Browsing by Author "Mufti, Hani N."

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    Anaphylactic and nonanaphylactic reactions to SARS-CoV-2 vaccines: a systematic review and meta-analysis
    (BMC, 2021-10) Alhumaid, Saad; Al Mutair, Abbas; Al Alawi, Zainab; Rabaan, Ali A.; Tirupathi, Raghavendra; Alomari, Mohammed A.; Alshakhes, Aqeel S.; Alshawi, Abeer M.; Ahmed, Gasmelseed Y.; Almusabeh, Hassan M.; Alghareeb, Tariq T.; Alghuwainem, Abdulaziz A.; Alsulaiman, Zainab A.; Alabdulmuhsin, Mohammed A.; AlBuwaidi, Emad A.; Dukhi, Amjad K. Bu; Mufti, Hani N.; Al-Qahtani, Manaf; Dhama, Kuldeep; Al-Tawfiq, Jaffar A.; Al-Omari, Awad; Medicine, School of Medicine
    Background Currently there is no systematic review and meta-analysis of the global incidence rates of anaphylactic and nonanaphylactic reactions to SARS-CoV-2 vaccines in the general adult population. Objectives To estimate the incidence rates of anaphylactic and nonanaphylactic reactions after COVID-19 vaccines and describe the demographic and clinical characteristics, triggers, presenting signs and symptoms, treatment and clinical course of confirmed cases. Design A systematic review and meta-analysis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] statement was followed. Methods Electronic databases (Proquest, Medline, Embase, Pubmed, CINAHL, Wiley online library, and Nature) were searched from 1 December 2020 to 31 May 2021 in the English language using the following keywords alone or in combination: anaphylaxis, non-anaphylaxis, anaphylactic reaction, nonanaphylactic reaction, anaphylactic/anaphylactoid shock, hypersensitivity, allergy reaction, allergic reaction, immunology reaction, immunologic reaction, angioedema, loss of consciousness, generalized erythema, urticaria, urticarial rash, cyanosis, grunting, stridor, tachypnoea, wheezing, tachycardia, abdominal pain, diarrhea, nausea, vomiting and tryptase. We included studies in adults of all ages in all healthcare settings. Effect sizes of prevalence were pooled with 95% confidence intervals (CIs). To minimize heterogeneity, we performed sub-group analyses. Results Of the 1,734 papers that were identified, 26 articles were included in the systematic review (8 case report, 5 cohort, 4 case series, 2 randomized controlled trial and 1 randomized cross-sectional studies) and 14 articles (1 cohort, 2 case series, 1 randomized controlled trial and 1 randomized cross-sectional studies) were included in meta-analysis. Studies involving 26,337,421 vaccine recipients [Pfizer-BioNTech (n = 14,505,399) and Moderna (n = 11,831,488)] were analyzed. The overall pooled prevalence estimate of anaphylaxis to both vaccines was 5.0 (95% CI 2.9 to 7.2, I2 = 81%, p =  < 0.0001), while the overall pooled prevalence estimate of nonanaphylactic reactions to both vaccines was 53.9 (95% CI 0.0 to 116.1, I2 = 99%, p =  < 0.0001). Vaccination with Pfizer-BioNTech resulted in higher anaphylactic reactions compared to Moderna (8.0, 95% CI 0.0 to 11.3, I2 = 85% versus 2.8, 95% CI 0.0 to 5.7, I2 = 59%). However, lower incidence of nonanaphylactic reactions was associated with Pfizer-BioNTech compared to Moderna (43.9, 95% CI 0.0 to 131.9, I2 = 99% versus 63.8, 95% CI 0.0 to 151.8, I2 = 98%). The funnel plots for possible publication bias for the pooled effect sizes to determine the incidence of anaphylaxis and nonanaphylactic reactions associated with mRNA COVID-19 immunization based on mRNA vaccine type appeared asymmetrical on visual inspection, and Egger’s tests confirmed asymmetry by producing p values < 0.05. Across the included studies, the most commonly identified risk factors for anaphylactic and nonanaphylactic reactions to SARS-CoV-2 vaccines were female sex and personal history of atopy. The key triggers to anaphylactic and nonanaphylactic reactions identified in these studies included foods, medications, stinging insects or jellyfish, contrast media, cosmetics and detergents, household products, and latex. Previous history of anaphylaxis; and comorbidities such as asthma, allergic rhinitis, atopic and contact eczema/dermatitis and psoriasis and cholinergic urticaria were also found to be important. Conclusion The prevalence of COVID-19 mRNA vaccine-associated anaphylaxis is very low; and nonanaphylactic reactions occur at higher rate, however, cutaneous reactions are largely self-limited. Both anaphylactic and nonanaphylactic reactions should not discourage vaccination.
