Drug-utilisation profiles and COVID-19

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

BACKGROUND

Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions.

METHODS

The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling.

RESULTS

From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68).

CONCLUSION

Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

Assessing medication use patterns by clinical outcomes severity among inpatients with COVID-19: A retrospective drug utilization study

PURPOSE

This study assessed medication patterns for inpatients at a central hospital in Portugal and explored their relationships with clinical outcomes in COVID-19 cases.

METHODS

A retrospective study analyzed inpatient medication data, coded using the Anatomical Therapeutic Chemical classification system, from electronic patient records. It investigated the association between medications and clinical severity outcomes such as ICU admissions, respiratory/circulatory support needs, and hospital discharge status, including mortality (identified by ICD-10-CM/PCS codes). Multivariate analyses incorporating demographic data and comorbidities were used to adjust for potential confounders and understand the impact of medication patterns on disease progression and outcomes.

RESULTS

The analysis of 2688 hospitalized COVID-19 patients (55.3% male, average age 62.8 years) revealed a significant correlation between medication types and intensity and disease severity. Cases requiring ICU admission or ECMO support often involved blood and blood-forming organ drugs. Increased use of nervous system and genitourinary hormones was observed in nonsurvivors. Corticosteroids, like dexamethasone, were common in critically ill patients, while tocilizumab was used in ECMO cases. Medications for the alimentary tract, metabolism, and cardiovascular system, although widely prescribed, were linked to more severe cases. Invasive mechanical ventilation correlated with higher usage of systemic anti-infectives and musculoskeletal medications. Trends in co-prescribing blood-forming drugs with those for acid-related disorders, analgesics, and antibacterials were associated with intensive interventions and worse outcomes.

CONCLUSIONS

The study highlights complex medication regimens in managing severe COVID-19, underscoring specific drug patterns associated with critical health outcomes. Further research is needed to explore these patterns.

Drug Utilization and Measurement of Medication Adherence: A Real World Study of Psoriasis in Italy

Exceptional advances have been made with systemic treatment for psoriasis (PSO). However, that disease still represents a heavy burden in terms of impact on healthcare systems worldwide. This study comprehensively assesses medication adherence in a real world setting in Italy across all phases-initiation, implementation, and persistence-of PSO therapies. By distinguishing between switches and swaps, it provides unique insights into the patient's own approach to prescribed therapy as well as clinical decision-making processes, enhancing our understanding of medication adherence and discontinuation in a real world daily setting. The study's refined methodology for assessing persistence, considering variations in refill gaps and complex dosing regimens, shows that anti-interleukin (IL) therapies are associated with longer periods of adherence compared with other available therapeutic strategies. Among the selected drugs, ixekizumab and secukinumab were the ones with higher rate of treatment adherence at the expense of anti-TNF-α and anti-PDE4 agents. Notably, patients who opt for swaps are approximately 2.8 times more likely to discontinue their PSO therapy within one year. These findings carry practical implications for optimizing medication adherence, including tailored patient counseling, monitoring, and therapeutic adjustments, highlighting the need for a comprehensive and patient-centered approach to managing these conditions.

Longitudinal Trajectory Modeling to Assess Adherence to Sacubitril/Valsartan among Patients with Heart Failure

