Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score

Background The past decade has seen tremendous growth in patients with ambulatory ventricular assist devices. We sought to identify patients that present to the emergency department (ED) at the highest risk of death. Methods and Results This retrospective analysis of ED encounters from the Nationwide Emergency Department Sample includes 2010 to 2017. Using a random sampling of patient encounters, 80% were assigned to development and 20% to validation cohorts. A risk model was derived from independent predictors of mortality. Each patient encounter was assigned to 1 of 3 groups based on risk score. A total of 44 042 ED ventricular assist device patient encounters were included. The majority of patients were male (73.6%), <65 years old (60.1%), and 29% presented with bleeding, stroke, or device complication. Independent predictors of mortality during the ED visit or subsequent admission included age ≥65 years (odds ratio [OR], 1.8; 95% CI, 1.3–4.6), primary diagnoses (stroke [OR, 19.4; 95% CI, 13.1–28.8], device complication [OR, 10.1; 95% CI, 6.5–16.7], cardiac [OR, 4.0; 95% CI, 2.7–6.1], infection [OR, 5.8; 95% CI, 3.5–8.9]), and blood transfusion (OR, 2.6; 95% CI, 1.8–4.0), whereas history of hypertension was protective (OR, 0.69; 95% CI, 0.5–0.9). The risk score predicted mortality areas under the curve of 0.78 and 0.71 for development and validation. Encounters in the highest risk score strata had a 16‐fold higher mortality compared with the lowest risk group (15.8% versus 1.0%). Conclusions We present a novel risk score and its validation for predicting mortality of patients with ED ventricular assist devices, a high‐risk, and growing, population.

Prediction of mortality in hospitalized Egyptian patients with Coronavirus disease-2019: A multicenter retrospective study

We aimed to assess the epidemiological, clinical, and laboratory characteristics associated with mortality among hospitalized Egyptian patients with COVID-19. A multicenter, retrospective study was conducted on all polymerase chain reaction (PCR)-confirmed COVID-19 cases admitted through the period from April to July 2020. A generalized linear model was reconstructed with covariates based on predictor’s statistical significance and clinically relevance. The odds ratio (OR) was calculated by using stepwise logistic regression modeling. A total of 3712 hospitalized patients were included; of them, 900 deaths were recorded (24.2%). Compared to survived patients, non-survived patients were more likely to be older than 60 years (65.7%), males (53.6%) diabetic (37.6%), hypertensive (37.2%), and had chronic renal insufficiency (9%). Non-survived patients were less likely to receive azithromycin (p <0.001), anticoagulants (p <0.001), and steroids (p <0.001). We found that age ≥ 60 years old (OR = 2.82, 95% CI 2.05–3.86; p <0.0001), diabetes mellitus (OR = 1.58, 95% CI 1.14–2.19; p = 0.006), hypertension (OR = 1.69, 95% CI 1.22–2.36; p = 0.002), chronic renal insufficiency (OR = 3.15, 95% CI 1.84–5.38; p <0.0001), tachycardia (OR = 1.65, 95% CI 1.22–2.23; p <0.001), hypoxemia (OR = 5.69, 95% CI 4.05–7.98; p <0.0001), GCS <13 (OR 515.2, 95% CI 148.5–1786.9; p <0.0001), the use of therapeutic dose of anticoagulation (OR = 0.4, 95% CI 0.22–0.74, p = 0.003) and azithromycin (OR = 0.16, 95% CI 0.09–0.26; p <0.0001) were independent negative predictors of mortality. In conclusion, age >60 years, comorbidities, tachycardia, hypoxemia, and altered consciousness level are independent predictors of mortality among Egyptian hospitalized patients with COVID-19. On the other hand, the use of anticoagulants and azithromycin is associated with reduced mortality.

