Fixed dose combination drugs for cardiovascular disease in a prolonged humanitarian crisis in Lebanon: an implementation study.

OBJECTIVES: This pre-post implementation study evaluated the introduction of fixed dose combination (FDC) medications for atherosclerotic cardiovascular disease (ASCVD) secondary prevention into routine care in a humanitarian setting. SETTING: Two Médecins sans Frontières (MSF) primary care clinics serving Syrian refugee and host populations in north Lebanon. PARTICIPANTS: Consenting patients ≥18 years with existing ASCVD requiring secondary prevention medication were eligible for study enrolment. Those with FDC contraindication(s) or planning to move were excluded. Of 521 enrolled patients, 460 (88.3%) were retained at 6 months, and 418 (80.2%) switched to FDC. Of these, 84% remained on FDC (n=351), 8.1% (n=34) discontinued and 7.9% (n=33) were lost to follow-up by month 12. INTERVENTIONS: Eligible patients, enrolled February-May 2019, were switched to Trinomia FDC (atorvastatin 20 mg, aspirin 100 mg, ramipril 2.5/5/10 mg) after 6 months' usual care. During the study, the COVID-19 pandemic, an economic crisis and clinic closures occurred. OUTCOME MEASURES: Descriptive and regression analyses compared key outcomes at 6 and 12 months: medication adherence, non-high density lipoprotein cholesterol (non-HDL-C) and systolic blood pressure (SBP) control. We performed per-protocol, intention-to-treat and secondary analyses of non-switchers. RESULTS: Among 385 switchers remaining at 12 months, total adherence improved 23%, from 63% (95% CI 58 to 68) at month 6, to 86% (95% CI 82 to 90) at month 12; mean non-HDL-C levels dropped 0.28 mmol/L (95% CI -0.38 to -0.18; p<0.0001), from 2.39 (95% CI 2.26 to 2.51) to 2.11 mmol/L (95% CI 2.00 to 2.22); mean SBP dropped 2.89 mm Hg (95% CI -4.49 to -1.28; p=0.0005) from 132.7 (95% CI 130.8 to 134.6) to 129.7 mm Hg (95% CI 127.9 to 131.5). Non-switchers had smaller improvements in adherence and clinical outcomes. CONCLUSION: Implementing an ASCVD secondary prevention FDC improved adherence and CVD risk factors in MSF clinics in Lebanon, with potential for wider implementation by humanitarian actors and host health systems.

The effects of systemic immunomodulatory treatments on COVID-19 outcomes in patients with atopic dermatitis: Results from the global SECURE-AD registry.

BACKGROUND: Limited data are available on the effects of systemic immunomodulatory treatments on COVID-19 outcomes in patients with atopic dermatitis (AD). OBJECTIVE: To investigate COVID-19 outcomes in patients with AD treated with or without systemic immunomodulatory treatments, using a global registry platform. METHODS: Clinicians were encouraged to report cases of COVID-19 in their patients with AD in the Surveillance Epidemiology of Coronavirus Under Research Exclusion for Atopic Dermatitis (SECURE-AD) registry. Data entered from 1 April 2020 to 31 October 2021 were analysed using multivariable logistic regression. The primary outcome was hospitalization from COVID-19, according to AD treatment groups. RESULTS: 442 AD patients (mean age 35.9 years, 51.8% male) from 27 countries with strongly suspected or confirmed COVID-19 were included in analyses. 428 (96.8%) patients were treated with a single systemic therapy (n = 297 [67.2%]) or topical therapy only (n = 131 [29.6%]). Most patients treated with systemic therapies received dupilumab (n = 216). Fourteen patients (3.2%) received a combination of systemic therapies. Twenty-six patients (5.9%) were hospitalized. No deaths were reported. Patients treated with topical treatments had significantly higher odds of hospitalization, compared with those treated with dupilumab monotherapy (odds ratio (OR) 4.65 [95%CI 1.71-14.78]), including after adjustment for confounding variables (adjusted OR (aOR) 4.99 [95%CI 1.4-20.84]). Combination systemic therapy which did not include systemic corticosteroids was associated with increased odds of hospitalization, compared with single agent non-steroidal immunosuppressive systemic treatment (OR 8.09 [95%CI 0.4-59.96], aOR 37.57 [95%CI 1.05-871.11]). Hospitalization was most likely in patients treated with combination systemic therapy which included systemic corticosteroids (OR 40.43 [95%CI 8.16-207.49], aOR 45.75 [95%CI 4.54-616.22]). CONCLUSIONS: Overall, the risk of COVID-19 complications appears low in patients with AD, even when treated with systemic immunomodulatory agents. Dupilumab monotherapy was associated with lower hospitalization than other therapies. Combination systemic treatment, particularly combinations including systemic corticosteroids, was associated with the highest risk of severe COVID-19.

Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation.

With over 117 million COVID-19-positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.

High sensitivity troponin and COVID-19 outcomes.

BACKGROUND: Recent reports have demonstrated high troponin levels in patients affected with COVID-19. In the present study, we aimed to determine the association between admission and peak troponin levels and COVID-19 outcomes. METHODS: This was an observational multi-ethnic multi-centre study in a UK cohort of 434 patients admitted and diagnosed COVID-19 positive, across six hospitals in London, UK during the second half of March 2020. RESULTS: Myocardial injury, defined as positive troponin during admission was observed in 288 (66.4%) patients. Age (OR: 1.68 [1.49-1.88], p < .001), hypertension (OR: 1.81 [1.10-2.99], p = .020) and moderate chronic kidney disease (OR: 9.12 [95% CI: 4.24-19.64], p < .001) independently predicted myocardial injury. After adjustment, patients with positive peak troponin were more likely to need non-invasive and mechanical ventilation (OR: 2.40 [95% CI: 1.27-4.56], p = .007, and OR: 6.81 [95% CI: 3.40-13.62], p < .001, respectively) and urgent renal replacement therapy (OR: 4.14 [95% CI: 1.34-12.78], p = .013). With regards to events, and after adjustment, positive peak troponin levels were independently associated with acute kidney injury (OR: 6.76 [95% CI: 3.40-13.47], p < .001), venous thromboembolism (OR: 11.99 [95% CI: 3.20-44.88], p < .001), development of atrial fibrillation (OR: 10.66 [95% CI: 1.33-85.32], p = .026) and death during admission (OR: 2.40 [95% CI: 1.34-4.29], p = .003). Similar associations were observed for admission troponin. In addition, median length of stay in days was shorter for patients with negative troponin levels: 8 (5-13) negative, 14 (7-23) low-positive levels and 16 (10-23) high-positive (p < .001). CONCLUSIONS: Admission and peak troponin appear to be predictors for cardiovascular and non-cardiovascular events and outcomes in COVID-19 patients, and their utilisation may have an impact on patient management.