Prophylactic Inferior Vena Cava Filters for Venous Thromboembolism in Adults With Trauma: An Updated Systematic Review and Meta-Analysis.

Background: Trauma is an independent risk factor for venous thromboembolism (VTE). Due to contraindications or delay in starting pharmacological prophylaxis among trauma patients with a high risk of bleeding, the inferior vena cava (IVC) filter has been utilized as alternative prevention for pulmonary embolism (PE). Albeit, its clinical efficacy has remained uncertain. Therefore, we performed an updated systematic review and meta-analysis on the effectiveness and safety of prophylactic IVC filters in severely injured patients. Methods: Three databases (MEDLINE, EMBASE, and Cochrane) were searched from August 1, 2012, to October 27, 2021. Independent reviewers performed data extraction and quality assessment. Relative risk (RR) at 95% confidence interval (CI) pooled in a randomized meta-analysis. A parallel clinical practice guideline committee assessed the certainty of evidence using the GRADE approach. The outcomes of interest included VTE, PE, deep venous thrombosis, mortality, and IVC filter complications. Results: We included 10 controlled studies (47 140 patients), of which 3 studies (310 patients) were randomized controlled trials (RCTs) and 7 were observational studies (46 830 patients). IVC filters demonstrated no significant reduction in PE and fatal PE (RR, 0.27; 95% CI, 0.06-1.28 and RR, 0.32; 95% CI, 0.01-7.84, respectively) by pooling RCTs with low certainty. However, it demonstrated a significant reduction in the risk of PE and fatal PE (RR, 0.25; 95% CI, 0.12-0.55 and RR, 0.09; 95% CI, 0.011-0.81, respectively) by pooling observational studies with very low certainty. IVC filter did not improve mortality in both RCTs and observational studies (RR, 1.44; 95% CI, 0.86-2.43 and RR, 0.63; 95% CI, 0.3-1.31, respectively). Conclusion: In trauma patients, moderate risk reduction of PE and fatal PE was demonstrated among observational data but not RCTs. The desirable effect is not robust to outweigh the undesirable effects associated with IVC filter complications. Current evidence suggests against routinely using prophylactic IVC filters.

Tocilizumab Outcomes in Critically Ill COVID-19 Patients Admitted to the ICU and the Role of Non-Tocilizumab COVID-19-Specific Medical Therapeutics.

BACKGROUND: Tocilizumab is a monoclonal antibody proposed to manage cytokine release syndrome (CRS) associated with severe COVID-19. Previously published reports have shown that tocilizumab may improve the clinical outcomes of critically ill patients admitted to the ICU. However, no precise data about the role of other medical therapeutics concurrently used for COVID-19 on this outcome have been published. OBJECTIVES: We aimed to compare the overall outcome of critically ill COVID-19 patients admitted to the ICU who received tocilizumab with the outcome of matched patients who did not receive tocilizumab while controlling for other confounders, including medical therapeutics for critically ill patients admitted to ICUs. METHODS: A prospective, observational, multicenter cohort study was conducted among critically ill COVID-19 patients admitted to the ICU of 14 hospitals in Saudi Arabia between 1 March 2020, and October 31, 2020. Propensity-score matching was utilized to compare patients who received tocilizumab to patients who did not. In addition, the log-rank test was used to compare the 28 day hospital survival of patients who received tocilizumab with those who did not. Then, a multivariate logistic regression analysis of the matched groups was performed to evaluate the impact of the remaining concurrent medical therapeutics that could not be excluded via matching 28 day hospital survival rates. The primary outcome measure was patients' overall 28 day hospital survival, and the secondary outcomes were ICU length of stay and ICU survival to hospital discharge. RESULTS: A total of 1470 unmatched patients were included, of whom 426 received tocilizumab. The total number of propensity-matched patients was 1278. Overall, 28 day hospital survival revealed a significant difference between the unmatched non-tocilizumab group (586; 56.1%) and the tocilizumab group (269; 63.1%) (p-value = 0.016), and this difference increased even more in the propensity-matched analysis between the non-tocilizumab group (466.7; 54.6%) and the tocilizumab group (269; 63.1%) (p-value = 0.005). The matching model successfully matched the two groups' common medical therapeutics used to treat COVID-19. Two medical therapeutics remained significantly different, favoring the tocilizumab group. A multivariate logistic regression was performed for the 28 day hospital survival in the propensity-matched patients. It showed that neither steroids (OR: 1.07 (95% CI: 0.75-1.53)) (p = 0.697) nor favipiravir (OR: 1.08 (95% CI: 0.61-1.9)) (p = 0.799) remained as a predictor for an increase in 28 day survival. CONCLUSION: The tocilizumab treatment in critically ill COVID-19 patients admitted to the ICU improved the overall 28 day hospital survival, which might not be influenced by the concurrent use of other COVID-19 medical therapeutics, although further research is needed to confirm this.

