Incidence, Risk Factors and Outcomes of Sepsis in Critically Ill Post-craniotomy Patients: A Single-Center Prospective Cohort Study

Impact of Metabolic Syndrome on Post-Operative Infection in Patients Undergoing Flexible Ureteroscopy Lithotripsy

Background: To investigate retrospectively whether metabolic syndrome (MetS) of flexible ureteroscopy (fURS) lithotripsy can be used to predict post-operative infection. Patients and Methods: After screening, 1,110 patients who received fURS lithotripsy for upper urinary tract stones in our center between January 2015 and December 2022 were analyzed retrospectively. Patients were divided into MetS-positive group and MetS-negative group. Post-operative infection was divided into fever, urosepsis, and septic shock. Relevant data during the peri-operative period were collected. Univariable and multivariable logistic regression analyses were adopted to estimate the impact of metabolic syndrome on post-operative infection in patients undergoing fURS lithotripsy. Results: Among the 1,110 patients, 427 tested positive for MetS, whereas 683 tested negative. Eighty-eight patients suffered from fever (67 patients in the MetS-positive group and 21 in the MetS-negative group). Forty-nine patients had urosepsis (29 patients in the MetS-positive group and 20 in the MetS-negative group), of whom seven patients developed septic shock. No patient developed multiple organ failure or died because of infection. The prevalence of post-operative infections in the MetS-positive group was higher than that in the MetS-negative group (p < 0.001). Multivariable logistic regression analyses showed that diabetes mellitus, MetS-positive, positive urine culture, and longer operation time were positively correlated with post-operative fever. Positive MetS, positive urine culture, and longer operation time were strongly correlated with post-operative urosepsis. Conclusions: Metabolic syndrome was found to be associated with post-operative infection in patients undergoing fURS lithotripsy, suggesting it can serve as a predictive factor.

A predictive model for the risk of sepsis within 30 days of admission in patients with traumatic brain injury in the intensive care unit: a retrospective analysis based on MIMIC-IV database

PURPOSE

Traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) are at a high risk of infection and sepsis. However, there are few studies on predicting secondary sepsis in TBI patients in the ICU. This study aimed to build a prediction model for the risk of secondary sepsis in TBI patients in the ICU, and provide effective information for clinical diagnosis and treatment.

METHODS

Using the MIMIC IV database version 2.0 (Medical Information Mart for Intensive Care IV), we searched data on TBI patients admitted to ICU and considered them as a study cohort. The extracted data included patient demographic information, laboratory indicators, complications, and other clinical data. The study cohort was divided into a training cohort and a validation cohort. In the training cohort, variables were screened by LASSO (Least absolute shrinkage and selection operator) regression and stepwise Logistic regression to assess the predictive ability of each feature on the incidence of patients. The screened variables were included in the final Logistic regression model. Finally, the decision curve, calibration curve, and receiver operating character (ROC) were used to test the performance of the model.

RESULTS

Finally, a total of 1167 patients were included in the study, and these patients were randomly divided into the training (N = 817) and validation (N = 350) cohorts at a ratio of 7:3. In the training cohort, seven features were identified as key predictors of secondary sepsis in TBI patients in the ICU, including acute kidney injury (AKI), anemia, invasive ventilation, GCS (Glasgow Coma Scale) score, lactic acid, and blood calcium level, which were included in the final model. The areas under the ROC curve in the training cohort and the validation cohort were 0.756 and 0.711, respectively. The calibration curve and ROC curve show that the model has favorable predictive accuracy, while the decision curve shows that the model has favorable clinical benefits with good and robust predictive efficiency.

CONCLUSION

We have developed a nomogram model for predicting secondary sepsis in TBI patients admitted to the ICU, which can provide useful predictive information for clinical decision-making.

Update on Sepsis Epidemiology in the Era of COVID-19

Timely and accurate data on the epidemiology of sepsis is essential to inform public policy, clinical practice, and research priorities. Recent studies have illuminated several ongoing questions about sepsis epidemiology, including the incidence and outcomes of sepsis in non-Western countries and in specialized populations such as surgical patients, patients with cancer, and the elderly. There have also been new insights into the limitations of current surveillance methods using administrative data and increasing experience tracking sepsis incidence and outcomes using "big data" approaches that take advantage of detailed electronic health record data. The COVID-19 pandemic, however, has fundamentally changed the landscape of sepsis epidemiology. It has increased sepsis rates, helped highlight ongoing controversies about how to define sepsis, and intensified debate about the possible unintended consequences of overly rigid sepsis care bundles. Despite these controversies, there is a growing consensus that severe COVID-19 causing organ dysfunction is appropriate to label as sepsis, even though it is treated very differently from bacterial sepsis, and that surveillance strategies need to be modified to reliably identify these cases to fully capture and delineate the current burden of sepsis. This review will summarize recent insights into the epidemiology of sepsis and highlight several urgent questions and priorities catalyzed by COVID-19.