A Call for Adopting High-Dose Influenza Vaccines for Adults Aged 65 and Above in Gulf Cooperation Council (GCC) Countries.

Seasonal influenza poses significant health and economic challenges globally each year, particularly impacting the elderly population (aged ≥ 65 years) with increased rates of hospitalization, and mortality. The population of older adults is steadily increasing in the Gulf Cooperation Council (GCC) countries and is likely to increase even further. In addition, there is a high burden of chronic comorbidities in these countries like diabetes and obesity, which increases the likelihood of severe consequences of influenza infection. The GCC countries also host mass gathering events like Hajj, Umrah pilgrimage, Arba'een (nearby Iraq) pilgrimage, and international sports and business events, which further intensify the risk of outbreaks like influenza. These events facilitate the mixing of visitors from various countries. Thus, influenza activity in this North Hemisphere (NH) geography is usually present even before the availability of NH seasonal influenza vaccine. This is especially problematic for the elderly, whose protection from the previous year's immunization would have waned. Higher dosages of antigens or adjuvants have been used to improve immunogenicity in older people with superior vaccine effectiveness. Therefore, there is a compelling argument in favor of the implementation of high-dose seasonal influenza vaccines in the GCC countries to improve the protection of individuals aged 65 years and older against influenza infection and associated severe complications.

Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study.

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients: Intensive care unit admission. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (cor(Xu1, Zv1) = 0.596, p value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Predictors of Severity of Influenza-Related Hospitalizations: Results From the Global Influenza Hospital Surveillance Network (GIHSN).

BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) has since 2012 provided patient-level data on severe influenza-like-illnesses from >100 participating clinical sites worldwide based on a core protocol and consistent case definitions. METHODS: We used multivariable logistic regression to assess the risk of intensive care unit admission, mechanical ventilation, and in-hospital death among hospitalized patients with influenza and explored the role of patient-level covariates and country income level. RESULTS: The data set included 73 121 patients hospitalized with respiratory illness in 22 countries, including 15 660 with laboratory-confirmed influenza. After adjusting for patient-level covariates we found a 7-fold increase in the risk of influenza-related intensive care unit admission in lower middle-income countries (LMICs), compared with high-income countries (P = .01). The risk of mechanical ventilation and in-hospital death also increased by 4-fold in LMICs, though these differences were not statistically significant. We also find that influenza mortality increased significantly with older age and number of comorbid conditions. Across all severity outcomes studied and after controlling for patient characteristics, infection with influenza A/H1N1pdm09 was more severe than with A/H3N2. CONCLUSIONS: Our study provides new information on influenza severity in underresourced populations, particularly those in LMICs.

Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19.

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (corr(Xu1, Zv1) = 0.596, p-value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction.

BACKGROUND: Renin-angiotensin-aldosterone system inhibitors (RAASi) are commonly used medications. Renal adverse events associated with RAASi are hyperkalemia and acute kidney injury. We aimed to evaluate the performance of machine learning (ML) algorithms in order to define event associated features and predict RAASi associated renal adverse events. MATERIALS AND METHODS: Data of patients recruited from five internal medicine and cardiology outpatient clinics were evaluated retrospectively. Clinical, laboratory, and medication data were acquired via electronic medical records. Dataset balancing and feature selection for machine learning algorithms were performed. Random forest (RF), k-nearest neighbor (kNN), naïve Bayes (NB), extreme gradient boosting (xGB), support vector machine (SVM), neural network (NN), and logistic regression (LR) were used to create a prediction model. RESULTS: 409 patients were included, and 50 renal adverse events occurred. The most important features predicting the renal adverse events were the index K and glucose levels, as well as having uncontrolled diabetes mellitus. Thiazides reduced RAASi associated hyperkalemia. kNN, RF, xGB and NN algorithms have the highest and similar AUC (≥ 98%), recall (≥ 94%), specifity (≥ 97%), precision (≥ 92%), accuracy (≥ 96%) and F1 statistics (≥ 94%) performance metrics for prediction. CONCLUSION: RAASi associated renal adverse events can be predicted prior to medication initiation by machine learning algorithms. Further prospective studies with large patient numbers are needed to create scoring systems as well as for their validation.

