Risk factors for slowly resolving pneumonia in the intensive care unit

Development of Extubation Success Prediction Model for Mechanically Ventilated Patients with Spontaneous Cerebellar Hemorrhage

Spontaneous cerebellar hemorrhage (SCH) patients have a low success rate in extubation, but there are currently no guidelines establishing specifically for SCH patients extubation. The study included 68 SCH patients who received mechanical ventilation for more than 24 h, with 39 cases (57.3%) resulting in successful extubation. The multivariate analysis identified four factors significantly associated with extubation success: patient age under 66 years, an Intracerebral Hemorrhage (ICH) score less than 4 points, the presence of tissue shift, and a Glasgow Coma Scale (GCS) score (excluding language) above 6 points at extubation. By simplifying the prediction model, we obtained the Spontaneous Cerebellar Hemorrhage Extubation Success scoring system (SCHES-SCORE). Within the scoring system, 2 points were allocated for a GCS score (excluding language) above 6 at extubation, 1 point each for age under 66 years and an ICH score below 4, while tissue shift was assigned a negative point. A score of Grade A (SCHES-SCORE = 3-4) was found to correlate with a 92.9% success rate for extubation. The area under the receiver operating characteristic curve was 0.923 (95% CI, 0.863 to 0.983). Notably, successful extubation was significantly linked to reduced durations of mechanical ventilation, intensive care unit (ICU) stay, and total hospital stay. In conclusion, the scoring system developed for assessing extubation outcomes in SCH patients has the potential to enhance the rate of successful extubation and overall patient outcomes.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic

BACKGROUND AND PURPOSE

The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features.

METHODS

A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features.

RESULTS

A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models.

CONCLUSION

Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

A Prognostic Model for In-Hospital Mortality in Critically Ill Patients with Pneumonia

Purpose

To determine the utility of a novel serum biomarker for the outcome prediction of critically ill patients with pneumonia.

Patients and Methods

A retrospective analysis of critically ill patients was performed at an emergency department. The expression and prediction value of parameters were assessed. Binary logistic regression analysis was utilized to determine the indicators associated with in-hospital mortality of pneumonia patients. The Last Absolute Shrinkage and Selection Operator was used to further determine the independent predictors, which were validated by multiple logistic regression. The receiver operator characteristic curve was performed to assess their prediction values. A prognostic nomogram model was finally established for the outcome prediction for critically ill patients with pneumonia.

Results

Retinol-binding protein (RBP) was significantly reduced in non-survived and pneumonia patients. CURB-65 score, levels of RBP, and blood urea nitrogen (BUN) were associated with in-hospital mortality of critically ill patients with pneumonia. Their combination was determined to be an ideal prognostic predictor (area under the curve of 0.762) and further developed into a nomogram prediction model (c-index 0.764).

Conclusion

RBP is a novel in-hospital mortality predictor, which well supplements the CURB-65 score for critical pneumonia patients.

Early Screening of Risk for Multidrug-Resistant Organisms in the Emergency Department in Patients With Pneumonia and Early Septic Shock: Single-Center, Retrospective Cohort Study

OBJECTIVES

Pneumonia is the fourth leading cause of death globally, with rapid progression during sepsis. Multidrug-resistant organisms (MDROs) are becoming more common with some healthcare-associated pneumonia events. Early detection of MDRO risk improves the outcomes; however, MDROs risk in pneumonia with sepsis is unknown. This study investigated the disease outcomes of pneumonia with septic shock in patients admitted in the emergency department (ED) intensive care unit (ICU), a population with a high prevalence of MDROs, after early screening of MDROs risk.

METHODS

In this retrospective cohort study, patients with pneumonia and early septic shock (n = 533) admitted to the ED at the Taipei Tzu Chi Hospital from 2013 to 2019 were selected. The study population was divided into four subgroups after the MDROs risk and screening procedure were completed within 1 or 6 h of admission. ICU mortality and multidrug antibiotic therapy were compared.

RESULTS

The high-risk MDROs groups had higher percentage of P aeruginosa than the low-risk group. Furthermore, the appropriate ED first antibiotics were higher in the 1-h subgroup than in the 6-h subgroup of the high-risk MDROs group. In multivariate analysis, the 6-h high-risk MDROs group had an adjusted odds ratio of 7.191 (95% CI: 2.911-17.767, P < 0.001) and 2.917 (95% CI: 1.456-5.847, P = 0.003) for ICU mortality and multidrug therapy in the ICU, respectively, after adjusting for other confounding factors.

CONCLUSIONS

MDRO screening within 1 h is recommended following admission of patients with pneumonia and early septic shock in the ED, especially in areas with a high prevalence of MDROs.

N-acetylcysteine improves oxidative stress and inflammatory response in patients with community acquired pneumonia: A randomized controlled trial

Oxidative stress is considered to be part of the pathogenic mechanism for community-acquired pneumonia (CAP) and is closely linked to inflammation. Attenuation of oxidative stress would be expected to reduce pulmonary damage. Antioxidants have been found to be effective in alleviating lung injury and protecting against damage of other organs.The aim of the study was to compare the effect of adding N-acetylcysteine (NAC) to conventional treatment versus conventional treatment on oxidative stress, inflammatory factors, and radiological changes in CAP patients.Eligible CAP patients at Weihai Municipal Hospital were stratified and randomly assigned to either NAC group or non-NAC group between August 2016 and March 2017. The NAC group received conventional treatment for pneumonia and NAC (1200 mg/d). Thenon-NAC group received conventional therapy. malondialdehyde (MDA), superoxide dismutase (SOD), total antioxidant capacity (TAOC), tumor necrosis factor-α (TNF-α), and computed tomography (CT) images were evaluated at baseline and after treatment. The primary endpoint indicators were the changes in oxidative stress parameters (MDA, TAOC, SOD) and TNF-α after treatment in the NAC group compared with those in the non-NAC group. The secondary endpoint indicator was any difference in CT scores after treatment in the NAC group compared with the non-NAC group.Baseline levels of MDA, TAOC, SOD, and TNF-α were similar between the 2 groups before treatment. Plasma levels of MDA and TNF-α decreased more (P < .05 MDA:p 0.004, TNF-α:p <0.001) in the NAC group than the non-NAC group, and there was a reliable increase in TAOC content (p 0.005). There was no significant difference in increased plasma SOD activity between the groups (p 0.368), and the NAC group did not show a greater improvement from CT scores. No NAC-related adverse effects were observed.Addition of NAC therapy for CAP patients reduced MDA and TNF-α and increased TAOC. Treatment with NAC may help to reduce oxidative and inflammatory damage in pneumonia patients.