Polypharmacy among COVID-19 patients: A systematic review

Patterns of co-administration and sequential administration of COVID-19 and influenza vaccinations: a US study using Adheris pharmacy dataset

BACKGROUND

This study assessed monthly patterns of co-administration of COVID-19 and influenza vaccines at visit level and quantified the number and proportion of adults who received both vaccines on the same day or sequentially.

RESEARCH DESIGN AND METHODS

This retrospective study utilized the Adheris pharmacy dataset and included adults aged ≥ 19 years with a record of ≥ 1 dose of COVID-19 or influenza vaccines between August 2022 and December 2023. Administration of both vaccines on the same day was defined as co-administration, and administration of vaccines within 1-180 days was referred to as sequential administration. Descriptive analyses were performed.

RESULTS

Among 31.5 million visits with either COVID-19 or influenza vaccines, 5.8 million (18.5%) involved co-administration. Co-administration visits were higher between September and November compared to other months, rising to 1.5 million in October 2023. Among 7.9 million adults with both vaccines, 65.8% had same day administration, while 34.2% had sequential administration. 60.7% of adults with sequential administration received the vaccines within 1-30 days.

CONCLUSIONS

About one in five COVID-19 or influenza vaccinations involved co-administration. The majority of sequential administrations happened within 30 days, highlighting missed opportunities for co-administration. These findings suggest substantial potential to improve uptake of both vaccines through co-administration.

Inappropriate polypharmacy during the COVID-19 pandemic: impact, challenges, and solutions - a narrative review

The COVID-19 pandemic has brought unprecedented challenges to healthcare systems worldwide, impacting various aspects of patient care. Polypharmacy, the concurrent use of multiple medications by a single patient, is a significant concern exacerbated by the pandemic. The dual threat of COVID-19 infection and polypharmacy for the same vulnerable group - the elderly and those with pre-existing multimorbidity - is particularly problematic, as polypharmacy has been shown to lead to suboptimal treatment outcomes in many chronic diseases. This comprehensive review explores the multifaceted issues surrounding polypharmacy during the COVID-19 pandemic, addressing its causes, consequences, and potential solutions.

Use of an extensively humanized mouse model to predict the risk of drug-drug interactions in patients receiving dexamethasone

The corticosteroid dexamethasone, which is used to treat numerous health conditions, remains the first-line treatment for patients hospitalized with COVID-19 requiring oxygen. Current British National Formulary prescribing advice warns of a "severe theoretical" or "severe anecdotal" risk of drug-drug interactions between dexamethasone and 138 different medications. In humans, dexamethasone is eliminated via the cytochrome P450 monooxygenase system, particularly CYP3A4. It is also described as a human cytochrome P450-inducing agent. To establish factors that affect concomitant therapy and dexamethasone efficacy in the treatment of COVID-19, we used a unique mouse model humanized for cytochrome P450s and the transcription factors that regulate their expression, the pregnane X receptor, and the constitutive androstane receptor. We found that induction of CYP3A4 with the anticancer drug dabrafenib or the herbal medicine St John's wort profoundly reduced dexamethasone exposure. These data suggest that comedications that induce cytochrome P450 expression can have a marked effect on dexamethasone exposure and, potentially, clinical efficacy. We also observed that rather than increasing CYP3A4 expression, dexamethasone at doses equivalent to or higher than those used in the treatment of COVID-19 reduced CYP3A4 expression and increased exposure to dabrafenib. These data indicate the need for a clinical trial to establish the risk of overexposure to comedications during dexamethasone treatment, including the treatment of COVID-19. SIGNIFICANCE STATEMENT: Current prescribing advice identifies a potential theoretical risk of severe side effects when dexamethasone, one of the most widely used drugs in clinical practice, is coadministered with many other drugs; it is, however, difficult to define the magnitude of this risk for specific drug combinations. We describe the use of cytochrome P450-humanized 8HUM mice to predict drug-drug interactions in patients on polypharmacy, a means of generating data that could better inform clinicians regarding foreseeable drug-drug interactions involving dexamethasone.

Association of Polypharmacy and Burden of Comorbidities on COVID-19 Adverse Outcomes in People with Type 1 or Type 2 Diabetes

INTRODUCTION

It is widely accepted that the higher the number of medications prescribed and taken by an individual, the higher the risk of poor health outcomes. We have investigated whether polypharmacy and comorbidities conveyed more risk of adverse health outcomes following COVID-19 infection (as a paradigm of serious viral infections in general) in people with type 1 diabetes (T1DM) or type 2 diabetes (T2DM).

METHODS

The Greater Manchester Care Record (GMCR) is an integrated database of electronic health records containing data collected from 433 general practices in Greater Manchester. Baseline demographic information (age, body mass index [BMI], gender, ethnicity, smoking status, deprivation index), hospital admission or death within 28 days of infection were extracted for adults (18+) diagnosed with either T1DM or T2DM.

RESULTS

The study cohort included individuals diagnosed as T1DM and T2DM separately. Across the Greater Manchester Region, a total of 145,907 individuals were diagnosed with T2DM and 9705 were diagnosed with T1DM. For the T2DM individuals, 45.2% were women and for the T1DM individuals, 42.7% were women. For T2DM, 16-20 medications (p = 0.005; odds ratio [OR] [95% confidence interval (CI) 2.375 [1.306-4.319]) and > 20 medications (p < 0.001; OR [95% CI] 3.141 [1.755-5.621]) were associated with increased risk of death following COVID-19 infection. Increased risk of hospital admissions in T2DM individuals was associated with 11 to 15 medications (p = 0.013; OR = 1.341 (95% CI) [1.063-1.692]). This was independent of comorbidities, metabolic and demographic factors. For T1DM, there was no association of polypharmacy with hospital admission. Additionally, respiratory, cardiovascular/cerebrovascular and gastrointestinal conditions were associated with increased risk of hospital admissions and deaths in T2DM (p < 0.001). Many comorbidities were common across both T1DM and T2DM.