Long-term outcomes of patients with COVID-19 treated with helmet noninvasive ventilation or usual respiratory support: follow-up study of the Helmet-COVID randomized clinical trial.

PURPOSE: To evaluate whether helmet noninvasive ventilation compared to usual respiratory support reduces 180-day mortality and improves health-related quality of life (HRQoL) in patients with acute hypoxemic respiratory failure due to COVID-19 pneumonia. METHODS: This is a pre-planned follow-up study of the Helmet-COVID trial. In this multicenter, randomized clinical trial, adults with acute hypoxemic respiratory failure (n = 320) due to coronavirus disease 2019 (COVID-19) were randomized to receive helmet noninvasive ventilation or usual respiratory support. The modified intention-to-treat population consisted of all enrolled patients except three who were lost at follow-up. The study outcomes were 180-day mortality, EuroQoL (EQ)-5D-5L index values, and EQ-visual analog scale (EQ-VAS). In the modified intention-to-treat analysis, non-survivors were assigned a value of 0 for EQ-5D-5L and EQ-VAS. RESULTS: Within 180 days, 63/159 patients (39.6%) died in the helmet noninvasive ventilation group compared to 65/158 patients (41.1%) in the usual respiratory support group (risk difference - 1.5% (95% confidence interval [CI] - 12.3, 9.3, p = 0.78). In the modified intention-to-treat analysis, patients in the helmet noninvasive ventilation and the usual respiratory support groups did not differ in EQ-5D-5L index values (median 0.68 [IQR 0.00, 1.00], compared to 0.67 [IQR 0.00, 1.00], median difference 0.00 [95% CI - 0.32, 0.32; p = 0.91]) or EQ-VAS scores (median 70 [IQR 0, 93], compared to 70 [IQR 0, 90], median difference 0.00 (95% CI - 31.92, 31.92; p = 0.55). CONCLUSIONS: Helmet noninvasive ventilation did not reduce 180-day mortality or improve HRQoL compared to usual respiratory support among patients with acute hypoxemic respiratory failure due to COVID-19 pneumonia.

Incidence of Bacterial and Fungal Secondary Infections in COVID-19 Patients Admitted to the ICU

Purpose Secondary infections have been observed among coronavirus disease 2019 (COVID-19) patients, especially in the intensive care unit (ICU) setting, which is associated with worse clinical outcomes. The current study aimed to investigate the incidence, common pathogens, and outcome of bacterial and fungal secondary infections among ICU patients with COVID-19. Methods A retrospective chart review of all patients admitted to the ICU at King Fahd Hospital of the University in Saudi Arabia. All adult patients aged ≥18 admitted in the ICU for ≥48 hours with positive COVID-19 reverse transcription-polymerase chain reaction test during the period between March 2020 till September 2021 were included. Results Out of 314 critically ill patients, 133 (42.4%) developed secondary infections. The incidence of secondary bacterial infection was 32.5% with Pseudomonas aeruginosa (n = 34), Acinetobacter baumannii (n = 33), and Klebsiella pneumoniae (n = 17) being the predominant pathogens, while secondary fungal infection was 25.2% mainly caused by Candida albicans (n = 43). Invasive mechanical ventilation was significantly associated with the development of secondary bacterial infections (odds ratio [OR] = 17.702, 95% confidence interval [CI] 7.842–39.961, p < 0.001) and secondary fungal infections (OR = 12.914, 95% CI 5.406–30.849, p < 0.001). Mortality among patients with secondary infections was 69.2% (n = 92). Secondary infections were associated with longer hospital and ICU stays with a median of 25 days (interquartile range [IQR] 17–42) and 19 days (IQR 13–32), respectively. Conclusion Bacterial and fungal secondary infections are common among COVID-19 patients admitted to the ICU with a predominance of gram-negative bacteria and Candida species. The development of secondary infections was significantly associated with invasive mechanical ventilation. Poor clinical outcomes have been observed, demonstrated with a prolonged hospital and ICU stays and higher mortality.

Prevalence, Causes, and Adverse Clinical Impact of Delayed Presentation of Non-COVID-19-Related Emergencies during the COVID-19 Pandemic: Findings from a Multicenter Observational Study.

