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.

Influenza vaccination is recommended for cancer patients; however, adherence is low. We aimed to identify predictive factors for vaccination among cancer patients. We conducted a case-control analysis of a patient cohort in the 2010-2011 influenza season. We included adult cancer patients with solid malignancies undergoing chemotherapy, and haematological patients with active disease. Patients who died between October and November 2010 (N = 43) were excluded from analysis. Cases received the 2011 seasonal influenza vaccine, and controls did not. Data were obtained from patients' records, and validated through personal interviews. We collected socio-demographic information, and data on the malignancy and co-morbidities and triggers for vaccination and non-vaccination. We performed bivariate and multivariable analyses, in which vaccination status was the dependent variable. Of 806 patients included in analysis, 387 (48%) were vaccinated. Variables associated with vaccination on bivariate analysis were older age, higher socio-economic status, lower crowding index, marital status (widowed > married > single), malignancy type (haematological > solid tumours) and time from diagnosis, low-risk malignancy, diabetes, past vaccination, country of birth (non-Russian origin), and physicians' recommendations. Predictive factors found to be independently associated with vaccination on multivariable analysis were past vaccinations, low-risk malignancy, and country of birth. In the analysis conducted among interviewees (N = 561), recommendations from the oncologist (OR 10.7, 95% CI 5.4-21.2) and from the primary-care physician (OR 3.35, 95% CI 2.05-5.49) were strong predictors for vaccination. We conclude that 'habitual vaccinees' continue influenza vaccinations when ill with cancer. Physicians' recommendations, especially the oncologist's, have a major influence on patients' compliance with influenza vaccination.

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. Cancer 2013;119:4028–4035. © 2013 American Cancer Society.

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<b><i>Background:</i></b> 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. <b><i>Methods:</i></b> 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 <i>p</i> value &#x3c;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. <b><i>Results:</i></b> 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; <i>p</i> &#x3c; 0.001). The statistically significant relationship was seen in the unadjusted regression model as well as after the adjustment for clinical variables. <b><i>Conclusions:</i></b> 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.

Background: Atrial fibrillation (AF) following cardiac surgery is common and has clinical impact on morbidity. The preoperative and intraoperative risk factors are still not well defined. The objective of the study was to examine preoperative and intraoperative risk factors for AF following cardiac surgery. Methods: A retrospective analysis of a database of cardiac surgeries was performed during 2017-2019 at Poriya Medical Center. Preoperative factors and intraoperative were recorded. Results: 208 patients were included in this analysis. Overall AF following cardiac surgery was detected in 50 (24%) patients. Of 175 patients who did not have history of AF prior to surgery, 27 (15.5%) had post-operative AF. In the 33 patients with previous AF, AF following surgery was detected in 23 (70%). Patients with AF following surgery who were older (66.2±8.0 vs. 60.7± 11.4 years, p=0.002), were treated more with anti-arrhythmic drugs (18.9% vs 4.5, p<0.001), and had higher rates of pre-operative AF (46% vs 6.3%, p=0.0001), prior cerebral vascular accidents (14% vs 4.4%, p=0.019), and prior valve replacement (10% vs 1.9%, p=0.009) compared to patients without AF following surgery. In multivariate Cox regression analysis, age (HR 1.04, CI 1.01-1.07, P=0.006) and history of preoperative AF (HR 6.01, CI 3.42-10.57, P<0.001) were predictors of AF following cardiac surgery. The probability of being free of postsurgical AF was 80% among patients without history of AF compared to 30% in patients with previous AF history (p<0.001). Conclusion: Preoperative AF and age were predictors of AF following cardiac surgery