CONCLUSIONS

We have shown in T2DM an independent association of multiple medications taken from 11 upwards with adverse health consequences following COVID-19 infection. We also found that individuals with diabetes develop comorbidities that were common across both T1DM and T2DM. This study has laid the foundation for future investigations into the way that complex pharmacological interactions may influence clinical outcomes in people with T2DM.

Impact of COVID-19 on Psychiatric Patients: The Role of Vaccination, Comorbidities, and Biomarkers in Clinical Outcomes

Background/Objectives: The COVID-19 pandemic has posed significant challenges, particularly for individuals residing in psychiatric facilities. This study aims to investigate the impact of COVID-19 on psychiatric patients, focusing on factors such as their vaccination status, comorbidities, medication regimens, and biomarkers like C-reactive protein (CRP) and ferritin. Methods: This retrospective study analyzed 100 patients with confirmed SARS-CoV-2 infections admitted to the private psychiatric clinic "Asclepius of Upper Volos" from March 2020 to March 2023. The data collected included sociodemographic characteristics, vaccination status, symptom severity, medication regimens, and levels of CRP and ferritin. Statistical analyses using IBM SPSS Statistics version 29 included Pearson's chi-square tests, Student's t-tests, and a survival time analysis via the log-rank test to assess associations between clinical characteristics and outcomes. Results: Among the participants, 64% were female and 74% received two doses of the COVID-19 vaccine. The majority experienced mild symptoms, with a survival rate of 74%. Statistically significant findings include a higher survival rate among vaccinated individuals (98.6%) versus unvaccinated individuals (1.4%, p < 0.001). Comorbidities like chronic obstructive pulmonary disease (COPD), coronary artery disease, and renal failure were associated with severe symptoms and higher mortality rates. Higher ferritin levels were significantly associated with poorer outcomes, with survivors having a mean ferritin level of 246.2 (SD = 150.3) compared to the 416.9 (SD = 215.9) seen in non-survivors (p < 0.001). Similarly, mean CRP levels were lower in survivors (1.58, SD = 1.96) than in non-survivors (3.46, SD = 2.92), with a p-value of 0.002. Conclusions: The findings underscore the importance of tailored health protocols and continued support for this vulnerable population. Enhanced strategies for managing comorbidities and utilizing biomarkers can aid in better predicting and improving psychiatric patient outcomes.

Prevalence of Potential Drug Interactions With Direct-Acting Antivirals for COVID-19 Among Hospitalized Patients

PURPOSE

Clinicians consider polypharmacy, comorbidities, and other factors including the potential for drug-drug interactions (DDIs) when evaluating therapeutic options for specific clinical diagnoses. Contemporary treatment for coronavirus disease 2019 (COVID-19) includes direct-acting antivirals (DAAs). We sought to characterize patients' characteristics, comorbidities, and medications received during their hospitalization for COVID-19 and quantify potential DDIs that clinicians consider in selecting appropriate DAAs.

METHODS

Patients hospitalized with a primary diagnosis of COVID-19 between May 2020 and December 2022 from the PINC AI Healthcare Database were identified. Medications administered during the hospitalization with the potential to cause DDIs with nirmatrelvir/ritonavir, remdesivir, or molnupiravir (per the Emergency Use Authorization factsheet or package insert) were assessed. For DDIs with nirmatrelvir/ritonavir, medications are categorized as "Contraindicated," "Avoid Concomitant Use," or "Other DDIs" (includes recommendation for dose modification or clinical and laboratory monitoring). For remdesivir, coadministration with chloroquine phosphate and hydroxychloroquine sulfate was not recommended. For molnupiravir, no drugs are listed as having potential DDIs. In a subset of patients, a multivariable logistic regression model was used to examine the association between documented patient/hospital characteristics and the likelihood of being "Contraindicated" to receive nirmatrelvir/ritonavir.

FINDINGS

Of the 788,238 patients hospitalized for COVID-19 in 920 hospitals, 53% were ≥ 65 years old, and 31% had Charlson Comorbidity Index (CCI) ≥ 3. During the study period, about half of the patients received medications categorized as "Contraindicated" (11%) and/or "Avoid Concomitant Use" (41%) with nirmatrelvir/ritonavir. The frequency of administered drugs was higher in those aged ≥ 65 years (68%), CCI ≥ 3 (78%), with high-risk underlying conditions (55%). About 1% of patients received medications that were not recommended to be coadmistered with remdesivir. Among a subset of patients hospitalized for COVID-19 in 2022, those who were older, had higher CCI, high-risk underlying conditions, severe hepatic impairment, Medicare insurance, and hospitalized in larger hospitals were significantly more likely to be categorized as "Contraindicated" when considering nirmatrelvir/ritonavir as a therapeutic option to manage COVID-19.

IMPLICATIONS

A significant proportion of patients hospitalized for COVID-19 receive medications for other conditions that have the potential to result in DDIs with DAAs; most predominantly with nirmatrelvir/ritonavir, a strong CYP3A enzyme inhibitor, fewer with remdesivir, and none with molnupiravir. Higher age and comorbidity burden were significantly associated with a higher likelihood of receiving medications that are "Contraindicated" with nirmatrelvir/ritonavir. In the evolving COVID-19 era, these findings provide insights into patients hospitalized for COVID-19, and the polypharmacy evaluations that clinicians may encounter when selecting among DAAs to manage COVID-19.

