Predictive value of the sequential organ failure assessment (SOFA) score for prognosis in patients with severe acute ischemic stroke: a retrospective study

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.

Machine learning-based prognostication of mortality in stroke patients

Objectives

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.

Materials and methods

Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.

Results

The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.

Conclusions

This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Development and verification of a nomogram for predicting short-term mortality in elderly ischemic stroke populations

Stroke is a major healthcare problem worldwide, particularly in the elderly population. Despite limited research on the development of prediction models for mortality in elderly individuals with ischemic stroke, our study aimed to address this knowledge gap. By leveraging data from the Medical Information Mart for Intensive Care IV database, we collected comprehensive raw data pertaining to elderly patients diagnosed with ischemic stroke. Through meticulous screening of clinical variables associated with 28-day mortality, we successfully established a robust nomogram. To assess the performance and clinical utility of our nomogram, various statistical analyses were conducted, including the concordance index, integrated discrimination improvement (IDI), net reclassification index (NRI), calibration curves and decision curve analysis (DCA). Our study comprised a total of 1259 individuals, who were further divided into training (n = 894) and validation (n = 365) cohorts. By identifying several common clinical features, we developed a nomogram that exhibited a concordance index of 0.809 in the training dataset. Notably, our findings demonstrated positive improvements in predictive performance through the IDI and NRI analyses in both cohorts. Furthermore, calibration curves indicated favorable agreement between the predicted and actual incidence of mortality (P > 0.05). DCA curves highlighted the substantial net clinical benefit of our nomogram compared to existing scoring systems used in routine clinical practice. In conclusion, our study successfully constructed and validated a prognostic nomogram, which enables accurate short-term mortality prediction in elderly individuals with ischemic stroke.

Red Cell Distribution Width-to-Platelet Count Ratio: A Promising Predictor of In-Hospital All-Cause Mortality in Critically Ill Patients with Acute Ischemic Stroke

INTRODUCTION

The red blood cell distribution width-to-platelet ratio (RPR), a novel inflammatory index, has already been proven as a prognostic factor in some other diseases, but its prognostic effect on critically ill patients with acute ischemic stroke (AIS) has been rarely investigated. This study aimed to investigate the association between RPR and in-hospital mortality in these patients.

METHODS

We extracted clinical data from the Medical Information Mart for Intensive Care IV 1.0 database. The primary outcome was in-hospital all-cause mortality of patients with critical AIS. The main independent variable was RPR. To investigate the association between RPR and in-hospital all-cause mortality in patients with critical AIS, multivariable logistic analyses, smooth curve fitting, and stratified analyses were conducted.

RESULTS

In total, 2,673 patients with AIS who were admitted to the intensive care unit were included in the study. In the multivariable analysis, in-hospital mortality was positively related to RPR (odds ratio [OR] 1.28, 95% confidence interval [CI] 1.02-1.59). According to the two-piecewise logistic regression model, we found that the inflection point of RPR was 1.89%. To the left of the inflection point (RPR ≤1.89%), we did not detect any relationship between RPR and in-hospital all-cause mortality (OR [95% CI]: 0.73 [0.41, 1.31], p = 0.2884). In contrast, to the right of the inflection point (RPR >1.89%), RPR was positively related to in-hospital all-cause mortality (OR [95% CI]: 1.61 [1.18, 2.19], p = 0.0027).

CONCLUSIONS

RPR showed a nonlinear relationship with in-hospital all-cause mortality in patients with critical AIS.

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Background

This study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients.

Methods

All raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system.

Results

Patients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system.

Conclusion

This proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.

Added value of systemic inflammation markers in predicting pulmonary infection in stroke patients: A retrospective study by machine learning analysis

Exploring candidate markers to predict the clinical outcomes of pulmonary infection in stroke patients have a high unmet need. This study aimed to develop machine learning (ML)-based predictive models for pulmonary infection.Between January 2008 and April 2021, a retrospective analysis of 1397 stroke patients who had CT angiography from skull to diaphragm (including CT of the chest) within 24 hours of symptom onset. A total of 21 variables were included, and the prediction model of pulmonary infection was established by multiple ML-based algorithms. Risk factors for pulmonary infection were determined by the feature selection method. Area under the curve (AUC) and decision curve analysis were used to determine the model with the best resolution and to assess the net clinical benefits associated with the use of predictive models, respectively.A total of 889 cases were included in this study as a training group, while 508 cases were as a validation group. The feature selection indicated the top 6 predictors were procalcitonin, C-reactive protein, soluble interleukin-2 receptor, consciousness disorder, dysphagia, and invasive procedure. The AUCs of the 5 models ranged from 0.78 to 0.87 in the training cohort. When the ML-based models were applied to the validation set, the results also remained reconcilable, and the AUC was between 0.891 and 0.804. The decision curve analysis also showed performed better than positive line and negative line, indicating the favorable predictive performance and clinical values of the models.By incorporating clinical characteristics and systemic inflammation markers, it is feasible to develop ML-based models for the presence and consequences of signs of pulmonary infection in stroke patients, and the use of the model may be greatly beneficial to clinicians in risk stratification and management decisions.