Clinical outcomes and economic burden of seasonal influenza and other respiratory virus infections in hospitalized adults.

The cost of influenza and other respiratory virus infections should be determined to analyze the real burden of these diseases. We aimed to investigate the clinical outcomes and cost of illness due to respiratory virus infections in hospitalized adult patients. Hospitalized patients who had nasal swab sampling for a suspected viral infection between August 1, 2018 to March 31, 2019 were included. Outcome variables were oxygen requirement, mechanical ventilation need, intensive care unit admission, and cost. At least one viral pathogen was detected in 125 (47.7%) of 262 patients who were included in the study. Fifty-five (20.9%) of the patients were infected with influenza. Influenza-positive patients had higher rates for respiratory support, intensive care unit admission, and mortality compared to all other patients. The average cost of hospitalization per person was 2879.76 USD in the influenza-negative group, while the same cost was 3274.03 USD in the influenza-positive group. Although all of the vaccinated influenza-positive patients needed oxygen support, neither of them required invasive mechanical ventilation or intensive care unit admission. The average hospitalization cost per person was 779.70 USD in the vaccinated group compared to 3762.01 USD in the unvaccinated group. Disease-related direct cost of influenza in the community was estimated as 22 776 075.61 USD in the 18-65 years of age group and 15 756 120.02 USD in the 65 years of age and over group per year. Influenza, compared to other respiratory virus infections, can lead to untoward clinical outcomes and mortality as well as higher direct medical costs in adults.

Efficacy, Immunogenicity, and Safety of the Two-Dose Schedules of TURKOVAC versus CoronaVac in Healthy Subjects: A Randomized, Observer-Blinded, Non-Inferiority Phase III Trial.

We present the interim results of the efficacy, immunogenicity, and safety of the two-dose schedules of TURKOVAC versus CoronaVac. This was a randomized, observer-blinded, non-inferiority trial (NCT04942405). Volunteers were 18-55 years old and randomized at a 1:1 ratio to receive either TURKOVAC or CoronaVac at Day 0 and Day 28, both of which are 3 µg/0.5 mL of inactivated severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) adsorbed to aluminum hydroxide. The primary efficacy outcome was the prevention of polymerase chain reaction (PCR)-confirmed symptomatic coronavirus disease 2019 (COVID-19) at least 14 days after the second dose in the modified per-protocol (mPP) group. Safety analyses were performed in the modified intention-to-treat (mITT) group. Between 22 June 2021 and 7 January 2022, 1290 participants were randomized. The mITT group consisted of 915 participants, and the mPP group consisted of 732 participants. During a median follow-up of 90 (IQR 86-90) days, the relative risk reduction with TURKOVAC compared to CoronaVac was 41.03% (95% CI 12.95-60.06) for preventing PCR-confirmed symptomatic COVID-19. The incidences of adverse events (AEs) overall were 58.8% in TURKOVAC and 49.7% in CoronaVac arms (p = 0.006), with no fatalities or grade four AEs. TURKOVAC was non-inferior to CoronaVac in terms of efficacy and demonstrated a good safety and tolerability profile.

Community-acquired pneumonia - An EFIM guideline critical appraisal adaptation for internists.

BACKGROUND: In real-life settings, guidelines frequently cannot be followed since many patients are multimorbid and/or elderly or have other complicating conditions which carry an increased risk of drug-drug interactions. This document aimed to adapt recommendations from existing clinical practice guidelines (CPGs) to assist physicians' decision-making processes concerning specific and complex scenarios related to acute CAP. METHODS: The process for the adaptation procedure started with the identification of unsolved clinical questions (PICOs) in patients with CAP and continued with critically appraising the updated existing CPGs and choosing the recommendations, which are most applicable to these specific scenarios. RESULTS: Seventeen CPGs were appraised to address five PICOs. Twenty-seven recommendations were endorsed based on 7 high, 9 moderate, 10 low, and 1 very low-quality evidence. The most valid recommendations applicable to the clinical practice were the following ones: Respiratory virus testing is strongly recommended during periods of increased respiratory virus activity. Assessing the severity with a validated prediction rule to discriminate where to treat the patient is strongly recommended along with reassessing the patient periodically for improvement as expected. In adults with multiple comorbidities, polypharmacy, or advanced age, it is strongly recommended to check for possible drug interactions before starting treatment. Strong graded recommendations exist on antibiotic treatment and its duration. Recommendations on the use of biomarkers such as C-reactive protein or procalcitonin to improve severity assessment are reported. CONCLUSION: This document provides a simple and reliable updated guide for clinical decision-making in the management of complex patients with multimorbidity and CAP in the real-life setting.

