Vancomycin therapy in critically ill patients on continuous renal replacement therapy; are we doing enough?

AbstractBackgroundRecommendations regarding vancomycin dosing and monitoring in critically ill patients on continuous renal replacement therapy (CRRT) are limited. This is a retrospective study to assess the adequacy of current vancomycin dosing and monitoring practice for patients on CRRT in a tertiary hospital in Riyadh, Saudi Arabia.MethodsA retrospective chart review of adult patients admitted between 1 April 2011 and 30 March 2013 to critical care and received intravenous vancomycin therapy whilst on CRRT was performed.ResultsA total of 68 patients received intravenous vancomycin therapy whilst on CRRT, of which 32 met the inclusion criteria. Fifty-one percent were males and median (range) age was 62.5 (19 – 90) years. Median APACHE II score was 33.5 (22–43) and median Charlson Comorbidity Score was 4 (0–8). The mean (±standard deviation) dose of vancomycin was 879.9mg (±281.2mg) for an average duration of 5.9days (±3.7days). All patients received continuous veno-venous haemofiltration (CVVH). A total of 55 vancomycin level readings were available from the study population, ranging from 6.6 to 41.3, with wide variations within the same sampling time frames. Vancomycin levels of>15mg/L or were achieved at least once in 24 patients (75.0%), but only 11 patients (34.3%) had 2 or more serum vancomycin level readings of 15mg/L or more.ConclusionTherapeutic vancomycin levels are difficult to maintain in critically ill patients who are receiving IV vancomycin therapy whilst on CRRT. Aggressive dosing schedules and frequent monitoring are required to ensure adequate vancomycin therapy in this setting

Viral shedding and antibody response in 37 patients with MERS-coronavirus infection

Background. The Middle East respiratory syndrome (MERS) coronavirus causes isolated cases and outbreaks of severe respiratory disease. Essential features of the natural history of disease are poorly understood. Methods. We studied 37 adult patients infected with MERS coronavirus for viral load in the lower and upper respiratory tracts (LRT and URT, respectively), blood, stool, and urine. Antibodies and serum neutralizing activities were determined over the course of disease. Results. One hundred ninety-nine LRT samples collected during the 3 weeks following diagnosis yielded virus RNA in 93% of tests. Average (maximum) viral loads were 5 × 106 (6 × 1010) copies/mL. Viral loads (positive detection frequencies) in 84 URT samples were 1.9 × 104 copies/mL (47.6%). Thirty-three percent of all 108 serum samples tested yielded viral RNA. Only 14.6% of stool and 2.4% of urine samples yielded viral RNA. All seroconversions occurred during the first 2 weeks after diagnosis, which corresponds to the second and third week after symptom onset. Immunoglobulin M detection provided no advantage in sensitivity over immunoglobulin G (IgG) detection. All surviving patients, but only slightly more than half of all fatal cases, produced IgG and neutralizing antibodies. The levels of IgG and neutralizing antibodies were weakly and inversely correlated with LRT viral loads. Presence of antibodies did not lead to the elimination of virus from LRT. Conclusions. The timing and intensity of respiratory viral shedding in patients with MERS closely matches that of those with severe acute respiratory syndrome. Blood viral RNA does not seem to be infectious. Extrapulmonary loci of virus replication seem possible. Neutralizing antibodies do not suffice to clear the infection

Steady improvement of infection control services in six community hospitals in Makkah following annual audits during Hajj for four consecutive years

BACKGROUND: the objective of this study was to evaluate the impact of annual review of the infection control practice in all Ministry of Health hospitals in the holy city of Makkah, Saudi Arabia, during the Hajj period of four lunar Islamic years, 1423 to 1426 corresponding to 2003 to 2006. METHODS: audit of infection control service was conducted annually over a 10-day period in six community hospitals with bed capacities ranging from 140 to 557 beds. Data were collected on standardized checklists on various infection control service items during surprise visits to the medical, pediatric, surgical, and critical care units, and the kitchens. Percentage scores were calculated for audited items. The results of the audit for hospitals were confidentially sent to them within four weeks after the end of Hajj. RESULTS: deficiencies observed in the first audit included lack of infection control committees, infection control units, infection control educational activities, and surveillance system and shortage of staff. These deficiencies were resolved in the subsequent audits. The average (range) scores of hospitals in 11 infection control items increased from 43% (20–67%) in the first audit to 78% (61–93%) in the fourth audit. CONCLUSION: regular hospital infection control audits lead to significant improvement of infection control practice. There is a need to build a rigorous infection control audit into hospitals' ongoing monitoring and reporting to the Ministry of Health and to provide these hospitals with feed back on such audits to continuously strengthen the safety standards for patients, visitors, and employees

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease

Deep Learning-based Method for Enhancing the Detection of Arabic Authorship Attribution using Acoustic and Textual-based Features