Medication adherence in chronic conditions is a long-term process. Modeling longitudinal trajectories using routinely collected prescription data is a promising method for describing adherence patterns and identifying at-risk groups. The study aimed to characterize distinct long-term sacubitril/valsartan adherence trajectories and factors associated with them in patients with heart failure (HF). Subjects with incident HF starting sac/val in 2017-2018 were identified from the Campania Regional Database for Medication Consumption. We estimated patients' continuous medication availability (CMA9; R package AdhereR) during a 12-month period. We selected groups with similar CMA9 trajectories (Calinski-Harabasz criterion; R package kml). We performed multinomial regression analysis, assessing the relationship between demographic and clinical factors and adherence trajectory groups. The cohort included 4455 subjects, 70% male. Group-based trajectory modeling identified four distinct adherence trajectories: high adherence (42.6% of subjects; CMA mean 0.91 ± 0.08), partial drop-off (19.6%; CMA 0.63 ± 0.13), moderate adherence (19.3%; CMA 0.54 ± 0.11), and low adherence (18.4%; CMA 0.17 ± 0.12). Polypharmacy was associated with partial drop-off adherence (OR 1.194, 95%CI 1.175-1.214), while the occurrence of ≥1 HF hospitalization (OR 1.165, 95%CI 1.151-1.179) or other hospitalizations (OR 1.481, 95%CI 1.459-1.503) were associated with low adherence. This study found that tailoring patient education, providing support, and ongoing monitoring can boost adherence within different groups, potentially improving health outcomes.

The role of INTERCheckWEB digital innovation in supporting polytherapy management

The study aims at defining the factors affecting the clinicians' decision of changing or confirming the treatment options for frail patients in polytherapy, supporting prescribing patterns, thus also figuring out if the inclination of the clinicians towards digital solutions (INTERCheckWEB) and specific guidelines, could play a role in their decision. A literature review was performed, revealing the main individual, organizational and decisional factors, impacting on the clinicians' propensity to change the current patients' therapy: the clinician perceptions of support in case of clinical guidelines use or INTERCheckWEB use were studied. A qualitative approach was implemented, and thirty-five clinicians completed a questionnaire, aimed at evaluating fifteen different clinical cases, defining if they would change the patient's current therapy depending on the level of information received. Three methodological approaches were implemented. (1) Bivariate correlations to test the relationships between variables. (2) Hierarchical sequential linear regression model to define the predictors of the clinician propensity to change therapy. (3) Fuzzy Qualitative Comparative Analysis-fsQCA, to figure out the combination of variables leading to the outcome. Patient's age and autonomy (p value = 0.000), as well as clinician's perception regarding IT ease of use (p value = 0.043) and seniority (p value = 0.009), number of drugs assumed by the patients (p value = 0.000) and number of concomitant diseases (p value = 0.000) are factors influencing a potential change in the current therapy. The fsQCA-crisp confirms that the clinical conditions of the patients are the driving factors that prompt the clinicians towards a therapy change.

Hospital Antibiotic Use during COVID-19 Pandemic in Italy

Antimicrobial resistance (AMR) represents a major issue in healthcare being correlated to global inappropriate use of antibiotics. The aim of this study was to compare the data on hospital antibiotic consumption in 2020-2021 with those related to 2019 in order to evaluate the impact of the COVID-19 pandemic on antibiotic prescriptions and appropriate use at national level and in the different geographical areas. To estimate the consumption of antibiotics, two indicators were calculated: "number of DDD per 1000 inhabitants per day" and "number of DDD per 100 hospitalisation days". Consumption data on antibiotics dispensed in public health facilities were based on the Italian "traceability of medicines" information flow. Data on hospitalisation days were extracted from the Italian "hospital discharge form" flow. Pearson correlation analysis was performed between the number of patients hospitalised for COVID-19 and the consumption of antibiotics in public healthcare facilities. During 2020, about 1.7 DDD/1000 inhabitants per day (12.3% of the overall consumption of reimbursed antibiotics) were dispensed exclusively in Italian hospitals (+0.8% compared to 2019). Considering the number of DDD per 100 hospitalisation days, consumption increased by 19.3% in 2020 compared to 2019. Comparing the first semester of 2020 and 2019, a decrease of DDD/1000 inhabitants per day was observed (-1.6%) at national level, with opposite trends in the different geographical areas; an increase in the use of azithromycin and carbapenems was also observed, with a stable consumption of third-generation cephalosporins. The use of antibiotics in the second semester of 2020 compared to the same period of 2019 showed a clear reduction at national level (-8.5%), appreciable to a similar extent in all geographic areas. In the first semester of 2021 compared to the same period of 2020, there was a huge reduction (-31.4%) in consumption at national level. However, the variations were heterogeneous between different geographical areas. To our knowledge, this study represents the most comprehensive analysis performed on antibiotic consumption data in hospital settings in Italy during the COVID-19 pandemic to date. Despite international and national guideline recommendations, a substantial overall increase in antibiotic prescriptions was observed during the COVID-19 pandemic, with variability in terms of geographical distribution and prescription strategies. These findings may be related to the dichotomy between perceived and real significance of guidelines, expert panels, or consensus. Therefore, new approaches or strategies to antimicrobial stewardship should be proposed.

