Age as a prognostic factor of 30-day mortality in hemorrhagic stroke patients: A Thai large tertiary care referral center

Associations of estimated plasma volume status with 30-day mortality and 1-year mortality in patients with intracerebral hemorrhage: a study of the MIMIC-IV database and the hospital information system

Aim

This study aims to investigated the associations between estimated plasma volume status (ePVS) and 30-day and 1-year mortality in intracerebral hemorrhage (ICH) patients, providing insights into the management in ICH.

Methods

Data of adult ICH patients were extracted from both the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the Hospital Information System (HIS) in this retrospective cohort study. Univariate and multivariate Cox regression analyses, and restricted cubic spline plots (RCS) were conducted to explore the associations between ePVS levels and both 30-day and 1-year mortality, with hazard ratios (HR) and 95% confidence intervals (CI) used for evaluation. Subgroup analyses were performed to further investigate these associations.

Results

Among 2,512 eligible patients from the MIMIC-IV database, 655 (26.07%) died within 30 days, with 1,254 (49.92%) had died by the 1-year follow-up. After adjusting for covariates, elevated ePVS was independently associated with both 30-day mortality (HR = 1.05, 95%CI: 1.01-1.09) and 1-year mortality (HR = 1.09, 95% CI: 1.06-1.13). Compared to patients with ePVS levels of [4.63-5.79), those with ePVS levels ≥5.79 had a higher risk of 30-day mortality (HR: 1.36, 95%CI: 1.12-1.64) and 1-year mortality (HR = 1.24, 95% CI: 1.08-1.42). Among 515 eligible patients from the HIS, 132 (25.60%) died within 30 days, with 288 (55.90%) mortality observed at 1-year follow-up. After adjusting for covariates, elevated ePVS was independently associated with both 30-day mortality (HR = 1.33, 95%CI: 1.23-1.43) and 1-year mortality (HR = 1.26, 95% CI: 1.18-1.35). Comparing to patients with ePVS levels of [4.63-5.79), those with ePVS levels of ≥5.79 had a higher risk of 30-day mortality (HR:2.21, 95%CI: 1.48-3.30) and 1-year mortality (HR = 2.75, 95% CI: 2.04-3.72). Additionally, subgroup analyses demonstrated that ePVS was significantly associated with 30-day mortality or 1-year mortality derived from MIMIC-IV and HIS in most subgroups (p < 0.05). And RCS analysis indicates that, whether using MIMIC-IV or HIS data, ePVS was linearly associated with 30-day or 1-year mortality.

Conclusion

Higher ePVS levels may be a potential risk factor for 30-day and 1-year mortality in ICH patients, suggesting that timely monitoring and stabilization of ePVS could improve prognosis in this population. However, further studies are needed to validate these fingings.

Hemorrhagic stroke and atherogenic markers - is there any relation?

Background

The triglyceride/high-density lipoprotein (TG/HDL) ratio emerges as a promising marker for cardiovascular risk. However, the relationship between overall serum lipid levels and hemorrhagic stroke (HS) remains uncertain. Therefore, our study aims to explore the association between this novel index and mortality in HS patients.

Methods

Utilizing a retrospective-prospective framework from January 2020 to August 2023, we scrutinized data from 104 hospitalized patients diagnosed with HS, with particular attention to their medical backgrounds and lipid profiles.

Results

Age (odds ratio [OR], 1.078; 95% confidence interval [CI], 1.032-1.125; P = 0.001), atrial fibrillation (OR, 0.237; 95% CI, 0.074-0.760; P = 0.015), glucose level (OR, 1.121; 95% CI, 1.007-1.247; P = 0.037), and TG/HDL index (OR, 0.368; 95% CI, 0.173-0.863; P = 0.020) emerged as independent predictors for in-hospital mortality, as determined by both univariable and multivariable logistic regression analyses.

Conclusion

Our results add weight to the growing evidence backing the utility of the TG/HDL index in assessing cardiovascular risk among HS patients. They emphasize the necessity of adopting a comprehensive risk assessment and management strategy that incorporates both traditional markers and novel indicators.

Independent Factors Associated with 30-Day In-Hospital Mortality from Acute Spontaneous Intracerebral Hemorrhage

OBJECTIVE

This study aims to investigate independent factors associated with 30-day mortality in patients with acute spontaneous intracerebral hemorrhage (SICH) before treatment.

METHODS

A retrospective analysis was performed on medical records of patients hospitalized with acute SICH between 2019 and 2021. Data included personal history, hospital stay duration, symptom onset, chief complaint, underlying diseases, medication, and alcohol/smoking habits. Physical examination records comprised baseline blood pressure, Glasgow Coma Scale assessment, and pupil reaction evaluation. Diagnostic imaging, specifically computed tomography brain scans, was examined for hemorrhage details. Multivariable logistic analysis was utilized for data analysis.

RESULTS

Among 663 cases, 185 (27.9%) experienced mortality. Risk factors for mortality included chronic kidney disease, ischemic heart disease, loss of follow-up in hypertension clinic, and pontine hemorrhage. Conversely, motor response (m), reactive pupils, and basal cistern persistence significantly decreased the risk of mortality in multivariable analysis. Receiver operating characteristic analysis identified a m score of 5 as the cutoff for predicting survival.