Authorship attribution (AA) is defined as the identification of the original author of an unseen text. It is found that the style of the author’s writing can change from one topic to another, but the author’s habits are still the same in different texts. The authorship attribution has been extensively studied for texts written in different languages such as English. However, few studies investigated the Arabic authorship attribution (AAA) due to the special challenges faced with the Arabic scripts. Additionally, there is a need to identify the authors of texts extracted from livestream broadcasting and the recorded speeches to protect the intellectual property of these authors. This paper aims to enhance the detection of Arabic authorship attribution by extracting different features and fusing the outputs of two deep learning models. The dataset used in this study was collected from the weekly livestream and recorded Arabic sermons that are available publicly on the official website of Al-Haramain in Saudi Arabia. The acoustic, textual and stylometric features were extracted for five authors. Then, the data were pre-processed and fed into the deep learning-based models (CNN architecture and its pre-trained ResNet34). After that the hard and soft voting ensemble methods were applied for combining the outputs of the applied models and improve the overall performance. The experimental results showed that the use of CNN with textual data obtained an acceptable performance using all evaluation metrics. Then, the performance of ResNet34 model with acoustic features outperformed the other models and obtained the accuracy of 90.34%. Finally, the results showed that the soft voting ensemble method enhanced the performance of AAA and outperformed the other method in terms of accuracy and precision, which obtained 93.19% and 0.9311 respectively

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes’ response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error

An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model

Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA

Presentation and outcome of Middle East respiratory syndrome in Saudi intensive care unit patients.

BACKGROUND: Middle East respiratory syndrome coronavirus infection is associated with high mortality rates but limited clinical data have been reported. We describe the clinical features and outcomes of patients admitted to an intensive care unit (ICU) with Middle East respiratory syndrome coronavirus (MERS-CoV) infection. METHODS: Retrospective analysis of data from all adult (>18 years old) patients admitted to our 20-bed mixed ICU with Middle East respiratory syndrome coronavirus infection between October 1, 2012 and May 31, 2014. Diagnosis was confirmed in all patients using real-time reverse transcription polymerase chain reaction on respiratory samples. RESULTS: During the observation period, 31 patients were admitted with MERS-CoV infection (mean age 59 ± 20 years, 22 [71 %] males). Cough and tachypnea were reported in all patients; 22 (77.4 %) patients had bilateral pulmonary infiltrates. Invasive mechanical ventilation was applied in 27 (87.1 %) and vasopressor therapy in 25 (80.6 %) patients during the intensive care unit stay. Twenty-three (74.2 %) patients died in the ICU. Nonsurvivors were older, had greater APACHE II and SOFA scores on admission, and were more likely to have received invasive mechanical ventilation and vasopressor therapy. After adjustment for the severity of illness and the degree of organ dysfunction, the need for vasopressors was an independent risk factor for death in the ICU (odds ratio = 18.33, 95 % confidence interval: 1.11-302.1, P = 0.04). CONCLUSIONS: MERS-CoV infection requiring admission to the ICU is associated with high morbidity and mortality. The need for vasopressor therapy is the main risk factor for death in these patients

Estimating potential incidence of MERS-CoV associated with Hajj pilgrims to Saudi Arabia, 2014

Between March and June 2014 the Kingdom of Saudi Arabia (KSA) had a large outbreak of MERS-CoV, renewing fears of a major outbreak during the Hajj this October. Using KSA Ministry of Health data, the MERS-CoV Scenario and Modeling Working Group forecast incidence under three scenarios. In the expected incidence scenario, we estimate 6.2 (95% Prediction Interval [PI]: 1-17) pilgrims will develop MERS-CoV symptoms during the Hajj, and 4.0 (95% PI: 0-12) foreign pilgrims will be infected but return home before developing symptoms. In the most pessimistic scenario, 47.6 (95% PI: 32-66) cases will develop symptoms during the Hajj, and 29.0 (95% PI: 17-43) will be infected but return home asymptomatic. Large numbers of MERS-CoV cases are unlikely to occur during the 2014 Hajj even under pessimistic assumptions, but careful monitoring is still needed to detect possible mass infection events and minimize introductions into other countries

Transmission of MERS-Coronavirus in Household Contacts

BACKGROUND: Strategies to contain the Middle East respiratory syndrome coronavirus (MERS-CoV) depend on knowledge of the rate of human-to-human transmission, including subclinical infections. A lack of serologic tools has hindered targeted studies of transmission. METHODS: We studied 26 index patients with MERS-CoV infection and their 280 household contacts. The median time from the onset of symptoms in index patients to the latest blood sampling in contact patients was 17.5 days (range, 5 to 216; mean, 34.4). Probable cases of secondary transmission were identified on the basis of reactivity in two reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assays with independent RNA extraction from throat swabs or reactivity on enzyme-linked immunosorbent assay against MERS-CoV S1 antigen, supported by reactivity on recombinant S-protein immunofluorescence and demonstration of neutralization of more than 50% of the infectious virus seed dose on plaque-reduction neutralization testing. RESULTS: Among the 280 household contacts of the 26 index patients, there were 12 probable cases of secondary transmission (4%; 95% confidence interval, 2 to 7). Of these cases, 7 were identified by means of RT-PCR, all in samples obtained within 14 days after the onset of symptoms in index patients, and 5 were identified by means of serologic analysis, all in samples obtained 13 days or more after symptom onset in index patients. Probable cases of secondary transmission occurred in 6 of 26 clusters (23%). Serologic results in contacts who were sampled 13 days or more after exposure were similar to overall study results for combined RT-PCR and serologic testing. CONCLUSIONS: The rate of secondary transmission among household contacts of patients with MERS-CoV infection has been approximately 5%. Our data provide insight into the rate of subclinical transmission of MERS-CoV in the home