A Quality Improvement Study on Delirium Awareness Day: In Pursuit of Missed Delirium Diagnoses.

The current study aimed to screen for delirium in hospitalized older adults and assess the validity of the Turkish version of the 4A's Test (4AT-TR) as a feasible tool to integrate in routine patient care. The point prevalence of delirium according to clinical evaluation in routine practice was detected among all patients aged ≥60 years in 12 pilot wards. Delirium screening was then conducted by two arms: (a) nurses using the 4AT-TR and (b) geriatricians according to Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) criteria. Prevalence of delirium according to clinical impression was 3.3% (n = 4), whereas prevalence was 12.4% (n = 17) according to DSM-5 criteria and 13.8% (n = 17) according to the 4AT-TR. The 4AT-TR performed by nurses had a sensitivity of 66.6% and specificity of 93.5%. Area under the receiver operating characteristic curve for delirium diagnosis was 0.819 (p < 0.001). Most delirium cases remain undetected unless a routine and formal delirium assessment is integrated in hospital care of high-risk patients. The 4AT-TR performed by nurses seems to be a valid tool for determining delirium in hospitalized older adults. [Journal of Gerontological Nursing, 48(8), 43-51.].

Relative Vaccine Effectiveness of the Third Dose of CoronaVac or BNT162b2 Following a Two-Dose CoronaVac Regimen: A Prospective Observational Cohort Study from an Adult Vaccine Center in Turkey.

Coronavirus disease 2019 (COVID-19) continues to pose a threat to public health with the potential for the emergence of new variants. Vaccines are the milestones to control and slow down the damage of the pandemic. As of January 2021, a two-dose regimen with CoronaVac was authorized in Turkey. Due to the waning seroprevalence rate of SARS-CoV-2 over time, BNT162b2 or CoronaVac has been administered as the third dose following a two-dose CoronaVac regimen as a national vaccination policy. As of 14 January 2021, 5243 volunteers who received two doses of the CoronaVac vaccine at Hacettepe University Adult Vaccine Center were followed prospectively. In our study, relative vaccine effectiveness (VEff) for the third dose of the CoronaVac was 58.24% and 87.27% for BNT162b2 in preventing symptomatic COVID-19 cases. There were no hospitalizations, intensive care unit admissions, or deaths in third-dose booster groups with either BNT162b2 or CoronaVac, yielding 100% effectiveness. Both homologous or heterologous third-dose boosters provided further protection against severe COVID-19 and should be prioritized as an effective strategy to combat the COVID-19 pandemic.

Independent predictors of in-hospital mortality and the need for intensive care in hospitalized non-critical COVID-19 patients: a prospective cohort study.

One of the most helpful strategies to deal with ongoing coronavirus pandemics is to use some prudence when treating patients infected with SARS-CoV-2. We aimed to evaluate the clinical, demographic, and laboratory parameters that might have predictive value for in-hospital mortality and the need for intensive care and build a model based on them. This study was a prospective, observational, single-center study including non-critical patients admitted to COVID-19 wards. Besides classical clinic-demographic features, basic laboratory parameters obtained on admission were tested, and then new models for each outcome were developed built on the most significant variables. Receiver operating characteristics (ROC) analyses were performed by calculating each model's probability. A total of 368 non-critical hospitalized patients were recruited, the need for ICU care was observed in 70 patients (19%). The total number of patients who died in either ICU or wards was 39 (10.6%). The first two models (based on clinical features and demographics) were developed to predict ICU and death, respectively; older age, male sex, active cancer, and low baseline saturation were noted to be independent predictors. The area under the curve values of the first two models were noted 0.878 and 0.882 (p < .001; confidence interval [CI] 95% [0.837-0.919], p < .001; CI 95% [0.844-0.922]). Following two models, the third and fourth were based on laboratory parameters with clinic-demographic features. Initial lower sodium and lower albumin levels were determined as independent factors in predicting the need for ICU care; higher blood urea nitrogen and lower albumin were independent factors in predicting in-hospital mortality. The area under the curve values of the third and fourth model was noted 0.938 and 0.929, respectively (p < .001; CI 95% [0.912-0.965], p < .001; CI 95% [0.895-962]). By integrating the widely available blood tests results with simple clinic demographic data, non-critical patients can be stratified according to their risk level. Such stratification is essential to filter the patients' non-critical underlying diseases and conditions that can obfuscate the physician's predictive capacity.

