Development and validation of a clinical risk model to predict the hospital mortality in ventilated patients with acute respiratory distress syndrome: a population-based study

Predictive validity of the sequential organ failure assessment score for mortality in patients with acute respiratory distress syndrome in Vietnam

Evaluating the prognosis of ARDS patients using grading systems can enhance treatment decisions. This retrospective observational study evaluated the predictive accuracy of the SOFA score, APACHE II score, SpO2/FiO2 ratio, and PaO2/FiO2 ratio for mortality in ARDS patients in Vietnam. The study included 335 adult ARDS patients admitted to a central hospital from August 2015 to August 2023. Among them, 66.9% were male, the median age was 55 years, and 61.5% died in the hospital. The SOFA (AUROC: 0.651) and APACHE II scores (AUROC: 0.693) showed poor discriminatory ability for hospital mortality. The SpO2/FiO2 (AUROC: 0.595) and PaO2/FiO2 ratios (AUROC: 0.595) also displayed poor discriminatory ability. In multivariable analyses, after adjusting for the same set of confounding variables, the APACHE II score (adjusted OR: 1.152), SpO2/FiO2 ratio (adjusted OR: 0.985), and PaO2/FiO2 ratio (adjusted OR: 0.989) were independently associated with hospital mortality. Although the SOFA score (adjusted OR: 1.132) indicated a potential association with hospital mortality, it did not reach statistical significance (p = 0.081). However, a SOFA score of ≥ 10 emerged as an independent predictor (adjusted OR: 3.398) of hospital mortality. These findings emphasize the need for further studies to develop more accurate scoring systems for predicting outcomes in ARDS patients.

Association of coagulation function with the risk of in-hospital mortality in patients with severe acute respiratory distress syndrome

BACKGROUND

To evaluate the association of coagulation disorder score with the risk of in-hospital mortality in acute respiratory distress syndrome (ARDS) patients.

METHODS

In this cohort study, 7,001 adult patients with ARDS were identified from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV). Univariate and multivariate Logistic stepwise regression models were used to explore the associations of coagulation-associated biomarkers with the risk of in-hospital mortality in patients with ADRS. Restricted cubic spline (RCS) was plotted to explore the association between coagulation disorder score and in-hospital mortality of ARDS patients.

RESULTS

The follow-up time for in-hospital death was 7.15 (4.62, 13.88) days. There were 1,187 patients died and 5,814 people survived in hospital. After adjusting for confounding factors, increased risk of in-hospital mortality in ARDS patients was observed in those with median coagulation disorder score [odds ratio (OR) = 1.22, 95% confidence interval (CI): 1.01-1.47) and high coagulation disorder score (OR = 1.38, 95% CI: 1.06-1.80). The results of RCS indicated that when the coagulation disorder score >2, the trend of in-hospital mortality rose gradually, and OR was >1.

CONCLUSIONS

Poor coagulation function was associated with increased risk of in-hospital mortality in ARDS patients. The findings implied that clinicians should regularly detect the levels of coagulation-associated biomarkers for the management of ARDS patients.

Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit

End-of-life care (EOLC) exemplifies the joint mission of intensive and palliative care (PC) in their human-centeredness. The explosion of technological advances in medicine must be balanced with the culture of holistic care. Inevitably, it brings together the science and the art of medicine in their full expression. High-quality EOLC in the ICU is grounded in evidence, ethical principles, and professionalism within the framework of the Law. Expert professional statements over the last two decades in India were developed while the law was evolving. Recent landmark Supreme Court judgments have necessitated a review of the clinical pathway for EOLC outlined in the previous statements. Much empirical and interventional evidence has accumulated since the position statement in 2014. This iteration of the joint Indian Society of Critical Care Medicine-Indian Association of Palliative Care (ISCCM-IAPC) Position Statement for EOLC combines contemporary evidence, ethics, and law for decision support by the bedside in Indian ICUs.

HOW TO CITE THIS ARTICLE

Mani RK, Bhatnagar S, Butola S, Gursahani R, Mehta D, Simha S, et al. Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit. Indian J Crit Care Med 2024;28(3):200-250.