Relationship between 2nd-generation angiotensin receptor blockers and the risk of hypotension in COVID-19 patients admitted to hospital

It has not yet been established whether angiotensin II receptor blockers (ARB), statins, and multiple drugs affect the severity of COVID-19. Therefore, we herein performed an observational study on the effects of 1st- and 2nd-generation ARB, statins, and multiple drugs, on COVID-19 in patients admitted to 15 Japanese medical facilities. The results obtained showed that ARB, statins, and multiple drugs were not associated with the primary outcome (odds ratio: 1.040, 95% confidence interval: 0.688-0.571; 0.696, 0.439-1.103; 1.056, 0.941-1.185, respectively), each component of the primary outcome (in-hospital death, ventilator support, extracorporeal membrane oxygenation support, and admission to the intensive care unit), or the secondary outcomes (oxygen administration, disturbed consciousness, and hypotension, defined as systolic blood pressure ≤90 mmHg). ARB were divided into 1st- and 2nd-generations based on their approval for use (before 2000 and after 2001), with the former consisting of losartan, candesartan, and valsartan, and the latter of telmisartan, olmesartan, irbesartan, and azilsartan. The difference of ARB generation was not associated with the primary outcome (odds ratio with 2nd-generation ARB relative to 1st-generation ARB: 1.257, 95% confidence interval: 0.613-2.574). The odd ratio for a hypotension as one of the secondary outcomes with 2nd-generation ARB was 1.754 (95% confidence interval: 1.745-1.763) relative to 1st-generation ARB. These results suggest that patients taking 2nd-generation ARB may be at a higher risk of hypotension than those taking 1st-generation ARB and also that careful observations are needed. Further studies are continuously needed to support decisions to adjust medications for co-morbidities.

Drug Synergism of Anticancer Action in Combination with Favipiravir and Paclitaxel on Neuroblastoma Cells

Background and Objectives: Favipiravir (FPV) is an antiviral medication and has an inhibitory effect on Cytochrome P450 (CYP2C8) protein, which is mainly involved in drug metabolism in the liver, and the expression of this gene is known to be enhanced in neuronal cells. The metabolization of Paclitaxel (PTX), a chemotherapeutic drug used in cancer patients, was analyzed for the first time in the human SH-SY5Y neuroblastoma cell line for monitoring possible synergistic effects when administered with FPV. Materials and Methods: Further, in vitro cytotoxic and genotoxic evaluations of FPV and PTX were also performed using wide concentration ranges in a human fibroblast cell culture (HDFa). Nuclear abnormalities were examined under a fluorescent microscope using the Hoechst 33258 fluorescent staining technique. In addition, the synergistic effects of these two drugs on cultured SH-SY5Y cells were determined by MTT cell viability assay. In addition, the death mechanisms that can occur in SHSY-5Y were revealed by using the flow cytometry technique. Results: Cell viability analyses on the HDFa healthy cell culture showed that both FPV and PTX have inhibitory effects at higher concentrations. On the other hand, there were no significant differences in nuclear abnormality numbers when both of the compounds were applied together. Cell viability analyses showed that FPV and PTX applications have higher cytotoxicity, which indicated synergistic toxicity against the SHSY-5Y cell line. Also, PTX exhibited higher anticancer properties against the neuroblastoma cell line when applied with FPV, as shown in both cytotoxicity and flow cytometry analyses. Conclusions: In light of our findings, the anticancer properties of PTX can be enhanced when the drug application is coupled with FPV exposure. Moreover, these results put forth that the anticancer drug dosage should be evaluated carefully in cancer patients who take COVID-19 treatment with FPV.

Poor regulation, desperation, and misinformation, a countrywide analysis of self-medication and prescription patterns in Ecuador during the COVID-19 pandemic

BACKGROUND

The rapid spread of the SARS-CoV-2 virus during the early phase of the pandemic led to an unprecedented global health crisis. Various factors have influenced self-medication practices among the general population and unsubstantiated prescribing practices among healthcare professionals.

OBJECTIVE

This study aimed to describe trends in the purchase and sale of medicines during the COVID-19 pandemic period (2020-2022) in Ecuador, by comparing them with pre-pandemic periods.

METHODS

In this study, a cross-sectional design was employed to conduct a comprehensive analysis of 28 pharmacological groups, categorized according to the Anatomical Therapeutic Chemical Classification (ATC). Utilizing an integrated drug consumption database, the study examined physician prescribing data, medicine usage, and spending levels in Ecuador during the COVID-19 pandemic. The analysis involved computing absolute differences in monthly resolution, calculating excessive expenditure in comparison to previous yearly averages, and using Defined Daily Dose (DDD) methodology for internationally comparable results. Furthermore, a correlation analysis was performed to investigate potential associations between prescribed and consumed medicines and the number of new cases and deaths.

RESULTS

In Ecuador, the average yearly expenditure among these groups prior to the pandemic (2017-2019) amounted to $150,646,206 USD, whereas during 2020 and 2021, the same groups represented a total expenditure of $228,327,210, reflecting a significant increase. The excess expenditure during this period reached 51.4%, equivalent to $77,681,004 USD. Notably, 13% of this expenditure consisted of Over the Counter (OTC) Medicines. The study also identified a remarkable surge in sales of ivermectin, which increased by 2,057%, and hydroxychloroquine, which increased by 171%, as measured by DDD.

CONCLUSIONS

This study highlights the substantial consumption of medicines by the population in Ecuador during the pandemic. It is concerning that many medications were sold without proven therapeutic indications, indicating that misinformation and desperation may have led to improper prescribing by physicians and patients resorting to ineffective drugs. Moreover, since the sale of these therapeutic drugs requires a prescription, poor regulation, and a lack of control within pharmacies likely contributed to such practices.

Polypharmacy and the In Silico Prediction of Potential Body Proteins Targeted by These Drugs Among Hospitalized COVID-19 Patients With Cytokine Storm

