Risk Factors for Nonsyndromic Orofacial Clefts Among Saudi Children

ABSTRACT Objectives The aim of this study was to identify the risk factors associated with nonsyndromic orofacial clefts (NSOFCs) among Saudi children. Materials and Methods A case–control study was carried out at the Ministry of National Guard Health Affairs. Cases were children with NSOFCs who were matched by gender and year of birth to healthy controls from the same setting. Data on risk factors were collected by interviewing parents of both cases and controls using a validated questionnaire. The questionnaire consisted of the father's and mother's information and the child's information. The level of significance was set at 0.05. Odds ratio (OR) and 95% confidence intervals (CIs) were used to determine the associated risk factors with NSOFCs. Results A total of 188 children were included (88 cases and 100 controls), with a mean age of 5.1 ± 2.3 years. Maternal fever during pregnancy was associated with a significantly higher risk of NSOFCs (OR = 3.4, 95% CI: 0.05–2.5, p < 0.05). Additionally, the presence of maternal relatives with orofacial clefts increased the risk (OR = 6.02, 95% CI: 0.43–3.16, p < 0.001), whereas the strongest predictor was paternal relatives with orofacial clefts (OR = 8.00, 95% CI: 0.41–3.75, p = 0.014). These findings are of utmost importance for the understanding and potential prevention of NSOFCs. Conclusions The presence of paternal or maternal relatives with orofacial clefts and maternal fever during the first trimester were predictors for NSOFCs, with having affected paternal relatives being the strongest predictor

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.</jats:p