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Drug–drug interactions (DDIs) are a significant concern for patients on complex therapeutic regimens, especially involving cardiovascular medications, which are frequently implicated in these interactions.
This study used a standardised interaction database to determine the frequency, severity and risk factors associated with potential DDIs (pDDIs) among cardiovascular disease (CVD) in-patients.
The prospective cross-sectional study was conducted at a tertiary care hospital in Nepal from April 2024 to October 2024. A total of 106 eligible CVD in-patients were evaluated for pDDIs using the Lexicomp DDI checker database, and the interactions were categorised based on severity and risk rating. Binary logistic regression identified factors associated with pDDIs.
The study identified 621 pDDIs using the Lexicomp database, with median values of 8 pDDIs per patient. Patients with at least one pDDI comprised 64.2% of the sample. Most pDDIs were of moderate severity (77.3%) with risk ratings of C (65.7%). The most common cardiovascular medications involved in the detected DDI pairs were diuretics (31.2%), antiplatelets and anticoagulants (23.8%) and calcium channel blockers (12.2%). Multivariate binary logistic regression revealed that patients who stayed longer (adjusted OR (AOR) 9.08, 95% CI 1.027 to 80.216, p=0.047), those receiving more medications (AOR 18.85, 95% CI 2.975 to 119.370, p=0.002) and those who were admitted to the intensive cardiac care unit (AOR 16.31, 95% CI 2.728 to 97.461, p=0.002) were significantly more likely to experience pDDIs.
This study found a higher prevalence of pDDIs. It is advisable to incorporate medication reviews into routine cardiac care and use a drug interaction checker to identify pDDIs.
by Paige K. Marty, Balaji Pathakumari, Thomas M. Cox, Virginia P. Van Keulen, Courtney L. Erskine, Maleeha Shah, Mounika Vadiyala, Pedro Arias-Sanchez, Snigdha Karnakoti, Kelly M. Pennington, Elitza S. Theel, Cecilia S. Lindestam Arlehamn, Tobias Peikert, Patricio Escalante
Clinical prediction of nontuberculous mycobacteria lung disease (NTM-LD) progression remains challenging. We aimed to evaluate antigen-specific immunoprofiling utilizing flow cytometry (FC) of activation-induced markers (AIM) and IFN-γ enzyme-linked immune absorbent spot assay (ELISpot) accurately identifies patients with NTM-LD, and differentiate those with progressive from nonprogressive NTM-LD. A Prospective, single-center, and laboratory technician-blinded pilot study was conducted to evaluate the FC and ELISpot based immunoprofiling in patients with NTM-LD (n = 18) and controls (n = 22). Among 18 NTM-LD patients, 10 NTM-LD patients were classified into nonprogressive, and 8 as progressive NTM-LD based on clinical and radiological features. Peripheral blood mononuclear cells were collected from patients with NTM-LD and control subjects with negative QuantiFERON results. After stimulation with purified protein derivative (PPD), mycobacteria-specific peptide pools (MTB300, RD1-peptides), and control antigens, we performed IFN-γ ELISpot and FC AIM assays to access their diagnostic accuracies by receiver operating curve (ROC) analysis across study groups. Patients with NTM-LD had significantly higher percentage of CD4+/CD8+ T-cells co-expressing CD25+CD134+ in response to PPD stimulation, differentiating between NTM-LD and controls. Among patients with NTM-LD, there was a significant difference in CD25+CD134+ co-expression in MTB300-stimulated CD8+ T-cells (p
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