OBJECTIVE: The coronavirus disease (COVID-19) pandemic has disrupted healthcare systems worldwide, resulting in decreased and delayed hospital visits of patients with non-COVID-19-related acute emergencies. We evaluated the impact of the COVID-19 pandemic on the presentation and outcomes of patients with non-COVID-19-related medical and surgical emergencies. METHOD: All non-COVID-19-related patients hospitalized through emergency departments in three tertiary care hospitals in Saudi Arabia and Bahrain in June and July 2020 were enrolled and categorized into delayed and non-delayed groups (presentation ≥/=24 or <24 h after onset of symptom). Primary outcome was the prevalence and cause of delayed presentation; secondary outcomes included comparative 28-day clinical outcomes (i.e., 28-day mortality, intensive care unit (ICU) admission, invasive mechanical ventilation, and acute surgical interventions). Mean, median, and IQR were used to calculate the primary outcomes and inferential statistics including chi-square/Fisher exact test, t-test where appropriate were used for comparisons. Stepwise multivariate regression analysis was performed to identify the factors associated with delay in seeking medical attention. RESULTS: In total, 24,129 patients visited emergency departments during the study period, compared to 48,734 patients in the year 2019. Of the 256 hospitalized patients with non-COVID-19-related diagnoses, 134 (52%) had delayed presentation. Fear of COVID-19 and curfew-related restrictions represented 46 (34%) and 25 (19%) of the reasons for delay. The 28-day mortality rates were significantly higher among delayed patients vs. non-delayed patients (n = 14, 10.4% vs. n = 3, 2.5%, OR: 4.628 (CI: 1.296-16.520), p = 0.038). CONCLUSION: More than half of hospitalized patients with non-COVID-19-related diagnoses had delayed presentation to the ED where mortality was found to be significantly higher in this group. Fear of COVID-19 and curfew restrictions were the main reasons for delaying hospital visit.

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients' need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.

Bacterial and Fungal Coinfection in Critically Ill COVID-19 Cases and Predictive Role of Procalcitonin During the First Wave at an Academic Health Center.

BACKGROUND: Coinfection at various sites can complicate the clinical course of coronavirus disease of 2019 (COVID-19) patients leading to worse prognosis and increased mortality. We aimed to investigate the occurrence of coinfection in critically ill COVID-19 cases, and the predictive role of routinely tested biomarkers on admission for mortality. METHODS: This is a retrospective study of all SARS-CoV-2-infected cases, who were admitted to King Fahad Hospital of the University between March 2020 and December 2020. We reviewed the data in the electronic charts in the healthcare information management system including initial presentation, clinical course, radiological and laboratory findings and reported all significant microbiological cultures that indicated antimicrobial therapy. The mortality data were reviewed for severely ill patients who were admitted to critical care units. RESULTS: Of 1091 admitted patients, there were 70 fatalities (6.4%). 182 COVID-19 persons were admitted to the critical care service, of whom 114 patients (62.6%) survived. The in-hospital mortality was 13.4%. Coinfection was noted in 67/68 non-survivors, and Gram-negative pathogens (Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumanni) represented more than 50% of the etiological agents. We noted that the serum procalcitonin on admission was higher for non-survivors (Median = 1.6 ng/mL ± 4.7) than in survivors (Median = 0.2 ng/mL ± 4.2) (p ≤ 0.05). CONCLUSION: Coinfection is a serious complication for COVID-19 especially in the presence of co-morbidities. High levels of procalcitonin on admission may predict non-survival in critically ill cases in whom bacterial or fungal co-infection is likely.

Predictors of Illness Severity in COVID-19 Cases in Saudi Arabia.

PURPOSE: Multiple studies worldwide have reported the clinical and epidemiological features of coronavirus disease 2019 (COVID-19), with limited reports from the Middle East. This study describes the clinical and epidemiological features of COVID-19 cases in the Eastern Province of Saudi Arabia and identified factors associated with the severity of illness. PATIENTS AND METHODS: This was an observational study of 341 COVID-19 cases. These cases were reported in the first three months after the first case in the country was identified. Clinical and demographic data were analyzed and described to identify the effects of age, sex, and ethnicity on illness severity. In addition, the duration of viral shedding and cycle threshold (Ct) values of real-time PCR were evaluated as predictors of severity. RESULTS: The median age was 45 years. Males were twice as likely to be infected than females (p <0.0001). The duration of viral shedding ranged from 9 to 36 days. The most common clinical presentations include fever, shortness of breath, cough, myalgia, sore throat, vomiting, and headache. Critical cases were significantly higher in men (23% vs 8.7%), senior adults (>65 years), individuals of Bengali ethnicity, and in patients with comorbidities including diabetes, hypertension, and dyslipidemia (p =0.001). The case fatality rate was found to be 10%. The fatality was significantly higher in males than females (13.8% vs 2.6%), and in Asians (17.9%) than Arabs (6%) and Africans (0) (p =0.002). No association was found between viral load, represented by the RT-PCR cycle threshold (Ct) values, and severity of illness. CONCLUSION: Age, sex, and ethnicity are important predictors of COVID-19 severity. The cycle threshold (Ct) of the SARS-CoV-2 RT-PCR test cannot be used as a predictor of the criticality of illness.

