Simulated Adoption of 2019 Community-Acquired Pneumonia Guidelines Across 114 Veterans Affairs Medical Centers: Estimated Impact on Culturing and Antibiotic Selection in Hospitalized Patients

A novel risk-adjusted metric to compare hospitals on their antibiotic-prescribing at hospital discharge

BACKGROUND

Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use.

METHODS

This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated negative binomial mixed-model with two random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts.

RESULTS

1,804,300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR 4-10). The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve=0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT.

CONCLUSION

A model using electronically-available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

Adherence to use of blood cultures according to current national guidelines and their impact in patients with community acquired pneumonia: A retrospective cohort

BACKGROUND

Community acquired pneumonia (CAP) is the most frequent cause of mortality secondary to infectious etiologies. Recommendations about the use of blood cultures in the diagnosis and treatment of CAP has been a contentious topic of debate and ever-changing recommendations.

METHODS

A cohort study was conducted in a community teaching hospital. All the patients that were admitted with a diagnosis of CAP, between January and December of 2019 were included. Sociodemographic and clinical characteristics were obtained. Blood cultures results were obtained, and it was evaluated if they were done in compliance with current recommendations by the Infectious Disease Society of America (IDSA).

RESULTS

721 patients were included in the study. Median age was 68 years and 50% of the patients were male (n = 293). Patients presented from home (84%) and the most common comorbidities were hypertension and diabetes (68% and 31%). 96 patients had positive blood culture and 34% (n = 247) of all the blood cultures were adequately ordered. 80 patients died or went to hospice and the median length of hospital stay in our cohort was 7 days. The multivariate model showed that mortality was associated with positive blood cultures (OR = 3.1 95%CI 1.63-5.87) and appropriateness of blood cultures (OR = 2.96 95% CI 1.2-5.7).

CONCLUSION

Adequate use of blood cultures in patients with CAP might have some association with the outcomes of this disease. However, a prospective study evaluating the utility of this test following current IDSA recommendations is needed to understand their impact in mortality and morbidity.

The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years

OBJECTIVE

To investigate the value of CT radiomics in the differentiation of mycoplasma pneumoniae pneumonia (MPP) from streptococcus pneumoniae pneumonia (SPP) with similar CT manifestations in children under 5 years.

METHODS

A total of 102 children with MPP (n = 52) or SPP (n = 50) with similar consolidation and surrounding halo on CT images in Qilu Hospital and Qilu Children's Hospital between January 2017 and March 2022 were enrolled in the retrospective study. Radiomic features of the both lesions on plain CT images were extracted including the consolidation part of the pneumonia or both consolidation and surrounding halo area which were respectively delineated at region of interest (ROI) areas on the maximum axial image. The training cohort (n = 71) and the validation cohort (n = 31) were established by stratified random sampling at a ratio of 7:3. By means of variance threshold, the effective radiomics features, SelectKBest and least absolute shrinkage and selection operator (LASSO) regression method were employed for feature selection and combined to calculate the radiomics score (Rad-score). Six classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) were used to construct the models based on radiomic features. The diagnostic performance of these models and the radiomic nomogram was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC), and the decision curve analysis (DCA) was used to evaluate which model achieved the most net benefit.

RESULTS

RF outperformed other classifiers and was selected as the backbone in the classifier with the consolidation + the surrounding halo was taken as ROI to differentiate MPP from SPP in validation cohort. The AUC value of MPP in validation cohort was 0.822, the sensitivity and specificity were 0.81 and 0.81, respectively.

CONCLUSION

The RF model has the best classification efficiency in the identification of MPP from SPP in children, and the ROI with both consolidation and surrounding halo is most suitable for the delineation.