Anxiety among hospitalized COVID-19 patients: a case-control study from a tertiary teaching hospital in Malaysia.

Introduction: Anxiety has been increasingly recognized as part of the psychosocial health issues in COVID-19 patients. However, the impact of this topic may be underestimated in low- and middle-income countries. This study aimed to estimate the prevalence of and risk factors of anxiety in COVID-19 patients compared to controls in a local tertiary teaching hospital in Malaysia. Methods: In this case-control study, we analyzed data on adult patients aged 18 years and above hospitalized for COVID-19 infection with matched hospitalized controls. The demographic, clinical data and anxiety measures using the Generalized Anxiety Disorder-7 questionnaire were analyzed using univariate and multivariate analysis. Results: 86.6% in the COVID-19 group had anxiety, significantly higher than 13.4% in the control group (p = 0.001). The COVID-19 group was significantly associated with the GAD-7 severity (p = 0.001). The number of COVID-19 patients in the mild, moderate, and severe anxiety groups was 48 (84.2%), 37 (86%), and 18 (94.7%), respectively. Multiple logistic regression showed significant predictors for anxiety, including COVID-19 diagnosis and neurological symptoms. Anxiety was found 36.92 times higher in the patients with COVID-19 compared to those without COVID-19 (OR 36.92;95% CI 17.09, 79.78, p = 0.001). Patients with neurological symptoms were at risk of having anxiety (OR 2.94; 95% CI 1.03, 8.41, p = 0.044). Discussion: COVID-19 patients experience a significant disruption in psychosocial functioning due to hospitalization. The burden of anxiety is notably high, compounded by a diagnosis of COVID-19 itself and neurological symptomatology. Early psychiatric referrals are warranted for patients at risk of developing anxiety symptoms.

COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology.

At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.

Neurological manifestations in SARS-CoV-2 infection: A single-center cross-sectional study in Malaysia.

Background: Neurological involvement associated with SARS-CoV-2 infection has been reported from different regions of the world. However, data from South East Asia are scarce. We described the neurological manifestations and their associated factors among the hospitalized COVID-19 patients from an academic tertiary hospital in Malaysia. Methods: A cross-sectional observational study of hospitalized COVID-19 patients was conducted. The neurological manifestations were divided into the self-reported central nervous system (CNS) symptoms, stroke associated symptoms, symptoms of encephalitis or encephalopathy and specific neurological complications. Multiple logistic regression was performed using demographic and clinical variables to determine the factors associated with outcome. Results: Of 156 hospitalized COVID-19 patients with mean age of 55.88 ± 6.11 (SD) years, 23.7% developed neurological complications, which included stroke, encephalitis and encephalopathy. Patients with neurological complications were more likely to have diabetes mellitus (p = 0.033), symptoms of stroke [limb weakness (p < 0.001), slurred speech (p < 0.001)]; and encephalitis or encephalopathy [confusion (p < 0.001), forgetfulness (p = 0.006) and seizure (p = 0.019)]. Unvaccinated patients had a 4.25-fold increased risk of having neurological complications (adjusted OR = 4.25; 95% CI: 1.02, 17.71, p = 0.047). Anosmia and dysgeusia were less associated with neurological complications (adjusted OR = 0.22; 95% CI: 0.05, 0.96, p = 0.044). The odds of neurological complications were increased by 18% in patients with leukocytosis (adjusted OR = 1.18, 95% CI: 1.003, p = 0.0460). Conclusions: Stroke, encephalitis and encephalopathy were the common neurological complications from our study. Diabetes mellitus, presence of symptoms of stroke, symptoms of encephalitis or encephalopathy, leukocytosis, and being unvaccinated against COVID-19 were the associated risk factors of developing neurological complications.

Corticosteroid effectiveness among hospitalised COVID-19 patients in Malaysia.

