Sepsis, a life-threatening condition caused by an inflammatory response to systemic infection, results in a significant social burden and healthcare costs. This study aimed to investigate the relationship between the C-reactive protein (CRP) trajectories of patients with sepsis in the intensive care unit (ICU) and the in-hospital mortality rate. We reviewed 1464 patients with sepsis treated in the ICU of Dongyang People’s Hospital from 2010 to 2020 and used latent growth mixture modeling to divide the patients into four classes according to CRP trajectory (intermediate, gradually increasing, persistently high, and persistently low CRP levels). We found that patients with intermediate and persistently high CRP levels had the lowest (18.1%) and highest (32.6%) in-hospital mortality rates, respectively. Multiple logistic regression analysis showed that patients with persistently high (odds ratio [OR] = 2.19, 95% confidence interval [CI] = 1.55–3.11) and persistently low (OR = 1.41, 95% CI = 1.03–1.94) CRP levels had a higher risk of in-hospital mortality than patients with intermediate CRP levels. In conclusion, in-hospital mortality rates among patients with sepsis differ according to the CRP trajectory, with patients with intermediate CRP levels having the lowest mortality rate. Further research on the underlying mechanisms is warranted.

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64–0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752–0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

Acute kidney injury (AKI) often occurs in patients in the intensive care unit (ICU). AKI duration is closely related to the prognosis of critically ill patients. Identifying the disease course length in AKI is critical for developing effective individualised treatment. To predict persistent AKI at an early stage based on a machine learning algorithm and integrated models. Overall, 955 patients admitted to the ICU after surgery complicated by AKI were retrospectively evaluated. The occurrence of persistent AKI was predicted using three machine learning methods: a support vector machine (SVM), decision tree, and extreme gradient boosting and with an integrated model. External validation was also performed. The incidence of persistent AKI was 39.4–45.1%. In the internal validation, SVM exhibited the highest area under the receiver operating characteristic curve (AUC) value, followed by the integrated model. In the external validation, the AUC values of the SVM and integrated models were 0.69 and 0.68, respectively, and the model calibration chart revealed that all models had good performance. Critically ill patients with AKI after surgery had high incidence of persistent AKI. Our machine learning model could effectively predict the occurrence of persistent AKI at an early stage.

Delirium is a neuropsychiatric syndrome commonly encountered in critically ill patients, and systemic inflammation has been strongly implicated to underlie its pathophysiology. This study aimed to investigate the predictive value of the platelet-to-lymphocyte ratio (PLR) for delirium in the intensive care unit (ICU).In this retrospective observational study, we analyzed the clinical and laboratory data of 319 ICU patients from October 2016 to December 2017. Using the Locally Weighted Scatterplot Smoothing technique, a PLR knot was detected at a value of approximately 100. Logistic regression was used to investigate the association between the PLR and delirium.Of the 319 patients included in this study, 29 (9.1%) were diagnosed with delirium. In the delirium group, the duration of mechanical ventilation was significantly longer than that in the no-delirium group (40.2 ± 65.5 vs. 19.9 ± 26.5 hours, respectively; P < .001). A multiple logistic regression analysis showed that PLR > 100 (odds ratio [OR]: 1.003, 95% confidence interval [CI]: 1.001–1.005), age (OR: 2.76, 95% CI: 1.110–6.861), and the ratio of arterial oxygen partial pressure to the inspired oxygen fraction (OR: 0.996, 95% CI: 0.992–0.999) were independent predictors of delirium.In our study, a high PLR value on ICU admission was associated with a higher incidence of delirium. Owing to easy calculability, the PLR could be a useful delirium predictive index in ICUs, thereby enabling early interventions to be implemented.

Sepsis, a syndrome of life-threatening organ dysfunction caused by dysregulated host responses to infection, exhibits profound pathobiological heterogeneity, hindering the development of effective therapies. Current subtyping approaches, often reliant on single-omics data or unsupervised clustering, yield poorly reproducible and therapeutically misaligned classifications. Here, we introduce a goal-directed subgroup identification (GD-SI) framework that optimizes patient stratification for differential treatment responses, integrating longitudinal multi-omics data (transcriptomic, proteomic, metabolomic, phenomic) from 1327 subjects across 43 hospitals. While supervised multi-omics integration frameworks (e.g., DIABLO) effectively capture shared biological signals, our approach anchors subgroup discovery directly to treatment-effect optimization. This strategy achieves substantial cross-omic concordance and, crucially, generalizes to predict differential treatment response across international critical care databases. Patients stratified by GD-SI-derived benefit scores for restrictive versus liberal fluid resuscitation exhibited marked survival differences, with similar advantages observed for ulinastatin immunomodulation. External validations in MIMIC-IV and ZiGongDB confirm prognostic generalizability. This framework reconciles biological heterogeneity with clinical actionability, offering a scalable infrastructure for precision trial design and personalized sepsis management. Our findings underscore the translational potential of omics-driven, goal-directed stratification to overcome decades of therapeutic stagnation in critical care.