Background and objective Polypharmacy is prevalent in coronavirus disease 2019 (COVID-19) patients with severe disease. However, information on polypharmacy among COVID-19 patients who also suffer from cytokine storm is scarce. In light of this, the purpose of the present study was to assess the incidence of polypharmacy and in silico prediction of potential body proteins targeted by these drugs among hospitalized COVID-19 patients who were identified to have the additional burden of cytokine storm in the city of Duhok, Kurdistan Region, Iraq. Methods This was a cross-sectional observational study conducted from June 2021 to April 2022; the phenomena of major polypharmacy (six to nine medications) and excessive polypharmacy (≥10 medications) were documented among 33 (15 males and 18 females) COVID-19 patients with cytokine storm during their hospital stay (8-45 days) in Duhok, Kurdistan Region, Iraq. The SwissTargetPrediction program was utilized in silico to predict and identify human body proteins that could be potentially targeted by selected medications involved in polypharmacy. Results All patients had tested positive for COVID-19 via PCR testing, and they showed different signs and symptoms of the disease. None of the patients recovered and all of them deceased. All 33 patients received many therapeutic agents that ranged in number from eight to 20/patient during their hospital stay. The mean number of medications was 15 ± 3. We identified 2/33 (6%) patients with major polypharmacy (eight and nine) and 31/33 (94%) with excessive polypharmacy (15.5 ± 2.7). The total number of medications identified in polypharmacy was 37, excluding vitamins, minerals, and intravenous solutions. The frequency of medications administered was as follows: antibiotics (67, 13.7%), mucolytic agents (56, 11.5%), corticosteroids (54, 11%), anticoagulants (48, 9.8%), antiviral agents (41, 8.4%), antihypertensive agents (32, 6.5%), analgesics (28, 5.7%), antifungal drugs (27, 5.5%), antidiabetics (26, 5.3%), and other medications (2-19, 0.41-3.9%). Using the SwissTargetPrediction program, various drugs including antiviral agents involved in polypharmacy were found to target, in silico, body proteins at a prediction percentage that ranged from 6.7% to 40%. Conclusions Major and extensive polypharmacy conditions were identified in hospitalized COVID-19 patients suffering from cytokine storm. The severity of COVID-19 with cytokine storm, comorbidities, and hospitalization were key factors associated with polypharmacy in the patients. The SwissTargetPrediction web server is useful for predicting in silico potential human body protein targets that could possibly be sources of additional information on the adverse/toxic effects of polypharmacy medications administered concurrently. Further research in current medication protocols prescribed for advanced COVID-19 illness with cytokine storm is warranted to gain deeper insights into the topic.

Role of SARS-COV-2 and ACE2 in the pathophysiology of peripheral vascular diseases

The occurrence of a novel coronavirus known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), created a serious challenge worldwide. SARS-CoV-2 has high infectivity, the ability to be transmitted even during the asymptomatic phase, and relatively low virulence, which has resulted in rapid transmission. SARS-CoV-2 can invade epithelial cells, hence, many patients infected with SARS-CoV-2 have suffered from vascular diseases (VDs) in addition to pulmonary manifestations. Accordingly, SARS-CoV-2 may can worsen the clinical condition of the patients with pre-existing VDs. Endothelial cells express angiotensin-converting enzyme 2 (ACE2). ACE2 is a biological enzyme that converts angiotensin (Ang)- 2 to Ang-(1-7). SARS-CoV-2 uses ACE2 as a cell receptor for viral entry. Thus, the SARS-CoV-2 virus promotes downregulation of ACE2, Ang-(1-7), and anti-inflammatory cytokines, as well as, an increase in Ang-2, resulting in pro-inflammatory cytokines. SARS-CoV-2 infection can cause hypertension, and endothelial damage, which can lead to intravascular thrombosis. In this review, we have concentrated on the effect of SARS-CoV-2 in peripheral vascular diseases (PVDs) and ACE2 as an enzyme in Renin-angiotensin aldosterone system (RAAS). A comprehensive search was performed on PubMed, Google Scholar, Scopus, using related keywords. Articles focusing on ("SARS-CoV-2", OR "COVID-19"), AND ("Vascular disease", OR "Peripheral vascular disease", OR interested disease name) with regard to MeSH terms, were selected. According to the studies, it is supposed that vascular diseases may increase susceptibility to severe SARS-CoV-2 infection due to increased thrombotic burden and endothelial dysfunction. Understanding SARS-CoV-2 infection mechanism and vascular system pathogenesis is crucial for effective management and treatment in pre-existing vascular diseases.

Effectiveness and safety of COVID-19 vaccines in patients with oncological diseases: State-of-the-art

Although the coronavirus disease 2019 (COVID-19) pandemic was declared to be no longer “a public health emergency of international concern” with its wide range of clinical manifestations and late complications, severe acute respiratory syndrome coronavirus 2 infection proved to be a serious threat, especially to the elderly and patients with comorbidities. Patients with oncologic diseases are vulnerable to severe infection and death. Indeed, patients with oncohematological diseases have a higher risk of severe COVID-19 and impaired post-vaccination immunity. Unfortunately, cancer patients are usually excluded from vaccine trials and investigations of post-vaccinal immune responses and the effectiveness of the vaccines. We aimed to elucidate to what extent patients with cancer are at increased risk of developing severe COVID-19 and what is their overall case fatality rate. We also present the current concept and evidence on the effectiveness and safety of COVID-19 vaccines, including boosters, in oncology patients. In conclusion, despite the considerably higher mortality in the cancer patient group than the general population, countries with high vaccination rates have demonstrated trends toward improved survival of cancer patients early and late in the pandemic.

Correlates of poor clinical outcomes related to COVID-19 among older people with psychiatric illness - a mixed methods study

OBJECTIVE

COVID-19 may lead to a range of clinical outcomes among older people with psychiatric and medical conditions. Evidence guiding management of future outbreaks among this vulnerable population in psychiatric hospital settings are sparse. In this study, we examined the correlates of poor clinical outcomes related to COVID-19 and explored the perspectives of COVID-19 survivors hospitalized in psychiatry settings.

METHOD

The correlates of poor clinical outcomes related to COVID-19 were examined using a retrospective chart review of 81 older people hospitalized in psychiatry settings. Correlates of clinical outcomes related to COVID-19 were assessed by multiple logistic regression models. In addition, the perspectives of 10 COVID-19 survivors were explored by qualitative interviews. The qualitative data was subject to thematic analysis.

RESULTS

Although 25.9% (n = 21) participants were asymptomatic, there was high COVID-19 related mortality (14.8%; n = 12). Vitamin-D deficiency, anticholinergic burden, and isolation policies within psychiatric wards were significantly (p < 0.05) related to COVID-19 related deaths. In qualitative interviews, participants emphasized the importance of strengthening local support networks and making vaccination centers more accessible.

