Plos One
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Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death worldwide, with acute coronary syndromes accounting for most of the cases. While the benefit of early revascularization has been clearly demonstrated in patients with ST-segment-elevation myocardial infarction (STEMI), diagnostic pathways remain unclear in the absence of STEMI. We aimed to characterize OHCA patients presenting to 2 tertiary cardiology centers and identify predicting factors associated with survival. ⋯ In our study, immediate CAG, ROSC at admission, witnessed arrest and former smoking were independent predictors of survival in cardiac arrest survivors. Improvement in prehospital management including bystander CPR and best practice post-resuscitation care with optimized triage of patients to an early invasive strategy may help ameliorate overall outcome of this critically-ill patient population.
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Acute respiratory failure (ARF) is a life-threatening complication in onco-hematology patients. Optimal ventilation strategy in immunocompromised patients has been highly controversial over the last decade. Data are lacking on patients presenting with ARF associating isolated cardiac dysfunction or in combination with another etiology. The aim of this study was to assess prognostic impact of initial ventilation strategy in onco-hematology patients presenting ARF with associated cardiac dysfunction. ⋯ In onco-hematology patients admitted for ARF with associated cardiac dysfunction, severity at ICU admission, invasive fungal infections and initial ventilation strategy were independently associated with ICU mortality. NIV was a protective factor on ICU mortality.
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Observational Study
Dedicated emergency department physical therapy is associated with reduced imaging, opioid administration, and length of stay: A prospective observational study.
Emergency department based Physical Therapy (ED-PT) has been practiced globally in various forms for over 20 years and is an emerging resource in the US. While there is a growing body of evidence suggesting that ED-PT has a positive effect on a number of clinical and operational outcomes in patients presenting with musculoskeletal (MSK) pain, there are few published narratives that quantify this in the US. Although there are international papers that offer outcome data on reduction of pain, imaging, throughput time, and the ability of physical therapists to appropriately manage MSK conditions in the ED setting, most papers to date have been descriptive in nature. The purpose of this study is to assess the impact of ED-PT on imaging studies obtained, rates of opioids prescribed, and ED length of stay. ⋯ In our experience, being seen by a physical therapist for MSK pain within the ED was associated with reduced use of imaging and time spent in the ED. Patients seeing a Physical Therapist were also less likely to receive an opioid prescription within the ED, a potentially significant finding given the need for opioid reduction strategies.
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This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. ⋯ On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.
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Meta Analysis
Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis.
Early and accurate prognosis prediction of the patients was urgently warranted due to the widespread popularity of COVID-19. We performed a meta-analysis aimed at comprehensively summarizing the clinical characteristics and laboratory abnormalities correlated with increased risk of mortality in COVID-19 patients. ⋯ Among the common symptoms of COVID-19 infections, fatigue, expectoration, hemoptysis, dyspnea and chest tightness were independent predictors of death. As for laboratory examinations, significantly increased pretreatment absolute leukocytosis count, LDH, PCT, D-Dimer and ferritin, and decreased pretreatment absolute lymphocyte count were found in non-survivors, which also have an unbeneficial impact on mortality among COVID-19 patients. Motoring these indicators during the hospitalization plays a very important role in predicting the prognosis of patients.