Delays In Antimicrobial Therapy Research Articles

The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.

Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record. Retrospective analysis of a large, multicenter inpatient data. Two academic tertiary medical centers between the years 2007 and 2018. All hospitalized patients who received a blood culture during hospitalization. The dataset was partitioned temporally into development and validation cohorts: the logistic regression and gradient boosting machine models were trained on the earliest 80% of hospital admissions and validated on the most recent 20%. There were 252,569 blood culture days-defined as nonoverlapping 24-hour periods in which one or more blood cultures were ordered. In the validation cohort, there were 50,514 blood culture days, with 3,762 cases of bacteremia (7.5%) and 370 cases of fungemia (0.7%). The gradient boosting machine model for bacteremia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001). The model identified a high-risk group with over 30 times the occurrence rate of bacteremia in the low-risk group (27.4% vs 0.9%; p < 0.001). Using the low-risk cut-off, the model identifies bacteremia with 98.7% sensitivity. The gradient boosting machine model for fungemia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.86-0.90]). The high-risk fungemia group had 252 fungemic cultures compared with one fungemic culture in the low-risk group (5.0% vs 0.02%; p < 0.001). Further, the high-risk group had a mortality rate 60 times higher than the low-risk group (28.2% vs 0.4%; p < 0.001). Our novel models identified patients at low and high-risk for bacteremia and fungemia using routinely collected electronic health record data. Further research is needed to evaluate the cost-effectiveness and impact of model implementation in clinical practice.

Patient and Organizational Factors Associated With Delays in Antimicrobial Therapy for Septic Shock.

To identify clinical and organizational factors associated with delays in antimicrobial therapy for septic shock. In a retrospective cohort of critically ill patients with septic shock. Twenty-four ICUs. A total of 6,720 patients with septic shock. None. Higher Acute Physiology Score (+24 min per 5 Acute Physiology Score points; p < 0.0001); older age (+16 min per 10 yr; p < 0.0001); presence of comorbidities (+35 min; p < 0.0001); hospital length of stay before hypotension: less than 3 days (+50 min; p < 0.0001), between 3 and 7 days (+121 min; p < 0.0001), and longer than 7 days (+130 min; p < 0.0001); and a diagnosis of pneumonia (+45 min; p < 0.01) were associated with longer times to antimicrobial therapy. Two variables were associated with shorter times to antimicrobial therapy: community-acquired infections (-53 min; p < 0.001) and higher temperature (-15 min per 1°C; p < 0.0001). After adjusting for confounders, admissions to academic hospitals (+52 min; p< 0.05), and transfers from medical wards (medical vs surgical ward admission; +39 min; p < 0.05) had longer times to antimicrobial therapy. Admissions from the emergency department (emergency department vs surgical ward admission, -47 min; p< 0.001) had shorter times to antimicrobial therapy. We identified clinical and organizational factors that can serve as evidence-based targets for future quality-improvement initiatives on antimicrobial timing. The observation that academic hospitals are more likely to delay antimicrobials should be further explored in future trials.

Loading dose of vancomycin in critically ill patients: 15 mg/kg is a better choice than 500 mg

Delays in antimicrobial therapy in high-risk patients with infection may have deleterious effects on clinical outcomes. Therefore, appropriate treatment must be initiated promptly. The objective of this prospective study was to determine the better loading dose of vancomycin in critically ill patients with suspected Gram-positive infections. Two groups of patients were studied successively: Group A, loading dose of 500 mg; and Group B, loading dose of 15 mg/kg. The mean post-loading dose serum vancomycin concentration was significantly higher in Group B than in Group A (19.1 ± 7.4 mg/L versus 10.4 ± 2.7 mg/L; P < 0.001), without producing toxic peak levels. Clinical cure rates were significantly different for infected patients in Group B compared with Group A: 93% (14 of 15 patients) versus 56% (10 of 18 patients), respectively. However, the proportion of patients surviving to Intensive Care Unit discharge was similar. Because vancomycin is believed to achieve maximum killing at concentrations in serum of four to five times the minimum inhibitory concentration for the infecting organism, our results suggest that the 15 mg/kg loading dose should be preferred.