Bias in artificial intelligence in vascular surgery.

Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning models to learn without a priori assumption, or without prior learning, to connect seemingly unrelated information mixes excitement alongside hesitation to fully understand AI's limitations. Bias, ranging from data collection and input to algorithm development to finally human review of algorithm output affects AI's application to clinical patient presents unique challenges that differ significantly from biases in traditional analyses. Algorithm fairness, a new field of research within AI, aims to mitigate bias by evaluating the data at the preprocessing stage, optimizing during algorithm development, and evaluating algorithm output at the postprocessing stage. As the field continues to develop, being cognizant of the inherent biases and limitations related to black box decision making, biased data sets agnostic to patient-level disparities, wide variation of present methodologies, and lack of common reporting standards will require ongoing research to provide transparency to AI and its applications.

An Ounce of Prevention is the Only Cure: Nationwide Clinical and Financial Outcomes of Self-Inflicted Firearm Injuries.

BACKGROUND: Self-inflicted gunshot wounds (SIGSWs) remain a leading, preventable cause of death in the United States. The present study evaluated patient demographics, operative characteristics, in-hospital outcomes, and resource utilization between patients with SIGSW and other GSW. METHODS: The 2016-2020 National Inpatient Sample was queried for patients ≥16 years old admitted following gunshot wounds. Patients were categorized as SIGSW if they were injured through self-harm. Multivariable logistic regression was used to evaluate the association of SIGSW on outcomes. The primary endpoint was in-hospital mortality with complications, costs, and length of stay secondarily considered. RESULTS: Of an estimated 157,795 surviving to hospital admission, 14,670 (9.30%) were SIGSW. Self-inflicted gunshot wounds were more commonly female (18.1 vs 11.3%), insured by Medicare (21.1 vs 5.0%), and white (70.8 vs 22.3%) (all P < .001) compared to non-SIGSW. Psychiatric illness was more prevalent in SIGSW (46.0 vs 6.6%, P < .001). Additionally, SIGSW more frequently underwent neurologic (10.7 vs 2.9%) and facial operations (12.5 vs 3.2%) (both P < .001). After adjustment, SIGSW was associated with greater odds of mortality (AOR: 12.4, 95% CI: 10.4-14.7). Length of stay (ß: +1.5 days, 95% CI: .8-2.1) and costs (ß: +$3.6 K, 95% CI: 1.4-5.7) were significantly greater in SIGSW. CONCLUSIONS: Self-inflicted gunshot wounds are associated with increased mortality compared to other GSW, likely due to the increased proportion of injuries in the head and neck region. This lethality, coupled with the high prevalence of psychiatric illness in this population, indicates that efforts must be made to intervene through primary prevention, including enhanced screening and weapon safety considerations for those at risk.

Multiple Sclerosis in a Patient With Prior West Nile Encephalitis.

Multiple sclerosis is a neurodegenerative disease characterized by an inflammatory demyelination of the central nervous system. The degenerative disease has been linked to numerous viral infections, geographical locations, and genetic predisposition. One link that has not been fully established is the relationship between West Nile virus infection and its role in the initiation of multiple sclerosis. This case study provides further evidence that the proinflammatory neurological processes induced by the West Nile virus may lead to systemic demyelination of neuronal axons, ultimately causing multiple sclerosis.