IS THERE A SAFETY SIGNAL GENERATION FOR SUDDEN SENSORINEURAL HEARING LOSS FOLLOWING mRNA COVID-19 VACCINATION: NATIONWIDE POST-MARKETING SURVEILLANCE USING THE FRENCH PHARMACOVIGILANCE SPONTANEOUS REPORTING DATABASE

BACKGROUND

The World Health Organization lately described sudden sensorineural hearing loss (SSNHL) as a possible adverse effect of COVID-19 vaccines. Recent discordant pharmacoepidemiologic studies invite robust clinical investigations of SSNHL after COVID mRNA vaccines. This post-marketing surveillance study, overseen by French public health authorities, is the first to clinically document post-vaccination SSNHL in terms of severity, duration, positive rechallenge cases and to examine the role of potential risk factors.

OBJECTIVE

This nationwide study aimed to assess the relationship between SSNHL and exposure to mRNA COVID-19 vaccines and to estimate the reporting rates (Rr) of SSNHL after mRNA vaccination per 1,000,000 doses (primary outcome).

METHODS

We performed a retrospective review of all suspected cases of SSNHL after mRNA COVID-19 vaccination spontaneously reported in France between January 2021 and February 2022, based on a comprehensive medical evaluation including patient medical history, side and range of hearing loss, hearing recovery outcomes after a minimum period of 3 months. Quantification of hearing loss and assessment of hearing recovery outcomes were performed according to a grading system modified from Siegel's criteria. A cutoff value of 21 days was used for the delay onset of SSNHL. The primary outcome was estimated using as a denominator the total number of doses of each vaccine administered over the study period in France.

RESULTS

From an initial number of 400 extracted cases for both mRNA vaccines, 345 spontaneous reports were selected for further analysis. After detailed review of complementary medical data, 171 fully documented cases of SSNHL were found. Of these, 142 SSNHL cases occurred after tozinameran vaccination, with the following characteristics: Rr=1.45/1,000,000 injections; no difference between the first, second, and booster injection; complete recovery for 32 cases; median delay onset before day 21 = 4 days; median (range) age=51 (13-83) years; no sex effect. A total of 29 SSNHL cases were identified after elasomeran vaccination, with the following characteristics: Rr=1.67/1,000,000 injections; rank effect in favour of the first injection (p=0.036); complete recovery for 7 cases; median delay onset before day 21 = 8 days; median (range) age=47 (33-81) years; no sex effect. Autoimmune, cardiovascular, or audiovestibular risk factors were present in nearly 30% of cases. SSNHL was more often unilateral than bilateral for both mRNA vaccines (p<0.001 for tozinameran; p<0.003 for elasomeran), and the hearing loss degree was slight to moderately severe (Siegel's grade 1 to 3) on 74% of audiogram tests. There was 23 (13%) of profound hearing loss (Siegel's grade 5), among which 17 (74%) did not recover a serviceable ear. A positive rechallenge was documented for 8 cases, reinforcing the hypothesis of a causal relationship between mRNA COVID-19 vaccination and the occurrence of SSNHL.

CONCLUSIONS

Episodes of SSNHL after COVID-19 mRNA vaccines are very rare adverse events that do not call into question the benefits of mRNA vaccines but deserve to be known given the potentially disabling impact of sudden deafness. It is, therefore, essential to properly characterize any post-injection SSNHL, especially in the case of a positive rechallenge, to provide appropriate individualized recommendations.

Potential drug-drug interactions when managing status epilepticus patients in intensive care: A cohort study

AIMS

The risk for drug-drug interactions (DDIs) associated with antiseizure drugs (ASDs) used to manage status epilepticus (SE) patients in the intensive care unit (ICU) has been poorly investigated. We aimed to quantify and describe those potential DDIs and determine SE patient risk profiles.

METHODS

We conducted an observational bi-centric cohort study including all SE patients admitted to the ICU in the period 2016-2020.

RESULTS

Overall, 431 SE patients were included and 5504 potential DDIs were identified including 1772 DDIs (33%) between ASDs, 2610 DDIs (47%) between ASDs and previous usual treatments (PUTs), and 1067 DDIs (20%) between ASDs and ICU treatments (ICUTs). DDIs were moderate (n = 4871), major (n = 562) or severe (n = 16). All patients exhibited potential DDIs, which were major-to-severe DDIs in 47% of the cases. DDIs were pharmacokinetic (n = 1972, 36%), mostly involving cytochrome P450 modulators, and pharmacodynamic (n = 3477, 64%), mainly leading to increased sedation. ASD/PUT DDIs were the most frequent and severe. Age, PUT and ASD drug numbers and length of ICU stay were significantly associated with increased DDI number. We identified four SE patient profiles with different DDI risks and outcomes including (1) epileptic or brain trauma patients, (2) withdrawal syndrome patients, (3) older patients with comorbidities and (4) self-poisoned patients with psychiatric disorders and/or past epilepsy.

CONCLUSION

SE patients are subject to potential DDIs between ASDs, ASD/PUT and ASD/ICUT. Major-to-severe DDIs mostly occur between ASDs and PUTs. Physicians should pay attention to SE patient characteristics and history to limit DDI numbers and prevent their consequences.

Clinical Pharmacokinetics and Bayesian Estimators for the Individual Dose Adjustment of a Generic Formulation of Tacrolimus in Adult Kidney Transplant Recipients

BACKGROUND

Tacrolimus has a narrow therapeutic range and requires dose adjustment, usually based on the trough blood concentration but preferably on the area under the concentration-time curve over 12 h post-dose (AUC_0-12h). The single-arm, multicentre, clinical study IMPAKT aimed: (i) to develop, in de novo kidney transplant recipients, pharmacokinetic models and maximum a-posteriori Bayesian estimators for a generic, immediate-release, oral formulation of tacrolimus to estimate tacrolimus AUC_0-12h at different post-transplant periods using a limited sampling strategy, and considering the CYP3A5*3 polymorphism as a covariate and (ii) to compare the performance of these Bayesian estimators to those previously developed for the original formulation.

METHODS

Thirty patients were enrolled and 29 provided nine blood samples over 9 h at day 7 and months 1 and 3 post-transplant. Tacrolimus blood profiles measured with liquid chromatography-tandem mass spectrometry were modelled using one-compartment, double gamma absorption, linear elimination models developed in-house. Different limited sampling strategies of three time-points within 4 h post-dose were tested for the maximum a-posteriori Bayesian estimator of tacrolimus AUC_0-12h. The models and estimators were validated internally and their performance compared to that of models previously developed for the original formulation.

RESULTS

The concentration-time curves, AUC_0-12h/dose and trough blood concentration/dose exhibited wide inter-individual variability. The covariate-free pharmacokinetic models developed for the three post-transplant periods closely fitted the individual profiles. Maximum a-posteriori Bayesian estimators based on three different limited sampling strategies and no covariate yielded accurate AUC_0-12h estimates, including for the five cytochrome P450 3A5 expressers and for the four patients without corticosteroids. The 0-1 h-3 h strategy finally chosen had very low bias (- 4.0 to - 2.5%) and imprecision (root mean square error 5.5-9.2%). The maximum a-posteriori Bayesian estimators previously developed for the reference product fitted the generic profiles with similar performance.

CONCLUSIONS

We developed original pharmacokinetic models and accurate maximum a-posteriori Bayesian estimators to estimate patient exposure and adjust the dose of generic tacrolimus, and confirmed that the robust tools previously developed for the original formulation can be applied to this generic.