Background Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model’s predictive value for these patients. Methods Clinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve. Results A total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival. Conclusions We constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.
Sepsis is a clinical syndrome characterized by a severe disorder of pathophysiology caused by infection of pathogenic microorganisms. The addition of antioxidant micronutrient therapies such as thiamine to sepsis treatment remains controversial. This study explored the effect of thiamine on the prognosis of patients with sepsis. This study was a retrospective study involving patients with sepsis from the Medical Information Mart for Intensive Care IV. Patients were divided into two groups, the thiamine received group (TR) and the thiamine unreceived group (TUR), according to whether they were supplemented with thiamin via intravenous while in the intensive care unit (ICU). The primary outcome was ICU mortality. The association between thiamine and outcome was analyzed using the Cox proportional hazard regression model, propensity score matching (PSM), generalized boosted model-based inverse probability of treatment weighting (IPTW), and doubly robust estimation. A total of 11,553 sepsis patients were enrolled in this study. After controlling for potential confounders using Cox regression models, the TR group had a statistically significantly lower ICU mortality risk than the TUR group. The hazard ratio (95% confidence interval) of ICU mortality for the TR group was 0.80 (0.70, 0.93). We obtained the same results after using PSM, IPTW, and doubly robust estimation. Supplementation with thiamine has a beneficial effect on the prognosis of patients with sepsis. More randomized controlled trials are needed to confirm the effectiveness of thiamine supplementation in the treatment of sepsis.
BackgroundSpinal fracture is a common traumatic condition in orthopaedics, accounting for 5%–6% of total body fractures, and is a high‐risk factor for venous thromboembolism (VTE), which seriously affects patient prognosis.AimThe aim of this study was to determine the impact of VTE prophylaxis on the prognosis of patients with spinal fractures in intensive care units (ICUs) and to provide a scientific basis for clinical treatment and nursing.DesignA retrospective study of patients with spinal fractures from the multicenter eICU Collaborative Research Database.MethodThe outcomes of this study were ICU mortality and in‐hospital mortality. Patients were divided into the VTE prophylaxis (VP) and no VTE prophylaxis (NVP) groups according to whether they had undergone VTE prophylaxis during their ICU admission. The association between groups and outcomes were analysed using Kaplan–Meier (KM) survival curve, log‐rank test and the Cox proportional‐hazards regression model.ResultsThis study included 1146 patients with spinal fractures: 330 in the VP group and 816 in the NVP group. KM survival curves and log‐rank tests revealed that both ICU and in‐hospital survival probabilities in the VP group were significantly higher than in the NVP group. After the Cox model was adjusted for all covariates, the hazard ratio for ICU mortality in the VP group was 0.38 (0.19–0.75); the corresponding value for in‐hospital mortality in the VP group was 0.38 (0.21–0.68).ConclusionsVTE prophylaxis is associated with reduced ICU and in‐hospital mortality in patients with spinal fractures in ICUs. More research is necessary to further define specific strategies and optimal timing for VTE prophylaxis.Relevance to clinical practiceThis study provides the basis that VTE prophylaxis may be associated with improved prognosis in patients with spinal fractures in ICUs. In clinical practice, an appropriate modality should be selected for VTE prophylaxis in such patients.
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