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Development and validation of a risk-prediction nomogram for in-hospital mortality in adults poisoned with drugs and nonpharmaceutical agents: An observational study.

ABSTRACT: Acute poisoning with drugs and nonpharmaceutical agents represents an important challenge in the emergency department (ED).The objective is to create and validate a risk-prediction nomogram for use in the ED to predict the risk of in-hospital mortality in adults from acute poisoning with drugs and nonpharmaceutical agents.This was a prospective cohort study involving adults with acute poisoning from drugs and nonpharmaceutical agents admitted to a tertiary referral center for toxicology between January and December 2015 (derivation cohort) and between January and June 2016 (validation cohort). We used a program to generate nomograms based on binary logistic regression predictive models. We included variables that had significant associations with death. Using regression coefficients, we calculated scores for each variable, and estimated the event probability. Model validation was performed using bootstrap to quantify our modeling strategy and using receiver operator characteristic (ROC) analysis. The nomogram was tested on a separate validation cohort using ROC analysis and goodness-of-fit tests.Data from 315 patients aged 18 to 91 years were analyzed (n?=?180 in the derivation cohort; n?=?135 in the validation cohort). In the final model, the following variables were significantly associated with mortality: age, laboratory test results (lactate, potassium, MB isoenzyme of creatine kinase), electrocardiogram parameters (QTc interval), and echocardiography findings (E wave velocity deceleration time). Sex was also included to use the same model for men and women. The resulting nomogram showed excellent survival/mortality discrimination (area under the curve [AUC] 0.976, 95% confidence interval [CI] 0.954-0.998, P?

SUBMITTER: Lionte C

PROVIDER: S-EPMC5371475 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Development and validation of a risk-prediction nomogram for in-hospital mortality in adults poisoned with drugs and nonpharmaceutical agents: An observational study.

Lionte Catalina C Sorodoc Victorita V Jaba Elisabeta E Botezat Alina A

Medicine 20170301 12

Acute poisoning with drugs and nonpharmaceutical agents represents an important challenge in the emergency department (ED).The objective is to create and validate a risk-prediction nomogram for use in the ED to predict the risk of in-hospital mortality in adults from acute poisoning with drugs and nonpharmaceutical agents.This was a prospective cohort study involving adults with acute poisoning from drugs and nonpharmaceutical agents admitted to a tertiary referral center for toxicology between ...[more]

PMID: 28328838

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Project description:BackgroundHospital mortality is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system (CNS) is defined as sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, especially gut microbiota, and there is no clinical tool to predict hospital mortality for SAE patients. The study aimed to explore the relationship between pathogenic microorganisms and the hospital mortality of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients.MethodsThe study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of SAE patients was developed by multivariable Cox regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) to evaluate the clinical utility of the model.ResultsUnfortunately, the results of our study did not find intestinal infection and microorganisms of the gastrointestinal (such as: Escherichia coli) that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate, international normalized ratio (INR), albumin, SpO2, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of 0.812 under the nomogram was more than that of the Simplified Acute Physiology Score (0.745), indicating excellent discrimination. DCA demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality.ConclusionThe prognosis of SAE patients has nothing to do with intestinal and microbial infections. We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

Project description:Background and Aims: Patients with heart failure with reduced ejection fraction (HFrEF) are among the most challenging patients undergoing coronary artery bypass grafting surgery (CABG). Several surgical risk scores are commonly used to predict the risk in patients undergoing CABG. However, these risk scores do not specifically target HFrEF patients. We aim to develop and validate a new nomogram score to predict the risk of in-hospital mortality among HFrEF patients after CABG. Methods: The study retrospectively enrolled 489 patients who had HFrEF and underwent CABG. The outcome was postoperative in-hospital death. About 70% (n = 342) of the patients were randomly constituted a training cohort and the rest (n = 147) made a validation cohort. A multivariable logistic regression model was derived from the training cohort and presented as a nomogram to predict postoperative mortality in patients with HFrEF. The model performance was assessed in terms of discrimination and calibration. Besides, we compared the model with EuroSCORE-2 in terms of discrimination and calibration. Results: Postoperative death occurred in 26 (7.6%) out of 342 patients in the training cohort, and in 10 (6.8%) out of 147 patients in the validation cohort. Eight preoperative factors were associated with postoperative death, including age, critical state, recent myocardial infarction, stroke, left ventricular ejection fraction (LVEF) ≤35%, LV dilatation, increased serum creatinine, and combined surgery. The nomogram achieved good discrimination with C-indexes of 0.889 (95%CI, 0.839-0.938) and 0.899 (95%CI, 0.835-0.963) in predicting the risk of mortality after CABG in the training and validation cohorts, respectively, and showed well-fitted calibration curves in the patients whose predicted mortality probabilities were below 40%. Compared with EuroSCORE-2, the nomogram had significantly higher C-indexes in the training cohort (0.889 vs. 0.762, p = 0.005) as well as the validation cohort (0.899 vs. 0.816, p = 0.039). Besides, the nomogram had better calibration and reclassification than EuroSCORE-2 both in the training and validation cohort. The EuroSCORE-2 underestimated postoperative mortality risk, especially in high-risk patients. Conclusions: The nomogram provides an optimal preoperative estimation of mortality risk after CABG in patients with HFrEF and has the potential to facilitate identifying HFrEF patients at high risk of in-hospital mortality.