High-Flow Nasal Cannula Treatment in Patients with COVID-19 Acute Hypoxemic Respiratory Failure: A Prospective Cohort Study.

BACKGROUND: Early use of high-flow nasal cannula (HFNC) decreases the need for endotracheal intubation (EI) in different respiratory failure causes. While HFNC is used in coronavirus disease 2019 (COVID-19)-related acute hypoxemic respiratory failure (AHRF) under weak recommendations, its efficacy remains to be investigated. OBJECTIVES: The primary objective was to examine HFNC efficacy in preventing EI among COVID-19 patients with AHRF. Secondary objectives were to determine predictors of HFNC success/failure, mortality rate, and length of hospital and intensive care unit (ICU) stay. PATIENTS AND METHODS: This is a prospective cohort study conducted at a single tertiary care centre in Saudi Arabia from April to August 2020. Adult patients admitted to the ICU with AHRF secondary to COVID-19 pneumonia and managed with HFNC were included. We excluded patients who were intubated or managed with non-invasive ventilation before HFNC. RESULTS: Forty-four patients received HFNC for a median duration of 3 days (interquartile range, 1-5 days). The mean age was 57 ± 14 years, and 86% were men. HFNC failure and EI occurred in 29 (66%) patients. Patients in whom HNFC treatment failed had a higher risk of death (52% versus 0%; P = 0.001). After adjusting for confounding factors, a high SOFA score and a low ROX index were significantly associated with HFNC failure (hazard ratio [HR], 1.42; 95% confidence interval [CI], 1.04-1.93; P = 0.025; and HR, 0.61; 95% CI, 0.42-0.88; P = 0.008, respectively). CONCLUSIONS: One-third of hypoxemic COVID-19 patients who received HFNC did not require intubation. High SOFA score and low ROX index were associated with HFNC failure.

Comorbidities and Risk Factors for Severe Outcomes in COVID-19 Patients in Saudi Arabia: A Retrospective Cohort Study.

PURPOSE: The first novel coronavirus disease-19 (COVID-19) case in the Kingdom of Saudi Arabia (KSA) was reported in Qatif in March 2020 with continual increase in infection and mortality rates since then. In this study, we aim to determine risk factors which effect severity and mortality rates in a cohort of hospitalized COVID-19 patients in KSA. METHOD: We reviewed medical records of hospitalized patients with confirmed COVID-19 positive results via reverse-transcriptase-polymerase-chain-reaction (RT-PCR) tests at Prince Mohammed Bin Abdulaziz Hospital, Riyadh between May and August 2020. Data were obtained for patient's demography, body mass index (BMI), and comorbidities. Additional data on patients that required intensive care unit (ICU) admission and clinical outcomes were recorded and analyzed with Python Pandas. RESULTS: A total of 565 COVID-19 positive patients were inducted in the study out of which, 63 (11.1%) patients died while 101 (17.9%) patients required ICU admission. Disease incidences were significantly higher in males and non-Saudi nationals. Patients with cardiovascular, respiratory, and renal diseases displayed significantly higher association with ICU admissions (p<0.001) while mortality rates were significantly higher in COVID-19 patients with cardiovascular, respiratory, renal and neurological diseases. Univariate cox proportional hazards regression model showed that COVID-19 positive patients requiring ICU admission [Hazard's ratio, HR=4.2 95% confidence interval, CI 2.5-7.2); p<0.001] with preexisting cardiovascular [HR=4.1 (CI 2.5-6.7); p<0.001] or respiratory [HR=4.0 (CI 2.0-8.1); p=0.010] diseases were at significantly higher risk for mortality among the positive patients. There were no significant differences in mortality rates or ICU admissions among males and females, and across different age groups, BMIs and nationalities. Hospitalized patients with cardiovascular comorbidity had the highest risk of death (HR=2.9, CI 1.7-5.0; p=0.020). CONCLUSION: Independent risk factors for critical outcomes among COVID-19 in KSA include cardiovascular, respiratory and renal comorbidities.

