Therapeutic drug monitoring in inflammatory bowel disease patients on vedolizumab

OBJECTIVE

We aimed to investigate whether vedolizumab (VDZ) levels were associated with inflammatory markers or clinical or endoscopic scoring in inflammatory bowel disease (IBD).

METHODS

Besides demographic data, clinical scoring, endoscopic data, and laboratory markers of IBD patients treated with VDZ from 2015 to 2020 who had trough levels drawn on maintenance therapy were collected at baseline and at follow-up (after at least 8 weeks on VDZ therapy or after change in dose frequency). Low drug levels were defined as VDZ trough <20 μg/mL.

RESULTS

We identified 89 patients with a mean age of 42.9 years. Of the 90 total trough levels drawn, 61.1% were low. Among patients on every 8 week (Q8 week) VDZ dosing, 81.5% had low troughs. After increasing dosing frequency to Q4 weeks, all patients showed improvement in VDZ levels, but 30.6% remained <20 μg/mL. Higher VDZ levels on Q8 week dosing were associated with higher albumin levels (P = 0.01). While higher VDZ levels on Q4 week dosing were associated with higher albumin (P = 0.02), lower erythrocyte sedimentation rate (P = 0.04) and higher likelihood of having mild disease or endoscopic remission (P = 0.01). No significant association was found between VDZ levels and clinical scoring, body mass index, hemoglobin, vitamin D or platelet levels on either Q8 or Q4 week dosing.

CONCLUSIONS

Higher VDZ troughs were associated with higher albumin, mild endoscopic disease or endoscopic remission. Patients who continue to have low VDZ troughs despite Q4 week dosing may require a change in therapy.

A Time-Series Feature-based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for future pandemic events.