Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial

Background In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. Methods We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment. Results Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: − 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: − 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model. Conclusion We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. Trial registration ClinicalTrials.gov: NCT00852423

The Association Between Gut Microbiome Affecting Concomitant Medication and The Related Effectiveness of Immunotherapy in Patients With Stage IV NSCLC

Objectives This historically matched cohort study investigated the influence of microbiome-affecting-medication on the effectiveness of immunotherapy in patients with stage IV non-small-cell lung cancer (NSCLC). We postulated that if the effectiveness of immunotherapy is mediated by drug-related changes of the microbiome, a stronger association between the use of co-medication and overall survival (OS) will be observed in patients treated with immunotherapy as compared to patients treated with chemotherapy. Methods Immunotherapy patients were matched (1:1) to patients treated with chemotherapy in the pre immunotherapy era. The association between the use of antibiotics, opioids, proton pump inhibitors, metformin and other antidiabetics on OS was assessed with multivariable cox-regression analyses. Interaction tests were applied to investigate whether the association differs between patients treated with immuno- or chemotherapy. Results A total of 442 patients were studied. The use of antibiotics was associated with worse OS (adjusted Hazard Ratio (aHR) 1.39, p = 0.02) independent of the type of therapy (chemotherapy or immunotherapy). The use of opioids was also associated with worse OS (aHR 1.33, p = 0.01). The other drugs studied showed no association with OS. Interaction term testing showed no effect modification by immuno- or chemotherapy for the association of antibiotics and opioids with OS. Conclusion The use of antibiotics and opioids is similarly associated with worse outcomes in both chemotherapy and immunotherapy treated NSCLC patients. This suggests that the association is likely to be a consequence of confounding by indication rather than disturbing the composition of the microbiome.

Drug–drug interactions between treatment specific pharmacotherapy and concomitant medication in patients with COVID-19 in the first wave in Spain

Primary aim was to assess prevalence and severity of potential and real drug–drug interactions (DDIs) among therapies for COVID-19 and concomitant medications in hospitalized patients with confirmed SARS-CoV-2 infection. The secondary aim was to analyze factors associated with rDDIs. An observational single center cohort study conducted at a tertiary hospital in Spain from March 1st to April 30th. rDDIs refer to interaction with concomitant drugs prescribed during hospital stay whereas potential DDIs (pDDIs) refer to those with domiciliary medication. DDIs checked with The University of Liverpool resource. Concomitant medications were categorized according to the Anatomical Therapeutic Chemical classification system. Binomial logistic regression was carried out to identify factors associated with rDDIs. A total of 174 patients were analyzed. DDIs were detected in 152 patients (87.4%) with a total of 417 rDDIs between COVID19-related drugs and involved hospital concomitant medication (60 different drugs) while pDDIs were detected in 105 patients (72.9%) with a total of 553 pDDIs. From all 417 rDDIs, 43.2% (n = 180) were associated with lopinavir/ritonavir and 52.9% (n = 221) with hydroxychloroquine, both of them the most prescribed (106 and 165 patients, respectively). The main mechanism of interaction observed was QTc prolongation. Clinically relevant rDDIs were identified among 81.1% (n = 338) (‘potential interactions’) and 14.6% (n = 61) (contraindicated) of the patients. Charlson index (OR 1.34, 95% IC 1.02–1.76) and number of drugs prescribed during admission (OR 1.42, 95% IC 1.12–1.81) were independently associated with rDDIs. Prevalence of patients with real and pDDIs was high, especially those clinically relevant. Both comorbidities and polypharmacy were found as risk factors independently associated with DDIs development.

Use of deep learning frameworks to detect super-responder and super-survivor stage IV squamous non-small-cell lung cancer (NSCLC) patients treated with a gemcitabine and cisplatin combination.

1548 Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF). Methods: Through www.projectdatasphere.org, we accessed the control arm clinical data (N = 548) of the randomised phase III SQUIRE trial (NCT00981058). This trial included patients with stage IV squamous NSCLC who had not received previous chemotherapy. These patients were treated with gemcitabine 1,250 mg/m2 (IV, 30-min infusion, d1/d8) and cisplatin 75 mg/m2 (IV, 120 min infusion, d1) on a 3-week cycle for a maximum of six cycles. Synthetic fingerprints resulted of the integration of 180 features collected during the first 3 cycles including demographics, medical history, physical exam, concomitant medication, histopathology, PK parameters, adverse events and common labs. These fingerprints were used as input for the UDLF. The resultant clusters were correlated with overall-response rate (ORR) and overall survival (OS). Results: After missing data removal and feature standardization, 192 patients were eligible for the study. The UDLF was able to generate two different clusters: P0 (n = 107) and P1 (n = 84). ORR was higher in the P1 than in the P0 cluster (mean 41.6% [95% CI 31.7-52.3] vs. 28.0% [95% CI 20.4-37.2]; p = 0.04). OS was significantly longer in the P1 than in the P0 cluster (median 13.2 months vs. 9.7 months; hazard ratio 1.56 [95% CI 1.12-2.17; p = 0.008]). Feature contribution analysis showed that P1 had more patients and more events of grade III/IV neutropenia. In contrast, P0 had more patients and more events of grade III/IV nausea and vomiting. Other major differences were observed on vital signs (SBP, DBP, HR, RR, Temp), concomitant medication (osmotically-active laxatives, dexamethasone, furosemide, granisetron and ondansetron) and in hematological (RBC, HGB, HCT, MCV, WBC, neutrophils, monocytes, lymphocytes) and biochemistry (albumin, globulins, ALP, LDH, creatinine, BUN, urea, sodium, magnesium and phosphate) tests. Conclusions: Our findings show that synthetic fingerprints and subsequent deep learning analysis can be of use to identify patients with clinical characteristics associated with high-response rate and long-term survival.

Surveillance of Adverse Drug Events Associated with Tocilizumab in Hospitalized Veterans with COVID-19 to Inform Patient Safety and Pandemic Preparedness

We evaluated adverse drug events (ADE) by chart review in a random national sample of 428 Veterans with COVID-19 who received tocilizumab (n=173/428). ADEs (median time=5 days) occurred in 51/173 (29%) and included hepatoxicity (n=29) and infection (n=13). Concomitant medication discontinuation occurred in 22% of ADE patients; mortality was 39%.