INTRODUCTION: Using steroids to manage hospitalised coronavirus disease 2019 (COVID-19) patients caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection has been shown to reduce the need for mechanical ventilation and mortality. To date, low-dose dexamethasone and methylprednisolone corticosteroids have been effective in reducing the infection's progress in hospitalised patients. However, it is unknown if high dosages of corticosteroids can achieve a better clinical outcome. This study aims to compare the clinical outcomes of hospitalised COVID-19 patients who are given a 10-day low-dose corticosteroid treatment (IV 2 mg/kg/day methylprednisolone loading dose (LD) then 0.25 mg/kg four times a day (q.i.d.)) with patients given a 10-day high-dose corticosteroid treatment (IV 20 mg dexamethasone once daily (o.d.) or a 1.5 mg/kg prednisolone tablet o.d.). METHODOLOGY: Retrospective data on hospitalised COVID-19 patients were collected for this study, and the primary outcome measure was the patients' clinical status based on the World Health Organization's (WHO) Ordinal Scale for Clinical Improvement (OSCI) on Day-5 and Day-10 post-steroid. RESULTS: The results demonstrated that using steroids significantly improved patients' clinical outcomes from a WHO OSCI level of 4 (0.1) on Day-1 to 2.6 (2.5) on Day-5 (p < 0.001). There was no significant difference in clinical outcome between low-dose and high-dose corticosteroid treatment on Day-5 (H = 2.15; p = 0.34) and Day-10 (H = 1.12; p = 0.58). CONCLUSIONS: This study concludes that using low-dose corticosteroids is recommended for hospitalised COVID-19 patients to ensure clinical outcomes are optimised.

Plethora of Antibiotics Usage and Evaluation of Carbapenem Prescribing Pattern in Intensive Care Units: A Single-Center Experience of Malaysian Academic Hospital.

Excessive antibiotic consumption is still common among critically ill patients admitted to intensive care units (ICU), especially during the coronavirus disease 2019 (COVID-19) period. Moreover, information regarding antimicrobial consumption among ICUs in South-East Asia remains scarce and limited. This study aims to determine antibiotics utilization in ICUs by measuring antibiotics consumption over the past six years (2016-2021) and specifically evaluating carbapenems prescribed in a COVID-19 ICU and a general intensive care unit (GICU) during the second year of the COVID-19 pandemic. (2) Methods: This is a retrospective cross-sectional observational analysis of antibiotics consumption and carbapenems prescriptions. Antibiotic utilization data were estimated using the WHO Defined Daily Doses (DDD). Carbapenems prescription information was extracted from the audits conducted by ward pharmacists. Patients who were prescribed carbapenems during their admission to COVID-19 ICU and GICU were included. Patients who passed away before being reviewed by the pharmacists were excluded. (3) Results: In general, antibiotics consumption increased markedly in the year 2021 when compared to previous years. Majority of carbapenems were prescribed empirically (86.8%). Comparing COVID-19 ICU and GICU, the reasons for empirical carbapenems therapy in COVID-19 ICU was predominantly for therapy escalation (64.7% COVID-19 ICU vs. 34% GICU, p < 0.001), whereas empirical prescription in GICU was for coverage of extended-spectrum beta-lactamases (ESBL) gram-negative bacteria (GNB) (45.3% GICU vs. 22.4% COVID-19 ICU, p = 0.005). Despite microbiological evidence, the empirical carbapenems were continued for a median (interquartile range (IQR)) of seven (5-8) days. This implies the need for a rapid diagnostic assay on direct specimens, together with comprehensive antimicrobial stewardship (AMS) discourse with intensivists to address this issue.

COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology

At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.

COVID-19 in Malaysia: exposure assessment and prevention practices among healthcare workers at a teaching hospital.

INTRODUCTION: During the second wave of the coronavirus disease 19 (COVID-19) pandemic, Malaysia reported several COVID-19 clusters related to healthcare workers. Thus, addressing and understanding the risk of exposure in healthcare workers is important to prevent future infection and reduce secondary COVID-19 transmission within the healthcare settings. In this study, we aim to assess exposure and prevention practices against COVID-19 among healthcare workers at the Hospital Canselor Tuanku Muhriz, a university teaching hospital based in Kuala Lumpur, Malaysia. METHODOLOGY: A total of 571 healthcare workers at COVID-19 and non-COVID-19 wards as well as the emergency department and laboratory staff at COVID-19 testing labs were recruited. The presence of novel human coronavirus (SARS-CoV-2) and IgM/IgG antibodies were confirmed in all healthcare workers. The healthcare workers responded to an online Google Forms questionnaire that evaluates demographic information and comorbidities, exposure and adherence to infection prevention and control measures against COVID-19. Descriptive analysis was performed using Statistical Package for the Social Sciences 24.0. RESULTS: Three healthcare workers (0.5%) tested positive for SARS-CoV-2, while the remaining 568 (99.5%) were negative. All were negative for IgM and IgG antibodies during recruitment (day 1) and follow-up (day 15). More than 90% of the healthcare workers followed infection prevention and control practices recommendations regardless of whether they have been exposed to occupational risk for COVID-19. CONCLUSIONS: The healthcare workers' high level of adherence to infection prevention practices at this hospital helped reduce and minimize their occupational exposure to COVID-19.