CONCLUSIONS

Reducing anticholinergic prescriptions and improving isolation policies may help to mitigate poor clinical outcomes. Future research investigating the impact of vitamin-D supplementation on COVID-19 related outcomes is warranted.

[Risk factors for mortality from COVID-19 Omicron variant: Retrospective analysis in elderly from the Canary Islands]

BACKGROUND AND AIMS

Since the beginning of the COVID-19 pandemic, the elderly population has had the highest rates of complications and mortality. This study aimed to determine the influence of different risk factors on deaths due to the Omicron variant in the Canary Islands.

MATERIALS AND METHODS

A retrospective observational study of 16,998 cases of COVID-19 over 40 years of age was conducted in the Canary Islands between August 1, 2022, and January 31, 2023. We extracted sociodemographic data (age and sex) and clinical data (death, vaccination history, hospital admission, previous diseases, and treatments).

RESULTS

Among the deaths, there was a higher proportion of males aged over 70 years, with diabetes, cardiovascular, renal, respiratory, and systemic diseases, and nursing home residents. Significant differences were observed in the number of doses of the vaccine. The multiple regression model showed that male sex (OR [95% CI]=1.92 [1.42-2.58]), age (70-79 years, 9.11 [4.27-19.43]; 80-89 years, 21.72 [10.40-45.36]; 90-99 years, 66.24 [31.03-141.38]; 100 years or older, 69.22 [12.97-369.33]), being unvaccinated (6.96, [4.01-12.08]), or having the last dose administered at least 12 months before the diagnosis (2.38, [1.48-3.81]) were significantly associated with mortality.

CONCLUSIONS

Multiple factors may increase the risk of mortality due to COVID-19 in the elderly population. In our study, we found that only three predictors can effectively explain the variability: older age, male sex, and not being vaccinated or last vaccination date prior to one year.

Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms

BACKGROUND

Polypharmacy (PP) is increasingly common in Iran, and contributes to the substantial burden of drug-related morbidity, increasing the potential for drug interactions and potentially inappropriate medications. Machine learning algorithms (ML) can be employed as an alternative solution for the prediction of PP. Therefore, our study aimed to compare several ML algorithms to predict the PP using the health insurance claims data and choose the best-performing algorithm as a predictive tool for decision-making.

METHODS

This population-based cross-sectional study was performed between April 2021 and March 2022. After feature selection, information about 550 thousand patients were obtained from National Center for Health Insurance Research (NCHIR). Afterwards, several ML algorithms were trained to predict PP. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated.

RESULTS

The study sample comprised 554 133 adults with a median (IQR) age of 51 years (40 - 62) that nested in 27 cities within the Khuzestan province of Iran. Most of the patients were female (62.5%), married (63.5%), and employed (83.2%) during the last year. The prevalence of PP in all populations was about 36.0%. After performing the feature selection, out of 23 features, the number of prescriptions, Insurance coverage for prescription drugs, and hypertension were found as the top three predictors. Experimental results showed that Random Forest (RF) performed better than other ML algorithms with recall, specificity, accuracy, precision and F1-score of 63.92%, 89.92%, 79.99%, 63.92% and 63.92% respectively.

CONCLUSION

It was found that ML provides a reasonable level of accuracy in predicting polypharmacy. Therefore, the prediction models based on ML, especially the RF algorithm, performed better than other methods for predicting PP in Iranian people in terms of the performance criteria.

Staging of COVID-19 disease; using selected laboratory profiles for prediction, prevention and management of severe SARS-CoV-2 infection in Africa-review

There are many uncertainties on the future management of the coronavirus disease 19 (COVID-19) in Africa. By July 2021, Africa had lagged behind the rest of the world in Covid-19 vaccines uptake, accounting for just 1.6% of doses administered globally. During that time COVID 19 was causing an average death rate of 2.6% in Africa, surpassing the then global average of 2.2%. There were no clear therapeutic guidelines, yet inappropriate and unnecessary treatments may have led to unwanted adverse events such as worsening of hyperglycemia and precipitating of ketoacidosis in administration of steroid therapy. in order to provide evidence-based policy guidelines, we examined peer-reviewed published articles in PubMed on COVID 19, or up-to date data, we focused our search on publications from 1st May 2020 to 15th July, 2021. For each of the studies, we extracted data on pathophysiology, selected clinical chemistry and immunological tests, clinical staging and treatment. Our review reports a gross unmet need for vaccination, inadequate laboratory capacity for immunological tests and the assessment of individual immune status, clinical staging and prediction of disease severity. We recommend selected laboratory tools in the assessment of individual immune status, prediction of disease severity and determination of the exact timing for suitable therapy, especially in individuals with co-morbidities.

Recent development of machine learning models for the prediction of drug-drug interactions

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Mortality Prediction of COVID-19 in Hospitalized Patients Using the 2020 Diagnosis Procedure Combination Administrative Database of Japan

Objectives Numerous people have died from coronavirus disease 2019 (COVID-19) infection. Identifying crucial predictive biomarkers of disease mortality is critical to support decision-making and logistic planning in healthcare systems. This study investigated the association between mortality and medical factors and prescription records in 2020 in Japan, where COVID-19 prevalence and mortality remain relatively low. Methods This retrospective cohort study analyzed anonymous administrative data from the Diagnosis Procedure Combination (DPC) database in Japan. Results A total of 22,795 patients were treated in DPC hospitals in 2020 in Japan, and of these, 5,980 patients over 50 years old were hospitalized, with 299 (5.0%) dying. There were 2,399 severe patients among 11,440 total hospitalized patients (all ages). The results of a logistic model analysis revealed that an older age, male sex, Parkinson's disease, cerebrovascular diseases, and chronic kidney diseases were risk factors for mortality. A machine learning analysis identified an older age, male sex (mortality), pneumonia, drugs for acid-related disorders, analgesics, anesthesia, upper respiratory tract disease, drugs for functional gastrointestinal disorders, drugs for obstructive airway diseases, topical products for joint and muscular pain, diabetes, lipid-modifying agents, calcium channel blockers, drugs for diabetes, and agents acting on the renin-angiotensin system as risk factors for a severe status. Conclusions This COVID-19 mortality risk tool is a well-calibrated and accurate model for predicting mortality risk among hospitalized patients with COVID-19 in Japan, which is characterized by a relatively low COVID-19 prevalence, aging society, and high population density. This COVID-19 mortality prediction model can assist in resource utilization and patient and caregiver education and be useful as a risk stratification instrument for future research trials.

