Dataset Information

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.

ABSTRACT: 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.

SUBMITTER: Edelson JB

PROVIDER: S-EPMC9238533 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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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.

Edelson Jonathan B JB Edwards Jonathan J JJ Katcoff Hannah H Mondal Antara A Chen Feiyan F Reza Nosheen N Hanff Thomas C TC Griffis Heather H Mazurek Jeremy A JA Wald Joyce J Burstein Danielle S DS Atluri Pavan P O'Connor Matthew J MJ Goldberg Lee R LR Zamani Payman P Groeneveld Peter W PW Rossano Joseph W JW Lin Kimberly Y KY Birati Edo Y EY

Journal of the American Heart Association 20220113 2

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 indepe ...[more]

PMID: 35023355

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Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/μL or lower than 4000/μL; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ≥18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.

Project description:BackgroundThe Model for End-Stage Liver Disease (MELD) predicts events in cirrhotic subjects undergoing major surgery and may offer similar prognostication in left ventricular assist device candidates with comparable degrees of multisystem dysfunction.Methods and resultsPreoperative MELD scores were calculated for subjects enrolled in the University of Michigan Health System (UMHS) mechanical circulatory support database. Univariate and multiple regression analyses were performed to investigate the ability of patient characteristics, laboratory data (including MELD scores), and hemodynamic measurements to predict total perioperative blood product exposure and operative mortality. The ability of preoperative MELD scores to predict operative mortality was evaluated in subjects enrolled in the Interagency Registry of Mechanically Assisted Circulatory Support (INTERMACS), and results were compared with those from the UMHS cohort. The mean+/-SD MELD scores for the UMHS (n=211) and INTERMACS (n=324) cohorts were 13.7+/-6.1 and 15.2+/-5.8, respectively, with 29 (14%) and 19 (6%) perioperative deaths. In the UMHS cohort, median total perioperative blood product exposure was 74 units (25th and 75th percentiles, 44 and 120 units). Each 5-unit MELD score increase was associated with 15.1+/-3.8 units (beta+/-SE) of total perioperative blood product exposure. Each 10-unit increase in total perioperative blood product exposure increased the odds of operative death (odds ratio, 1.05; 95% confidence interval, 1.01 to 1.10). Odds ratios, measuring the ability of MELD scores to predict perioperative mortality, were 1.5 (95% confidence interval, 1.1 to 2.0) and 1.5 (95% confidence interval, 1.1 to 2.1) per 5 MELD units for the UMHS and INTERMACS cohorts, respectively. When MELD scores were dichotomized as >or=17 and <17, risk-adjusted Cox proportional-hazard ratios for 6-month mortality were 2.5 (95% confidence interval, 1.2 to 5.3) and 2.5 (95% confidence interval, 1.1 to 5.4) for the UMHS and INTERMACS cohorts, respectively.ConclusionsThe MELD score identified left ventricular assist device candidates at high risk for perioperative bleeding and mortality.

Project description:ObjectivesThis study investigates the use of a Bayesian statistical model to address the limited predictive capacity of existing risk scores derived from multivariate analyses. This is based on the hypothesis that it is necessary to consider the interrelationships and conditional probabilities among independent variables to achieve sufficient statistical accuracy.BackgroundRight ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation.MethodsData used for this study were derived from 10,909 adult patients from the Inter-Agency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who had a primary LVAD implanted between December 2006 and March 2014. An initial set of 176 pre-implantation variables were considered. RVF post-implant was categorized as acute (<48 h), early (48 h to 14 daysays), and late (>14 days) in onset. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables employing an open source Bayesian inference engine.ResultsThe acute RVF model consisted of 33 variables including systolic pulmonary artery pressure (PAP), white blood cell count, left ventricular ejection fraction, cardiac index, sodium levels, and lymphocyte percentage. The early RVF model consisted of 34 variables, including systolic PAP, pre-albumin, lactate dehydrogenase level, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation, and body mass index. The late RVF model included 33 variables and was predicted mostly by peripheral vascular resistance, model for end-stage liver disease score, albumin level, lymphocyte percentage, and mean and diastolic PAP. The accuracy of all Bayesian models was between 91% and 97%, with an area under the receiver operator characteristics curve between 0.83 and 0.90, sensitivity of 90%, and specificity between 98% and 99%, significantly outperforming previously published risk scores.ConclusionsA Bayesian prognostic model of RVF based on the large, multicenter INTERMACS registry provided highly accurate predictions of acute, early, and late RVF on the basis of pre-operative variables. These models may facilitate clinical decision making while screening candidates for LVAD therapy.

Dataset Information

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.

Publications

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.

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