Prognostic value of inflammation biomarkers for 30-day mortality in critically ill patients with stroke

Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke

Background

Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis.

Objective

To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS.

Methods

This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model's predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes.

Results

Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility.

Conclusion

The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BACKGROUND

This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.

METHODS

Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.

RESULTS

A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients.

CONCLUSIONS

The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

The lactate-to-albumin ratio relationship with all-cause mortality in cerebral infarction patients: analysis from the MIMIC-IV database

Objective

To examine the association of lactate-to-albumin ratio (LAR) with 30-day and 90-day mortality in patients with cerebral infarction admitted to the intensive care unit (ICU).

Methods

In this retrospective observational study, 1,089 patients with cerebral infarction were recruited. The concentration of blood lactate and serum albumin on the first day of ICU admission were recorded. The relationship between LAR levels and mortality was evaluated through univariate and multivariate Cox regression analyses, four-knot multivariate restricted cubic spline regression, and Kaplan-Meier (KM) curves.

Results

The overall 30-day and 90-day mortality rates in the entire cohort were 27.3 and 35.8%, respectively. KM analysis revealed a significant relationship between high LAR index and the risk of all-cause mortality (log-rank p < 0.001). Furthermore, multivariate Cox proportional risk analysis showed that the LAR index independently predicted the risk of 30-day mortality (HR: 1.38, 95% CI 1.15-1.64, p = 0.004) and 90-day mortality (HR: 1.53, 95% CI 1.32-1.77, p < 0.001) in the study population. Furthermore, a higher LAR exceeding 0.53 was positively correlated with the risk of 30-day and 90-day mortalities. Subsequent subgroup analyses demonstrated that LAR could predict the primary outcome.

Conclusion

In summary, the LAR index is a reliable and independent predictor of increased mortality among critically ill patients suffering from cerebral infarction. Nonetheless, there is a need for additional comprehensive prospective studies to validate these findings.

Relationship Between Systemic Immune-Inflammation Index and Risk of Respiratory Failure and Death in COPD: A Retrospective Cohort Study Based on the MIMIC-IV Database

Purpose

Chronic obstructive pulmonary disease (COPD) concurrent with respiratory failure (RF) is devastating, and may result in death and disability. Systemic immune-inflammation index (SII) is a new prognostic biomarker linked to unfavorable outcomes of acute coronary syndrome, ischemic stroke, and heart failure. Nonetheless, its role in COPD is rarely investigated. Consequently, this study intends to assess the accuracy of SII in predicting the prognosis of COPD.

Patients and Methods

The clinical information was retrospectively acquired from the Medical Information Mart for Intensive Care-IV database. The outcomes encompassed the incidence of RF and mortality. The relationship between different SII and outcomes was examined utilizing the Cox proportional-hazards model and restricted cubic splines. Kaplan-Meier analysis was employed for all-cause mortality.

Results

The present study incorporated 1653 patients. During hospitalization, 697 patients (42.2%) developed RF, and 169 patients (10.2%) died. And 637 patients (38.5%) died during long-term follow-up. Higher SII increased the risk of RF (RF: HR: 1.19, 95% CI 1.12-1.28, P<0.001), in-hospital mortality (HR: 1.22, 95% CI 1.07-1.39, P=0.003), and long-term follow-up mortality (HR: 1.12, 95% CI 1.05-1.19, P<0.001). Kaplan-Meier analysis suggested a significantly elevated risk of all-cause death (log-rank P<0.001) in patients with higher SII, especially during the short-term follow-up period of 21 days.

Conclusion

SII is closely linked to an elevated risk of RF and death in COPD patients. It appears to be a potential predictor of the prognosis of COPD patients, which is helpful for the risk stratification of this population. However, more prospective studies are warranted to consolidate our conclusion.

Exploring the utility of a latent variable as comprehensive inflammatory prognostic index in critically ill patients with cerebral infarction

Objective

We introduce the comprehensive inflammatory prognostic index (CIPI), a novel prognostic tool for critically ill cerebral infarction patients, designed to meet the urgent need for timely and convenient clinical decision-making in this high-risk patient group.

Methods

Using exploratory factor analysis on selected indices-neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SIII)-we derived CIPI, a latent variable capturing their combined predictive power. Data from 1,022 patients in the Medical Information Mart for Intensive Care (MIMIC)-IV database were used to develop CIPI-based survival models, with the robustness and area under the receiver operating characteristic curve (AUC) performance of CIPI validated against an independent dataset of 326 patients from the MIMIC-III CareVue subset. The CIPI's predictive power for in-hospital and intensive care unit (ICU) mortality was assessed through Kaplan-Meier analysis, univariate and multivariate Cox regression models, and time-dependent AUC analysis. Linearity, subgroup sensitivity analyses and interaction effects with CIPI were also evaluated.

Results

CIPI was an independent prognostic factor, demonstrating a statistically significant association with in-hospital and ICU mortality, when assessed as a continuous and a categorical variable. It showed a linear relationship with mortality rates and demonstrated stability across most subgroups, with no significant interactions observed. Its predictive capabilities for in-hospital and ICU mortality among critically ill cerebral infarction patients matched those of established prognostic indices in the MIMIC database.

Conclusion

Our study indicates that CIPI is a reliable and effective prognostic tool for critically ill cerebral infarction patients in predicting in-hospital and ICU mortality. Its straightforward calculation, rooted in routine blood tests, enhances its practicality, promising significant utility in clinical settings.

Association of prognostic nutritional index with the risk of all-cause mortality and cardiovascular mortality in patients with type 2 diabetes: NHANES 1999-2018

INTRODUCTION

There is little bulk clinical evidence on nutritional status and mortality in patients with diabetes. The purpose of this study was to examine the relationship between prognostic nutritional index (PNI) and all-cause mortality and cardiovascular mortality in adults with diabetes.

RESEARCH DESIGN AND METHODS

This study included 5916 adult patients with diabetes from the National Health and Nutrition Examination Survey 1999-2018. Cox proportional risk models were used to estimate risk ratios (HRs) and 95% CIs for all-cause mortality, cardiovascular disease (CVD) mortality.

RESULTS

During a mean follow-up of 8.17 years, there were 1248 deaths from all causes and 370 deaths from CVD. After multivariate adjustment, the risk of all-cause mortality was reduced by 24%, 38%, and 28% in Q2 (49.0-52.99), Q3 (53.0-57.99), and Q4 (≥58.0), respectively, compared with Q1 (PNI<49.0). The risk of cardiovascular mortality was reduced by 30%, 27%, and 26%, respectively. Consistent results were observed in the subgroup analysis.

CONCLUSIONS

Lower serum PNI levels were significantly associated with higher all-cause and CVD mortality. These findings suggest that maintaining an appropriate range of serum PNI status may reduce the risk of death in patients with diabetes.