The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current ...situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients.
Using electronic medical records, we retrospectively extracted all records of patients' visits that were admitted under adult cardiology service. Admission diagnosis and primary treating physician were reviewed to verify selection criteria. A predictive machine learning-based model approach was applied to incorporate simple baseline health data at admission time to predict LOS. Patients were divided into three groups based on their LOS: short (<3 days), intermediate (3–5 days) and long (>5 days). Information gain algorithm was utilized to select the most relevant attributes. Only attributes with information gain of more than zero were used in model building. Four different machine learning techniques were evaluated and their diagnostic accuracy measures were compared.
The dataset of this study included adult patients who were admitted between 2008 and 2016 in King Abdulaziz Cardiac Center (KACC). The center is located in King Abdulaziz Medical City Complex in Riyadh, the capital of Saudi Arabia.
A total of 16,414 consecutive inpatient visits for 12,769 unique patients (mean age of 58.8 ± 16 years of which 68.2% were males) between 2008 and 2016 were included. The study cohort had a high prevalence of cardiovascular risk factors (hypertension 56%, diabetes 56%, dyslipidemia 52%, obesity 33% and smoking 24%). The most common admitting diagnosis was acute coronary syndrome (36%).
The variables with highest impact on the prediction of in-hospital LOS were on admission heart rate, on admission systolic and diastolic blood pressure, age and insurance status (eligibility). Using machine learning models; Random Forest (RF) model outperformed among all other models (sensitivity (0.80), accuracy (0.80), and AUROC (0.94)).
We showed that machine learning methods provide accurate prediction of LOS for cardiac patients. This is can be used in clinical bed management and resources allocation.
•The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages.•Accurately predicting LOS would have a positive impact on healthcare metrics.•Machine learning methods provide accurate prediction of LOS for cardiac patients.•Random Tree Forest (RF) model outperformed other machine learning models and achieved high accuracy (ROC (0.94))•Developed model can be used in clinical bed management and resources allocation among many other applications.
Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification ...techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality).
We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used.
Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling.
The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.
This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from ...interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.
BACKGROUND: Community-acquired pneumonia (CAP) is a leading cause of intensive care unit (ICU) morbidity and mortality. Despite extensive international epidemiological and clinical studies to improve ...those patients' outcomes, local statistics in Saudi Arabia are limited. The objective of this study is to describe the clinical characteristics and outcomes of patients admitted to the ICU with the diagnosis of CAP reflecting the experience of a tertiary center over an 18-year period.
METHODS: A retrospective cohort study included all consecutive adult ICU patients diagnosed with CAP between 1999 and 2017. Baseline demographics, patients' risk factors, and initial admission laboratory investigations were compared between survivors and nonsurvivors. A multivariate regression model was used to predict mortality.
RESULTS: During the study period, there were 3438 patients admitted to the ICU with CAP (median age 67 Quartile 1, 3 (Q1, Q3) 51, 76 years) and 54.4% were males, of whom 1007 (29.2%) died. The survivors compared with nonsurvivors were younger (65 vs. 70 years), less likely to have chronic liver disease (2.4% vs. 10.5%), chronic renal failure (8.1% vs. 14.4%), and be immunocompromised (10.2% vs. 18.2%), and less frequently required mechanical ventilation or vasopressors (46.2% vs. 80.5% and 29.6% vs. 55.9%, respectively). Acute Physiology and Chronic Health Evaluation (APACHE) II score was significantly higher among nonsurvivors (median score 26 vs. 20) with a longer duration of mechanical ventilation and ICU stay. Using a multivariate regression model, age, APACHE II score, bilirubin level, vasopressors, and mechanical ventilation were significantly associated with increased mortality, while diabetes was associated with lower mortality.
CONCLUSION: Around one-third of patients admitted to the ICU with CAP died. Mortality was significantly associated with age, APACHE II score, vasopressor use, and mechanical ventilation. A comprehensive national registry is needed to enhance epidemiological data and to guide initiatives for improving CAP patients' outcomes.
