Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey.

BACKGROUND: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. METHODS: An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. RESULTS: 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. DISCUSSION: The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.

Population Health Strategies to Support Hospital and Intensive Care Unit Resiliency During the COVID-19 Pandemic: The Italian Experience.

Italy was one of the countries most affected by the number of people infected and dead during the first COVID-19 wave. The authors describe the rapid rollout of a population health clinical and organizational response in preparedness and capabilities to support the first wave of the COVID-19 pandemic in the Italian province of Modena. The authors review the processes, the challenges faced, and describe how excess demand for hospital services was successfully mitigated and thus overwhelming the healthcare services avoided the collapse of the local health care system. An analysis of bed occupancy in the region predicted during the first weeks of the epidemic. The SEIR model estimated the number of infected people under different containment measures. Community resources were mobilized to reduce provincial hospitals' burden of care. A population health approach, based on a radical reorganization of the workflow and emergency patient management, was implemented. The bed saturation of the Modena Healthcare Agency was measured by an ad hoc, newly implemented intensive care unit (ICU) bed occupancy and COVID-19 centralized governance dashboard. ICU bed occupancy increased by 114%, avoiding saturation of the Modena Healthcare Agency system. The Emilia-Romagna region achieved a higher rate of ICU bed availability at 2.15 ICU beds per 10,000 inhabitants as compared with community 1 ICU bed availability prior to the pandemic. Rapid and radical local reorganization of regional efforts helped inform the successful development and implementation of strategic choices within the hospital and the community to prevent the saturation of key facilities.

Estimating the glomerular filtration rate in the general population using different equations: effects on classification and association.

BACKGROUND/AIMS: Several formulas for glomerular filtration rate (GFR) estimation, based on serum creatinine or cystatin C, have been proposed. We assessed the impact of some of these equations on estimated GFR (eGFR) and chronic kidney disease (CKD) prevalence, and on the association with cardiovascular risk factors, in a general population sample characterized by a young mean age. METHODS: We studied 1,199 individuals from three Alpine villages enrolled into the MICROS study. eGFR was obtained with the 4- and 6-parameter MDRD study equations, the Virga equation, and with the three CKD-EPI formulas for creatinine, cystatin C, and the combination of creatinine and cystatin C. We assessed the concordance between quantitative eGFR levels, CKD prevalence, and in terms of association with total, LDL, and HDL cholesterol. RESULTS: The highest and lowest eGFR levels corresponded to the cystatin C-based and MDRD-4 equations, respectively. CKD prevalence varied from 1.8% (Virga) to 5.8% (MDRD-4). The CKD-EPI based on creatinine showed the highest agreement with all other equations. Agreement between methods was higher at lower eGFR levels, older age, and in the presence of diabetes and hypertension. Creatinine-based estimates of eGFR were associated with total and low-density lipoprotein but not high-density lipoprotein cholesterol. The opposite was observed for the cystatin C-based GFR. CONCLUSION: GFR estimation is strongly affected by the chosen equation. Differences are more pronounced in healthy and younger individuals. To identify CKD risk factors, the choice of the equation is of secondary importance to the choice of the biomarker used in the formula. If eGFR is not calibrated to a gold standard GFR in the general population, reports about CKD prevalence should be considered with caution.

Importance of different types of prior knowledge in selecting genome-wide findings for follow-up.

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts' opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

SNP prioritization using a Bayesian probability of association.

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone.