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An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.

The worldwide demand for oil has been rising rapidly for many decades, being the first indicator of economic development. Oil is extracted from underneath reservoirs found below land or ocean using oil wells. An offshore oil well is an oil well type where a wellbore is drilled underneath the ocean bed to obtain oil to the surface that demands more stability than other oil wells. The sensors of oil wells generate massive amounts of multivariate time-series data for surveillance engineers to analyze manually and have continuous insight into drilling operations. The manual analysis of data is challenging and time-consuming. Additionally, it can lead to several faulty events that could increase costs and production losses since the engineers tend to focus on the analysis rather than detecting the faulty events. Recently, machine learning (ML) techniques have significantly solved enormous real-time data anomaly problems by decreasing the data engineers’ interaction processes. Accordingly, this study aimed to utilize ML techniques to reduce the time spent manually to establish rules that detect abnormalities in oil wells, leading to rapid and more precise detection. Four ML algorithms were utilized, including random forest (RF), logistic regression (LR), k-nearest neighbor (K-NN), and decision tree (DT). The dataset used in this study suffers from the class imbalance issue; therefore, experiments were conducted using the original and sampled datasets. The empirical results demonstrated promising outcomes, where RF achieved the highest accuracy, recall, precision, F1-score, and AUC of 99.60%, 99.64%, 99.91%, 99.77%, and 1.00, respectively, using the sampled data, and 99.84%, 99.91%, 99.91%, 99.91%, and 1.00, respectively, using the original data. Besides, the study employed Explainable Artificial Intelligence (XAI) to enable surveillance engineers to interpret black box models to understand the causes of abnormalities. The proposed models can be used to successfully identify anomalous events in the oil wells.

Background Critically ill patients are at a higher risk of developing infections and related complications, which can lead to death. Each additional day of antipseudomonal β-lactam use increases the risk of resistance; therefore, de-escalation is highly recommended to improve antibiotic use. To our knowledge, there are limited studies that have evaluated the clinical impact of de-escalation in critically ill patients with proven Methicillin-Susceptible Staphylococcus aureus (MSSA) pneumonia alone. Therefore, our study aimed to assess the clinical impact of the de-escalation strategy compared with the non-de-escalation strategy in critically ill patients with proven MSSA pneumonia. Methods This multicenter retrospective cohort study was conducted in three tertiary hospitals from January 2016 to July 2021. Adult critically ill patients admitted to the intensive care unit with proven MSSA respiratory culture who received antibiotics with anti-MSSA activity were screened for eligibility. Eligible patients were categorized into two groups according to their de-escalation status: De-escalated and Non-de-escalated. The De-escalation was defined as the reduction of the antimicrobial activity spectrum of antibiotics by switching to a narrower-spectrum agent that targets MSSA. The primary outcome was treatment failure rate, while other outcomes were considered secondary. Propensity score (PS) matching was applied at a 1:1 ratio, and multivariate regression analyses were utilized as appropriate. Results After PS matching (1:1), 58 patients were included in the study (29 patients in non-deescalated vs 29 patients in de-escalated). The treatment failure rate was significantly higher in the de-escalated group compared to the non-de-escalated (OR 16.98; 95% CI (3.304–87.225), p  = 0.0007). In contrast, no significant differences were found in 30-day mortality, hospital and ICU length of stays, ventilator-free days, ICU readmission rate, or MSSA infection recurrence rate. Conclusion Our results showed that de-escalation of antibiotics in critically ill patients with confirmed MSSA pneumonia was associated with significantly higher rate of treatment failure while no significant differences were observed in the other clinical outcomes. These findings highlight the need for prospective studies to better inform safe and effective de-escalation strategies in this population.

