7-65.1 years). The control was associated with higher cost, €342 (95% CI, €318-€368) (PPP, $458; 95% CI, $426-$493) and fewer tooth retention-years (60.9 years; 95% CI, 60.5-63.1 years). In ophthalmology, AI accrued costs of R $1321 (95% CI, R $1283-R $1364) (PPP, $559; 95% CI, $543-$577) at 8.4 QALYs (95% CI, 8.0-8.7 QALYs), while the control was less expensive (R $1260; 95% CI, R $1222-R $1303) (PPP, $533; 95% CI, $517-$551) and associated with similar QALYs. Dominance in favor of AI was dependent on small differences in the fee paid for the service and the treatment assumed after diagnosis. The fee paid for AI was a factor in patient preferences in cost-effectiveness between strategies. Conclusions and Relevance: The findings of this study suggest that marginal improvements in diagnostic accuracy when using AI may translate into a marginal improvement in outcomes. The current evidence supporting AI as decision support from a cost-effectiveness perspective is limited; AI should be evaluated on a case-specific basis to capture not only differences in costs and payment mechanisms but also treatment after diagnosis.

The role of personality traits and social support in relations of health-related behaviours and depressive symptoms.

BACKGROUND: Previous evidence has suggested that physically inactive individuals and extensive media users are at high risk for experiencing depressive symptoms. We examined personality traits and perceived social support as potential moderators of this association. Personality and perceived social support were included as two of the most frequently considered variables when determining predispositioning factors for media use phenomena also discussed in relation to physical activity. METHODS: We analysed cross-sectional data from 1402 adults (18-31 years old) who participated in a national health survey in Germany (KiGGS, Study on the health of children and adolescents in Germany, wave 2). The data included one-week accelerometer assessments as objective indicators of physical activity, self-reported media use, depressive symptoms, perceived social support and Big 5 personality traits. An elastic net regression model was fit with depressive symptoms as outcome. Ten-fold cross-validation was implemented. RESULTS: Amongst the main effects, we found that high media use was positively correlated with depressive symptoms, whereas physical activity was not correlated. Looking at support and individual differences as moderators, revealed that PC use was more strongly correlated with depressive symptoms in cases of low levels of perceived social support. Positive associations of social media use with depressive symptoms were more pronounced, whereas negative associations of moderate to vigorous physical activity with depressive symptoms were less pronounced in extraverts than they were in introverts. CONCLUSIONS: Results highlight the importance of considering individual factors for deriving more valid recommendations on protective health behaviours.