Empirical prescribing of penicillin G/V reduces risk of readmission of hospitalized patients with community-acquired pneumonia in Norway: a retrospective observational study

New paradigms on antibiotic recommendations for community-acquired infections in Spain

Over the last years, the susceptibility activity of the most common microorganisms causing community-acquired infections has significantly changed in Spain. Based on the susceptibility rates of Streptococcus pyogenes, Streptococcus pneumoniae, Haemophilus influenzae, Escherichia coli, and Klebsiella pneumoniae collected from outpatients aged 15 or older with symptoms of respiratory or urinary tract infections in several Microbiology Departments in Catalonia in 2021, penicillin V should be first choice for most respiratory tract infections, amoxicillin and clavulanate for chronic obstructive pulmonary disease exacerbations and a single dose of fosfomycin or a short-course nitrofurantoin should remain first-line treatments for uncomplicated urinary tract infections. Updated information on antimicrobial resistance for general practitioners is crucial for achieving appropriate empirical management of the most common infections by promoting more rational antibiotic use.

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database

Background

Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.

Methods

This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.

Results

Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.

Conclusion

The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Antimicrobial therapy of community-acquired pneumonia during stewardship efforts and a coronavirus pandemic: an observational study

Background

Community-acquired pneumonia (CAP) is the most frequent infection diagnosis in hospitals. Antimicrobial therapy for CAP is depicted in clinical practice guidelines, but adherence data and effect of antibiotic stewardship measures are lacking.

Methods

A dedicated antibiotic team pointed out CAP as a potential target for antimicrobial stewardship (AMS) measures at a 1.000-bed, tertiary care, teaching university hospital in Norway from March until May for the years 2016 throughout 2021. The aim of the AMS program was to increase diagnostic and antimicrobial therapy adherence to national clinical practice guideline recommendations through multiple and continuous AMS efforts. Descriptive statistics were retrospectively used to delineate antimicrobial therapy for CAP. The primary outcomes were proportions that received narrow-spectrum beta-lactams, and broad-spectrum antimicrobial therapy.

Results

1.112 CAP episodes were identified. The annual proportion that received narrow-spectrum beta-lactams increased from 56.1 to 74.4% (p = 0.045). Correspondingly, the annual proportion that received broad-spectrum antimicrobial therapy decreased from 34.1 to 17.1% (p = 0.002). Trends were affected by the coronavirus pandemic. Mortality and 30-day readmission rates remained unchanged. De-escalation strategies were frequently unutilized, and overall therapy duration exceeded clinical practice guideline recommendations substantially. Microbiologically confirmed CAP episodes increased from 33.7 to 56.2% during the study period.

Conclusion

CAP is a suitable model condition that is sensitive to AMS measures. A continuous focus on improved microbiological diagnostics and antimicrobial therapy initiation is efficient in increasing adherence to guideline recommendations. There is an unmet need for better antimicrobial de-escalation strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02178-6.

Nationwide audit and feedback on implementation of antibiotic stewardship programmes in Norwegian hospitals

Background

Implementation of antibiotic stewardship programmes (ASPs) in hospitals is challenging and there is a knowledge gap on how to pursue this process efficiently.

Objectives

To evaluate whether audit and feedback (A&F) is a feasible and useful methodology to assess and support the implementation of ASPs in hospitals.

Methods

A multidisciplinary team performed document reviews and on-site interviews with professionals involved in the implementation of ASPs. Oral feedback on preliminary findings and areas of improvement were provided on-site, followed by feedback reports summarizing major findings and recommendations. Descriptive statistics were used to present number of hospital trusts, interviewees, professions, disciplines, workload and costs.

Results

All 22 hospital trusts in Norway participated in the A&F conducted October 2017 to April 2019. Altogether, 446 leaders and healthcare workers were interviewed: 110 leaders, 336 health professionals of whom 89 were antimicrobial stewardship team members. Median number of days from audits were performed till reporting were 36 (IQR 30-49). Median workload for auditors per visit was 7 days (6-8). Total costs were €133 952. Main audit findings were that ASP structures were established in most hospital trusts, but leadership commitment and implementation of interventions were often lacking. The hospital trusts received feedback on establishing governance structures, setting local targets, implementing interventions and increased involvement of nurses.

Conclusions

Nationwide A&F provides a unique and comprehensive insight into the implementation of ASPs in hospitals and is feasible with a reasonable amount of resources. This approach can identify targets for improved implementation of ASPs in hospitals.