The use of tocilizumab for the management of COVID-19 emerged since it modulates inflammatory markers by blocking interleukin 6 receptors. Concerns regarding higher thrombosis risk while using tocilizumab were raised in the literature. The aim of this study is to investigate the association between tocilizumab therapy and the development of thromboembolic events in critically ill COVID-19 patients. A propensity score-matched, multicenter cohort study for critically ill adult patients with COVID-19. Eligible patients admitted to ICU between March 2020 and July 2021 were categorized into two sub-cohorts based on tocilizumab use within 24 h of ICU admission. The primary endpoint was to assess the incidence of all thrombosis cases during ICU stay. The secondary endpoints were 30-day mortality, in-hospital mortality, and the highest coagulation parameters follow-up (i.e., D-dimer, Fibrinogen) during the stay. Propensity score matching (1:2 ratio) was based on nine matching covariates. Among a total of 867 eligible patients, 453 patients were matched (1:2 ratio) using propensity scores. The thrombosis events were not statistically different between the two groups in crude analysis (6.8% vs. 7.7%; p-value = 0.71) and regression analysis [OR 0.83, 95% CI (0.385, 1.786)]. Peak D-dimer levels did not change significantly when the patient received tocilizumab (beta coefficient (95% CI): 0.19 (− 0.08, 0.47)), while there was a significant reduction in fibrinogen levels during ICU stay (beta coefficient (95% CI): − 0.15 (− 0.28, − 0.02)). On the other hand, the 30-day and in-hospital mortality were significantly lower in tocilizumab-treated patients (HR 0.57, 95% CI (0.37, 0.87), [HR 0.67, 95% CI (0.46, 0.98), respectively). The use of tocilizumab in critically ill patients with COVID-19 was not associated with higher thrombosis events or peak D-dimer levels. On the other hand, fibrinogen levels, 30-day and in-hospital mortality were significantly lower in the tocilizumab group. Further randomized controlled trials are needed to confirm our findings.

Background Recent guidelines recommend using direct oral anticoagulants (DOACs) as first-line agents in patients with non-valvular atrial fibrillation (NVAF). Research is currently investigating the use of Apixaban in underweight patients, with some results suggesting altered pharmacokinetics, decreased drug absorption, and potential overdosing in this population. This study examined the effectiveness and safety of standard Apixaban dosing in adult patients with atrial NVAF weighing less than 50 kg. Methods This is a retrospective cohort study conducted at King Abdulaziz Medical City (KAMC); adult patients with a body mass index (BMI) below 25 who received a standard dose of Apixaban (5 mg twice daily) were categorized into two sub-cohorts based on their weight at the time of Apixaban initiation. Underweight was defined as patients weighing ≤ 50 kg, while the control group (Normal weight) comprised patients weighing > 50 kg. We followed the patients for at least one year after Apixaban initiation. The study’s primary outcome was the incidence of stroke events, while secondary outcomes included bleeding (major or minor), thrombosis, and venous thromboembolism (VTE). Propensity score (PS) matching with a 1:1 ratio was used based on predefined criteria and regression model was utilized as appropriate. Results A total of 1,433 patients were screened; of those, 277 were included according to the eligibility criteria. The incidence of stroke events was lower in the underweight than in the normal weight group at crude analysis (0% vs. 9.1%) p-value = 0.06), as well in regression analysis (OR (95%CI): 0.08 (0.001, 0.76), p-value = 0.002). On the other hand, there were no statistically significant differences between the two groups in the odds of major and minor bleeding (OR (95%CI): 0.39 (0.07, 2.03), p-value = 0.26 and OR (95%CI): 1.27 (0.56, 2.84), p-value = 0.40, respectively). Conclusion This exploratory study revealed that underweight patients with NVAF who received standard doses of Apixaban had fewer stroke events compared to normal-weight patients, without statistically significant differences in bleeding events. To confirm these findings, further randomized controlled trials with larger sample sizes and longer observation durations are required.

The use of erythropoietin-stimulating agents (ESAs) as adjunctive therapy in critically ill patients with COVID-19 may have a potential benefit. This study aims to evaluate the effect of ESAs on the clinical outcomes of critically ill COVID-19 patients. A multicenter, retrospective cohort study was conducted from 01-03-2020 to 31-07-2021. We included adult patients who were ≥ 18 years old with a confirmed diagnosis of COVID-19 infection and admitted to intensive care units (ICUs). Patients were categorized depending on ESAs administration during their ICU stay. The primary endpoint was the length of stay; other endpoints were considered secondary. After propensity score matching (1:3), the overall included patients were 120. Among those, 30 patients received ESAs. A longer duration of ICU and hospital stay was observed in the ESA group (beta coefficient: 0.64; 95% CI: 0.31-0.97; P = < .01, beta coefficient: 0.41; 95% CI: 0.12-0.69; P = < .01, respectively). In addition, the ESA group's ventilator-free days (VFDs) were significantly shorter than the control group. Moreover, patients who received ESAs have higher odds of liver injury and infections during ICU stay than the control group. The use of ESAs in COVID-19 critically ill patients was associated with longer hospital and ICU stays, with no survival benefits but linked with lower VFDs.

