Dataset Information

Simple Scoring System to Predict In-Hospital Mortality After Surgery for Infective Endocarditis.

ABSTRACT:

Background

Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis.

Methods and results

Outcomes of 361 consecutive patients (mean age, 59.1±15.4 years) who had undergone surgery for IE in 8 European centers of cardiac surgery were recorded prospectively, and a risk factor analysis (multivariable logistic regression) for in-hospital death was performed. The discriminatory power of a new predictive scoring system was assessed with the receiver operating characteristic curve analysis. Score validation procedures were carried out. Fifty-six (15.5%) patients died postsurgery. BMI >27 kg/m² (odds ratio [OR], 1.79; P=0.049), estimated glomerular filtration rate <50 mL/min (OR, 3.52; P<0.0001), New York Heart Association class IV (OR, 2.11; P=0.024), systolic pulmonary artery pressure >55 mm Hg (OR, 1.78; P=0.032), and critical state (OR, 2.37; P=0.017) were independent predictors of in-hospital death. A scoring system was devised to predict in-hospital death postsurgery for IE (area under the receiver operating characteristic curve, 0.780; 95% CI, 0.734-0.822). The score performed better than 5 of 6 scoring systems for in-hospital death after cardiac surgery that were considered.

Conclusions

A simple scoring system based on risk factors for in-hospital death was specifically created to predict mortality risk postsurgery in patients with IE.

SUBMITTER: Gatti G

PROVIDER: S-EPMC5586260 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Simple Scoring System to Predict In-Hospital Mortality After Surgery for Infective Endocarditis.

Gatti Giuseppe G Perrotti Andrea A Obadia Jean-François JF Duval Xavier X Iung Bernard B Alla François F Chirouze Catherine C Selton-Suty Christine C Hoen Bruno B Sinagra Gianfranco G Delahaye François F Tattevin Pierre P Le Moing Vincent V Pappalardo Aniello A Chocron Sidney S

Journal of the American Heart Association 20170720 7

<h4>Background</h4>Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis.<h4>Methods and results</h4>Outcomes of 361 consecutive patients (mean age, 59.1±15.4 years) who had undergone surgery for IE in 8 European centers of cardia ...[more]

PMID: 28729412

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Project description:BackgroundDespite novel improvements in the diagnosis and treatment of infective endocarditis (IE), there has been no significant improvement in the survival rate of IE, which indicates that many details still need to be optimized in the preoperative assessment. We sought to evaluate preoperative serum albumin as a biomarker for predicting early mortality after IE surgery.MethodsBetween October 2013 and June 2019, patients with a definite diagnosis of IE were enrolled in this study. Patients' albumin levels at admission were used as the preoperative albumin levels. Restricted cubic spline and multivariate logistic regression analyses were performed to evaluate the relationship between albumin and early mortality. Receiver operating characteristic curve analyses were performed to assess the role of albumin in predicting early mortality and compare the predictive capacity of traditional models with models that included albumin.ResultsOf the 276 IE patients, 20 (7.2%) died in hospital or within 30 days of surgery. Hypoalbuminemia (an albumin level <3.5 g/dL) was present in 109 (39.5%) patients. The multivariate logistic regression analysis showed that preoperative albumin was inversely associated with early mortality [adjusted odds ratio (OR) =0.22 per 1 g/dL, 95% confidence interval (CI): 0.07-0.65, P=0.006] after full adjustment. Preoperative albumin had value in predicting early mortality [area under the curve (AUC) =0.72, 95% CI: 0.61-0.84; P<0.01]. After adding albumin to the European System for Cardiac Operative Risk Evaluation (EuroSCORE) and Charlson score, the predictive ability of the model was further improved (EuroSCORE II: AUC =0.55; 95% CI: 0.42-0.67 to AUC =0.72; 95% CI: 0.61-0.84; Charlson score: AUC =0.73; 95% CI: 0.64-0.83 to AUC =0.78; 95% CI: 0.68-0.88).ConclusionsPreoperative serum albumin is inversely associated with early mortality after IE surgery, and is a promising prognostic indicator in preoperative risk stratification assessments of IE patients.

Project description:BackgroundInfective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis.MethodsRetrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer-Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted.ResultsThe research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems.ConclusionsThe nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.

Dataset Information

Simple Scoring System to Predict In-Hospital Mortality After Surgery for Infective Endocarditis.

Background

Methods and results

Conclusions

Publications

Simple Scoring System to Predict In-Hospital Mortality After Surgery for Infective Endocarditis.

Similar Datasets

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