Showing papers by "Ryan D. Kilpatrick published in 2021"

Chronic opioid use and complication risks in women with endometriosis: A cohort study in US administrative claims.

Abstract: Background Women with endometriosis are prescribed opioids for pain relief but may be vulnerable to chronic opioid use given their comorbidity profile. Methods A cohort study was conducted in the Clinformatics™ DataMart database between 2006 and 2017 comparing women aged 18-50 years with endometriosis (N = 36 373) to those without (N = 2 172 936) in terms of risk of chronic opioid use, opioid dependence diagnosis, and opioid overdose. Chronic opioid use was defined as ≥120 days' supply dispensed or ≥10 fills of an opioid during any 365-day interval. Among women with endometriosis, we evaluated factors associated with higher risk of chronic opioid use and quantified the risk of complications associated with the use of opioids. Results Women with endometriosis were at greater risk for chronic opioid use (OR: 3.76; 95%CI: 3.57-3.96), dependence (OR: 2.73, 95%CI: 2.38-3.13) and overdose (OR: 4.34, 95%CI: 3.06-6.15) compared to women without. Chronic users displayed dose escalation and increase in days supplied over time, as well as co-prescribing with benzodiazepines and sedatives. Approximately 34% of chronic users developed constipation, 20% experienced falls, and 8% reported dizziness. Among endometriosis patients, women in younger age groups, those with other comorbidities associated with pain symptoms, as well as those with depression or anxiety were at a higher risk of developing chronic opioid use. Conclusions Women with endometriosis had a four times greater risk of chronic opioid use compared to women without. Multimorbidity among these patients was associated with the elevated risk of chronic opioid use and should be taken into account during treatment selection.

Incidence and Risk Factors of Pneumonitis in Patients with Non-Small Cell Lung Cancer: An Observational Analysis of Real-World Data

Abstract: The incidence of pneumonitis, a treatment-related adverse event (AE) in non-small cell lung cancer (NSCLC) patients, has been studied in the United States mostly through clinical trials and retrospective chart reviews. Few analyses of real-world data have been published. This study of a large nationally representative health records database estimated the incidence and predictors of pneumonitis among treated NSCLC patients between 2008 and 2018. The Optum® electronic health records (EHR) database includes data on over 80 million patients from more than 50 healthcare plans. The cohort of primary NSCLC patients was identified using ICD-9/10 codes. Natural language processing of unstructured data from physicians’ notes facilitated extraction of biomarker (epidermal growth factor receptor [EGFR] and programmed death ligand-1 [PD-L1]) status. Cumulative incidence was estimated as the proportion with pneumonitis overall, by clinical characteristics, and line of therapy (LOT) after diagnosis and treatment. Univariate analysis of incidence rates (cases/1000 person-years) enabled the identification of significant predictors of risk. Competing risk regression identified predictors of pneumonitis. The cohort included 81,628 patients. Overall, 19.0% developed pneumonitis during any LOT, with a cumulative incidence of 33.7% and 17.0% for patients with a prior history of pneumonitis and those without, respectively. Univariate analyses revealed several factors associated with pneumonitis (p < 0.05). While factors varied between LOTs, common factors included male gender, squamous histology, history of diabetes or pneumonitis, EGFR-negative status, monotherapy immunomodulatory drugs, or history of radiation therapy. Multivariable competing risk regression showed that male gender, history of pneumonitis, EGFR-negative status, use of other targeted therapies, use of immunomodulatory drugs, and history of radiation therapy predicted pneumonitis. Pneumonitis is significantly associated with NSCLC treatment. Knowledge of its predictors identified in this study may help devise strategies to mitigate its impact, enhancing treatment adherence and improving outcomes. Pneumonitis is a side effect of non-small cell lung cancer (NSCLC) treatment. Real-world data on its incidence in the United States is not extensive. In this study, the Optum® electronic health records database with data on over 80 million patients from more than 50 healthcare plans across the United States was used to estimate the incidence and predictors of pneumonitis in NSCLC patients treated between 2008 and 2018. A total of 81,628 NSCLC patients were identified using disease-specific codes. Physicians’ notes in their health records were subjected to natural language processing to identify presence of epidermal growth factor receptor (EGFR) and programmed death ligand-1 (PD-L1) receptors in tumors. Proportions of patients with pneumonitis overall, by clinical characteristics, and line of therapy (LOT) were calculated. Univariate analysis of incidence (cases per 1000 person-years) a multivariable competing risk regression helped identify risk predictors. Overall, 19.0% of patients developed pneumonitis during any LOT. Incidence was 33.7% and 17.0% in patients with and without prior pneumonitis, respectively. Univariate analysis revealed factors associated with pneumonitis, including male gender, squamous histology, history of diabetes or pneumonitis, EGFR-negative status, monotherapy immunomodulatory drugs, or history of radiation therapy. Multivariable competing risks regression analysis showed that male gender, history of pneumonitis, EGFR-negative status, use of other targeted therapies, use of immunomodulatory drugs, and history of radiation therapy were significantly associated with pneumonitis. Pneumonitis is significantly associated with NSCLC treatment. Knowledge of its predictors may help design interventions to lessen its impact, promoting compliance with treatment and improving outcomes.

