Objective Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). Materials and methods ML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test. Results Of 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO2, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively. Conclusions The ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate.Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population.Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902–0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)—over other models across a range of threshold probability values.Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).

Background In recent years, autologous fat grafting (AFG), also known as fat transfer or lipofilling, has been widely performed for periorbital rejuvenation and defect correction, although the evidence regarding its efficacy and safety is still lacking. Besides, with respect to the periorbital region, it is invariably the earliest appearance area of the facial aging phenomenon. Therefore, a systematic review and meta-analysis is needed to evaluate the efficacy and safety of this technique. Methods A literature search was performed in PubMed, Embase, and the Cochrane library databases on November 20, 2020, adhering to the PRISMA guidelines, to identify all relevant articles. Then, a data extraction and standardization process was performed to assess all outcome data. Ultimately, the data were assessed using a random effects regression model with comprehensive meta-analysis software. Results Thirty-nine studies consisting of 3 cohorts and 36 case series with a total of 4046 cases were included. Meta-analysis revealed a relatively high satisfaction rate of 90.9% (95% CI, 86.4%–94.0%). Frequent complications in 4046 patients receiving AFG were edema, chemosis, and contour irregularity, with an overall complication rate of 7.9% (95% CI, 4.8%–12.8%). Conclusion This systematic review and meta-analysis showed that AFG for rejuvenation of eyelids and periorbital area provided a high satisfaction rate and did not result in severe complications. Therefore, AFG might be performed safely for periorbital rejuvenation and reconstruction.

Objective:The objective of the study was to characterize the longitudinal, dynamic intracranial pressure (ICP) trajectory in acute brain injury (ABI) patients admitted to intensive care unit (ICU) and explore whether it added sights over traditional thresholds in predicting outcomes.Methods: ABI patients with ICP monitoring were identified from two public databases named Medical Information Mart for the Intensive Care (MIMIC)-IV and eICU Collaborative Research Database (eICU-CRD). Group-based trajectory modeling (GBTM) was employed to identify 4-h ICP trajectories in days 0-5 post-ICU admission. Then, logistic regression was used to compare clinical outcomes across distinct groups. To further validate previously reported thresholds, we created the receiver operating characteristic (ROC) curve in our dataset.Results: A total of 810 eligible patients were ultimately enrolled in the study. GBTM analyses generated 6 distinct ICP trajectories, differing in the initial ICP, evolution pattern, and number/proportion of spikes >20/22 mmHg. Compared with patients in "the highest, declined then rose" trajectory, those belonging to the "lowest, stable," "low, stable," and "medium, stable" ICP trajectories were at lower risks of 30-day mortality (odds ratio [OR] 0.04; 95% confidence interval [CI] 0.01, 0.21), (OR 0.04; 95% CI 0.01, 0.19), (OR 0.08; 95% CI 0.01, 0.42), respectively. ROC analysis demonstrated an unfavorable result, for example, 30-day mortality in total cohort: an area under the curve (AUC): 0.528, sensitivity: 0.11, and specificity: 0.94. Conclusions:This study identified three ICP trajectories associated with elevated risk, three with reduced risks for mortality during ICU hospitalization. Notably, a fixed ICP threshold should not be applied to all kinds of patients. GBTM, a granular method for describing ICP evolution and their association with clinical outcomes, may add to the current knowledge in intracranial hypertension treatment.

Background: Previous studies revealed that larger liposuction volumes were related to an increased risk of complications. However, no concrete data exist to support the most critical factor which affects the liposuction volume in the waist and abdominal area. This study was undertaken to investigate the relationship between the anthropometric measurements and lipoaspirate volume.Methods: The present study was a single-center retrospective study. 742 patients who met the inclusion and exclusion criteria in our hospital, from January 2001 to August 2020, were reviewed. Spearman correlation analyses and multivariable regressions were used to assess the relationship between the anthropometric measurements and lipoaspirate volume. Linear-by-linear association chi-square statistic and Goodman-Kruskal gamma method were used to test the consistency and to develop a rank prediction formula.Results: A total of 742 patients aged 18-59 years old met the inclusion criteria. Among all the anthropometric measurements, the highest correlation coefficient was observed in waist circumference. Subgroup analyses indicated that there was an interaction between the BMI and waist circumference on liposuction volume. Formula was generated to estimate the range of liposuction volume based on the nine grouped waist circumferences [liposuction volume (mean) = 106.3 waist circumference (mean) - 7497, P < 0.001, adjusted R2 = 0.9638].Conclusions: Waist circumference was the most influential factor for lipoaspirate volume. Roughly predicting the lipoaspirate volume allows surgeons to estimate their operating volume even if no iconography machine is available during suction-assisted lipectomy. This can increase safety, potentially decreasing the number of adverse events.