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Machine Learning Algorithms-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa during COVID-19 Pandemic
Version 1
: Received: 9 September 2024 / Approved: 10 September 2024 / Online: 10 September 2024 (17:20:54 CEST)
How to cite:
Mmatsinhe, C.; Kagodora, S. B.; Mukheli, T.; Mokoena, T. P.; Malebati, W.; Moeng, M. S.; Luvhengo, T. E. E. (. Machine Learning Algorithms-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa during COVID-19 Pandemic. Preprints2024, 2024090764. https://doi.org/10.20944/preprints202409.0764.v1
Mmatsinhe, C.; Kagodora, S. B.; Mukheli, T.; Mokoena, T. P.; Malebati, W.; Moeng, M. S.; Luvhengo, T. E. E. (. Machine Learning Algorithms-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa during COVID-19 Pandemic. Preprints 2024, 2024090764. https://doi.org/10.20944/preprints202409.0764.v1
Mmatsinhe, C.; Kagodora, S. B.; Mukheli, T.; Mokoena, T. P.; Malebati, W.; Moeng, M. S.; Luvhengo, T. E. E. (. Machine Learning Algorithms-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa during COVID-19 Pandemic. Preprints2024, 2024090764. https://doi.org/10.20944/preprints202409.0764.v1
APA Style
Mmatsinhe, C., Kagodora, S. B., Mukheli, T., Mokoena, T. P., Malebati, W., Moeng, M. S., & Luvhengo, T. E. E. (. (2024). Machine Learning Algorithms-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa during COVID-19 Pandemic. Preprints. https://doi.org/10.20944/preprints202409.0764.v1
Chicago/Turabian Style
Mmatsinhe, C., Maeyane Stephens Moeng and Thifhelimbilu Emmanuel Emmanuel (E) Luvhengo. 2024 "Machine Learning Algorithms-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa during COVID-19 Pandemic" Preprints. https://doi.org/10.20944/preprints202409.0764.v1
Abstract
Background: Diabetic foot sepsis (DFS) accounts for approximately 60% of hospital admissions in patients with diabetes mellitus (DM). Individuals with DM are at risk of severe COVID-19. This study investigated investigated factors associated with major amputation and mortality in patients admitted with DFS during the COVID-19 pandemic. Methods: Demographic information, COVID-19 and HIV status, clinical findings, laboratory results, treatment and outcome from records of patients with diabetic foot sepsis. The chi-square test, Fisher’s test, Student t-test or Mann-Whitney compare findings when appropriate. Multivariate logistic regression to determine factors that were associated with major amputation and mortality. Results of logistic regression were compared with outputs from analysis using machine learning algorithms. Kaplan-Meier survival curve to compare survival between COVID-19 positive and negative patients. Statistical was a p-value below 0.05. Supervised machine learning algorithms were used to compare their ability to predict major and deaths. Results: Overall, 114 records were found and 57.9% (66/114) were of male patients. The mean age of the patients was 55.7 (14) years and 47.4% (54/114) and 36% (41/114) tested positive for COVID-19 and HIV, respectively. The median c-reactive protein was 168mg/dl, urea 7.8mmol/l and creatinine 92µmol/l. The mean potassium level was 4.8±0.9mmol, and glycosylated haemoglobin 11.2±3%. The main outcomes included major amputation in 69.3% (79/114) and mortality of 37.7% (43/114) died. AI. The level of potassium, urea, creatinine and HbA1c were significantly higher in the deceased. Conclusions: The COVID-19 pandemic led to an increase in the rate of major amputation and mortality in patients with DFS. The in-hospital mortality was higher in patients above 60 years of age who tested positive for COVID-19. The Random Forest algorithm of ML can be highly effective predicting major amputation and death in patients with DFS.
Public Health and Healthcare, Health Policy and Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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