Diabetes Mellitus (DM) is a fast-growing health problem that imposes an enormous economic burden. Several studies demonstrated the association between physical inactivity and predicting the incidence ...of diabetes. However, these prediction models have limited validation locally. Therefore, we aim to explore the predictive value of exercise capacity in the incidence of diabetes within a high diabetes prevalence population.
A retrospective cohort study including consecutive patients free of diabetes who underwent clinically indicated treadmill stress testing. Diabetic patients at baseline or patients younger than 18 years of age were excluded. Incident diabetes was defined as an established clinical diagnosis post-exercise testing date. The predictive value of exercise capacity was examined using Harrell's c-index, net reclassification index (NRI), and integrated discrimination index (IDI).
A total of 8,722 participants (mean age 46 ± 12 years, 66.3% were men) were free of diabetes at baseline. Over a median follow-up period of 5.24 (2.17-8.78) years, there were 2,280 (≈ 26%) new cases of diabetes. In a multivariate model adjusted for conventional risk factors, we found a 12% reduction in the risk of incident diabetes for each METs achieved (HR, 0.9; 95% CI, 0.88-0.92; P < 0.001). Using Cox regression, exercise capacity improved the prediction ability beyond the conventional risk factors (AUC = 0.62 to 0.66 and c-index = 0.62 to 0.68).
Exercise capacity improved the overall predictability of diabetes. Patients with reduced exercise capacity are at high risk for developing incidence diabetes. Improvement of both physical activity and functional capacity represents a preventive measure for the general population.
Limited data exist on the epidemiology of cardiovascular risk factors in Saudi Arabia, particularly in relation to the differences between Saudi nationals and expatriates in Saudi Arabia. The aim of ...this analysis was to describe the current prevalence of cardiovascular risk factors among patients attending general practice clinics across Saudi Arabia.
In this cross-sectional epidemiological analysis of the Africa Middle East Cardiovascular Epidemiological (ACE) study, the prevalence of cardiovascular risk factors (hypertension, diabetes, dyslipidemia, obesity, smoking, abdominal obesity) was evaluated in adults attending primary care clinics in Saudi Arabia. Group comparisons were made between patients of Saudi ethnicity (SA nationals) and patients who were not of Saudi ethnicity (expatriates).
A total of 550 participants were enrolled from different clinics across Saudi Arabia aged (mean±standard deviation) 43±11years; 71% male. Nearly half of the study cohort (49.8%) had more than three cardiovascular risk factors. Dyslipidemia was the most prevalent risk factor (68.6%). The prevalence of hypertension (47.5%) and dyslipidemia (75.5%) was higher among expatriates when compared with SA nationals (31.4% vs. 55.1%, p=0.0003 vs. p<0.0001, respectively). Conversely, obesity (52.6% vs. 41.0%; p=0.008) and abdominal obesity (65.5% vs. 52.2%; p=0.0028) were higher among SA nationals vs. expatriates.
Modifiable cardiovascular risk factors are highly prevalent in SA nationals and expatriates. Programmed community-based screening is needed for all cardiovascular risk factors in Saudi Arabia. Improving primary care services to focus on risk factor control may ultimately decrease the incidence of coronary artery disease and improve overall quality of life.
The ACE trial is registered under NCT01243138.
Coronavirus Disease 2019 is a life-threatening disease, especially for people suffering from chronic diseases. As the vaccine is considered an essential tool to confront pandemics, many international ...medical institutions have developed vaccines. Countries around the world started immunizing their citizens. This study aims to assess the acceptance and barriers of COVID-19 vaccine uptake among Saudi Arabian people who suffer from chronic diseases.
In February-March 2021, a cross-sectional study of Saudi Arabian people who have chronic diseases was undertaken. It was based on an Arabic self-administered online questionnaire and used a convenience sampling technique. 310 people were invited. The response rate was 97%.
