Anosmia in Parkinson’s Disease in Pakistan: A Matched Case – Control Study

Objective:  To assess olfactory dysfunction in Parkinson's disease (PD) patients in Pakistan utilizing an autochthonous smell test. Setting:  Tertiary care center, single-center study. Materials and Methods:  Eighty-seven non-demented patients with PD, who fulfilled Queen Square Brain Bank Criteria were enrolled at the Movement Disorder Clinic, Lahore General Hospital (LGH), Lahore. Fifty-eight controls matched by gender, age, and place of residence were enrolled among patients and visitors attending other hospital clinics. Both groups underwent olfactory testing using the Pakistani Smell Identification test (PKSIT). The participants were required to identify the smell from a set of choices and were scored out of 10. Results:  Among patients in the study group, the mean duration of disease was 4.7 years (range 6 months to 19 years). The PD onset mean age was 52.15 ± 13.02 years among patients. The mean number of smell test items accurately recognized by the PD patients was 4.55 ± 2.4. A multiple linear regression demonstrated that age (P < 0.05) but not disease duration (P = 0.899) was a significant determinant of the smell test result in PD and control groups. The mean number of smell test items appropriately recognized by the controls was 7.33 ± 1.69. Logistic regression showed that the PKSIT had 73.2% sensitivity and 84.3% specificity to distinguish PD from control. Conclusion:  PKSIT being easily available, cheap, and more convenient to use in the Pakistani population, can be used in the evaluation of olfactory dysfunction in PD subjects

Pattern of benzodiazepine use in psychiatric outpatients in Pakistan: a cross-sectional survey.

Background: Benzodiazepines (BDZ) are the largest-selling drug group in the world. The potential of dependence with BDZ has been known for almost three decades now. In countries like Pakistan where laws against unlicensed sale of BDZ are not implemented vigorously the risk of misuse of and dependence on these drugs is even higher. Previous studies have shown that BDZ prevalence among Patients/visitors to general outPatient clinics in Pakistan may be as high as 30%. However, no research has been carried out on the prevalence of BDZ use in psychiatric Patients in Pakistan. Methods: We carried out a cross-sectional survey over 3 months in psychiatry outPatient clinics of two tertiary care hospitals in Karachi and Lahore. Besides basic socio-demographic data the participants were asked if they were taking a BDZ at present and if yes, the frequency, route and dosage of the drug, who had initiated the drug and why it had been prescribed. We used chi-square test and t-test to find out which socio-demographic or clinical factors were associated with an increased risk of BDZ use. We used Logistic Regression to find out which variable(s) best predicted the increased likelihood of BDZ use. Results: Out of a total of 419 participants 187 (45%) of the participants had been currently using at least one BDZ. Seventy-three percent of the users had been using the drug for 4 weeks or longer and 87% were taking it every day. In 90% of cases the BDZ had been initiated by a doctor, who was a psychiatrist in 70% of the cases. Female gender, increasing age, living in Lahore, and having seen a psychiatrist before, were associated with an increased likelihood of using BDZ. Conclusion: The study shows how high BDZ use is in psychiatric outPatients in Pakistan. Most of the users were taking it for a duration and with a frequency which puts them at risk of becoming dependent on BDZ. In most of the cases it had been initiated by a doctor. Both Patients and doctors need to be made aware of the risk of dependence associated with the use of BDZ

Tissue characterization of benign cardiac tumors by cardiac magnetic resonance imaging, a review of core imaging protocol and benign cardiac tumors

Generally, cardiac masses are initially suspected on routine echocardiography. Cardiac magnetic resonance (CMR) imaging is further performed to differentiate tumors from pseudo-tumors and to characterize the cardiac masses based on their appearance on T1/T2-weighted images, detection of perfusion and demonstration of gadolinium-based contrast agent uptake on early and late gadolinium enhancement images. Further evaluation of cardiac masses by CMR is critical because unnecessary surgery can be avoided by better tissue characterization. Different cardiac tissues have different T1 and T2 relaxation times, principally owing to different internal biochemical environments surrounding the protons. In CMR, the signal intensity from a particular tissue depends on its T1 and T2 relaxation times and its proton density. CMR uses this principle to differentiate between various tissue types by weighting images based on their T1 or T2 relaxation times. Generally, tumor cells are larger, edematous, and have associated inflammatory reactions. Higher free water content of the neoplastic cells and other changes in tissue composition lead to prolonged T1/T2 relaxation times and thus an inherent contrast between tumors and normal tissue exists. Overall, these biochemical changes create an environment where different cardiac masses produce different signal intensity on their T1- weighted and T2- weighted images that help to discriminate between them. In this review article, we have provided a detailed description of the core CMR imaging protocol for evaluation of cardiac masses. We have also discussed the basic features of benign cardiac tumors as well as the role of CMR in evaluation and further tissue characterization of these tumors

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use