How to best use procalcitonin to diagnose infections and manage antibiotic treatment

Evaluating the utility of procalcitonin and a clinical decision support tool to determine duration of antimicrobial therapy for respiratory tract infections

PURPOSE

Procalcitonin (PCT) levels may play a role in decreasing the duration of antimicrobial therapy in institutions that have long durations of therapy for management of community-acquired pneumonia. We assessed the impact of the combination of pharmacist stewardship interventions assisted by a clinical decision support (CDS) tool and PCT assessment on the antimicrobial days of therapy (DOT) prescribed for respiratory tract infections (RTIs).

METHODS

We conducted a quasi-experimental study in which patients in the preintervention group were admitted between April and June 2021 and patients in the intervention group were admitted between April and June 2022. In the intervention phase, a CDS tool was utilized to alert clinical pharmacists when patients met specific criteria. This alert was programmed to activate for individual patients when a reported PCT level was less than 0.25 ng/mL and the patient was on antimicrobials prescribed for an RTI as indicated by providers in the electronic health record. Stewardship interventions were made by pharmacists via prospective audit and feedback. The primary endpoint was inpatient antimicrobial DOT for RTIs.

RESULTS

There were 90 patients in the preintervention group and 104 patients in the intervention group. Although baseline characteristics were not well matched between the groups, favoring the preintervention group, the median DOT was lower in the intervention group, at 3 days (interquartile range [IQR], 2-4 days), compared to 4 days (IQR, 2.8-5 days) in the preintervention group (P = 0.001).

CONCLUSION

The results of our study demonstrate the utility of pharmacist interventions coupled with CDS and PCT in reducing antimicrobial DOT prescribed for RTIs. Antimicrobial stewardship programs may benefit from implementing a PCT bundle.

Machine learning risk prediction model for bloodstream infections related to totally implantable venous access ports in patients with cancer

Objective

This study aimed to develop and validate a machine learning-based risk prediction model for catheter-related bloodstream infection (CRBSI) following implantation of totally implantable venous access ports (TIVAPs) in patients.

Methods

A retrospective cohort study design was employed, utilizing the R software package mlr3. Various algorithms including logistic regression, naive Bayes, K nearest neighbor, classification tree, and random forest were applied. Addressing class imbalance, benchmarks were used, and model performance was assessed using the area under the curve (AUC). The final model, chosen for its superior performance, was interpreted using variable importance scores. Additionally, a nomogram was developed to calculate individualized risk probabilities, enhancing clinical utility.

Results

The study involved 755 patients across both development and validation cohorts, with a TIVAP-CRBSI rate of 14.17%. The random forest model demonstrated the highest discrimination ability, achieving a validated AUC of 0.94, which was consistent in the validation cohort.

Conclusions

This study successfully developed a robust predictive model for TIVAP-CRBSI risk post-implantation. Implementation of this model may aid healthcare providers in making informed decisions, thereby potentially improving patient outcomes.

Procalcitonin and C-reactive protein as early predictors in patients at high risk of colorectal anastomotic leakage

OBJECTIVE

To assess the diagnostic value of C-reactive protein (CRP) and procalcitonin (PCT) for anastomotic leakage (AL) following colorectal surgery.

METHODS

We retrospectively analyzed data for patients who underwent colorectal surgery at our hospital between November 2019 and December 2023. CRP and PCT were measured postoperatively to compare patients with/without AL, and changes were compared between low- and high-risk groups. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic accuracy of CRP and PCT to identify AL in high-risk patients.

RESULTS

Mean CRP was 142.53 mg/L and 189.57 mg/L in the low- and high-risk groups, respectively, on postoperative day (POD)3. On POD2, mean PCT was 2.75 ng/mL and 8.16 ng/mL in low- and high-risk patients, respectively; values on POD3 were 3.53 ng/mL and 14.86 ng/mL, respectively. The areas under the curve (AUC) for CRP and PCT on POD3 were 0.71 and 0.78, respectively (CRP cut-off: 235.64 mg/L; sensitivity: 96%; specificity: 89.42% vs PCT cut-off: 3.94 ng/mL; sensitivity: 86%; specificity: 93.56%; AUC: 0.78). The AUC, sensitivity, and specificity for the combined diagnostic ability of CRP and PCT on POD3 were 0.92, 90%, and 100%, respectively (cut-off: 0.44).

CONCLUSIONS

Combining PCT and CRP on POD3 enhances the diagnostic accuracy for AL.

Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

OBJECTIVE

To explore and model factors affecting antibiotic prescribing decision-making early in the pandemic.

DESIGN

Semistructured qualitative interview study.

SETTING

National Health Service (NHS) trusts/health boards in England and Wales.

PARTICIPANTS

Clinicians from NHS trusts/health boards in England and Wales.

METHOD

Individual semistructured interviews were conducted with clinicians in six NHS trusts/health boards in England and Wales as part of the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients study, a wider study that included statistical analysis of procalcitonin (PCT) use in hospitals during the first wave of the pandemic. Thematic analysis was used to identify key factors influencing antibiotic prescribing decisions for patients with COVID-19 pneumonia during the first wave of the pandemic (March to May 2020), including how much influence PCT test results had on these decisions.

RESULTS

During the first wave of the pandemic, recommendations to prescribe antibiotics for patients with COVID-19 pneumonia were based on concerns about secondary bacterial infections. However, as clinicians gained more experience with COVID-19, they reported increasing confidence in their ability to distinguish between symptoms and signs caused by SARS-CoV-2 viral infection alone, and secondary bacterial infections. Antibiotic prescribing decisions were influenced by factors such as clinician experience, confidence, senior support, situational factors and organisational influences. A decision-making model was developed.

CONCLUSION

This study provides insight into the decision-making process around antibiotic prescribing for patients with COVID-19 pneumonia during the first wave of the pandemic. The importance of clinician experience and of senior review of decisions as factors in optimising antibiotic stewardship is highlighted. In addition, situational and organisational factors were identified that could be optimised. The model presented in the study can be used as a tool to aid understanding of the complexity of the decision-making process around antibiotic prescribing and planning antimicrobial stewardship support in the context of a pandemic.

TRIAL REGISTRATION NUMBER

ISRCTN66682918.