'Anxiety is still ongoing!' Evaluation of the effect of the COVID-19 pandemic on anxiety severity of physicians working in the internal medicine department after 1 year: a collaborative cross-sectional study.

The COVID-19 pandemic has increased anxiety in society and particularly in healthcare workers, as shown with a questionnaire in our centre at the beginning of the pandemic. In this collaborative study, we aimed to evaluate the effects of the pandemic on anxiety 1 year later by applying the same questionnaire to the physicians working in the same department. A total of 77 participants consented to the study. The median age was 28 (interquartile range = 4) years and 55.8% were male. As in the first survey, female gender, having family members over 65 years of age, and having family members with chronic diseases were significantly associated with high anxiety scores and levels. There were no statistically significant differences between the first and second survey participants in any of the anxiety scales, which means anxiety persists.

"COVID-19: The final nail in the coffin for physical examination" Evaluation of the effects of COVID-19 pandemic on physical examination habits of residents in a university hospital: A cross-sectional survey.

AIMS: It is evident that the COVID-19 pandemic has affected the medical practice and training of residents. In this study, we evaluated the physical examination (PE) habits of residents working in a university hospital and how their PE practices did change during the pandemic. METHODS: This single-centre, non-interventional, cross-sectional descriptive study was conducted in a university hospital using an online survey questionnaire between 5 and 20 October 2020. RESULTS: Of the 308 residents who participated in the study, 172 of them (55.8%) were female and the median age was 27 (IQR (3) = Q1 (29)-Q3 (26)). Amongst all, 263 participants (85.4%) declared that they have worked in the areas where suspected/confirmed COVID-19 patients were being served. A total of 262 (85%) residents stated that PE habits have changed generally during the pandemic. There was a significant difference with regards to the change in PE habits between those residents who have worked in the COVID-19 areas (n = 230, 87.5%) and those who have not (n = 32, 71.1%) (P = .004). PE habits of Internal Medicine Residents were changed more than others (P < .001). The main reason for the change in PE habits in general (77.9%) and during the examination of suspected/confirmed COVID-19 patients (89.7%) were "self-protection." Independent factors for limited PE in suspected/confirmed COVID-19 patients were found as "Avoiding performing physical examination to be exposed less/to protect (adjusted ORs = 13.067)," "relying on laboratory and radiological investigations during practice (adjusted ORs = 4.358)," and "not having a thought that reduced physical examination will render the diagnosis and course of COVID-19 (adjusted ORs = 2.244)." CONCLUSIONS: This study clearly demonstrated that the COVID-19 pandemic has had a serious impact on the PE habits of the residents while examining patients in general and with COVID-19.

Characteristics and management of asymptomatic SARS-CoV-2 infections.

The coronavirus disease 2019 (COVID-19) pandemic has caused and is still causing tremendous morbidity, mortality, and damage to our societies. The disease course of COVID-19 can be unpredictable ranging from asymptomatic infections to multi-organ failure and death. Transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from an asymptomatic infected individual to others has been observed early in the pandemic. Asymptomatic individuals have been shown to have quantitative SARS-CoV-2 viral loads, there may or may not be radiological and/or laboratory abnormalities. No antiviral therapy has been approved for the treatment of asymptomatic SARS-CoV2- infection. The management of asymptomatic individuals at home requires that the person can be monitored for any signs and symptoms of deterioration and that the requirements for infection prevention and control measures can be fulfilled. It is crucial to properly diagnose and manage asymptomatic COVID-19 cases with effective testing, contact tracing, quarantine, and isolation strategies. Preventing asymptomatic SARS-CoV-2 infections that have a major role in the unhindered transmission of the virus is a milestone to take control of the pandemic. Vaccination has been proven to be the crucial pillar for preventing asymptomatic infections and real-life data will continue to exhibit the effects of community vaccination in breaking the transmission chain of SARS-CoV-2 infections.

