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.

Background: Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. Methods: Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. Results: The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. Conclusion: Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

The effect of high-flow nasal cannula (HFNC) therapy in patients after planned extubation remains inconclusive. We aimed to perform a rigorous and comprehensive systematic meta-analysis to robustly quantify the benefits of HFNC for patients after planned extubation by investigating postextubation respiratory failure and other outcomes. We searched MEDLINE, EMBASE, Web of Science, and the Cochrane Library from inception to August 2018. Two researchers screened studies and collected the data independently. Randomized controlled trials (RCTs) and crossover studies were included. The main outcome was postextubation respiratory failure. Ten studies (seven RCTs and three crossover studies; HFNC group: 856 patients; Conventional oxygen therapy (COT) group: 852 patients) were included. Compared with COT, HFNC may significantly reduce postextubation respiratory failure (RR, 0.61; 95% CI, 0.41, 0.92; z = 2.38; P = 0.02) and respiratory rates (standardized mean differences (SMD), − 0.70; 95% CI, − 1.16, − 0.25; z = 3.03; P = 0.002) and increase PaO2 (SMD, 0.30; 95% CI, 0.04, 0.56; z = 2.23; P = 0.03). There were no significant differences in reintubation rate, length of ICU and hospital stay, comfort score, PaCO2, mortality in ICU and hospital, and severe adverse events between HFNC and COT group. Our meta-analysis demonstrated that compared with COT, HFNC may significantly reduce postextubation respiratory failure and respiratory rates, increase PaO2, and be safely administered in patients after planned extubation. Further large-scale, multicenter studies are needed to confirm our results.

Trials have demonstrated lower rates of acute kidney injury in critically ill patients receiving magnesium supplementation, but they have yielded conflicting results regarding mortality. This is a retrospective cohort study based on the MIMIC-IV (Medical Information Mart in Intensive Care-IV) database. Adult critically ill patients with sepsis were included in the analysis. The exposure was magnesium sulfate use during ICU stay. The primary outcome was 28-day all-cause mortality. Propensity score matching (PSM) was conducted at a 1:1 ratio. Multivariable analyses were used to adjust for confounders. The pre-matched and propensity score-matched cohorts included 10 999 and 6052 patients, respectively. In the PSM analysis, 28-day all-cause mortality rate was 20.2% (611/3026) in the magnesium sulfate use group and 25.0% (757/3026) in the no use group. Magnesium sulfate use was associated with lower 28-day all-cause mortality (hazard ratio [HR], 0.70; 95% CI, 0.61-0.79; P<0.001). Lower mortality was observed regardless of baseline serum magnesium status: for hypomagnesaemia, HR, 0.64; 95% confidence interval (CI), 0.45-0.93; P=0.020; for normomagnesaemia, HR, 0.70; 95% CI, 0.61-0.80; P<0.001. Magnesium sulfate use was also associated with lower ICU mortality (odds ratio [OR], 0.52; 95% CI, 0.42-0.64; P<0.001), lower in-hospital mortality (OR, 0.65; 95% CI, 0.55-0.77; P<0.001), and renal replacement therapy (OR, 0.67; 95% CI, 0.52-0.87; P=0.002). A sensitivity analysis using the entire cohort also demonstrated lower 28-day all-cause mortality (HR, 0.62; 95% CI, 0.56-0.69; P<0.001). Magnesium sulfate use was associated with lower mortality in critically ill patients with sepsis. Prospective studies are needed to verify this finding.

Sepsis-associated acute kidney injury (S-AKI) is very common and early prediction is beneficial. This study aiming to develop an accurate ensemble model to predict the risk of S-AKI based on easily available clinical information. Patients with sepsis from the United States (US) database Medical Information Mart for Intensive Care-IV were used as a modeling cohort to predict the occurrence of AKI by combining Support Vector Machine, Random Forest, Neural Network, and Extreme Gradient Boost as four first-level learners via stacking algorithm. The external validation databases were the eICU Collaborative Research Database from US and Critical Care Database comprising infection patients at Zigong Fourth People’s Hospital from China, whose AUROC values for the ensemble model 48–12 h before the onset of AKI were 0.774–0.788 and 0.756–0.813, respectively. In this study, an ensemble model for early prediction of S-AKI onset was developed and it demonstrated good performance in multicenter external datasets.

Background. Research has previously been done into the risk factors for mortality in septic shock patients. However, there has been no epidemiological study investigating the effect of the blood urea nitrogen/creatinine ratio (BCR) on the prognosis of critically ill septic shock patients. This study is aimed at determining the relationship between BCR and all‐cause mortality in adult septic shock patients. Methods. Data were extracted from the MIMIC‐III database. The clinical endpoints were 28‐, 90‐, and 365‐day all‐cause mortality rates in critically ill septic shock patients. Cox proportional hazards models and subgroup analyses were used to analyze the relationship between BCR quartiles and all‐cause mortality in septic shock patients. Receiver operator characteristic (ROC) curves and areas under the ROC curves (AUCs) were calculated to evaluate how accurately BCR predicts the mortality of septic shock patients. Results. Among the 2484 septic shock patients extracted from the database, 619, 563, 677, and 625 fell into the first (<14.4 mg/dL), second (≥14.4 mg/dL and <20.0 mg/dL), third (≥20.0 mg/dL and <27.3 mg/dL), and fourth (≥27.3 mg/dL) quartiles of BCR, respectively. Male and white patients accounted for 53.8% (1336 patients) and 74.8% (1857 patients) of the population, respectively. The mean age of the population was 67.7 ± 15.8 years. An inverse M‐shaped relationship between BCR and mortality in septic shock patients was identified, with a value of ≥27.3 mg/dL providing the highest risk (HR = 1.596, 95% CI: 1.396‐1.824, P < 0.001). In the Cox regression model adjusted for different confounding variables, BCR values in the fourth quartiles were significantly associated with increased mortality, using the first quartiles as a reference. The areas under the ROC curves (AUCs) for BCR plus the Sequential Organ Failure Assessment (SOFA) score and BCR plus Acute Physiology Score III (APSIII) were 0.694 (95% CI: 0.673‐0.716) and 0.724 (95% CI: 0.703‐0.744), respectively. Conclusion. An inverse M‐shaped curve was determined between BCR and the mortality of septic shock patients. BCR was identified as a readily available and independent prognostic biomarker for septic shock patients, and higher BCRs were associated with increased mortality in these patients.