CONCLUSIONS

Chronic kidney disease, ischemic heart disease, loss of hypertension follow-up, m, reactive pupils, pontine hemorrhage, and basal cistern persistence were independent variables associated with the 30-day mortality rate in SICH patients before treatment initiation. A m, pupil reaction, and basal cistern persistence serve as predictive tools for assessing mortality in SICH before treatment.

Identification of key predictors of hospital mortality in critically ill patients with embolic stroke using machine learning

Embolic stroke (ES) is characterized by high morbidity and mortality. Its mortality predictors remain unclear. The present study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: Medical Information Mart for Intensive Care (MIMIC)-IV for training and internal validation, and eICU Collaborative Research Database (eICU-CRD) for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the receiver operating characteristic (AUROC). We adopted recursive feature elimination (RFE) for feature selection. We assessed model performance using three disease-severity scoring systems as benchmarks. Of the 1566 and 207 ES patients enrolled in the two databases, there were 173 (15.70%), 73 (15.57%), and 36 (17.39%) hospital mortality in the training, internal validation, and external validation cohort, respectively. The random forest (RF) model had the largest AUROC (0.806) in the internal validation phase and was chosen as the best model. The AUROC of the RF compact (RF-COM) model containing the top six features identified by RFE was 0.795. In the external validation phase, the AUROC of the RF model was 0.838, and the RF-COM model was 0.830, outperforming other models. Our findings suggest that the RF model was the best model and the top six predictors of ES hospital mortality were Glasgow Coma Scale, white blood cell, blood urea nitrogen, bicarbonate, age, and mechanical ventilation.

Effect of COPD on the Hospital Outcomes and Mortality among Hemorrhagic Stroke Patients. Sex Differences in a Population-Based Study

(1) Background: It is not well known whether there is an association between COPD and hemorrhagic stroke (HS). We aim to analyze the incidence, clinical characteristics, procedures, and outcomes of HS in patients with and without COPD and to assess sex differences. Secondly, to identify factors associated with in-hospital mortality (IHM). (2) Methods: Patients aged ≥40 years hospitalized with HS included in the Spanish National Hospital Discharge Database (2016-2018) were analyzed. Propensity score matching (PSM) was used to compare patients according to sex and COPD status. (3) Results: We included 55,615 patients (44.29% women). Among men with COPD the HS adjusted incidence was higher (IRR 1.31; 95% CI 1.24-1.57) than among non-COPD men. COPD men had higher adjusted incidence of HS than COPD women (IRR 1.87; 95% CI 1.85-1.89). After matching, COPD men had a higher IHM (29.96% vs. 27.46%; p = 0.032) than non-COPD men. Decompressive craniectomy was more frequently conducted among COPD men than COPD women (6.74% vs. 4.54%; p = 0.014). IHM increased with age and atrial fibrillation, while decompressive craniectomy reduced IHM. (4) Conclusions: COPD men had higher incidence and IHM of HS than men without COPD. COPD men had higher incidence of HS than COPD women. Decompressive craniectomy was more frequently conducted in COPD men than COPD women and this procedure was associated to better survival.

Predictors of death within 6 months of stroke onset: A model with Barthel index, platelet/lymphocyte ratio and serum albumin

AIMS

To develop and internally validate a nomogram to predict the risk of death within 6 months of onset of stroke in Chinese. Identifying risk factors with potentially direct effects on the nomogram will improve the quality of risk assessment and help nurses implement preventive measures based on patient-specific risk factors.

DESIGN

A retrospective study.

METHODS

We performed a least absolute shrinkage and selection operator (LASSO) regression modelling and multivariate logistic regression analysis to establish a prediction model of death risk in stroke patients within 6 months of onset. LASSO and time-dependent Cox regression models were further used to analyse the 6-month survival of stroke patients. Data were collected from 21 October 2013-6 May 2019.

RESULTS

The independent predictors of the nomogram were Barthel index (odds ratio (OR) = 0.980, 95% confidence interval (CI) = 0.961-0.998, p = .03), platelet/lymphocyte ratio (OR = 1.005, 95% CI = 1.000-1.010, p = .04) and serum albumin (OR = 0.854, 95% CI = 0.774-0.931, p < .01). This model showed good discrimination and consistency, and its discrimination evaluation C-statistic was 0.879 in the training set and 0.891 in the internal validation set. The DCA indicated that the nomogram had a higher overall net benefit over most of the threshold probability range. The time-dependent Cox regression model established the impact of the time effect of the age variable on survival time.

CONCLUSIONS

Our results identified three predictors of death within 6 months of stroke in Chinese. These predictors can be used as risk assessment indicators to help caregivers performing clinical nursing work, and in clinical practice, it is suggested that nurses should evaluate the self-care ability of stroke patients in detail. The constructed nomogram can help identify patients at high risk of death within 6 months, so that intervention can be performed as early as possible.