Abstract 238: Validation of In-hospital Mortality Prediction Models for Patients with Heart Failure

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Tara Lagu ◽  
Mihaela Stefan ◽  
Quinn Pack ◽  
Auras Atreya ◽  
Mohammad A Kashef ◽  
...  
Keyword(s):  
Heart Failure ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Clinical Data ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
Mortality Prediction Models ◽  
Clinical Models ◽  
Administrative Model

Background: Mortality prediction models, developed with the goal of improving risk stratification in hospitalized heart failure (HF) patients, show good performance characteristics in the datasets in which they were developed but have not been validated in external populations. Methods: We used a novel multi-hospital dataset [HealthFacts (Cerner Corp)] derived from the electronic health record (years 2010-2012). We examined the performance of four published HF inpatient mortality prediction models developed using data from: the Acute Decompensated Heart Failure National Registry (ADHERE), the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study, and the Get With the Guidelines-Heart Failure (GWTG-HF) registry. We compared to an administrative HF mortality prediction model (Premier model) that includes selected patient demographics, comorbidities, prior heart failure admissions, and therapies administered (e.g., inotropes, mechanical ventilation) in the first 2 hospital days. We also compared to a model that uses clinical data but is not heart failure-specific: the Laboratory-Based Acute Physiology Score (LAPS2). We included patients aged ≥18 years admitted with HF to one of 62 hospitals in the database. We applied all 6 models to the data and calculated the c-statistics. Results: We identified 13,163 patients ≥18 years old with a diagnosis of heart failure. Median age was 74 years; approximately half were women; 65% of patients were white and 27% were black. In-hospital mortality was 4.3%. Bland-Altman plots revealed that, at higher predicted mortality, the Premier model outperformed the clinical models. Discrimination of the models varied: ADHERE model (0.68); EFFECT (0.70); GWTG-HF, Peterson (0.69); GWTG-HF, Eapen (0.70); LAPS2 (0.74); Premier (0.81) (Figure). Conclusions: Clinically-derived inpatient heart failure mortality models exhibited similar performance with c statistics hovering around 0.70. A generic clinical mortality prediction model (LAPS2) had slightly better performance, as did a detailed administrative model. Any of these models may be useful for severity adjustment in comparative effectiveness studies of heart failure patients. When clinical data are not available, the administrative model performs similarly to clinical models.

To TQIP or Not to TQIP? that is the Question

2014 ◽  
Vol 80 (4) ◽  
pp. 386-390
Author(s):  
Jiselle Bock Heaney ◽  
Chrissy Guidry ◽  
Eric Simms ◽  
Jennifer Turney ◽  
Peter Meade ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Prediction Model ◽  
Trauma Center ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Improvement Program ◽  
Injury Score ◽  
Mortality Prediction Model ◽  
Mortality Prediction Models ◽  
Abbreviated Injury Score

The Trauma Quality Improvement Program (TQIP) reports a feasible mortality prediction model. We hypothesize that our institutional characteristics differ from TQIP aggregate data, questioning its applicability. We conducted a 2-year (2008 to 2009) retrospective analysis of all trauma activations at a Level 1 trauma center. Data were analyzed using TQIP methodology (three groups: blunt single system, blunt multisystem, and penetrating) to develop a mortality prediction model using multiple logistic regression. These data were compared with TQIP data. Four hundred fifty-seven patients met TQIP inclusion criteria. Penetrating and blunt trauma differed significantly at our institution versus TQIP aggregates (61.9 vs 7.8%; 38.0 vs 92.2%, P < 0.01). There were more firearm mechanisms of injury and less falls compared with TQIP aggregates (28.9 vs 4.2%; 8.5 vs 34.8%, P < 0.01). All other mechanisms were not significantly different. Variables significant in the TQIP model but not found to be predictors of mortality included Glasgow Coma Score motor 2 to 5, systolic blood pressure greater than 90 mmHg, age, initial pulse rate in the emergency department, mechanism of injury, head Abbreviated Injury Score, and abdominal Abbreviated Injury Score. External benchmarking of trauma center performance using mortality prediction models is important in quality improvement for trauma patient care. From our results, TQIP methodology from the pilot study may not be applicable to all institutions.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

scholarly journals Validation of U.S. mortality prediction models for hospitalized heart failure in the United Kingdom and Japan

