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BACKGROUND: Procalcitonin (PCT) has been used as a biomarker to guide antibiotic therapy in various patient populations. However, its role in optimizing antibiotic use in COVID-19 patients has not been well studied to date. Thus, we aimed to evaluate the use of serial PCT monitoring as an antimicrobial stewardship tool for COVID-19 patients. METHODS: This retrospective study included 240 COVID-19 patients who were admitted to a tertiary medical institution in Saudi Arabia between January 2020 and February 2022. Patients who received empiric antibiotic therapy for community-acquired pneumonia (CAP) and had serial procalcitonin levels were included. The patients were divided into two groups: the normal procalcitonin arm (PCT level < 0.5 ng/mL) and the elevated PCT arm (PCT level > 0.5 ng/mL). The primary and secondary outcomes were the effect of PCT monitoring on the duration of antibiotic exposure and the length of hospital stay, respectively. To measure the accuracy of PCT, the receiver-operating characteristic area under the curve (ROC-AUC) was determined. RESULTS: Among the included patients, 142 were in the normal procalcitonin arm (median PCT, 0.12 ng/mL), and 78 were in the elevated PCT arm (median PCT, 4.04 ng/mL). The baseline characteristics were similar between the two arms, except for the higher prevalence of kidney disease in the elevated PCT arm. There was no statistically significant difference in the duration of antibiotic exposure between the normal and elevated PCT arms (median duration: 7 days in both arms). However, the length of hospital stay was significantly shorter in the normal PCT arm (median stay, 9 days) than in the elevated PCT arm (median stay, 13 days; p = 0.028). The ROC-AUC value was 0.54 (95% CI: 0.503-0.595). CONCLUSIONS: Serial PCT monitoring did not lead to a reduction in the duration of antibiotic exposure in COVID-19 patients. However, it was associated with a shorter hospital stay. These findings suggest that PCT monitoring may be useful for optimizing antibiotic use and improving outcomes in COVID-19 patients. While PCT-guided algorithms have the potential to enable antibiotic stewardship, their role in the context of COVID-19 treatment requires further investigation.
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Introduction: Medication errors (MEs) are a huge burden on any healthcare system and have been associated with significant morbidity and mortality. The medical literature reported heavily on MEs but lacked focus on analyzing high-risk medications such as antimicrobials. Research design and methods: This was a retrospective analysis of the MEs database reported by the eastern region's medical centers in Saudi Arabia from January 1, 2019 to December 31, 2019. We used descriptive analysis to identify most common antimicrobials with errors, the stage of antimicrobial errors, type of the errors, contributing factors to the errors, and categories of errors based on the National Coordinating Council for Medication Error Reporting and Prevention (NCC-MERP) classification of errors. Results: A total of 1422 (22.1%) antimicrobial errors were identified out of 6412 MEs. Amoxicillin/Clavulanate (18%) was the most common antimicrobial reported in the database. Most errors occurred in the prescribing phase (87.6%) and included mainly incorrect doses (32.1%) and duplicate therapy (20.5%). In addition, most errors were identified as category B (72.5%). Finally, inexperienced personnel (57.9%) was the most cited contributing factor. Conclusion: This study revealed that antimicrobial errors occur primarily during prescription and that policy gaps and inexperienced staff were contributory factors. To improve, the focus should shift to physician education, clear dosing guidelines, efficient workload management, and implementing antimicrobial stewardship programs to promote appropriate antimicrobial use.
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Objective: The objective was to evaluate the 30-day re-admission predictive performance of the HOSPITAL score and Diabetes Early Re-admission Risk Indicator (DERRI™) in hospitalized diabetes patients. Methods: This was a case-control study in an academic, tertiary center in the United States. Adult hospitalized diabetes patients were randomly identified between January 1, 2014, and September 30, 2017. Patients were categorized into two groups: (1) re-admitted within 30 days, and (2) not re-admitted within 30 days. Predictive performance of the HOSPITAL and DERRI™ scores was evaluated by calculating receiver operating characteristics curves (c-statistic), Hosmer-Lemeshow goodness-of-fit tests, and Brier scores. Results: A total of 200 patients were included (100 re-admitted, 100 non-re-admitted). The HOSPITAL score had a c-statistic of 0.731 (95% confidence interval [CI], 0.661 to 0.800), Hosmer-Lemeshow test P = .211, and Brier score 0.212. The DERRI™ score had a c-statistic of 0.796 (95% CI, 0.734 to 0.857), Hosmer-Lemeshow test P = .114, and Brier score 0.212. The difference in receiver operating characteristic curves was not statistically significant between the two scores but showed a higher c-statistic with the DERRI™ score (P = .055). Conclusion: Both HOSPITAL and DERRI™ scores showed good predictive performance in 30-day re-admission of adult hospitalized diabetes patients. There was no significant difference in discrimination and calibration between the scores. Abbreviations: CI = confidence interval; DERRI™ = Diabetes Early Re-admission Risk Indicator; IQR = interquartile range.