Does the Initial Screening of Syncope in the Emergency Department and During Hospitalization Adhere to ESC Guidelines?

BACKGROUND: The evaluation of syncope in emergency departments (EDs) and during hospitalization can be ineffective. The European Society of Cardiology (ESC) guidelines were established to perform the evaluation based on risk stratification. OBJECTIVES: To investigate whether the initial screening of syncope adheres to the recent ESC guidelines. METHODS: Patients with syncope who were evaluated in our ED were included in the study and retrospectively classified based on whether they were treated according to ESC guidelines. Patients were divided into two groups according to the ESC guideline risk profile: high risk or low risk. RESULTS: The study included 114 patients (age 50.6 ± 21.9 years, 43% females); 74 (64.9%) had neurally mediated syncope, 11 (9.65%) had cardiac syncope, and 29 (25.45%) had an unknown cause. The low-risk group included 70 patients (61.4%), and the high-risk group included 44 (38.6%). Only 48 patients (42.1%) were evaluated according to the ESC guidelines. In fact, 22 (36.7%) of 60 hospitalizations and 41 (53.2%) of 77 head computed tomography (CT) scans were not mandatory according to guidelines. The rate of unnecessary CT scans (67.3% vs. 28.6%, respectively, P = 0.001) and unnecessary hospitalization (66.7% vs. 6.7%, respectively, P < 0.02) were higher among low-risk patients than high-risk patients. Overall, a higher percentage of high-risk patients were treated according to guidelines compared to low-risk patients (68.2% vs. 25.7% respectively, P < 0.0001). CONCLUSIONS: Most syncope patients, particularly those with a low-risk profile, were not evaluated in accordance with the ESC guidelines.

Does Duration of Symptoms Reliably Predict Detection of Left Atrial Thrombus in Newly Diagnosed Atrial Fibrillation.

BACKGROUND: Large prospective trials attribute minimal thromboembolic risk for cardioversion of atrial fibrillation (AF) when duration of symptoms is shorter than 48 hours. Our goal is to compare the prevalence of left atrial appendage (LAA) thrombus as demonstrated by a Trans esophageal echocardiography (TEE) exam between patients presenting with less or more than 48 hours of AF symptoms. METHODS: Observational cohort study including consecutive patients hospitalized with primary diagnosis of new onset AF, not previously treated with oral anticoagulation. All patients underwent TEE to exclude LAA thrombus, regardless of symptoms duration. Patients were divided into two groups based on AF duration: 1) early presenters: up to 48 hours, 2) later presenters: longer than 48 hours. RESULTS: The study included 122 patients mean age 65.8 years). The "early presenters" were younger, with less co-morbidities. LAA thrombus was detected in 13(21%) of 62 early presenters, compared to 20 (33%) of 60 patients of the second group (P=0.12). Significant predictors of LAA thrombus in the whole cohort by univariate analysis were ≥65 years of age (1.051, P=0.017), acute heart failure (2.394, P=0.038), and history of coronary artery/ peripheral vascular disease (2.7, P= 0.019). Notably neither duration of symptoms nor CHA2DS2-VASc score significantly predicted LAA thrombus. Inmultivariate analysis, only age ≥65 was found to be a significant predictor of LAA thrombus. CONCLUSIONS: LAA thrombus in patients presenting within 48 hours of AF symptoms onset is not uncommon. Duration of symptoms is not reliable for excluding LAA thrombus.

Pre- and Post-Transcatheter Aortic Valve Replacement Serum Brain Natriuretic Peptide Levels and All-Cause Mortality.

BACKGROUND: Risk stratification in patients post-transcatheter aortic valve replacement (TAVR) is limited to and is based on clinical judgment and surgical scoring systems. Serum natriuretic peptides are used for general risk stratification in patients with aortic stenosis, reflecting the increase in their afterload and thereby stressing the need for valve intervention. The objective of this study was to determine the predictive value of pre- and post-procedural serum brain natriuretic peptide (BNP) on 1-year all-cause mortality in patients who underwent TAVR. METHODS: In this population-based study, we included 148 TAVR patients treated at the Poriya Medical Center between June 1, 2015, and May 31, 2018. Routine blood samples for serum BNP levels (pg/mL) were taken just before the TAVR and 24 h post-TAVR. Our primary clinical outcome was defined as 1-year all-cause mortality. We used backward regression models and included all variables that had a p value <0.1 in the univariable analysis. A receiver-operating characteristic curve was calculated for the prediction of all-cause mortality by serum BNP levels using the median as the cut-off point. RESULTS: In this study cohort, BNP levels 24 h post-TAVR higher than the cohort median versus lower than the cohort median (387.5 pg/mL; IQR 195-817.6) were the strongest predictor of 1-year mortality (hazard ratio 9; 95% CI 2.72-30.16; p < 0.001). The statistically significant relationship was seen in the unadjusted regression model as well as after the adjustment for clinical variables. CONCLUSIONS: Serum BNP levels 24 h post-procedure were found to be a meaningful marker in predicting 1-year all-cause mortality in patients after TAVR procedure.

Predictive modeling of inpatient mortality in departments of internal medicine.

