Key result
Higher anion gap is linked to ~10% higher mortality in ICU cerebral infarction patients.
Why the study?
Cerebral infarction is a significant cause of mortality and long-term disability, creating a need to identify prognostic variables and develop a mortality risk model to guide patient care.
Does a prognostic risk model based on anion gap, platelet count, and glucose concentration predict short-term mortality risk in ICU patients with cerebral infarction?
Cohort (n=109)
No
Does a prognostic risk model based on anion gap, platelet count, and glucose concentration predict short-term mortality risk in ICU patients with cerebral infarction?
Hazard Ratio: 1.104 (95% CI 1.007–1.211)
Absolute Event Rate: 40.7% vs 32.7%
p-value: p=0.0356
In ICU patients with cerebral infarction, an exploratory risk model combining admission anion gap, platelet count, and blood glucose concentration stratified short-term mortality risk, with the anion gap providing the vast majority of predictive information.
May inform short-term mortality risk stratification in ICU cerebral infarction; hypothesis-generating and requires prospective validation.
Cerebral infarction represents a significant health threat, causing mortality and long-term disability. This study investigated prognostic variables associated with cerebral infarction and developed a risk model to predict mortality, thereby offering new strategies to enhance patient care and improve overall outcomes. Participants were selected from the Medical Information Mart for Intensive Care IV database, covering the period 2008–2019. The study population comprised patients with cerebral infarction admitted to the intensive care unit. Univariate Cox regression analysis, proportional hazards assumption testing, machine learning (least absolute shrinkage and selection operator regression), and multivariate Cox regression analysis were performed to identify prognostic variables. Based on these variables, a risk model was developed and subsequently evaluated. After excluding ineligible individuals, 109 patients with cerebral infarction were included. The anion gap [hazard ratio (HR) = 1.1040, 95% confidence interval (CI) = 1.0070–1.2110, P = 0.0356], platelet count (HR = 0.9965, 95% CI = 0.9939–0.9990, P = 0.0072), and glucose concentration (HR = 1.0050, 95% CI = 1.0000–1.0090, P = 0.0483) were identified as prognostic variables. A risk model utilizing these prognostic variables classified patients into high- and low-risk cohorts, with high-risk patients displaying lower survival probabilities. Receiver operating characteristic curve analysis demonstrated the strong predictive performance of this risk model, with all areas under the curve exceeding 0.7. The study identified the anion gap, platelet count, and glucose concentration as prognostic variables for cerebral infarction. The findings underscore the potential of the risk model to enhance clinical decision-making and management for patients with cerebral infarction.
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张金枝 et al. (2026) conducted a cohort in Cerebral infarction admitted to the intensive care unit (n=109). Anion gap, platelet count, and glucose concentration risk model vs. Low-risk group (median risk score cutoff 1.19) or per-unit biomarker change was evaluated on In-hospital / short-term all-cause mortality (HR 1.1040, 95% CI 1.0070-1.2110, p=0.0356). Anion gap (HR 1.1040, P=0.0356), platelet count (HR 0.9965, P=0.0072), and glucose (HR 1.0050, P=0.0483) predicted mortality in ICU patients with cerebral infarction.
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