51.95% of the participants agreed to take the COVID-19 vaccine, 33.5% were unsure about being vaccinated, and 14.5% refused. The most frequent concerns between participants and receiving the vaccine were about the side effects and the perceived misconception that following preventative measures is enough to protect against the virus. Significant associations between age, education, and occupation with acceptance rate were found (p < 0.05).
Although a higher acceptance for the targeted group was expected, the participants showed a moderate acceptance of the COVID-19 vaccine. Addressing the barriers in the current study regarding vaccine uptake and focusing on building trust in the safety and efficacy of the vaccine will aid in hesitancy and resistance toward the vaccine, specifically if these measures were undertaken by an authority such as the Saudi Ministry of Health.
Abstract
Background
Cardiac myxomas are the most common benign primary cardiac tumours. The natural history of left cardiac myxomas is thought to be of slowly growing tumours. Cardiac myxomas are a ...heterogeneous group with a variable growth rate. They present usually with stroke, valve obstruction, or non-specific symptoms. Surgical resection is the effective treatment.
Case summary
This case report describes a 56-year-old hypertensive and dyslipidaemic female, when she was admitted in January 1990, complaining of loss of appetite, aches, pains, and palpitations. Her workup included a transthoracic echocardiography and transoesophageal echocardiography, which showed a left atrial mass attached to the inter-atrial septum, highly suggestive of left atrial myxoma. She was referred for surgical removal of the left atrial mass. However, she was reluctant to undergo surgery as she felt better. The patient was followed-up for almost 30 years with the left atrial mass confirmed as left atrial myxoma by cardiac magnetic resonance imaging. The left atrial mass became smaller in size and more calcified.
Discussion
Cardiac myxomas are a group of heterogeneous tumours, thought to be slowly growing. The growth rate of cardiac myxomas prior to diagnosis is not well known, as the vast majority is treated with surgical resection immediately after diagnosis. Our case showed the natural progression of an unoperated smooth-surfaced left atrial myxoma followed-up for almost 30 years, which slowly became smaller and more calcified.
Prior studies have demonstrated cardiorespiratory fitness (CRF) to be a strong marker of cardiovascular health. However, there are limited data investigating the association between CRF and risk of ...progression to heart failure (HF). The purpose of this study was to determine the relationship between CRF and incident HF.
We included 66,329 patients (53.8% men, mean age 55 years) free of HF who underwent exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009. Incident HF was determined using International Classification of Diseases, Ninth Revision codes from electronic medical records or administrative claim files. Cox proportional hazards models were performed to determine the association between CRF and incident HF.
A total of 4,652 patients developed HF after a median follow-up duration of 6.8 (±3) years. Patients with incident HF were older (63 vs 54 years, P<.001) and had higher prevalence of known coronary artery disease (42.3% vs 11%, P<.001). Peak metabolic equivalents (METs) of task were 6.3 (±2.9) and 9.1 (±3) in the HF and non-HF groups, respectively. After adjustment for potential confounders, patients able to achieve ≥12 METs had an 81% lower risk of incident HF compared with those achieving <6 METs (hazard ratio 0.19 95% CI 0.14-0.29, P for trend < .001). Each 1 MET achieved was associated with a 16% lower risk (hazard ratio 0.84 95% CI 0.82-0.86, P<.001) of incident HF.
Our analysis demonstrates that higher level of fitness is associated with a lower incidence of HF independent of HF risk factors.
Ischemic heart disease (IHD) remains the single most common cause of death worldwide. Ischemic cardiomyopathy is a major sequel of coronary artery disease. The economic health burden of IHD is ...substantial. In patients with old myocardial infarction (OMI), the extent of viable myocardium (VM) directly affects the short- and long-term outcome. There is a considerable collection of observational data showing substantial improvement in patients with significant left ventricular dysfunction when the need for revascularization is guided by preoperative assessment of viability and hibernation. However, a major challenge for present cardiovascular imaging is to identify better ways to assess viable but inadequately perfused myocardium and thus optimize selection of patients for coronary revascularization. Several non-invasive techniques have been developed to detect signs of viability. Hence, our aim is to provide the reader a state-of-the art review for the assessment of myocardial viability.
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