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.

Applications of Big Data Analytics to Control COVID-19 Pandemic.

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

Safety and Efficacy of Convalescent Plasma for Severe COVID-19: Interim Report of a Multicenter Phase II Study from Saudi Arabia.

OBJECTIVE: To present the interim findings from a national study investigating the safety and efficacy of convalescent plasma (CP) containing detectable IgG antibodies as a treatment strategy for severe coronavirus disease 2019 (COVID-19). TRIAL DESIGN AND PARTICIPANTS: An open label, two-arm, phase-II clinical trial conducted across 22 hospitals from Saudi Arabia. The intervention group included 40 adults (aged ≥18 years) with confirmed severe COVID-19 and the control group included 124 patients matched using propensity score for age, gender, intubation status, and history of diabetes and/or hypertension. Intervention group included those (a) with severe symptoms (dyspnea; respiratory rate, ≥30/min; SpO2, ≤93%, PaO2/FiO2 ratio, <300; and/or lung infiltrates >50% within 24-48 h), (b) requiring intensive care unit (ICU) care or (c) experiencing life-threatening conditions. The control group included confirmed severe COVID-19 patients of similar characteristics who did not consent for CP infusion or were not able to receive CP due to its nonavailability. INTERVENTIONS: The intervention group participants were infused 300 ml (200-400 ml/treatment dose) CP at least once, and if required, daily for up to 5 sessions, along with receiving the best standard of care. The control group only received the best standard of care. OUTCOMES: The primary endpoints were safety and ICU length of stay (LOS). The secondary endpoints included 30-day mortality, days on mechanical ventilation and days to clinical recovery. RESULTS: CP transfusion did not result in any adverse effects. There was no difference in the ICU LOS (median 8 days in both groups). The mortality risk was lower in the CP group: 13% absolute risk reduction (P = 0.147), hazard ratio (95% confidence interval): 0.554 (0.299-1.027; P = 0.061) by log-rank test. There was no significant difference in the days on mechanical ventilation and days to clinical recovery. CONCLUSION: CP containing detectable antibodies is a safe strategy and may result in a decrease in mortality in patients with severe COVID-19. The results of the completed trial with a larger study sample would provide more clarity if this difference in mortality is significant. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04347681; Saudi Clinical Trials Registry No.: 20041102.

Prevalence of the SARS-CoV-2 Infection Among Post-Quarantine Healthcare Workers.

BACKGROUND: Coronavirus disease 2019 is an emerging highly communicable disease. Nosocomial transmission needs to be prevented through the implementation of stringent screening and infection control measures. OBJECTIVE: The objective of the study is to estimate the prevalence of severe acute respiratory syndrome- coronavirus 2 (SARS-CoV-2) infection among health care workers (HCWs) post quarantine period. METHODS: This is a prospective, observational study conducted at a teaching University hospital in Alkhobar, Saudi Arabia, during the period between May 1 and June 15, 2020. All (HCWs) joining work back from the quarantine areas had a real-time polymerase chain reaction (qRT-PCR) test for SARS-CoV-2. The demographic and clinical data from the staff were collected. RESULTS: Of the 301 HCWs screened, 18 (6%) had positive PCR. The age means of the positive cases was 32.9 Y ± 8.7 compared to 33.8 Y ± 7.0 in the negatively tested group (p value = 0.90). Of the 18 PCR-positive HCWs, 7 (38.9%) were male. Majority of those who tested positive were trainees (8.2%) followed by nurses (5.1%). In PCR-positive group, a clear epidemiological exposure was found in 4/18 cases (22.2%). Male gender and residency in specific districts were observed more in the positive cases (p value = 0.01 and 0.0001, respectively). In regards to symptoms, most of the positive PCR tested HCWs (n=12, 66.7%) remained asymptomatic. Most prevalent initial symptoms were gastrointestinal symptoms (diarrhea, abdominal pain) in six HCWs representing 33.3%. No significant difference was noted in co-morbidities reported by both groups. CONCLUSION: Health care workers tested post-quarantine period were found to be at risk of SARS-CoV-2 infection despite very minimal or no known risks of exposure, where most of them were asymptomatic. This potentially carries risk of nosocomial transmission inside healthcare facilities. Implanting policies for routine post-quarantine screening for HCWs is recommended.