COVID-19 Mortality in Patients Aged 80 and over Residing in Nursing Homes-Six Pandemic Waves: OCTA-COVID Study

During the first COVID-19 pandemic wave in Spain, 50% of deaths occurred in nursing homes, making it necessary for some hospitals to support these facilities with the care of infected patients. This study compares origin, characteristics, and mortality of patients admitted with COVID-19 during six pandemic waves in the Hospital Central de la Cruz Roja in Madrid. It is a retrospective observational study of patients ≥80 years old, admitted with an acute SARS-CoV-2 infection, with a total of 546 patients included, whose final outcome was death or discharge. During the first wave, those from nursing homes had a higher risk of death than those from home; during the two successive waves, the risk was higher for those from home; and in the last two waves, the risk equalized and decreased exponentially in both groups. Men had 72% higher risk of death than women. For each year of age, the risk increased by 4% (p = 0.036). For each Charlson index point, the risk increased by 14% (p = 0.019). Individuals in nursing homes, despite being older with higher comorbidity, did not show a higher overall lethality. The mortality decreased progressively in each successive wave due to high vaccination rates and COVID-19 control measures in this population.

Risk of SARS-CoV-2 Infection Breakthrough among the Non-Vaccinated and Vaccinated Population in Italy: A Real-World Evidence Study Based on Big Data

SARS-CoV-2 infection after vaccination can occur because COVID-19 vaccines do not offer 100% protection. The study aim was to assess duration of vaccination coverage, disease symptoms and type of hospitalization among non-vaccinated and vaccinated subjects to evaluate the vaccination trend over time. A retrospective cohort study was carried out among people testing COVID-19 positive in Campania Region using information from the Health Information System of Campania Region (Sinfonia). Vaccination status was assessed considering: no vaccination, partial vaccination and effective vaccination. Univariate and multivariate logistic regression models were constructed to evaluate the association between ICU admissions caused by COVID-19 and gender, age groups and vaccine type. Vaccine coverage duration trends were investigated using segmented linear regression and breakpoint estimations. Vaccination coverage was assessed by analyzing COVID-19 positive subjects in the 9 months after an effective dose vaccination. A significant risk of hospitalization in the ICU was caused by vaccination status: subjects non-vaccinated (OR: 7.14) and partially vaccinated (OR: 3.68) were 3 and 7 times more at risk of hospitalization, respectively, than subjects effectively vaccinated. Regarding subjects with an effective vaccination, the vaccine’s ability to protect against infection in the months following vaccination decreased. The risk of contracting COVID-19 after vaccination was higher 5 months (β = 1441, p < 0.001) and 7 months (β = 3110, p < 0.001) after administration of an effective dose. COVID-19 vaccines were demonstrated to protect from symptomatic infection by significantly reducing hospitalization risk, and their full protection against SARS-CoV-2 was demonstrated to decrease after 5 months regardless of age, gender or vaccine type.

