Glycaemic Characteristics and Outcomes of COVID-19 Patients Admitted to a Tertiary Hospital in Johannesburg

Mohamed, Farzahna; Raal, Frederick J.; Mbelle, Mzamo; Zamparini, Jarrod; Venturas, Jacqueline; Shaddock, Erica; Diana, Nina; Mer, Mervyn; Dingezweni, Sithandiwe; Bolon, Stefan; Smith, Oliver; Mahomed, Adam; Kalla, Ismail

doi:10.18772/26180197.2020.v2n3a1

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…[34] Borghesi et al [20] did not include obesity or CRP in their final predictive models for the Brixia score. Similar to other data, however, we identified both obesity and CRP as predictors of in-hospital mortality, [37,38,40,44] and CRP was successfully introduced into a risk stratification model with the Brixia score. Comparable with local and international publications, [40,43] LDH was also statistically significantly higher for participants who died, but could not be introduced into a multivariate prediction model for participant demise.…”

Section: Researchsupporting

confidence: 79%

“…Similar to other data, however, we identified both obesity and CRP as predictors of in-hospital mortality, [37,38,40,44] and CRP was successfully introduced into a risk stratification model with the Brixia score. Comparable with local and international publications, [40,43] LDH was also statistically significantly higher for participants who died, but could not be introduced into a multivariate prediction model for participant demise. In accord with Venturas et al [45] we also concluded that being HIV-positive did not confer a higher risk of in-hospital fatality.…”

Section: Researchsupporting

confidence: 79%

“…[39] It has been demonstrated that diabetes mellitus and substandard glycaemic control may confer a worse prognosis in those hospitalised with COVID-19. [40,41] The study focus was to evaluate the utility of the Brixia scoring system combined with participant demographics, comorbidities and laboratory parameters for predicting disease course and patient outcome (in-hospital demise v. discharge) in the SA context, where we have a high prevalence of HIV co-infection, [42] among other comorbidities.…”

Section: Researchmentioning

confidence: 99%

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Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

Labuschagne

Venturas²,

Moodley³

2023

S Afr Med J

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Background. Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries.Objectives. To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters.Methods. Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise.Results. The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality.Conclusion. We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.

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Section: Researchsupporting

confidence: 79%

Section: Researchsupporting

confidence: 79%

Section: Researchmentioning

confidence: 99%

See 1 more Smart Citation

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

Labuschagne

Venturas²,

Moodley³

2023

S Afr Med J

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“…COVID-19 puts individuals with co-existing DM at higher risk of severe disease and mortality. [ 14 15 ] According to a recent pooled meta-analysis study, COVID-19-associated NODM patients had the highest mortality rate (24.96%), followed by patients with pre-existing DM (16.03%), and non-diabetic patients (9.29%). COVID-19 associated NODM patients had the highest adverse effects, followed by patients with pre-existing DM, COVID-19 associated hyperglycemia, and non-diabetic patients.…”

Section: Discussionmentioning

confidence: 99%

New onset diabetes mellitus in post-COVID-19 patients

Keerthi

Sushmita

Khan

et al. 2022

Journal of Family Medicine and Primary Care

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Background: Diabetes, is known to have a bilateral relationship with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Precise mechanism of diabetes onset in COVID-19 patients remains unclear. Aim: To analyse the incidence of new onset diabetes (NODM) among COVID-19 patients, as well as the effect of body mass index (BMI), family history, and steroid use on the incidence of the disease. Methods: Adult, not known diabetic patients, tested positive with Rapid Antigen Test or RT-PCR admitted to a tertiary care hospital and research institute were included in the present prospective observational study. The patients who developed NODM and NOPD (New Onset Pre-diabetes) during the three months follow-up and the risk factors associated were assessed. Patients with HbA1c >6.4% were diagnosed with NODM. An HbA1c of 5.7% to 6.4% was used to characterize NOPD. Results: Out of 273 previously not known diabetic COVID-19 infected individuals, a total of 100 were studied for three months after consent. Mean age of the patients 48.31 ± 19.07 years with male predominance (67%). Among these, 58% were non-diabetics and 42% were pre-diabetics. 6 (10.3%) of the 58 non-diabetics developed NOPD, and 8 (13.8%) developed NODM. 6 (14.2%) of the 42 pre-diabetics became non-diabetic, and 16.6% (7) developed NODM. Family history of DM ( P < 0.001), severity at admission ( P < 0.006), diabetic ketoacidosis ( P < 0.0275), and persistent symptoms were associated significantly with NODM. Those with NODM had significantly greater BMI, O 2 duration, steroid duration, FBS, and PPBS ( P < 0.001 for all). Nearly 67% of the patients who developed NOPD had shortness of breath as the common symptom at time of admission ( P = 0.0165). Conclusion: The incidence of NODM was strongly influenced by positive family history of DM, higher BMI, steroid dosage, and its duration. Hence, patients with COVID-19 need to be under surveillance for blood glucose screening.