Validation of Claims-Based Algorithms to Identify Patients with Psoriasis

Abstract: Purpose Accurately identifying patients with psoriasis (PsO) is crucial for generating real-world evidence on PsO disease course and treatment utilization. Methods We developed 9 claims-based algorithms for PsO using a combination of the International Classification of Diseases (ICD)-9 codes, specialist visit, and medication dispensing using Medicare linked to electronic health records data (2013-2014) in two healthcare provider networks in Boston, Massachusetts. We calculated positive predictive value (PPV) and 95% confidence interval (CI) for each algorithm using the treating physician's diagnosis of PsO via chart review as the gold standard. Among the confirmed PsO cases, we assessed their PsO disease activity. Results The nine claims-based algorithms identified 990 unique patient records. Of those, 918 (92.7%) with adequate information were reviewed. The PPV of the algorithms ranged from 65.1% to 82.9%. An algorithm defined as ≥1 ICD-9 diagnosis code for PsO and ≥ 1 prescription claim for topical vitamin D agents showed the highest PPV (82.9%). The PPV of the algorithm requiring ≥2 ICD-9 diagnosis codes and ≥ 1 prescription claim for PsO treatment excluding topical steroids was 81.1% but higher (82.5%) when ≥1 diagnosis was from a dermatologist. Among 411 PsO patients with adequate information on PsO disease activity in EHRs, 1.5-5.8% had no disease activity, 31.3-36.8% mild, and 26.9-35.1% moderate-to-severe across the algorithms. Conclusions Claims-based algorithms based on a combination of PsO diagnosis codes and dispensing for PsO-specific treatments had a moderate-to-high PPV. These algorithms can serve as a useful tool to identify patients with PsO in future real-world data pharmacoepidemiologic studies. This article is protected by copyright. All rights reserved.

Exploring Completeness of Adverse Event Reports as a Tool for Signal Detection in Pharmacovigilance.

Abstract: Completeness of adverse event (AE) reports is an important component of quality for good pharmacovigilance practices. We aimed to evaluate the impact of incorporating a measure of completeness of AE reports on quantitative signal detection. An internal safety database from a global pharmaceutical company was used in the analysis. vigiGrade, an index score of completeness, was derived for each AE report. Data from various patient support programs (PSPs) were categorized based on average vigiGrade score per PSP. Performance of signal detection was compared between: (1) weighting and not weighting by vigiGrade score; and, (2) well documented and poorly documented PSPs using sensitivity, specificity, area under the receiver operating characteristics curve (AUC) and time-to-signal detection. The ability to detect signals did not differ significantly when weighting by vigiGrade score [sensitivity (50% vs. 45%, p = 1), specificity (82.8% vs. 82.8%, p = 1), AUC (0.66 vs. 0.63, p = 0.051) or time-to-signal detection (HR 0.81, p = 0.63)] compared to not weighting. Well documented PSPs were better at detecting signals than poorly documented PSPs (AUC 0.66 vs. 0.52; p = 0.041) but time-to-signal detection did not differ significantly (HR 1.54, p = 0.42). Completeness of AE reports did not significantly impact the ability to detect signals when weighting by vigiGrade score or restricting the database based on the level of completeness. While the vigiGrade helps provide quality assessments of AE reports and prioritize cases for review, our findings indicate the tool might not be useful for quantitative signal detection when used by itself.