Potential of Microneedle Systems for COVID-19 Vaccination: Current Trends and Challenges

To prevent the coronavirus disease 2019 (COVID-19) pandemic and aid restoration to prepandemic normality, global mass vaccination is urgently needed. Inducing herd immunity through mass vaccination has proven to be a highly effective strategy for preventing the spread of many infectious diseases, which protects the most vulnerable population groups that are unable to develop immunity, such as people with immunodeficiencies or weakened immune systems due to underlying medical or debilitating conditions. In achieving global outreach, the maintenance of the vaccine potency, transportation, and needle waste generation become major issues. Moreover, needle phobia and vaccine hesitancy act as hurdles to successful mass vaccination. The use of dissolvable microneedles for COVID-19 vaccination could act as a major paradigm shift in attaining the desired goal to vaccinate billions in the shortest time possible. In addressing these points, we discuss the potential of the use of dissolvable microneedles for COVID-19 vaccination based on the current literature.

Postvaccination SARS-CoV-2 Infections among Healthcare Professionals: A Real World Evidence Study

Due to the COVID-19 pandemic, all countries with a global mobilization started to produce and authorize vaccines, prioritizing healthcare professionals (HCPs) to reduce transmission. The aim of this study was to assess post-vaccination infections’ occurrence among HCPs and their correlation with symptom onset. A retrospective cohort study was carried out in the Campania Region from December 2020 to April 2021. Data were retrieved from the Regional Health Information System of the Campania Region (Sinfonia). The study cohort included subjects that had all received at least one vaccine dose. Risk ratios (RRs) adjusted for age and sex (95% confidence intervals) were performed to assess differences in the prevalence between HCPs who tested positive or negative for COVID-19. Univariate and multivariate logistic regression models were used to evaluate the association between symptoms and vaccination status. Findings revealed that HCPs had a lower risk of contracting COVID-19 after receiving at least one vaccine dose, and this risk decreased with age. Furthermore, not having full vaccination coverage may predict a severe/critical evolution of the disease. This study provides a snapshot of the initial state of the Italian vaccination campaign on HCPs. A surveillance approach using Big Data matched to clinical conditions could offer a real analysis in the categorization of subjects most at risk.

Baseline Drug Treatments as Indicators of Increased Risk of COVID-19 Mortality in Spain and Italy

This study aims to identify baseline medications that, as a proxy for the diseases they are dispensed for, are associated with increased risk of mortality in COVID-19 patients from two regions in Spain and Italy using real-world data. We conducted a cross-country, retrospective, observational study including 8570 individuals from both regions with confirmed SARS-CoV-2 infection between 4 March and 17 April 2020, and followed them for a minimum of 30 days to allow sufficient time for the studied event, in this case death, to occur. Baseline demographic variables and all drugs dispensed in community pharmacies three months prior to infection were extracted from the PRECOVID Study cohort (Aragon, Spain) and the Campania Region Database (Campania, Italy) and analyzed using logistic regression models. Results show that the presence at baseline of potassium-sparing agents, antipsychotics, vasodilators, high-ceiling diuretics, antithrombotic agents, vitamin B12, folic acid, and antiepileptics were systematically associated with mortality in COVID-19 patients from both countries. Treatments for chronic cardiovascular and metabolic diseases, systemic inflammation, and processes with increased risk of thrombosis as proxies for the conditions they are intended for can serve as timely indicators of an increased likelihood of mortality after the infection, and the assessment of pharmacological profiles can be an additional approach to the identification of at-risk individuals in clinical practice.

Pre-Exposure Prophylaxis with Hydroxychloroquine Does Not Prevent COVID-19 nor Virus Related Venous Thromboembolism

Different and several public health strategies have been planned to reduce transmission of pandemic due to SARS-CoV-2 since it started. None drugs have been confirmed as able to prevent viral transmission. Hydroxychloroquine with its immunomodulatory properties has been proposed as potential anti-viral drug in particular for prevention once viral exposure has been happen or in first phases of infection. Furthermore, in several immunological systemic disease hydroxychloroquine was able to reduce the number of thrombotic complications. So, because COVID-19 was associated to immunological imbalance and to thrombotic complications, we retrospectively analyzed the rate of infection in those patients being under treatment with this drug during COVID-19 epidemic outbreak from 8 March until 28 April in particular comparing those with pre-exposure to this treatment and those that were not taking this medication before SARS-CoV-2 viral infections.