RESUMEN
The use of artificial intelligence in the field of medicine - including spine surgery - is now widespread and prominent. Kyphosis is a prevalent disease in spine surgery with abundant morbidity. Predicting the development of kyphosis disease has been somewhat difficult, and the use of AI to aid in the prediction of kyphosis disease may yield new opportunities for spine surgeons. The aim of this review is to recognize the contributions of AI in predicting the development of kyphosis. Five databases/registers were searched to identify suitable records for this review. Nine studies were included in this review. The studies demonstrated that AI could be utilized to predict the development of kyphosis disease after corrective surgery for a variety of spinal pathologies, including thoracolumbar burst fracture, cervical deformity, previous kyphosis disease, and adult degenerative scoliosis. The studies utilized a variety of AI modalities, including support vector machines, decision trees, random forests, and artificial neural networks. Two of the included studies also compared the use of different AI modalities in predicting the development of kyphosis disease. The literature has demonstrated that AI can be utilized effectively to predict the development of kyphosis disease. However, the current research is limited and only sparsely covers this broad field. Therefore, we suggest that further research is needed to explore the uncharted opportunities in predicting the development of kyphosis disease. Also, further research would confirm and consolidate the benefits demonstrated by the literature included in this review.
RESUMEN
Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.
RESUMEN
AIM: The aim of this study is to investigate the prevalence of asymptomatic hyperuricemia in Qatar and to examine its association with changes in markers of dyslipidemia, prediabetes and subclinical inflammation. METHODS: A cross-sectional study of young adult participants aged 18 - 40 years old devoid of comorbidities collected between 2012 and 2017. Exposure was defined as uric acid level, and outcomes were defined as levels of different blood markers. De-identified data were collected from Qatar Biobank. T-tests, correlation tests and multiple linear regression were all used to investigate the effects of hyperuricemia on blood markers. Statistical analyses were conducted using STATA 16. RESULTS: The prevalence of asymptomatic hyperuricemia is 21.2% among young adults in Qatar. Differences between hyperuricemic and normouricemic groups were observed using multiple linear regression analysis and found to be statistically and clinically significant after adjusting for age, gender, BMI, smoking and exercise. Significant associations were found between uric acid level and HDL-c p = 0.019 (correlation coefficient -0.07 (95% CI [-0.14, -0.01]); c-peptide p = 0.018 (correlation coefficient 0.38 (95% CI [0.06, 0.69]) and monocyte to HDL ratio (MHR) p = 0.026 (correlation coefficient 0.47 (95% CI [0.06, 0.89]). CONCLUSIONS: Asymptomatic hyperuricemia is prevalent among young adults and associated with markers of prediabetes, dyslipidemia, and subclinical inflammation.
