Vishvas Garg’s research while affiliated with AbbVie and other places

Hidradenitis suppurativa (HS) is a chronic inflammatory follicular skin condition that is associated with significant psychosocial and economic burden and a diminished quality of life and work productivity. Accurate diagnosis of HS is challenging due to its unknown etiology, which can lead to underdiagnosis or misdiagnosis that results in increased patient and healthcare system burden. We applied machine learning (ML) to a medical and pharmacy claims database using data from 2000 through 2018 to develop a novel model to better understand HS underdiagnosis on a healthcare system level. The primary results demonstrated that high-performing models for predicting HS diagnosis can be constructed using claims data, with an area under the curve (AUC) of 81%–82% observed among the top-performing models. The results of the models developed in this study could be input into the development of an impact of inaction model that determines the cost implications of HS diagnosis and treatment delay to the healthcare system.

Background The impact of psoriasis in special areas (i.e., scalp, nails, palms, soles, genitals) on patient physical functioning, health-related quality of life (HRQoL), and work abilities has not been fully characterized. We assessed associations between disease severity and special area involvement in psoriasis symptoms, HRQoL, and work/activity impairment. Methods Patients with psoriasis from the CorEvitas Psoriasis Registry who initiated systemic treatment between 04/2015–06/2020 were included. Outcomes were change from baseline in psoriasis symptoms, Dermatology Life Quality Index (DLQI), and work/activity impairment at 6 months stratified by baseline disease severity and special area involvement. Results Among 2620 patients, increasing disease severity was associated with worsening patient-reported outcomes. Patients with (46.0%; N = 1205) versus without (54.0%; N = 1415) psoriasis in special areas reported greater HRQoL and work/activity impairment. Over 6 months, patients with unchanged or worsening disease severity had reduced HRQoL and increased symptom severity; incremental increases in patient HRQoL and decreases in symptom severity were associated with improved disease severity. Conclusions Higher disease severity and special area involvement was associated with worse outcomes and impaired work abilities. These data highlight the significant impact that adequate treatment of severe psoriasis and/or special area involvement may have on patient HRQoL and function.

Background Switching therapies is common for patients with psoriasis. Objective To quantify real-world switching rates and characteristics among patients initiating biologics over 24 months. Methods Patients aged ≥18 years with ≥2 confirmed psoriasis diagnoses who initiated a new biologic were identified from a US-payer claims database (Merative® MarketScan®) Switching rates were reported over 24 months using Kaplan–Meier survival analysis, and multivariable Cox regression analyses were performed to identify associated patient characteristics. Results A total of 7997 patients were included, with overall treatment switch rates at 14.4% at 12 months and 26.0% at 24 months. IL-23 inhibitors were associated with the lowest risk of switching compared with TNF, IL-17, and IL-12/23 inhibitors over 24 months (p < 0.0001). Switch rates varied between specific biologics, with the lowest switch rates observed for patients treated with risankizumab at 8.5% followed by guselkumab at 15.7% over 24 months. Prior targeted immune modulator use, age, and female gender were predictors of switching (adjusted hazard ratio; 1.23, 1.31, and 1.40, respectively; p ≤ 0.0005). Limitations Claims data may be subject to data errors and reasons for switching cannot be determined. Conclusion Switching was common in psoriasis patients using biologics over 24 months, with the lowest risk of switching observed with IL-23 inhibitors.