Predictors of Mortality in Patients with Advanced Cancer—A Systematic Review and Meta-Analysis

To timely initiate advance care planning in patients with advanced cancer, physicians should identify patients with limited life expectancy. We aimed to identify predictors of mortality. To identify the relevant literature, we searched Embase, MEDLINE, Cochrane Central, Web of Science, and PubMed databases between January 2000–April 2020. Identified studies were assessed on risk-of-bias with a modified QUIPS tool. The main outcomes were predictors and prediction models of mortality within a period of 3–24 months. We included predictors that were studied in ≥2 cancer types in a meta-analysis using a fixed or random-effects model and summarized the discriminative ability of models. We included 68 studies (ranging from 42 to 66,112 patients), of which 24 were low risk-of-bias, and 39 were included in the meta-analysis. Using a fixed-effects model, the predictors of mortality were: the surprise question, performance status, cognitive impairment, (sub)cutaneous metastases, body mass index, comorbidity, serum albumin, and hemoglobin. Using a random-effects model, predictors were: disease stage IV (hazard ratio [HR] 7.58; 95% confidence interval [CI] 4.00–14.36), lung cancer (HR 2.51; 95% CI 1.24–5.06), ECOG performance status 1+ (HR 2.03; 95% CI 1.44–2.86) and 2+ (HR 4.06; 95% CI 2.36–6.98), age (HR 1.20; 95% CI 1.05–1.38), male sex (HR 1.24; 95% CI 1.14–1.36), and Charlson comorbidity score 3+ (HR 1.60; 95% CI 1.11–2.32). Thirteen studies reported on prediction models consisting of different sets of predictors with mostly moderate discriminative ability. To conclude, we identified reasonably accurate non-tumor specific predictors of mortality. Those predictors could guide in developing a more accurate prediction model and in selecting patients for advance care planning.

Treatment, clinical outcomes, and predictors of mortality among a national cohort of admitted patients with Acinetobacter baumannii infection

Objectives: To analyze treatment, clinical outcomes, and predictors of mortality in hospitalized patients with Acinetobacter baumannii infection. Methods: Retrospective cohort study of inpatients with A. baumannii cultures and treatment from 2010-2019. Patients who died during admission were compared to those who survived to identify predictors of inpatient mortality, using multivariable unconditional logistic regression models. Results: We identified 4,599 inpatients with A. baumannii infection; 13.6% died during admission. Fluoroquinolones (26.8%), piperacillin/tazobactam (24%) and carbapenems (15.6%) were used for treatment. Tigecycline (3%) and polymyxins (3.7%) were not used often. Predictors of inpatient mortality included current acute respiratory failure (adjusted odds ratio [aOR] 3.94), shock (aOR 3.05), and acute renal failure (aOR 2.01); blood (aOR 1.94) and respiratory (aOR 1.64) infectious source; multidrug-resistant A. baumannii (MDRAB) infection (aOR 1.66); liver disease (aOR 2.15); and inadequate initial treatment (aOR 1.30). Inpatient mortality was higher in those with MDRAB vs. non-MDRAB (aOR 1.61) and in those with CRAB vs. non-CRAB infection (aOR 1.68). Length of stay >10 days was higher among those with MDRAB vs. non-MDRAB (aOR 1.25) and in those with CRAB vs. non-CRAB infection (aOR 1.31). Conclusions: In our national cohort of inpatients with A. baumannii infection, clinical outcomes were worse among those with MDRAB and/or CRAB infection. Predictors of inpatient mortality included several current conditions associated with severity, infectious source, underlying illness, and inappropriate treatment. Our study may assist healthcare providers in the early identification of admitted patients with A. baumannii infection who are at higher risk of death.

Meta-analysis of COVID-19 patients to understand the key predictors of mortality in the non-vaccinated groups in remote settings

Various studies have looked into the impact of the COVID-19 vaccine on large populations. However, very few studies have looked into the remote setting of hospitals where vaccination is challenging due to social structure, myths, and misconceptions. There is a consensus that elevated inflammatory markers such as CRP, ferritin, D-dimer correlate with increased severity of COVID-19 and are associated with worse outcomes. In the present study, through retrospective meta-analysis, we have looked into ~20 months of SARS-COV2 infected patients with known mortality status and identified predictors of mortality concerning their comorbidities, various clinical parameters, inflammatory markers, superimposed infections, length of hospitalization, length of mechanical ventilation and ICU stay. Studies with larger sample sizes have covered the outcomes through epidemiological, social, and survey-based analysis; however, most studies cover larger cohorts from tertiary medical centers. In the present study, we assessed the outcome of non-vaccinated COVID 19 patients in a remote setting for 20 months from January 1, 2020, to August 30, 2021, at CHI Mercy Health in Roseburg, Oregon. We also included two vaccinated patients from September 2021 to add to the power of our cohort. The study will provide a comprehensive methodology and deep insight into multi-dimensional data in the unvaccinated group, translational biomarkers of mortality, and state-of-art to conduct such studies in various remote hospitals.