2025 IMJ Reviewers

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<h4>Background</h4>Post-operative delirium is a serious neurocognitive complication of cardiac surgery. The stress hyperglycaemia ratio (SHR), which adjusts acute glucose levels to baseline glycaemia using glycated haemoglobin, has demonstrated superior prognostic value in critical care; however, its association with delirium remains under-investigated.<h4>Aims</h4>This study's primary aim was to investigate the association between the SHR and post-operative delirium. The secondary aim was to develop machine-learning models for early risk stratification.<h4>Methods</h4>This retrospective study analysed 8346 adult patients from the Medical Information Mart for Intensive Care IV database. The SHR was calculated using the peak glucose level within the first 24 h after intensive care unit admission. Delirium was assessed using the Confusion Assessment Method for the Intensive Care Unit. Multivariable Cox regression and restricted cubic splines evaluated associations, while six machine-learning models were constructed using Boruta-selected features.<h4>Results</h4>Delirium incidence was significantly higher in the high-ratio group than in the low-ratio group (18% vs 11%; P < 0.001). After multivariable adjustment, each one-unit increase in the ratio was associated with a 60% increased delirium risk (hazard ratio, 1.60; 95% confidence interval, 1.29-1.99). A high ratio independently predicted delirium (hazard ratio, 1.54; 95% confidence interval, 1.31-1.82), with restricted cubic splines confirming a linear dose-response relationship (P for non-linearity = 0.203). The support vector machine model achieved the best discriminative performance (area under the curve = 0.8042).<h4>Conclusions</h4>An elevated SHR is an independent risk factor for post-operative delirium. These machine-learning models may facilitate timely perioperative risk stratification.

Abstract: PubMed · Datensatz