scholarly journals Development of a new score for early mortality prediction in trauma ICU patients: RETRASCORE

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Luis Serviá ◽  
Juan Antonio Llompart-Pou ◽  
Mario Chico-Fernández ◽  
Neus Montserrat ◽  
Mariona Badia ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Mortality Prediction ◽  
Early Mortality ◽  
Icu Patients ◽  
Internal Validation ◽  
Predicted Probability ◽  
Severity Scores ◽  
Validation Set ◽  
Unilateral Mydriasis ◽  
Specific Score

Abstract Background Severity scores are commonly used for outcome adjustment and benchmarking of trauma care provided. No specific models performed only with critically ill patients are available. Our objective was to develop a new score for early mortality prediction in trauma ICU patients. Methods This is a retrospective study using the Spanish Trauma ICU registry (RETRAUCI) 2015–2019. Patients were divided and analysed into the derivation (2015–2017) and validation sets (2018–2019). We used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 h after ICU admission. Using logistic regression methodology, a simple score (RETRASCORE) was created with points assigned to each selected variable. The performance of the model was carried out according to global measures, discrimination and calibration. Results The analysis included 9465 patients: derivation set 5976 and validation set 3489. Thirty-day mortality was 12.2%. The predicted probability of 30-day mortality was determined by the following equation: 1/(1 + exp (− y)), where y = 0.598 (Age 50–65) + 1.239 (Age 66–75) + 2.198 (Age > 75) + 0.349 (PRECOAG) + 0.336 (Pre-hospital intubation) + 0.662 (High-risk mechanism) + 0.950 (unilateral mydriasis) + 3.217 (bilateral mydriasis) + 0.841 (Glasgow ≤ 8) + 0.495 (MAIS-Head) − 0.271 (MAIS-Thorax) + 1.148 (Haemodynamic failure) + 0.708 (Respiratory failure) + 0.567 (Coagulopathy) + 0.580 (Mechanical ventilation) + 0.452 (Massive haemorrhage) − 5.432. The AUROC was 0.913 (0.903–0.923) in the derivation set and 0.929 (0.918–0.940) in the validation set. Conclusions The newly developed RETRASCORE is an early, easy-to-calculate and specific score to predict in-hospital mortality in trauma ICU patients. Although it has achieved adequate internal validation, it must be externally validated.

scholarly journals Development of a New Score for Early Mortality Prediction in Trauma ICU patients. RETRASCORE

2021 ◽  
Author(s):  
Luis Serviá ◽  
Juan Antonio Llompart-Pou ◽  
Mario Chico-Fernández ◽  
Neus Montserrat ◽  
Mariona Badia ◽  
...  
Keyword(s):  
Mortality Prediction ◽  
Early Mortality ◽  
Icu Patients ◽  
Internal Validation ◽  
Predicted Probability ◽  
Severity Scores ◽  
Validation Set ◽  
Unilateral Mydriasis ◽  
Hemodynamic Failure ◽  
Specific Score

Abstract BackgroundSeverity scores are commonly used for outcome adjustment and benchmarking of trauma care provided. No specific models performed only with critically ill patients are available. Our objective was to develop a new score for early mortality prediction in trauma ICU patients.MethodsRetrospective study using the Spanish Trauma ICU registry (RETRAUCI) 2015-2019. Patients were divided and analysed into the derivation (2015-2017) and validation sets (2018-2019). We used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 hours after ICU admission. Using logistic regression methodology, a simple score (RETRASCORE) was created with points assigned to each selected variable. The performance of the model was carried out according to global measures, discrimination and calibration.ResultsThe analysis included 9465 patients. Derivation set 5976 and validation set 3489. Thirty-day mortality was 12.2%. The predicted probability of 30-day mortality was determined by the following equation: 1 / (1+exp (-y)), where y=0.598 (Age 50–65) + 1.239 (Age 66–75) + 2.198 (Age > 75) + 0.349 (PRECOAG) + 0.336 (Pre-hospital intubation) + 0.662 (High risk mechanism) + 0.950 (unilateral mydriasis) + 3.217 (bilateral mydriasis) + 0.841 (Glasgow ≤ 8) + 0.495 (MAIS-Head) - 0.271 (MAIS-Thorax) + 1.148 (Hemodynamic failure) + 0.708 (Respiratory failure) + 0.567 (Coagulopathy) + 0.580 (Mechanical ventilation) + 0.452 (Massive haemorrhage) - 5.432. The AUROC was 0.913 (0.903-0.923) in the derivation set and 0.929 (0.918-0.940) in the validation set.ConclusionsThe newly developed RETRASCORE is an early, easy-to-calculate and specific score to predict in-hospital mortality in trauma ICU patients. Although it has achieved adequate internal validation, it must be externally validated.