Background Due to the high complexity of healthcare during mass gatherings (MG), the integration of Artificial Intelligence (AI) might be crucial. AI can enhance healthcare delivery, improve patient care, optimize resources, and ensure efficient management of the large-scale healthcare demands during Hajj. This paper aims to provide an overview of AI utilization specifically during Hajj and explore the potential role of AI-driven tools in healthcare and clinical services provided to pilgrims. Methods A task force was formed and included experts healthcare providers, AI specialists, and members from the Saudi Society for Multidisciplinary Research Development and Education (SCAPE Society), Saudi Critical Care Pharmacy Research (SCAPE) platform, Saudi Society of Clinical Pharmacy (SSCP), policymakers, and frontline healthcare practitioners involved in Hajj. The task force first agreed on the framework and voting system, then organized into teams to draft content for specific domains. Consensus was reached using a voting system requiring over 80% agreement, and all task force members reviewed and finalized the drafts. The selection of AI specialists, policymakers, and frontline healthcare practitioners for the task force was based on their expertise and relevance to healthcare during Hajj. Results The task force identified key focus areas: (1) Patient Care: AI tools for predictive analytics, triage, resource management, and virtual healthcare. (2) Healthcare Providers: AI in medical imaging, care delivery, provider-patient communication, and training. (3) Operational Management: AI for healthcare documentation and reducing administrative burden. (4) Healthcare Systems: AI for early detection and automation during Hajj. The task force constructed ten statements to guide future initiatives. Conclusion Expanding the role of AI in healthcare during MGs will help optimize healthcare outcomes and utilization. Concerns about AI ethics and data security need to be addressed. Additional data is needed to address the gaps in the literature regarding AI's applicability in healthcare services during MGs.

Atrial fibrillation (Afib) can contribute to a significant increase in mortality and morbidity in critically ill patients. Thus, our study aims to investigate the incidence and clinical outcomes associated with the new-onset Afib in critically ill patients with COVID-19. A multicenter, retrospective cohort study includes critically ill adult patients with COVID-19 admitted to the intensive care units (ICUs) from March, 2020 to July, 2021. Patients were categorized into two groups (new-onset Afib vs control). The primary outcome was the in-hospital mortality. Other outcomes were secondary, such as mechanical ventilation (MV) duration, 30-day mortality, ICU length of stay (LOS), hospital LOS, and complications during stay. After propensity score matching (3:1 ratio), 400 patients were included in the final analysis. Patients who developed new-onset Afib had higher odds of in-hospital mortality (OR 2.76; 95% CI: 1.49-5.11, = .001). However, there was no significant differences in the 30-day mortality. The MV duration, ICU LOS, and hospital LOS were longer in patients who developed new-onset Afib (beta coefficient 0.52; 95% CI: 0.28-0.77; < .0001,beta coefficient 0.29; 95% CI: 0.12-0.46; < .001, and beta coefficient 0.35; 95% CI: 0.18-0.52; < .0001; respectively). Moreover, the control group had significantly lower odds of major bleeding, liver injury, and respiratory failure that required MV. New-onset Afib is a common complication among critically ill patients with COVID-19 that might be associated with poor clinical outcomes; further studies are needed to confirm these findings.

Metabolic syndrome (MBS) and periodontitis are distinct conditions with overlapping and unique risk factors. Periodontitis is a chronic destructive disease of the periodontium, driven by alterations in the host immune-inflammatory response to virulent periodontal pathogens. MBS is characterized by various abnormalities, including visceral abdominal obesity, dyslipidemia (low high-density lipoprotein (HDL) and high triglyceride (TG) levels), hypertension, and hyperglycemia. These factors collectively increase the risk of atherosclerotic cardiovascular disease (CVD) and diabetes. Several pro-inflammatory mediators are involved in the pathogenesis of periodontitis and MBS, and the deleterious bidirectional effects of these mediators exacerbate the severity and progression of both conditions. This comprehensive review focuses on the intricate relationship between MBS and periodontitis. Specifically, it explores the pathophysiological mechanisms of each disease component of MBS and its impact on periodontitis, and vice versa.