Despite overwhelming data on predictors of inpatient mortality, it is unclear which variables are the most instructive in predicting mortality of patients in departments of internal medicine. This study aims to identify the most informative predictors of inpatient mortality, and builds a prediction model on an individual level, given a constellation of patient characteristics. We use a penalized method for developing the prediction model by applying the least-absolute-shrinkage and selection-operator regression. We utilize a cohort of adult patients admitted to any of 5 departments of internal medicine during 3.5 years. We integrated data from electronic health records that included clinical, epidemiological, administrative, and laboratory variables. The prediction model was evaluated using the validation sample. Of 10,788 patients hospitalized during the study period, 874 (8.1%) died during admission. We find that the strongest predictors of inpatient mortality are prior admission within 3 months, malignant morbidity, serum creatinine levels, and hypoalbuminemia at hospital admission, and an admitting diagnosis of sepsis, pneumonia, malignant neoplastic disease, or cerebrovascular disease. The C-statistic of the risk prediction model is 89.4% (95% CI 88.4-90.4%). The predictive performance of this model is better than a multivariate stepwise logistic regression model. By utilizing the prediction model, the AUC for the independent (validation) data set is 85.7% (95% CI 84.1-87.3%). Using penalized regression, this prediction model identifies the most informative predictors of inpatient mortality. The model illustrates the potential value and feasibility of a tool that can aid physicians in decision-making.

The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards.

Limited information is available about clinical predictors of in-hospital mortality in acute unselected medical admissions. Such information could assist medical decision-making.To develop a clinical model for predicting in-hospital mortality in unselected acute medical admissions and to test the impact of secondary conditions on hospital mortality.This is an analysis of the medical records of patients admitted to internal medicine wards at one university-affiliated hospital. Data obtained from the years 2013 to 2014 were used as a derivation dataset for creating a prediction model, while data from 2015 was used as a validation dataset to test the performance of the model. For each admission, a set of clinical and epidemiological variables was obtained. The main diagnosis at hospitalization was recorded, and all additional or secondary conditions that coexisted at hospital admission or that developed during hospital stay were considered secondary conditions.The derivation and validation datasets included 7268 and 7843 patients, respectively. The in-hospital mortality rate averaged 7.2%. The following variables entered the final model; age, body mass index, mean arterial pressure on admission, prior admission within 3 months, background morbidity of heart failure and active malignancy, and chronic use of statins and antiplatelet agents. The c-statistic (ROC-AUC) of the prediction model was 80.5% without adjustment for main or secondary conditions, 84.5%, with adjustment for the main diagnosis, and 89.5% with adjustment for the main diagnosis and secondary conditions. The accuracy of the predictive model reached 81% on the validation dataset.A prediction model based on clinical data with adjustment for secondary conditions exhibited a high degree of prediction accuracy. We provide a proof of concept that there is an added value for incorporating secondary conditions while predicting probabilities of in-hospital mortality. Further improvement of the model performance and validation in other cohorts are needed to aid hospitalists in predicting health outcomes.

The association between infections and chemotherapy interruptions among cancer patients: prospective cohort study.

OBJECTIVES: Adherence to scheduled chemotherapy is important for optimal outcomes of cancer patients. We examined causes for delay or cancellation of planned chemotherapy, focusing on mild respiratory infections during the winter. METHODS: Prospective cohort study. We included all adults with solid or hematologic cancer receiving active chemotherapy treatment during the winter of 2010-2011 in a cancer center. We compared baseline characteristics and outcomes between patients with and without chemotherapy delays, cancellations, or dose-reductions ("chemotherapy delay"). RESULTS: We included 547 patients receiving chemotherapy during the winter of 2011. Of these, 213 (38.9%) patients experienced 306 episodes of chemotherapy delays. The main documented reasons for the chemotherapy delay were neutropenia (84/306, 27.4%), fever or infection (73/306, 23.9%) and thrombocytopenia (26/306, 8.5%). Independent risk factors for chemotherapy delays were upper respiratory infections (OR 1.87, 95% CI 1.27-2.76), lymphopenia, prior hospitalization, peripheral vascular disease and colon cancer relative to hematologic cancer. In the adjusted analysis focusing on chemotherapy delays due to infection alone, upper respiratory infections (OR 5.25, 95% I 2.81-9.84) and age were significant independent risk factors. DISCUSSION: Mild respiratory infections were associated with chemotherapy delays. Our results should encourage modalities to prevent influenza and other upper respiratory infections among cancer patients.

Clinical effectiveness of seasonal influenza vaccine among adult cancer patients.

BACKGROUND: Patients with cancer are at increased risk of developing complications of influenza. In this study, the authors assessed the effectiveness of influenza vaccination among cancer patients. METHODS: A prospective, noninterventional cohort study was conducted during the 2010 to 2011 influenza season. The cohort included adult cancer patients with solid malignancies who were receiving chemotherapy and hematologic patients who had active disease. Patients who died between October and November 2010 (N = 43) were excluded. A comparison was made between patients who received the 2011 seasonal influenza vaccine with those who did not. The primary outcome was a composite of hospitalizations for fever or acute respiratory infections, pneumonia, and/or infection-related chemotherapy interruptions. All-cause mortality was a secondary outcome. A propensity-matched analysis was conducted based on the propensity for vaccination. RESULTS: Of 806 patients who were included, 387 (48%) were vaccinated. Factors that were associated independently with vaccination included past influenza vaccination, past pneumococcal vaccination, >6 months since cancer diagnosis, country of birth, and cancer type/status. The primary outcome occurred in 111 of 387 (28.7%) vaccinated patients versus 112 of 419 (26.7%) unvaccinated patients (P = .54). No association was observed between vaccination and the primary outcome in a propensity-matched analysis (N = 436) or during peak influenza activity. The mortality rate was 11.9% (46 of 387 patients) in vaccinated patients versus 19.1% (80 of 419 patients) in unvaccinated patients (P = .005). Vaccination retained a significant association with mortality on multivariable analysis (odds ratio, 2.31; 95% confidence interval, 1.4-3.79) and in a propensity-matched analysis (odds ratio, 2.39; 95% confidence interval, 1.32-4.32). CONCLUSIONS: Influenza vaccination was associated with lower mortality among cancer patients, although an association with infection-related complications could not be demonstrated. The current results support efforts to promote influenza vaccination in patients with cancer.