SIMPLE COAGULATION TESTS IMPROVE EARLY MORTALITY PREDICTION IN ICU PATIENTS WITH SEPTIC SHOCK

2007 ◽  
Vol 5 ◽  
pp. P-M-230-P-M-230
Author(s):  
G. Lissalde-Lavigne ◽  
C. Combescures ◽  
E. Dorangeon ◽  
L. Muller ◽  
C. Bengler ◽  
...  
Keyword(s):  
Septic Shock ◽  
Mortality Prediction ◽  
Early Mortality ◽  
Icu Patients ◽  
Coagulation Tests

scholarly journals Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

2021 ◽  
Vol 7 ◽  
Author(s):  
Kai Zhang ◽  
Shufang Zhang ◽  
Wei Cui ◽  
Yucai Hong ◽  
Gensheng Zhang ◽  
...  
Keyword(s):  
Intensive Care ◽  
Mortality Risk ◽  
External Validation ◽  
Model Development ◽  
Mortality Prediction ◽  
Sepsis Mortality ◽  
Severity Scores ◽  
Validation Set ◽  
Development And Validation ◽  
Risk Stratification Score

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients.Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores.Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit.Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

scholarly journals A 2-Biomarker Model Augments Clinical Prediction of Mortality in Melioidosis

2020 ◽  
Author(s):  
Shelton W Wright ◽  
Taniya Kaewarpai ◽  
Lara Lovelace-Macon ◽  
Deirdre Ducken ◽  
Viriya Hantrakun ◽  
...  
Keyword(s):  
Interleukin 6 ◽  
External Validation ◽  
Predictive Biomarkers ◽  
Mortality Prediction ◽  
Interleukin 8 ◽  
Lasso Regression ◽  
Clinical Prediction ◽  
Internal Validation ◽  
Clinical Variables ◽  
Validation Set

Abstract Background Melioidosis, infection caused by Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis. Methods In a derivation set (N = 113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1β, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-ɑ, granulocyte-colony stimulating factor, and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared with clinical variables. We repeated select analyses in an internal validation set (N = 78) and in a prospectively enrolled external validation set (N = 161) of hospitalized adults with melioidosis. Results All 8 cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8, and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables [AUC (95% confidence interval [CI]): 0.86 (.79–.92) vs 0.78 (.69–.87); P = .01]. In both the internal validation set (0.91 [0.84–0.97]) and the external validation set (0.81 [0.74–0.88]), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables. Conclusions A 2-biomarker model augments clinical prediction of 28-day mortality in melioidosis.

scholarly journals Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

2021 ◽  
Vol 8 ◽  
Author(s):  
Zongqing Lu ◽  
Jin Zhang ◽  
Jianchao Hong ◽  
Jiatian Wu ◽  
Yu Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Support Vector ◽  
Short Term ◽  
Icu Patients ◽  
Severity Scores ◽  
Significant Difference ◽  
Short Term Mortality ◽  
Validation Set ◽  
Mimic Iii

Background: Sepsis-induced coagulopathy (SIC) is a common cause for inducing poor prognosis of critically ill patients in intensive care unit (ICU). However, currently there are no tools specifically designed for assessing short-term mortality in SIC patients. This study aimed to develop a practical nomogram to predict the risk of 28-day mortality in SIC patients.Methods: In this retrospective cohort study, we extracted patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sepsis was defined based on Sepsis 3.0 criteria and SIC based on Toshiaki Iba's criteria. Kaplan–Meier curves were plotted to compare the short survival time between SIC and non-SIC patients. Afterward, only SIC cohort was randomly divided into training or validation set. We employed univariate logistic regression and stepwise multivariate analysis to select predictive features. The proposed nomogram was developed based on multivariate logistic regression model, and the discrimination and calibration were verified by internal validation. We then compared model discrimination with other traditional severity scores and machine learning models.Results: 9432 sepsis patients in MIMIC III were enrolled, in which 3280 (34.8%) patients were diagnosed as SIC during the first ICU admission. SIC was independently associated with the 7- and 28-day mortality of ICU patients. K–M curve indicated a significant difference in 7-day (Log-Rank: P < 0.001 and P = 0.017) and 28-day survival (Log-Rank: P < 0.001 and P < 0.001) between SIC and non-SIC groups whether the propensity score match (PSM) was balanced or not. For nomogram development, a total of thirteen variables of 3,280 SIC patients were enrolled. When predicted the risk of 28-day mortality, the nomogram performed a good discrimination in training and validation sets (AUROC: 0.78 and 0.81). The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. And the nomogram calibration slope was 0.91, the Brier value was 0.15. As presented by the decision curve analyses, the nomogram always obtained more net benefit when compared with other severity scores.Conclusions: SIC is independently related to the short-term mortality of ICU patients. The nomogram achieved an optimal prediction of 28-day mortality in SIC patient, which can lead to a better prognostics assessment. However, the discriminative ability of the nomogram requires validation in external cohorts to further improve generalizability.

scholarly journals Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhixuan Zeng ◽  
Shuo Yao ◽  
Jianfei Zheng ◽  
Xun Gong
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Mortality Prediction ◽  
Research Database ◽  
Training Set ◽  
Icu Patients ◽  
Test Set ◽  
Saps Ii ◽  
Severity Scores ◽  
Mimic Iii

Abstract Background Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. Methods Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. Results Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. Conclusions The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.

scholarly journals Prognostic biomarkers in predicting mortality in respiratory patients with ventilator-associated pneumonia