Older People Hospitalized for COVID-19: Prognostic Role of Multidimensional Prognostic Index and Other Prognostic Scores

During the SARS-CoV-2 pandemic, frailty and patients’ poor outcomes seem to be closely related. However, there is no clear indication of the significance of this connection and the most adequate risk index in clinical practice. In this study, we compared a short version of MPI (multidimensional prognostic index) and other two prognostic scores for COVID-19 as potential predictors of poor patient outcomes. The patients were consecutively enrolled in the hospital of Palermo for COVID-19. The accuracy of Brief-MPI, 4C score and COVID-GRAM score in points was evaluated using the area under the curve (AUC) with 95% CI, taking mortality or sub-ICU admission as outcome. The study included 112 participants (mean age 77.6, 55.4% males). During a mean of 16 days of hospitalization, Brief-MPI significantly increased by 0.03 ± 0.14 (p = 0.04), whilst COVID-GRAM did not. Brief-MPI, 4C score and COVID-GRAM scores had good accuracy in predicting negative outcomes (AUC > 0.70 for all three scores). Brief-MPI was significantly associated with an increased mortality/ICU admission risk, indicating the importance of multidimensional impairment in clinical decision-making with an accuracy similar to other prognostic scores commonly used in COVID-19 study, providing information regarding domains for which interventions can be proposed.

Polypharmacy and Drug Interactions in the COVID-19 Pandemic

The COVID-19 pandemic generated a great impact on health systems. We compared evolution, polypharmacy, and potential drug-drug interactions (P-DDIs) in COVID-19 and non-COVID-19 hospitalizations during first wave of pandemic. Prescriptions for hospitalized patients ≥ 18 years (COVID-19 and non-COVID-19 rooms) between April and September 2020 were included. The computerized medical decision support system SIMDA and the physician order entry system Hdc.DrApp.la were used. Patients in COVID-19 rooms were divided into detectable and non-detectable, according to real-time reverse transcription polymerase chain reaction (RT-PCR). Number of drugs, prescribed on day 1, after day 1, and total; polypharmacy, excessive polypharmacy, and P-DDIs were compared. 1,623 admissions were evaluated: 881 COVID-19, 538 detectable and 343 non-detectable, and 742 non-COVID-19. Mortality was 15% in COVID-19 and 13% in non-COVID-19 (RR [non-COVID-19 vs. COVID-19]: 0.84 [95% CI] [0.66-1.07]). In COVID-19, mortality was 19% in detectable and 9% in non-detectable (RR: 2.07 [1.42-3.00]). Average number of drugs was 4.54/patient (SD ± 3.06) in COVID-19 and 5.92/patient (±3.24) in non-COVID-19 (p<0.001) on day 1 and 5.57/patient (±3.93) in COVID-19 and 9.17/patient (±5.27) in non-COVID-19 (p<0.001) throughout the hospitalization. 45% received polypharmacy in COVID-19 and 62% in non-COVID-19 (RR: 1.38 [1.25-1.51]) and excessive polypharmacy 7% in COVID-19 and 14% in non-COVID-19 (RR: 2.09 [1.54-2.83]). The frequency of total P-DDIs was 0.31/patient (±0.67) in COVID-19 and 0.40/patient (±0.94) in non-COVID-19 (p=0.022). Hospitalizations in the COVID-19 setting are associated with less use of drugs, less polypharmacy and less P-DDIs. Detectable patients had higher mortality.

Polypharmacy and associated factors: a gender perspective in the elderly Spanish population (2011-2020)

Background: Few studies have examined the epidemiology of polypharmacy in non-institutionalized elderly adults with regard to sex differences. This study aimed to identify the prevalence of polypharmacy among people ≥65 years old residing in Spain, analyze trends in that prevalence from 2011/12 to 2020, describe the use of the medicines involved and study the possible relationship between polypharmacy and certain sociodemographic, health-related variables, as well as the use of care services by sex. Methods: A nationwide cross-sectional study with 21,841 non-institutionalized people ≥65 years old from the Spanish National Health Survey (2011/2012 and 2017) and the European Health Survey in Spain (2014 and 2020) was performed. We used descriptive statistics, performing two binary logistic regressions to determine the factors related to polypharmacy. Results: The prevalence of polypharmacy was 23.2% (women: 28.1%, men: 17.2%; p < 0.001). The most commonly consumed medicines were analgesics and tranquillizers, relaxants or sleeping pills in elderly women, compared with antihypertensives, antacids and antiulcer drugs and statins for elderly men. In both sexs, the positive predictors of polypharmacy included average, poor and very poor self-perceived states of health, people with overweight and obesity, being severely/non-severely limited due to a health problem, having ≥ three chronic conditions, visits to the family doctor and hospitalization. Among elderly women, negative predictors were alcohol intake, whereas in elderly men positive predictors were being 75-84 years old, being current smokers and having 1, 2 chronic conditions. Conclusion: Polypharmacy has a prevalence of 23.2%, with women accounting for 28.1% and men 17.2% of the total. Knowledge of positive and negative predictors of polypharmacy have important implications for public health efforts to develop or improve health guidelines and strategies for promoting the proper use of medication, particularly in the elderly population by sex.