Clinical Characteristics of Patients with Coronavirus Disease 2019 (COVID-19) in a Teaching Hospital in Malaysia.

INTRODUCTION: Information on the clinical characteristics of local patients with confirmed COVID-19 is limited. This study aims to report the clinical characteristics of 147 patients admitted and receiving treatment at a teaching hospital. METHODOLOGY: Patients' socio-demographic and epidemiological data, clinical features, laboratory findings and clinical outcomes were extracted using a data sheet. RESULTS: The median patient age was 25 [interquartile range (IQR)] 20-44) years, and most of patients were male (68.7%) and of Malaysian nationality (88.4%). Almost half of the patients were from a case cluster related to a religious event (48.3%) and 12.9% had a history of overseas travel. A total of 33.3% of patients were not related to any case cluster, i.e. sporadic cases. Radiological investigation showed that 13.6% of the patients had chest X-ray changes and all laboratory parameters were within the normal ranges. Sixty-six patients (44.9%) experienced symptoms. The most common symptoms were rhinitis (66.7%), followed by fever (19.7%) and cough (15.2%). Age, gender, case cluster, comorbidity status, haemoglobin, albumin, total protein, bilirubin total and alkaline phosphatase level were associated with symptomatic status. CONCLUSIONS: In this single-centre study, COVID-19 infection led not only to case clusters, but also to sporadic infections, with patients being either symptomatic or asymptomatic. These sporadic cases and asymptomatic patients may hamper effective contact tracing, leading to rapid human-to-human transmission in our population. Future studies on the prevalence and clinical significance of asymptomatic and presymptomatic COVID-19 patients would pre-emptively address issues on further containment of the pandemic.

The prevalence and clinical significance of Presymptomatic COVID-19 patients: how we can be one step ahead in mitigating a deadly pandemic.

BACKGROUND: Presymptomatic COVID-19 patients have been identified as a major stumbling block in efforts to break the chain of transmission. Studies on temporal dynamics of its shedding suggests it peaks 1-2 days prior to any symptom onset. Therefore, a large proportion of patients are actively spreading the disease unknowingly whilst undetected. However, lengthy lockdowns and isolation leads to a host of socioeconomic issues and are impractical. Conversely, there exists no study describing this group and their clinical significance despite their key role in disease transmission. METHODS: As a result, we devised a retrospective study to look at the prevalence of presymptomatic patients with COVID-19 from data sourced via our medical records office. Subsequently, we identify early indicators of infection through demographic information, biochemical and radiological abnormalities which would allow early diagnosis and isolation. In addition, we will look into the clinical significance of this group and their outcome; if it differs from asymptomatic or symptomatic patients. Descriptive statistics were used in addition to tabulating the variables and corresponding values for reference. Variables are compared between the presymptomatic group and others via Chi-square testing and Fisher's exact test, accepting a p value of < 0.05 as significant. RESULTS: Our analysis shows a higher proportion of presymptomatic patients with atypical symptoms like chest pain while symptomatic patients commonly present with respiratory symptoms like cough and shortness of breath. Besides that, there were more females presenting as presymptomatic patients compared to males (p = 0.019) and these group of patients were likely to receive treatment (p < 0.001). Otherwise, we were not able to identify other statistically significant markers suggesting a patient is presymptomatic. CONCLUSION: As we have little means of identifying these silent spreaders, it highlights further the importance of general measures implemented to stop COVID-19 transmission like social distancing, face mask, and widespread testing.

Pulmonary Embolism in an Asymptomatic COVID-19 Patient Detected on Ventilation/Perfusion SPECT/CT.

ABSTRACT: Severe COVID-19 infection is associated with significant coagulopathy. We would like share a case of an asymptomatic 26-year-old man who tested positive for COVID-19 and had elevated d-dimer levels. Because of inconclusive CTPA findings, V/Q (ventilation/perfusion) SPECT/CT was performed, which confirmed the presence of pulmonary embolism. This case highlights the fact that pulmonary embolism should not be overlooked in a COVID-19 patient who has raised d-dimer levels, even in the absence of symptoms. It also highlights the importance of performing a V/Q study when CTPA results are inconclusive or when there are contraindications for iodinated contrast media.