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“…Most of those performed were concerned with poor health outcomes in diabetic patients with COVID-19 and had small sample sizes and cohorts of admitted patients during the pandemic. 38 , 39 , 40 , 41 A study performed in a lower-middle income setting in South Asia in 2020 demonstrated a decrease in mean HbA1c level by 0.3% during the pandemic and found improved HbA1c results in 37.6% of participants. 42 A South African study performed in Gauteng province in 2021, found similar findings to our study, where they showed decreased HbA1c testing volumes during the COVID-19 lockdown periods and, while it reported variability in HbA1c results and glycaemic control between these pre-pandemic and pandemic periods, the significance of this was not determined.…”

Section: Discussionmentioning

confidence: 99%

Effect of COVID-19 lockdown on glycated haemoglobin testing and utilisation in KwaZulu-Natal, South Africa

Severaj,

Gounden

2024

African Journal of Laboratory Medicine

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Background: Diabetic monitoring and treatment guidelines are easily accessible, but compliance is poor in KwaZulu-Natal. The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on diabetic healthcare, both directly and through public health interventions.Objective: This study aimed to close the gaps in knowledge concerning glycated haemoglobin (HbA1c) test utilisation and how this was affected by the COVID-19 lockdown in KwaZulu-Natal.Methods: We reviewed HbA1c test volumes and results from public health facilities in the 11 health districts in KwaZulu-Natal, South Africa. We compared testing trends and glycaemic control between two 10-month study periods before (March 2019 – December 2019) and during (March 2020 – December 2020) the COVID-19 pandemic.Results: The number of HbA1c tests performed decreased 6.1% during the pandemic period, with 173 760 HbA1c tests performed in 2019 and 163 236 HbA1c tests performed in 2020. There was a statistically significant increase in the average HbA1c level during the pandemic (mean HbA1c level in the pre-pandemic period: 70.5 mmol/mol [8.6%] versus mean HbA1c level in the pandemic period: 72.7 mmol/mol [8.8%]; p-value 0.001). Of patients with suboptimal HbA1c results (83 421 in 2019, 83 259 in 2020), 11 656 (14.0%) in 2019 and 10 086 (12.1%) in 2020 had more than one HbA1c test performed during the study period.Conclusion: The COVID-19 pandemic impacted glycaemic monitoring in KwaZulu-Natal with lower HbA1c test volumes and worse glycaemic control seen during the pandemic. HbA1c testing practices are not in keeping with recommended guidelines.What this study adds: The study demonstrates that the COVID-19 pandemic impacted HbA1c utilisation in KwaZulu-Natal. Importantly, HbA1c testing practices in KwaZulu-Natal are not in keeping with Society for Endocrinology, Metabolism and Diabetes of South Africa guidelines regarding the monitoring of diabetic patients, and this requires more attention for future diabetic healthcare interventions.

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Glycaemic Characteristics and Outcomes of COVID-19 Patients Admitted to a Tertiary Hospital in Johannesburg

Cited by 4 publications

References 35 publications

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

New onset diabetes mellitus in post-COVID-19 patients

Effect of COVID-19 lockdown on glycated haemoglobin testing and utilisation in KwaZulu-Natal, South Africa

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