Comparison of Bleeding Risk Between Colistin-Tigecycline and Colistin-Carbapenem Treatment Regimens: A Retrospective Cohort Study

Machine learning-based prediction model for hypofibrinogenemia after tigecycline therapy

BACKGROUND

In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers.

OBJECTIVE

We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF.

METHODS

This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort.

RESULTS

Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668-0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652-0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups.

CONCLUSIONS

The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.

Tigecycline-associated hypofibrinogenemia: A single center, retrospective, controlled study

BACKGROUND

Tigecycline-associated hypofibrinogenemia has been reported as an important adverse effect in recent years, but controlled studies minimizing confounding factors are needed. The objective of our study was to assess changes in fibrinogen levels in patients for hospitalization, comparing two antibiotic episodes (tigecycline and other) within the same patients.

METHODS

The retrospective, self-controlled case series study was conducted at our University Hospitals. The study compared the change in fibrinogen levels during the patient's hospitalization for tigecycline (TigePer) and another antibiotic period (OtherPer). In addition, bleeding events, bleeding risk (determined by the IMPROVE bleeding risk score), as well as 15- and 30-day mortality rates between TigePer and OtherPer were compared.

RESULTS

The study enrolled 50 patients with 100 episodes of antibiotic treatment. The median age (interquartile range) of the patients was 68.5 (21.5) years, and 38 % were female. As compared to OtherPer, TigePer had a statistically significant reduction in fibrinogen levels (p < 0.001), with a hypofibrinogenemia rate of 40 % in TigePer as compared to 2 % in OtherPer (p < 0.001). TigePer demonstrated a significantly higher 15-day mortality rate (p = 0.006). No significant differences were observed between the two periods in terms of bleeding risk, rate of bleeding events, and 30-day mortality rate (p > 0.05).

CONCLUSION

Hypofibrinogenemia and other coagulopathies, without associated bleeding events, are more frequently observed in patients receiving tigecycline. Therefore, it is crucial for clinicians to monitor fibrinogen levels during tigecycline use.

Safety of Tigecycline in Patients on Antithrombotic Therapy: A Single-Center Retrospective Study

INTRODUCTION

The aims of the study were to investigate the risk factors of tigecycline-induced hypofibrinogenemia and to evaluate the safety of tigecycline with concomitant antithrombotic drugs.

METHODS

We performed a retrospective analysis of patients who received tigecycline for more than 3 days between January 2015 and June 2019. Clinical and laboratory data were collected including fibrinogen concertation, tigecycline dose, duration of treatment, disease severity, complete blood count, indicators of infection, liver and renal function. Risk factors of hypofibrinogenemia were analyzed by univariate and multivariate analysis. To evaluate the safety of tigecycline and concomitant antithrombotic drugs, bleeding events were assessed by comparing the decline in hemoglobin and the amount of red blood cell transfusion in patients with antithrombotic drugs and those without.

RESULTS

This study included a total of 68 cases, 20 of which experienced hypofibrinogenemia while receiving tigecycline treatment. Duration of treatment, cefoperazone/sulbactam combination therapy, and fibrinogen levels prior to initiation of tigecycline were risk factors associated with tigecycline-induced hypofibrinogenemia. There were 26 recorded bleeding incidents, 25 of which happened before the start of tigecycline. Antithrombotic and non-antithrombotic patients did not differ in their hemoglobin decline or need for red blood cell transfusions while taking tigecycline.

CONCLUSION

A longer treatment duration, cefoperazone/sulbactam combination therapy, and a lower level of fibrinogen before tigecycline were associated with an increased risk of tigecycline-induced hypofibrinogenemia. A combination of antithrombotic drugs and tigecycline did not aggravate the bleeding events during tigecycline treatment.