Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

doi:10.1016/j.ijregi.2022.03.024

. 2022 Jun:3:242-247.

doi: 10.1016/j.ijregi.2022.03.024. Epub 2022 Apr 1.

Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Brian W Allwood¹, Coenraad F Koegelenberg¹, Veranyuy D Ngah², Lovemore N Sigwadhi², Elvis M Irusen¹, Usha Lalla¹, Anteneh Yalew^{2

3

4}, Jacques L Tamuzi², Marli McAllister², Annalise E Zemlin⁵, Thumeka P Jalavu⁵, Rajiv Erasmus⁵, Zivanai C Chapanduka⁶, Tandi E Matsha⁷, Isaac Fwemba⁸, Alimuddin Zumla^{9

10}, Peter S Nyasulu^{2

11}; COVID-19 Research Response Collaboration

Affiliations

¹ Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa.
² Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
³ Department of Statistics, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
⁴ National Data Management Centre for Health, Ethiopian Public Health Institute, Addis Ababa, Ethiopia.
⁵ Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and NHLS Tygerberg Hospital, Cape Town, South Africa.
⁶ Division of Haematological Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and NHLS Tygerberg Hospital, Cape Town, South Africa.
⁷ Faculty of Health and Wellness Sciences, Peninsula University of Technology, Bellville Campus, Cape Town.
⁸ School of Public Health, University of Zambia, Lusaka, Zambia.
⁹ Division of Infection and Immunity, Centre for Clinical Microbiology, University College London Royal Free Campus, London, UK.
¹⁰ NIHR Biomedical Research Centre, UCL Hospitals NHS Foundation Trust, London, UK.
¹¹ Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

PMID: 35720137
PMCID: PMC8971059
DOI: 10.1016/j.ijregi.2022.03.024

Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Brian W Allwood et al. IJID Reg. 2022 Jun.

. 2022 Jun:3:242-247.

doi: 10.1016/j.ijregi.2022.03.024. Epub 2022 Apr 1.

Authors

Affiliations

¹ Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa.
² Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
³ Department of Statistics, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
⁴ National Data Management Centre for Health, Ethiopian Public Health Institute, Addis Ababa, Ethiopia.
⁵ Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and NHLS Tygerberg Hospital, Cape Town, South Africa.
⁶ Division of Haematological Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and NHLS Tygerberg Hospital, Cape Town, South Africa.
⁷ Faculty of Health and Wellness Sciences, Peninsula University of Technology, Bellville Campus, Cape Town.
⁸ School of Public Health, University of Zambia, Lusaka, Zambia.
⁹ Division of Infection and Immunity, Centre for Clinical Microbiology, University College London Royal Free Campus, London, UK.
¹⁰ NIHR Biomedical Research Centre, UCL Hospitals NHS Foundation Trust, London, UK.
¹¹ Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

PMID: 35720137
PMCID: PMC8971059
DOI: 10.1016/j.ijregi.2022.03.024

Abstract

Background: The second wave of coronavirus disease 2019 (COVID-19) in South Africa was caused by the Beta variant of severe acute respiratory syndrome coronavirurus-2. This study aimed to explore clinical and biochemical parameters that could predict outcome in patients with COVID-19.

Methods: A prospective study was conducted between 5 November 2020 and 30 April 2021 among patients with confirmed COVID-19 admitted to the intensive care unit (ICU) of a tertiary hospital. The Cox proportional hazards model in Stata 16 was used to assess risk factors associated with survival or death. Factors with P<0.05 were considered significant.

Results: Patients who died were found to have significantly lower median pH (P<0.001), higher median arterial partial pressure of carbon dioxide (P<0.001), higher D-dimer levels (P=0.001), higher troponin T levels (P=0.001), higher N-terminal-prohormone B-type natriuretic peptide levels (P=0.007) and higher C-reactive protein levels (P=0.010) compared with patients who survived. Increased standard bicarbonate (HCO3std) was associated with lower risk of death (hazard ratio 0.96, 95% confidence interval 0.93-0.99).

Conclusions: The mortality of patients with COVID-19 admitted to the ICU was associated with elevated D-dimer and a low HCO3std level. Large studies are warranted to increase the identification of patients at risk of poor prognosis, and to improve the clinical approach.

Keywords: Biomarkers; COVID-19; ICU; Mortality; SARS-CoV-2; Second wave.

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Conflict of interest statement

None declared.

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References

1. Bezuidenhout MC, Wiese OJ, Moodley D, Maasdorp E, Davids MR, Koegelenberg CF, et al. Correlating arterial blood gas, acid-base and blood pressure abnormalities with outcomes in COVID-19 intensive care patients. Ann Clin Biochem. 2021;58:95–101. - PubMed
1. Chan KH, Slim J, Shaaban HS. Pulmonary embolism and increased levels of D-dimer in patients with coronavirus disease. Emerg Infect Dis. 2020;26:2522–2533. - PMC - PubMed
1. Cobre AF, Stremel DP, Noleto GR, Fachi MM, Surek M, Wiens A, et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput Biol Med. 2021;134 - PMC - PubMed
1. Coccia M. The impact of first and second wave of the COVID-19 pandemic in society: comparative analysis to support control measures to cope with negative effects of future infectious diseases. Environ Res. 2021;197 - PMC - PubMed
1. Connors JM, Levy JH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. 2020;135:2033–2040. - PMC - PubMed

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[1] Bezuidenhout MC, Wiese OJ, Moodley D, Maasdorp E, Davids MR, Koegelenberg CF, et al. Correlating arterial blood gas, acid-base and blood pressure abnormalities with outcomes in COVID-19 intensive care patients. Ann Clin Biochem. 2021;58:95–101. - PubMed

[2] Bezuidenhout MC, Wiese OJ, Moodley D, Maasdorp E, Davids MR, Koegelenberg CF, et al. Correlating arterial blood gas, acid-base and blood pressure abnormalities with outcomes in COVID-19 intensive care patients. Ann Clin Biochem. 2021;58:95–101. - PubMed

[3] Chan KH, Slim J, Shaaban HS. Pulmonary embolism and increased levels of D-dimer in patients with coronavirus disease. Emerg Infect Dis. 2020;26:2522–2533. - PMC - PubMed

[4] Chan KH, Slim J, Shaaban HS. Pulmonary embolism and increased levels of D-dimer in patients with coronavirus disease. Emerg Infect Dis. 2020;26:2522–2533. - PMC - PubMed

[5] Cobre AF, Stremel DP, Noleto GR, Fachi MM, Surek M, Wiens A, et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput Biol Med. 2021;134 - PMC - PubMed

[6] Cobre AF, Stremel DP, Noleto GR, Fachi MM, Surek M, Wiens A, et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput Biol Med. 2021;134 - PMC - PubMed

[7] Coccia M. The impact of first and second wave of the COVID-19 pandemic in society: comparative analysis to support control measures to cope with negative effects of future infectious diseases. Environ Res. 2021;197 - PMC - PubMed

[8] Coccia M. The impact of first and second wave of the COVID-19 pandemic in society: comparative analysis to support control measures to cope with negative effects of future infectious diseases. Environ Res. 2021;197 - PMC - PubMed

[9] Connors JM, Levy JH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. 2020;135:2033–2040. - PMC - PubMed

[10] Connors JM, Levy JH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. 2020;135:2033–2040. - PMC - PubMed

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Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Affiliations

Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Authors

Affiliations

Abstract

Conflict of interest statement

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References

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