Efficacy and safety of an inactivated whole-virion SARS-CoV-2 vaccine (CoronaVac): interim results of a double-blind, randomised, placebo-controlled, phase 3 trial in Turkey.

BACKGROUND: CoronaVac, an inactivated whole-virion SARS-CoV-2 vaccine, has been shown to be well tolerated with a good safety profile in individuals aged 18 years and older in phase 1/2 trials, and provided a good humoral response against SARS-CoV-2. We present the interim efficacy and safety results of a phase 3 clinical trial of CoronaVac in Turkey. METHODS: This was a double-blind, randomised, placebo-controlled phase 3 trial. Volunteers aged 18-59 years with no history of COVID-19 and with negative PCR and antibody test results for SARS-CoV-2 were enrolled at 24 centres in Turkey. Exclusion criteria included (but were not limited to) immunosuppressive therapy (including steroids) within the past 6 months, bleeding disorders, asplenia, and receipt of any blood products or immunoglobulins within the past 3 months. The K1 cohort consisted of health-care workers (randomised in a 1:1 ratio), and individuals other than health-care workers were also recruited into the K2 cohort (randomised in a 2:1 ratio) using an interactive web response system. The study vaccine was 3 µg inactivated SARS-CoV-2 virion adsorbed to aluminium hydroxide in a 0·5 mL aqueous suspension. Participants received either vaccine or placebo (consisting of all vaccine components except inactivated virus) intramuscularly on days 0 and 14. The primary efficacy outcome was the prevention of PCR-confirmed symptomatic COVID-19 at least 14 days after the second dose in the per protocol population. Safety analyses were done in the intention-to-treat population. This study is registered with ClinicalTrials.gov (NCT04582344) and is active but no longer recruiting. FINDINGS: Among 11 303 volunteers screened between Sept 14, 2020, and Jan 5, 2021, 10 218 were randomly allocated. After exclusion of four participants from the vaccine group because of protocol deviations, the intention-to-treat group consisted of 10 214 participants (6646 [65·1%] in the vaccine group and 3568 [34·9%] in the placebo group) and the per protocol group consisted of 10 029 participants (6559 [65·4%] and 3470 [34·6%]) who received two doses of vaccine or placebo. During a median follow-up period of 43 days (IQR 36-48), nine cases of PCR-confirmed symptomatic COVID-19 were reported in the vaccine group (31·7 cases [14·6-59·3] per 1000 person-years) and 32 cases were reported in the placebo group (192·3 cases [135·7-261·1] per 1000 person-years) 14 days or more after the second dose, yielding a vaccine efficacy of 83·5% (95% CI 65·4-92·1; p<0·0001). The frequencies of any adverse events were 1259 (18·9%) in the vaccine group and 603 (16·9%) in the placebo group (p=0·0108) with no fatalities or grade 4 adverse events. The most common systemic adverse event was fatigue (546 [8·2%] participants in the vaccine group and 248 [7·0%] the placebo group, p=0·0228). Injection-site pain was the most frequent local adverse event (157 [2·4%] in the vaccine group and 40 [1·1%] in the placebo group, p<0·0001). INTERPRETATION: CoronaVac has high efficacy against PCR-confirmed symptomatic COVID-19 with a good safety and tolerability profile. FUNDING: Turkish Health Institutes Association.

Pulmonary nocardiosis caused by Nocardia abscessus mimicking pulmonary thromboembolism in a patient with atypical anti-glomerular basement membrane glomerulonephritis.

Nocardia species are opportunistic bacteria that are frequently contagious by inhalation. Recently, Nocardia abscessus has been described as a different species. We report a 54-year-old male who presented with acute pleuritic chest pain, mimicking pulmonary thromboembolism on the 5th day of discharge from the hospital. The patient was receiving immunosuppressive therapy for newly diagnosed atypical anti-glomerular basement membrane disease. Thorax computed tomography revealed a 17x19 mm soft tissue lesion in the lateral segment of the middle lobe of the right lung. After further examinations, a biopsy decision was made and Nocardia abscessus was isolated in the aerobic culture of the aspiration material.