2018 ◽  
Vol 20 (8) ◽  
pp. 1179-1190 ◽  
Author(s):  
Toshiyuki Nagai ◽  
Varun Sundaram ◽  
Ahmad Shoaib ◽  
Yasuyuki Shiraishi ◽  
Shun Kohsaka ◽  
...  
Keyword(s):  
Heart Failure ◽  
United Kingdom ◽  
Prediction Models ◽  
Mortality Prediction ◽  
The United Kingdom ◽  
Mortality Prediction Models

scholarly journals Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinwoo Jeong ◽  
Sung Woo Lee ◽  
Won Young Kim ◽  
Kap Su Han ◽  
Su Jin Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Scoring System ◽  
Operating Characteristic ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
National Emergency ◽  
Receiver Operating

Abstract Background In-hospital mortality and short-term mortality are indicators that are commonly used to evaluate the outcome of emergency department (ED) treatment. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of the condition of ED patients, methods for comparing severity-adjusted mortality in general ED patients between different systems have yet to be developed. The aim of the present study was to develop a scoring system to predict mortality in ED patients using data collected at the initial evaluation and to validate the usefulness of the scoring system for comparing severity-adjusted mortality between institutions with different severity distributions. Methods The study was based on the registry of the National Emergency Department Information System, which is maintained by the National Emergency Medical Center of the Republic of Korea. Data from 2016 were used to construct the prediction model, and data from 2017 were used for validation. Logistic regression was used to build the mortality prediction model. Receiver operating characteristic curves were used to evaluate the performance of the prediction model. We calculated the standardized W statistic and its 95% confidence intervals using the newly developed mortality prediction model. Results The area under the receiver operating characteristic curve of the developed scoring system for the prediction of mortality was 0.883 (95% confidence interval [CI]: 0.882–0.884). The Ws score calculated from the 2016 dataset was 0.000 (95% CI: − 0.021 – 0.021). The Ws score calculated from the 2017 dataset was 0.049 (95% CI: 0.030–0.069). Conclusions The scoring system developed in the present study utilizing the parameters gathered in initial ED evaluations has acceptable performance for the prediction of in-hospital mortality. Standardized W statistics based on this scoring system can be used to compare the performance of an ED with the reference data or with the performance of other institutions.

scholarly journals A hybrid mortality prediction model of patients with cavitary pulmonary carcinomas

2018 ◽  
Author(s):  
Maliazurina Saad
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Added Value ◽  
Medical Community ◽  
Clinical Predictors ◽  
Clinical Covariates ◽  
Mortality Prediction Model ◽  
Roc Area ◽  
High Degree

AbstractTo date, developing a reliable mortality prediction model remains challenging. Although clinical predictors like age, gender and laboratory results are of considerable predictive value, the accuracy often ranges only between 60-80%. In this study, we proposed prediction models built on the basis of clinical covariates with adjustment for additional variables that was radiographically induced. The proposed method exhibited a high degree of prediction accuracy of between 83-92%, as well as overall improvement of between 6-20% in all other metrics, such as ROC Area, False Positive Rates, Recall and Root Mean Square Error. We provide a proof of concept that there is an added value for incorporating the additional variables while predicting 24-month mortality in pulmonary carcinomas patients with cavitary lesions. It is hoped that the findings will be clinically useful to the medical community.

scholarly journals Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Mohammad M. Banoei ◽  
Roshan Dinparastisaleh ◽  
Ali Vaeli Zadeh ◽  
Mehdi Mirsaeidi
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Biochemical Markers ◽  
Mortality Prediction ◽  
Blood Biochemical ◽  
Mortality Prediction Model ◽  
Predict Hospital Mortality ◽  
Risk Patients

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Building ICU In-hospital Mortality Prediction Model with Federated Learning

2020 ◽  
pp. 255-268
Author(s):  
Trung Kien Dang ◽  
Kwan Chet Tan ◽  
Mark Choo ◽  
Nicholas Lim ◽  
Jianshu Weng ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Model ◽  
Mortality Prediction ◽  
Mortality Prediction Model
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