A novel risk model based on white blood cell-related biomarkers for acute kidney injury prediction in patients with ischemic stroke admitted to the intensive care unit

Sex differences in clinical risk factors in obese ischemic stroke patients with a history of smoking

Clinical risk factors associated obesity and smoking, as well as their combined effect, are not fully understood. This study aims to determine sex differences in risk factors in a population of acute ischemic stroke (AIS) patients who are obese and with a history of previous or current smoking.

METHODS

A retrospective analysis of risk factors in male and female AIS patients with baseline data of obesity and current or previous history of smoking, smoking, and obesity alone was determined. The primary predictor and outcome are risk factors associated with male and female AIS patients. Baseline risk factors were analyzed using a multivariate regression analysis to determine specific risk factors linked with the combined effect of obesity and current or previous history of smoking''.

RESULTS

Male obese AIS patients who are current or previous smokers were more likely to be older patients(OR = 1.024, 95% CI, 1.022-1.047, P = 0.033) that present with coronary artery disease (OR = 1.806, 95% CI, 1.028-3.174, P = 0.040), a history of alcohol use (OR = 2.873, 95% CI, 1.349-6.166, P = 0.006), elevated serum creatinine (OR = 4.724, 95% CI, 2.171-10.281, P < 0.001) and systolic blood pressure (OR = 1.029, 95% CI, 1.011-1.047, P < 0.002). Females were more associated with depression (OR = 0.432, 95% CI, 0.244-0.764, P = 0.004), previous TIA (OR = 0.319, 95% CI, 0.142-0.714, P < 0.005), and higher levels of HDL (OR = 0.938, 95% CI, 0.915-0.962, P < 0.001).

CONCLUSION

Our results reveal sex differences in risk factors in obese AIS patients with a current or past history of smoking. This finding emphasizes the need to develop management strategies to improve the care of obese AIS patients who are either current or former smokers.

Prognostic Value of Leukocyte-Based Risk Model for Acute Kidney Injury Prediction in Critically Ill Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients

Purpose

Acute kidney injury (AKI) is a common complication of acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and inflammation is the potential link between AKI and AECOPD. However, little is known about the incidence and risk stratification of AKI in critically ill AECOPD patients. In this study, we aimed to establish risk model based on white blood cell (WBC)-related indicators to predict AKI in critically ill AECOPD patients.

Material and Methods

For the training cohort, data were taken from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database, and for the validation cohort, data were taken from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The study employed logistic regression analysis to identify the major predictors of WBC-related biomarkers on AKI prediction. Subsequently, a risk model was developed by multivariate logistic regression, utilizing the identified significant indicators.

Results

Finally, 3551 patients were enrolled in training cohort, 926 patients were enrolled in validation cohort. AKI occurred in 1206 (33.4%) patients in training cohort and 521 (56.3%) patients in validation cohort. According to the multivariate logistic regression analysis, four WBC-related indicators were finally included in the novel risk model, and the risk model had a relatively good accuracy for AKI in the training set (C-index, 0.764, 95% CI 0.749-0.780) as well as in the validation set (C-index, 0.738, 95% CI: 0.706-0.770). Even after accounting for other models, the critically ill AECOPD patients in the high-risk group (risk score > 3.44) still showed an increased risk of AKI (odds ratio: 4.74, 95% CI: 4.07-5.54) compared to those in low-risk group (risk score ≤ 3.44). Moreover, the risk model showed outstanding calibration capability as well as therapeutic usefulness in both groups for AKI and ICU mortality and in-hospital mortality of critical ill AECOPD patients.

Conclusion

The novel risk model showed good AKI prediction performance. This risk model has certain reference value for the risk stratification of AECOPD complicated with AKI in clinically.

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.