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    Diabetic ketoacidosis in patients with SARS-CoV-2: a systematic review and meta-analysis
    (BMC, 2021-10) Alhumaid, Saad; Al Mutair, Abbas; Al Alawi, Zainab; Rabaan, Ali A.; Alomari, Mohammed A.; Al Salman, Sadiq A.; Al-Alawi, Ahmed S.; Al Hassan, Mohammed H.; Alhamad, Hesham; Al-Kamees, Mustafa A.; Almousa, Fawzi M.; Mufti, Hani N.; Alwesabai, Ali M.; Dhama, Kuldeep; Al-Tawfiq, Jaffar A.; Al-Omari, Awad; Medicine, School of Medicine
    BACKGROUND: One possible reason for increased mortality due to SARS-CoV-2 in patients with diabetes is from the complication of diabetic ketoacidosis (DKA). OBJECTIVES: To re-evaluate the association of SARS-CoV-2 and development of DKA and analyse the demographic and biochemical parameters and the clinical outcomes in COVID-19 patients with DKA. DESIGN: A systematic review and meta-analysis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was followed. METHODS: Electronic databases (Proquest, Medline, Embase, Pubmed, CINAHL, Wiley online library, Scopus and Nature) were searched from 1 December 2019 to 30 June 2021 in the English language using the following keywords alone or in combination: COVID-19 OR SARS-CoV-2 AND diabetic ketoacidosis OR DKA OR ketosis OR ketonemia OR hyperglycaemic emergency OR hyperglycaemic crisis. We included studies in adults and children of all ages in all healthcare settings. Binary logistic regression model was used to explore the effect of various demographic and biochemical parameters variables on patient's final treatment outcome (survival or death). RESULTS: Of the 484 papers that were identified, 68 articles were included in the systematic review and meta-analysis (54 case report, 10 case series, and 4 cohort studies). Studies involving 639 DKA patients with confirmed SARS-CoV-2 [46 (7.2%) were children and 334 (52.3%) were adults] were analyzed. The median or mean patient age ranged from < 1 years to 66 years across studies. Most of the patients (n = 309, 48.3%) had pre-existing type 2 diabetes mellitus. The majority of the patients were male (n = 373, 58.4%) and belonged to Hispanic (n = 156, 24.4%) and black (n = 98, 15.3%) ethnicity. The median random blood glucose level, HbA1c, pH, bicarbonate, and anion gap in all included patients at presentation were 507 mg/dl [IQR 399-638 mg/dl], 11.4% [IQR 9.9-13.5%], 7.16 [IQR 7.00-7.22], 10 mmol/l [IQR 6.9-13 mmol/l], and 24.5 mEq/l [18-29.2 mEq/l]; respectively. Mortality rate was [63/243, 25.9%], with a majority of death in patients of Hispanic ethnicity (n = 17, 27%; p = 0.001). The odd ratios of death were significantly high in patients with pre-existing diabetes mellitus type 2 [OR 5.24, 95% CI 2.07-15.19; p = 0.001], old age (≥ 60 years) [OR 3.29, 95% CI 1.38-7.91; p = 0.007], and male gender [OR 2.61, 95% CI 1.37-5.17; p = 0.004] compared to those who survived. CONCLUSION: DKA is not uncommon in SARS-CoV-2 patients with diabetes mellitus and results in a mortality rate of 25.9%. Mortality key determinants in DKA patients with SARS-CoV-2 infection are individuals with pre-existing diabetes mellitus type 2, older age [≥ 60 years old], male gender, BMI ≥ 30, blood glucose level > 1000 mg/dl, and anion gap ≥ 30 mEq/l.
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    Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU
    (Elsevier, 2022) Elhazmi, Alyaa; Al-Omari, Awad; Sallam, Hend; Mufti, Hani N.; Rabie, Ahmed A.; Alshahrani, Mohammed; Mady, Ahmed; Alghamdi, Adnan; Altalaq, Ali; Azzam, Mohamed H.; Sindi, Anees; Kharaba, Ayman; Al-Aseri, Zohair A.; Almekhlafi, Ghaleb A.; Tashkandi, Wail; Alajmi, Saud A.; Faqihi, Fahad; Alharthy, Abdulrahman; Al-Tawfiq, Jaffar A.; Melibari, Rami Ghazi; Al-Hazzani, Waleed; Arabi, Yaseen M.; Medicine, School of Medicine
    Background: Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic. Methods: This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses. Results: There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio. Conclusion: DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.
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    Tocilizumab Outcomes in Critically Ill COVID-19 Patients Admitted to the ICU and the Role of Non-Tocilizumab COVID-19-Specific Medical Therapeutics
    (MDPI, 2023-03-16) Elhazmi, Alyaa; Rabie, Ahmed A.; Al-Omari, Awad; Mufti, Hani N.; Sallam, Hend; Alshahrani, Mohammed S.; Mady, Ahmed; Alghamdi, Adnan; Altalaq, Ali; Azzam, Mohamed H.; Sindi, Anees; Kharaba, Ayman; Al-Aseri, Zohair A.; Almekhlafi, Ghaleb A.; Tashkandi, Wail; Alajmi, Saud A.; Faqihi, Fahad; Alharthy, Abdulrahman; Al-Tawfiq, Jaffar A.; Melibari, Rami Ghazi; Arabi, Yaseen M.; Medicine, School of Medicine
    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.