Drug-Drug Interactions among Patients Hospitalized with COVID-19 in Greece

The modulation of the pharmacological action of drugs due to drug-drug interactions (DDIs) is a critical issue in healthcare. The aim of this study was to evaluate the prevalence and the clinical significance of potential DDIs in patients admitted to the University Hospital of Heraklion in Greece with coronavirus disease 2019 (COVID-19). Cardiovascular disorders (58.4%) and diabetes (types I and II) (29.6%) were the most common comorbidities. A high occurrence of DDIs was observed, and clinically significant DDIs that may hamper response to treatment represented 40.3% of cases on admission, 21% during hospitalization, and 40.7% upon discharge. Polypharmacy and comorbidities were associated with a higher prevalence of DDIs in a statistically significant way (p < 0.05, 95% CI). Clinically significant DDIs and increased C-reactive protein values upon admission were associated with prolonged hospitalization. The results reveal that patients admitted due to COVID-19 in Greece often have an additional burden of DDIs that healthcare teams should approach and resolve.

Global prevalence of polypharmacy among the COVID-19 patients: a comprehensive systematic review and meta-analysis of observational studies

Background

Polypharmacy has traditionally been defined in various texts as the use of 5 or more chronic drugs, the use of inappropriate drugs, or drugs that are not clinically authorized. The aim of this study was to evaluate the prevalence of polypharmacy among the COVID-19 patients, and the side effects, by systematic review and meta-analysis.

Methods

This study was performed by systematic review method and in accordance with PRISMA 2020 criteria. The protocol in this work is registered in PROSPERO (CRD42021281552). Particular databases and repositories have been searched to identify and select relevant studies. The quality of articles was assessed based on the Newcastle–Ottawa Scale checklist. Heterogeneity of the studies was measured using the I² test.

Results

The results of meta-analysis showed that the prevalence of polypharmacy in 14 studies with a sample size of 189,870 patients with COVID-19 is 34.6% (95% CI: 29.6–40). Studies have shown that polypharmacy is associated with side effects, increased morbidity and mortality among patients with COVID-19. The results of meta-regression analysis reported that with increasing age of COVID-19 patients, the prevalence of polypharmacy increases (p < 0.05).

Discussion

The most important strength of this study is the updated search to June 2022 and the use of all databases to increase the accuracy and sensitivity of the study. The most important limitation of this study is the lack of proper definition of polypharmacy in some studies and not mentioning the number of drugs used for patients in these studies.

Conclusion

Polypharmacy is seen in many patients with COVID-19. Since there is no definitive cure for COVID-19, the multiplicity of drugs used to treat this disease can affect the severity of the disease and its side effects as a result of drug interactions. This highlights the importance of controlling and managing prescription drugs for patients with COVID-19.

Drug-Disease Severity and Target-Disease Severity Interaction Networks in COVID-19 Patients

Drug interactions with other drugs are a well-known phenomenon. Similarly, however, pre-existing drug therapy can alter the course of diseases for which it has not been prescribed. We performed network analysis on drugs and their respective targets to investigate whether there are drugs or targets with protective effects in COVID-19, making them candidates for repurposing. These networks of drug-disease interactions (DDSIs) and target-disease interactions (TDSIs) revealed a greater share of patients with diabetes and cardiac co-morbidities in the non-severe cohort treated with dipeptidyl peptidase-4 (DPP4) inhibitors. A possible protective effect of DPP4 inhibitors is also plausible on pathophysiological grounds, and our results support repositioning efforts of DPP4 inhibitors against SARS-CoV-2. At target level, we observed that the target location might have an influence on disease progression. This could potentially be attributed to disruption of functional membrane micro-domains (lipid rafts), which in turn could decrease viral entry and thus disease severity.

Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

Of the patients infected with coronavirus disease 2019 (COVID-19), approximately 14–53% developed liver injury resulting in poor outcomes. Drug-induced liver injury (DILI) is the primary cause of liver injury in COVID-19 patients. In this study, we elucidated liver injury mechanism induced by drugs of pharmacologic treatments against SARS-CoV-2 (DPTS) using bioinformatics and systems biology. Totally, 1209 genes directly related to 216 DPTS (DPTSGs) were genes encoding pharmacokinetics and therapeutic targets of DPTS and enriched in the pathways related to drug metabolism of CYP450s, pregnane X receptor (PXR), and COVID-19 adverse outcome. A network, constructed by 110 candidate targets which were the shared part of DPTSGs and 445 DILI targets, identified 49 key targets and four Molecular Complex Detection clusters. Enrichment results revealed that the 4 clusters were related to inflammatory responses, CYP450s regulated by PXR, NRF2-regualted oxidative stress, and HLA-related adaptive immunity respectively. In cluster 1, IL6, IL1B, TNF, and CCL2 of the top ten key targets were enriched in COVID-19 adverse outcomes pathway, indicating the exacerbation of COVID-19 inflammation on DILI. PXR-CYP3A4 expression of cluster 2 caused DILI through inflammation-drug interaction and drug-drug interactions among pharmaco-immunomodulatory agents, including tocilizumab, glucocorticoids (dexamethasone, methylprednisolone, and hydrocortisone), and ritonavir. NRF2 of cluster 3 and HLA targets of cluster four promoted DILI, being related to ritonavir/glucocorticoids and clavulanate/vancomycin. This study showed the pivotal role of PXR associated with inflammation-drug and drug-drug interactions on DILI and highlighted the cautious clinical decision-making for pharmacotherapy to avoid DILI in the treatment of COVID-19 patients.

Trends in Excess Winter Mortality (EWM) from 1900/01 to 2019/20-Evidence for a Complex System of Multiple Long-Term Trends

Trends in excess winter mortality (EWM) were investigated from the winter of 1900/01 to 2019/20. During the 1918-1919 Spanish flu epidemic a maximum EWM of 100% was observed in both Denmark and the USA, and 131% in Sweden. During the Spanish flu epidemic in the USA 70% of excess winter deaths were coded to influenza. EWM steadily declined from the Spanish flu peak to a minimum around the 1960s to 1980s. This decline was accompanied by a shift in deaths away from the winter and spring, and the EWM calculation shifted from a maximum around April to June in the early 1900s to around March since the late 1960s. EWM has a good correlation with the number of estimated influenza deaths, but in this context influenza pandemics after the Spanish flu only had an EWM equivalent to that for seasonal influenza. This was confirmed for a large sample of world countries for the three pandemics occurring after 1960. Using data from 1980 onward the effect of influenza vaccination on EWM were examined using a large international dataset. No effect of increasing influenza vaccination could be discerned; however, there are multiple competing forces influencing EWM which will obscure any underlying trend, e.g., increasing age at death, multimorbidity, dementia, polypharmacy, diabetes, and obesity-all of which either interfere with vaccine effectiveness or are risk factors for influenza death. After adjusting the trend in EWM in the USA influenza vaccination can be seen to be masking higher winter deaths among a high morbidity US population. Adjusting for the effect of increasing obesity counteracted some of the observed increase in EWM seen in the USA. Winter deaths are clearly the outcome of a complex system of competing long-term trends.

