Early assessment of blood culture negativity as a potential support tool for antimicrobial stewardship.

Objective: To assess whether 48-h negative blood culture (BC) bottles are still negative at the classic 120-h incubation endpoint and whether 48 h might be the time to make antimicrobial therapy decisions. Methods: Data from the first collected bottles from bloodstream infection (BSI) episodes of single patients were retrospectively analyzed. Probabilities of bottles being negative at the classic endpoint were calculated from 0 to 120 h of incubation. Results: Among BC-negative episodes (4018/4901 [82.0%]), most (2097/4018 (52.2%) occurred in medicine patients. At 48 h, probability was 100.0% (95% CI, 99.9-100.0) for all 4018 patients. Of these, 1244 (31.0%) patients remained on antibiotics until 120 h. Excluding 401 (32.2%) patients who received antibiotics for another (non-bloodstream) infection, 843 (67.8%) of 1244 patients could have merited early (48-h) discontinuation of antibiotics. Stopping treatment in these patients would have led to saving 5201 days of access (943 [18.1%] days), watch (3624 [69.7%] days), or reserve (634 [12.2%]) AWaRe groups' antibiotics, which correspond to 65.6% (5201/7928) of days of administered antibiotics in all 1244 patients. Conclusion: As an early indicator of BC negativity, the 48-h endpoint could reliably support antimicrobial stewardship, but the clinical judgment remains imperative especially when BSI is highly suspected.

A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.

The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016-2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.

Impact of DCM-Causing Genetic Background on Long-Term Response to Cardiac Resynchronization Therapy.

BACKGROUND: Patients with nonischemic dilated cardiomyopathy (DCM), severe left ventricular (LV) dysfunction, and complete left bundle branch block benefit from cardiac resynchronization therapy (CRT). However, a large heterogeneity of response to CRT is described. Several predictors of response to CRT have been identified, but the role of the underlying genetic background is still poorly explored. OBJECTIVES: In the present study, the authors sought to define differences in LV remodeling and outcome prediction after CRT when stratifying patients according to the presence or absence of DCM-causing genetic background. METHODS: From our center, 74 patients with DCM subjected to CRT and available genetic testing were retrospectively enrolled. Carriers of causative monogenic variants in validated DCM-causing genes, and/or with documented family history of DCM, were classified as affected by genetically determined disease (GEN+DCM) (n = 25). Alternatively, by idiopathic dilated cardiomyopathy (idDCM) (n = 49). The primary outcome was long-term LV remodeling and prevalence of super response to CRT (evaluated at 24-48 months after CRT); the secondary outcome was heart failure-related death/heart transplant/LV assist device. RESULTS: GEN+DCM and idDCM patients were homogeneous at baseline with the exception of QRS duration, longer in idDCM. The median follow-up was 55 months. Long-term LV reverse remodeling and the prevalence of super response were significantly higher in the idDCM group (27% in idDCM vs 5% in GEN+DCM; P = 0.025). The heart failure-related death/heart transplant/LV assist device outcome occurred more frequently in patients with GEN+DCM (53% vs 24% in idDCM; P = 0.028). CONCLUSIONS: Genotyping contributes to the risk stratification of patients with DCM undergoing CRT implantation in terms of LV remodeling and outcomes.

Magnetic Resonance Imaging Characterization and Clinical Outcomes of Dilated and Arrhythmogenic Left Ventricular Cardiomyopathies.

BACKGROUND: Nondilated left ventricular cardiomyopathy (NDLVC) has been recently differentiated from dilated cardiomyopathy (DCM). A comprehensive characterization of these 2 entities using cardiac magnetic resonance (CMR) and genetic testing has never been performed. OBJECTIVES: This study sought to provide a thorough characterization and assess clinical outcomes in a large multicenter cohort of patients with DCM and NDLVC. METHODS: A total of 462 patients with DCM (227) or NDLVC (235) with CMR data from 4 different referral centers were retrospectively analyzed. The study endpoint was a composite of sudden cardiac death or major ventricular arrhythmias. RESULTS: In comparison to DCM, NDLVC had a higher prevalence of pathogenic or likely pathogenic variants of arrhythmogenic genes (40% vs 23%; P < 0.001), higher left ventricular (LV) systolic function (LV ejection fraction: 51% ± 12% vs 36% ± 15%; P < 0.001) and higher prevalence of free-wall late gadolinium enhancement (LGE) (27% vs 14%; P < 0.001). Conversely, DCM showed higher prevalence of pathogenic or likely pathogenic variants of nonarrhythmogenic genes (23% vs 12%; P = 0.002) and septal LGE (45% vs 32%; P = 0.004). Over a median follow-up of 81 months (Q1-Q3: 40-132 months), the study outcome occurred in 98 (21%) patients. LGE with septal location (HR: 1.929; 95% CI: 1.033-3.601; P = 0.039) was independently associated with the risk of sudden cardiac death or major ventricular arrhythmias together with LV dilatation, older age, advanced NYHA functional class, frequent ventricular ectopic activity, and nonsustained ventricular tachycardia. CONCLUSIONS: In a multicenter cohort of patients with DCM and NDLVC, septal LGE together with LV dilatation, age, advanced disease, and frequent and repetitive ventricular arrhythmias were powerful predictors of major arrhythmic events.