Prediction model for in-hospital mortality in patients at high altitudes with ARDS due to COVID-19

INTRODUCTION

The diagnosis of acute respiratory distress syndrome (ARDS) includes the ratio of pressure arterial oxygen and inspired oxygen fraction (P/F) ≤ 300, which is often adjusted in locations more than 1,000 meters above sea level (masl) due to hypobaric hypoxemia. The main objective of this study was to develop a prediction model for in-hospital mortality among patients with ARDS due to coronavirus disease 2019 (COVID-19) (C-ARDS) at 2,600 masl with easily available variables at patient admission and to compare its discrimination capacity with a second model using the P/F adjusted for this high altitude.

METHODS

This study was an analysis of data from patients with C-ARDS treated between March 2020 and July 2021 in a university hospital located in the city of Bogotá, Colombia, at 2,600 masl. Demographic and laboratory data were extracted from electronic records. For the prediction model, univariate analyses were performed to screen variables with p <0.25. Then, these variables were automatically selected with a backward stepwise approach with a significance level of 0.1. The interaction terms and fractional polynomials were also examined in the final model. Multiple imputation procedures and bootstraps were used to obtain the coefficients with the best external validation. In addition, total adjustment of the model and logistic regression diagnostics were performed. The same methodology was used to develop a second model with the P/F adjusted for altitude. Finally, the areas under the curve (AUCs) of the receiver operating characteristic (ROC) curves of the two models were compared.

RESULTS

A total of 2,210 subjects were included in the final analysis. The final model included 11 variables without interaction terms or nonlinear functions. The coefficients are presented excluding influential observations. The final equation for the model fit was g(x) = age(0.04819)+weight(0.00653)+height(-0.01856)+haemoglobin(-0.0916)+platelet count(-0.003614)+ creatinine(0.0958)+lactate dehydrogenase(0.001589)+sodium(-0.02298)+potassium(0.1574)+systolic pressure(-0.00308)+if moderate ARDS(0.628)+if severe ARDS(1.379), and the probability of in-hospital death was p (x) = e g (x)/(1+ e g (x)). The AUC of the ROC curve was 0.7601 (95% confidence interval (CI) 0.74-0, 78). The second model with the adjusted P/F presented an AUC of 0.754 (95% CI 0.73-0.77). No statistically significant difference was found between the AUC curves (p value = 0.6795).

CONCLUSION

This study presents a prediction model for patients with C-ARDS at 2,600 masl with easily available admission variables for early stratification of in-hospital mortality risk. Adjusting the P/F for 2,600 masl did not improve the predictive capacity of the model. We do not recommend adjusting the P/F for altitude.

Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation

Background

Timely and accurate outcome prediction plays a critical role in guiding clinical decisions for hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. However, interpreting and translating the predictive models into clinical applications are as important as the prediction itself. This study aimed to develop an interpretable machine learning (IML) model that accurately predicts 28-day all-cause mortality in hypertensive ischemic or hemorrhagic stroke patients.

Methods

A total of 4,274 hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU in the USA from multicenter cohorts were included in this study to develop and validate the IML model. Five machine learning (ML) models were developed, including artificial neural network (ANN), gradient boosting machine (GBM), eXtreme Gradient Boosting (XGBoost), logistic regression (LR), and support vector machine (SVM), to predict mortality using the MIMIC-IV and eICU-CRD database in the USA. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Model performance was evaluated based on the area under the curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV). The ML model with the best predictive performance was selected for interpretability analysis. Finally, the SHapley Additive exPlanations (SHAP) method was employed to evaluate the risk of all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU.

Results

The XGBoost model demonstrated the best predictive performance, with the AUC values of 0.822, 0.739, and 0.700 in the training, test, and external cohorts, respectively. The analysis of feature importance revealed that age, ethnicity, white blood cell (WBC), hyperlipidemia, mean corpuscular volume (MCV), glucose, pulse oximeter oxygen saturation (SpO2), serum calcium, red blood cell distribution width (RDW), blood urea nitrogen (BUN), and bicarbonate were the 11 most important features. The SHAP plots were employed to interpret the XGBoost model.

Conclusions

The XGBoost model accurately predicted 28-day all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. The SHAP method can provide explicit explanations of personalized risk prediction, which can aid physicians in understanding the model.