Exploring the associations between polypharmacy and COVID-19-related hospitalisations and deaths: a population-based cohort study among older adults in Quebec, Canada

OBJECTIVES

To study the association between polypharmacy and the risk of hospitalisation and death in cases of COVID-19 in the population over the age of 65.

DESIGN

Population-based cohort study.

SETTING

Quebec Integrated Chronic Disease Surveillance System, composed of five medico-administrative databases, in the province of Quebec, Canada.

PARTICIPANTS

32 476 COVID-19 cases aged over 65 whose diagnosis was made between 23 February 2020 and 15 March 2021, and who were covered by the public drug insurance plan (thus excluding those living in long-term care). We counted the number of different medications they claimed between 1 April 2019 and 31 March 2020.

OUTCOME MEASURES

Robust Poisson regression was used to calculate relative risk of hospitalisation and death associated with the use of multiple medications, adjusting for age, sex, chronic conditions, material and social deprivation and living environment.

RESULTS

Of the 32 476 COVID-19 cases included, 10 350 (32%) were hospitalised and 4146 (13%) died. Compared with 0-4 medications, polypharmacy exposure was associated with increased hospitalisations, with relative risks ranging from 1.11 (95% CI 1.04 to 1.19) for those using 5-9 medications to 1.62 (95% CI 1.51 to 1.75) for those using 20+. Similarly, the risk of death increased with the number of medications, from 1.13 (95% CI 0.99 to 1.30) for those using (5-9 medications to 1.97 (95% CI 1.70 to 2.27) (20+). Increased risk was mainly observed in younger groups.

CONCLUSIONS

Polypharmacy was significantly associated with the risk of hospitalisations and deaths related to COVID-19 in this cohort of older adults. Polypharmacy may represent a marker of vulnerability, especially for younger groups of older adults.

Factors Associated with Severe COVID-19 and Post-Acute COVID-19 Syndrome in a Cohort of People Living with HIV on Antiretroviral Treatment and with Undetectable HIV RNA

SARS-CoV-2 can produce both severe clinical conditions and long-term sequelae, but data describing post-acute COVID-19 syndrome (PACS) are lacking for people living with HIV (PLWH). We aimed at assessing the prevalence and factors associated with severe COVID-19 and PACS in our cohort. We included all unvaccinated adult PLWH on antiretroviral treatment and plasma HIV-RNA < 40 cp/mL since at least six months before SARS-CoV-2 infection at the Infectious and Tropical Diseases Unit of Padua (Italy), from 20 February 2020 to 31 March 2021. COVID-19 severity was defined by WHO criteria; PACS was defined as the persistence of symptoms or development of sequelae beyond four weeks from SARS-CoV-2 infection. Demographic and clinical variables were collected, and data were analyzed by non-parametric tests. 123 subjects meeting the inclusion criteria among 1800 (6.8%) PLWH in care at the Infectious and Tropical diseases Unit in Padua were diagnosed with SARS-CoV-2 infection/COVID-19 during the study period. The median age was 51 years (40−58), 79.7% were males, and 77.2% of Caucasian ethnicity. The median CD4+ T-cell count and length of HIV infection were 560 cells/mmc (444−780) and 11 years, respectively. Of the patients, 35.0% had asymptomatic SARS-CoV-2 infection, 48% developed mild COVID-19, 17.1% presented moderate or severe COVID-19 requiring hospitalization and 4.1% died. Polypharmacy was the single independent factor associated with severe COVID-19. As for PACS, among 75 patients who survived SARS-CoV-2 symptomatic infection, 20 (26.7%) reported PACS at a median follow-up of six months: asthenia (80.0%), shortness of breath (50.0%) and recurrent headache (25.0%) were the three most common complaints. Only the severity of the COVID-19 episode predicted PACS after adjusting for relevant demographic and clinical variables. In our study, PLWH with sustained viral suppression and good immunological response showed that the risk of hospital admission for COVID-19 was low, even though the severity of the disease was associated with high mortality. In addition, the likelihood of developing severe COVID-19 and PACS was mainly driven by similar risk factors to those faced by the general population, such as polypharmacy and the severity of SARS-CoV-2 infection.

Pharmacological Adherence Behavior Changes during COVID-19 Outbreak in a Portugal Patient Cohort

Concerns, behaviours, and beliefs influence how people deal with COVID-19. Understanding the factors influencing adherence behaviour is of utmost importance to develop tailored interventions to increase adherence within this context. Hence, we aimed to understand how COVID-19 affected adherence behaviour in Portugal. A cross-sectional online survey was conducted between 1 March and 3 April 2021. Descriptive statistics were performed, as well as univariable and multivariable regression models. Of the 1202 participants, 476 who were taking at least one medication prescribed by the doctor were selected. Of these, 78.2% were female, and the mean age was 40.3 ± 17.9 years old. About 74.2% were classified as being highly adherent. During the pandemic, 8.2% of participants reported that their adherence improved, while 5.9% had worsened adherence results. Compared with being single, widowers were 3 times more prone to be less adherent (OR:3.390 [1.106–10.390], p = 0.033). Comorbid patients were 1.8 times (OR:1.824 [1.155–2.881], p = 0.010) more prone to be less adherent. Participants who reported that COVID-19 negatively impacted their adherence were 5.6 times more prone to be less adherent, compared with those who reported no changes (OR:5.576 [2.420–12.847], p < 0.001). None of the other variables showed to be significantly associated with pharmacological adherence.

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.