Project description:While deep neural networks (DNNs) and other machine learning models often have higher accuracy than simpler models like logistic regression (LR), they are often considered to be "black box" models and this lack of interpretability and transparency is considered a challenge for clinical adoption. In healthcare, intelligible models not only help clinicians to understand the problem and create more targeted action plans, but also help to gain the clinicians' trust. One method of overcoming the limited interpretability of more complex models is to use Generalized Additive Models (GAMs). Standard GAMs simply model the target response as a sum of univariate models. Inspired by GAMs, the same idea can be applied to neural networks through an architecture referred to as Generalized Additive Models with Neural Networks (GAM-NNs). In this manuscript, we present the development and validation of a model applying the concept of GAM-NNs to allow for interpretability by visualizing the learned feature patterns related to risk of in-hospital mortality for patients undergoing surgery under general anesthesia. The data consists of 59,985 patients with a feature set of 46 features extracted at the end of surgery to which we added previously not included features: total anesthesia case time (1 feature); the time in minutes spent with mean arterial pressure (MAP) below 40, 45, 50, 55, 60, and 65 mmHg during surgery (6 features); and Healthcare Cost and Utilization Project (HCUP) Code Descriptions of the Primary current procedure terminology (CPT) codes (33 features) for a total of 86 features. All data were randomly split into 80% for training (n = 47,988) and 20% for testing (n = 11,997) prior to model development. Model performance was compared to a standard LR model using the same features as the GAM-NN. The data consisted of 59,985 surgical records, and the occurrence of in-hospital mortality was 0.81% in the training set and 0.72% in the testing set. The GAM-NN model with HCUP features had the highest area under the curve (AUC) 0.921 (0.895-0.95). Overall, both GAM-NN models had higher AUCs than LR models, however, had lower average precisions. The LR model without HCUP features had the highest average precision 0.217 (0.136-0.31). To assess the interpretability of the GAM-NNs, we then visualized the learned contributions of the GAM-NNs and compared against the learned contributions of the LRs for the models with HCUP features. Overall, we were able to demonstrate that our proposed generalized additive neural network (GAM-NN) architecture is able to (1) leverage a neural network's ability to learn nonlinear patterns in the data, which is more clinically intuitive, (2) be interpreted easily, making it more clinically useful, and (3) maintain model performance as compared to previously published DNNs.

Project description:OBJECTIVES:To develop a clinical prediction score for predicting mortality in children following return of spontaneous circulation after in-hospital cardiac arrest. DESIGN:Observational study using prospectively collected data. SETTING:This was an analysis using data from the Get With The Guidelines-Resuscitation registry between January 2000 and December 2015. PATIENTS:Pediatric patients (< 18 yr old) who achieved return of spontaneous circulation. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary outcome was in-hospital mortality. Patients were divided into a derivation (3/4) and validation (1/4) cohort. A prediction score was developed using a multivariable logistic regression model with backward selection. Patient and event characteristics for the derivation cohort (n = 3,893) and validation cohort (n = 1,297) were similar. Seventeen variables associated with the outcome remained in the final reduced model after backward elimination. Predictors of in-hospital mortality included age, illness category, pre-event characteristics, arrest location, day of the week, nonshockable pulseless rhythm, duration of chest compressions, and interventions in place at time of arrest. The C-statistic for the final score was 0.77 (95% CI, 0.75-0.78) in the derivation cohort and 0.77 (95% CI, 0.74-0.79) in the validation cohort. The expected versus observed mortality plot indicated good calibration in both the derivation and validation cohorts. The score showed a stepwise increase in mortality with an observed mortality of less than 15% for scores 0-9 and greater than 80% for scores greater than or equal to 25. The model also performed well for neurologic outcome and in sensitivity analyses for events within the past 5 years and for patients with or without a pulse at the onset of chest compressions. CONCLUSIONS:We developed and internally validated a prediction score for initial survivors of pediatric in-hospital cardiac arrest. This prediction score may be useful for prognostication following cardiac arrest, stratifying patients for research, and guiding quality improvement initiatives.

Dataset Information

Development and validation of a risk-prediction nomogram for in-hospital mortality in adults poisoned with drugs and nonpharmaceutical agents: An observational study.

Publications

Development and validation of a risk-prediction nomogram for in-hospital mortality in adults poisoned with drugs and nonpharmaceutical agents: An observational study.

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