Risk stratification and outcomes of intracranial hemorrhage in patients with immune thrombocytopenia under 60 years of age

A post-marketing pharmacovigilance study of avapritinib: Adverse event data mining and analysis based on the United States Food and Drug Administration Adverse Event Reporting System database

AIMS

Avapritinib was first approved by the FDA in January 2020 and represents the first precision-targeted drug for gastrointestinal stromal tumours. However, there is a lack of large-scale data relating to adverse events (AEs) related to its use. We aimed to explore the avapritinib-related AEs in real-world practice based on the post-marketing data.

METHODS

We extracted all avapritinib-related reports submitted to the FDA Adverse Event Reporting System (FAERS) by June 2022. Based on disproportionality analysis and Bayesian analysis, we then calculated the reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC) and empirical Bayes geometric mean (EBGM) to evaluate whether there is a significant association between avapritinib and AEs. Gender, age and time to onset were comparable between haemorrhage/non-haemorrhage, serious/non-serious, death/non-death AEs, respectively.

RESULTS

In total, 3120 cases related to avapritinib were documented in the FAERS database, and 44% were reported within 30 days of commencing avapritinib. A total of 331 different AE signals were detected, and no significant differences between males and females was identified. Although the number of AEs associated with an abnormal skin texture and executive dysfunction was small, the signal intensity is high, suggesting that these events are strongly correlated with avapritinib. Subgroup analysis showed that elderly male patients were more likely to suffer from serious AEs compared to females (P < .01), but there was no significant difference between the haemorrhage group and the non-haemorrhage group. Analysis of fatalities due to avapritinib-related AEs indicated that sex, age and time-to-onset were all significantly related to death (P < .05).

CONCLUSION

Our study provides a more precise description of the incidence and characteristics of AEs after using avapritinib, clinicians should be particularly careful when prescribing avapritinib to elderly male patients, especially within the 30 days.

A life-threatening bleeding prediction model for immune thrombocytopenia based on personalized machine learning: a nationwide prospective cohort study

Rare but critical bleeding events in primary immune thrombocytopenia (ITP) present life-threatening complications in patients with ITP, which severely affect their prognosis, quality of life, and treatment decisions. Although several studies have investigated the risk factors related to critical bleeding in ITP, large sample size data, consistent definitions, large-scale multicenter findings, and prediction models for critical bleeding events in patients with ITP are unavailable. For the first time, in this study, we applied the newly proposed critical ITP bleeding criteria by the International Society on Thrombosis and Hemostasis for large sample size data and developed the first machine learning (ML)-based online application for predict critical ITP bleeding. In this research, we developed and externally tested an ML-based model for determining the risk of critical bleeding events in patients with ITP using large multicenter data across China. Retrospective data from 8 medical centers across the country were obtained for model development and prospectively tested in 39 medical centers across the country over a year. This system exhibited good predictive capabilities for training, validation, and test datasets. This convenient web-based tool based on a novel algorithm can rapidly identify the bleeding risk profile of patients with ITP and facilitate clinical decision-making and reduce the occurrence of adversities.

Developing and validating a mortality prediction model for ICH in ITP: a nationwide representative multicenter study

Intraparenchymal hemorrhage, platelet count, serious infections, severe bleeding events, and Glasgow coma scale predict poor prognosis.

A prognostic model was developed and validated, and an application was established.

Intracranial hemorrhage (ICH) is a rare and life-threatening hemorrhagic event in patients with immune thrombocytopenia (ITP). However, its mortality and related risk factors remain unclear. Herein, we conducted a nationwide multicenter real-world study of ICH in adult ITP patients. According to data from 27 centers in China from 2005 to 2020, the mortality rate from ICH was 33.80% (48/142) in ITP adults. We identified risk factors by logistic univariate and multivariate logistic regression for 30-day mortality in a training cohort of 107 patients as follows: intraparenchymal hemorrhage (IPH), platelet count ≤10 × 10⁹/L at ICH, a combination of serious infections, grade of preceding bleeding events, and Glasgow coma scale (GCS) level on admission. Accordingly, a prognostic model of 30-day mortality was developed based on the regression equation. Then, we evaluated the performance of the prognostic model through a bootstrap procedure for internal validation. Furthermore, an external validation with data from a test cohort with 35 patients from 11 other centers was conducted. The areas under the receiver operating characteristic (ROC) curves for the internal and external validation were 0.954 (95% confidence interval [CI], 0.910-0.998) and 0.942 (95% CI, 0.871-1.014), respectively. Both calibration plots illustrated a high degree of consistency in the estimated and observed risk. In addition, the decision curve analysis showed a considerable net benefit for patients. Thus, an application (47.94.162.105:8080/ich/) was established for users to predict 30-day mortality when ICH occurred in adult patients with ITP.