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Nermeen A. Abdelaleem ◽  
Hoda A. Makhlouf ◽  
Eman M. Nagiub ◽  
Hassan A. Bayoumi
Keyword(s):  
Sofa Score ◽  
Diagnostic Performance ◽  
Mortality Prediction ◽  
Ventilator Associated Pneumonia ◽  
Distribution Width ◽  
Mechanically Ventilated ◽  
Neutrophil Lymphocyte Ratio ◽  
Prognostic Accuracy ◽  
Severity Scores ◽  
Critical Patients

Abstract Background Ventilator-associated pneumonia (VAP) is the most common nosocomial infection. Red cell distribution width (RDW) and neutrophil-lymphocyte ratio (NLR) are prognostic factors to mortality in different diseases. The aim of this study is to evaluate prognostic efficiency RDW, NLR, and the Sequential Organ Failure Assessment (SOFA) score for mortality prediction in respiratory patients with VAP. Results One hundred thirty-six patients mechanically ventilated and developed VAP were included. Clinical characteristics and SOFA score on the day of admission and at diagnosis of VAP, RDW, and NLR were assessed and correlated to mortality. The average age of patients was 58.80 ± 10.53. These variables had a good diagnostic performance for mortality prediction AUC 0.811 for SOFA at diagnosis of VAP, 0.777 for RDW, 0.728 for NLR, and 0.840 for combined of NLR and RDW. The combination of the three parameters demonstrated excellent diagnostic performance (AUC 0.889). A positive correlation was found between SOFA at diagnosis of VAP and RDW (r = 0.446, P < 0.000) and with NLR (r = 0.220, P < 0.010). Conclusions NLR and RDW are non-specific inflammatory markers that could be calculated quickly and easily via routine hemogram examination. These markers have comparable prognostic accuracy to severity scores. Consequently, RDW and NLR are simple, yet promising markers for ICU physicians in monitoring the clinical course, assessment of organ dysfunction, and predicting mortality in mechanically ventilated patients. Therefore, this study recommends the use of blood biomarkers with the one of the simplest ICU score (SOFA score) in the rapid diagnosis of critical patients as a daily works in ICU.

scholarly journals The Impact of Intervention-Related Risk Factors on the Risk of Ventilator-Associated Pneumonia Is High in a Neurosurgical Intensive Care Unit

2020 ◽  
Vol 41 (S1) ◽  
pp. s407-s409
Author(s):  
Ksenia Ershova ◽  
Oleg Khomenko ◽  
Olga Ershova ◽  
Ivan Savin ◽  
Natalia Kurdumova ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mechanical Ventilation ◽  
Negative Slope ◽  
Infection Prevention And Control ◽  
Ventilator Associated Pneumonia ◽  
Related Factors ◽  
Icu Patients ◽  
The Impact ◽  
Adf Test ◽  
Over Time

Background: Ventilator-associated pneumonia (VAP) represents the highest burden among all healthcare-associated infections (HAIs), with a particularly high rate in patients in neurosurgical ICUs. Numerous VAP risk factors have been identified to provide a basis for preventive measures. However, the impact of individual factors on the risk of VAP is unclear. The goal of this study was to evaluate the dynamics of various VAP risk factors given the continuously declining prevalence of VAP in our neurosurgical ICU. Methods: This prospective cohort unit-based study included neurosurgical patients who stayed in the ICU >48 consecutive hours in 2011 through 2018. The infection prevention and control (IPC) program was implemented in 2010 and underwent changes to adopt best practices over time. We used a 2008 CDC definition for VAP. The dynamics of VAP risk factors was considered a time series and was checked for stationarity using theAugmented Dickey-Fuller test (ADF) test. The data were censored when a risk factor was present during and after VAP episodes. Results: In total, 2,957 ICU patients were included in the study, 476 of whom had VAP. Average annual prevalence of VAP decreased from 15.8 per 100 ICU patients in 2011 to 9.5 per 100 ICU patients in 2018 (Welch t test P value = 7.7e-16). The fitted linear model showed negative slope (Fig. 1). During a study period we observed substantial changes in some risk factors and no changes in others. Namely, we detected a decrease in the use of anxiolytics and antibiotics, decreased days on mechanical ventilation, and a lower rate of intestinal dysfunction, all of which were nonstationary processes with a declining trend (ADF testP > .05) (Fig. 2). However, there were no changes over time in such factors as average age, comorbidity index, level of consciousness, gender, and proportion of patients with brain trauma (Fig. 2). Conclusions: Our evidence-based IPC program was effective in lowering the prevalence of VAP and demonstrated which individual measures contributed to this improvement. By following the dynamics of known VAP risk factors over time, we found that their association with declining VAP prevalence varies significantly. Intervention-related factors (ie, use of antibiotics, anxiolytics and mechanical ventilation, and a rate of intestinal dysfunction) demonstrated significant reduction, and patient-related factors (ie, age, sex, comorbidity, etc) remained unchanged. Thus, according to the discriminative model, the intervention-related factors contributed more to the overall risk of VAP than did patient-related factors, and their reduction was associated with a decrease in VAP prevalence in our neurosurgical ICU.Funding: NoneDisclosures: None

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