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机器学习算法辅助确定南非一家地区医院在 COVID-19 大流行期间糖尿病足败血症死亡率的预测因素。

Machine Learning Algorithm-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa During the COVID-19 Pandemic.

机构信息

Department of Surgery, Thelle Mogoerane Hospital, University of the Witwatersrand, Johannesburg 2017, South Africa.

Department of Nuclear Medicine, University of the Witwatersrand, Johannesburg 2017, South Africa.

出版信息

Medicina (Kaunas). 2024 Oct 20;60(10):1718. doi: 10.3390/medicina60101718.


DOI:10.3390/medicina60101718
PMID:39459505
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11509229/
Abstract

: 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 factors associated with major amputation and mortality in patients admitted with DFS during the COVID-19 pandemic. : Demographic information, COVID-19 and HIV status, clinical findings, laboratory results, treatment and outcome from records of patients with diabetic foot sepsis, were collected and analysed. Supervised machine learning algorithms were used to compare their ability to predict mortality due to diabetic foot sepsis. : 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 168 mg/dl, urea 7.8 mmol/L and creatinine 92 µmol/L. The mean potassium level was 4.8 ± 0.9 mmol, 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 levels of potassium, urea, creatinine and HbA1c were significantly higher in the deceased. : 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 in predicting major amputation and death in patients with DFS.

摘要

糖尿病足败血症(DFS)约占糖尿病(DM)患者住院的 60%。患有 DM 的人患严重 COVID-19 的风险较高。本研究调查了 COVID-19 大流行期间因 DFS 入院的患者中与主要截肢和死亡率相关的因素。

从糖尿病足败血症患者的记录中收集并分析了人口统计学信息、COVID-19 和 HIV 状态、临床发现、实验室结果、治疗和结果。使用监督机器学习算法比较了它们预测糖尿病足败血症死亡率的能力。

总体而言,发现了 114 份记录,其中 57.9%(66/114)为男性患者。患者的平均年龄为 55.7(14)岁,47.4%(54/114)和 36%(41/114)的 COVID-19 和 HIV 检测呈阳性。中位 C 反应蛋白为 168mg/dl,尿素 7.8mmol/L,肌酐 92µmol/L。平均钾水平为 4.8±0.9mmol,糖化血红蛋白 11.2±3%。主要结局包括 69.3%(79/114)的主要截肢和 37.7%(43/114)的死亡率。AI。死亡患者的钾、尿素、肌酐和 HbA1c 水平明显升高。

COVID-19 大流行导致 DFS 患者的主要截肢和死亡率上升。COVID-19 检测呈阳性且年龄在 60 岁以上的患者住院死亡率更高。ML 的随机森林算法可以非常有效地预测 DFS 患者的主要截肢和死亡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/b334cc2f5aeb/medicina-60-01718-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/fbbb63c697fd/medicina-60-01718-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/1dc3eec3f80a/medicina-60-01718-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/0697cb4e9d4e/medicina-60-01718-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/b0252a85fc2c/medicina-60-01718-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/b334cc2f5aeb/medicina-60-01718-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/fbbb63c697fd/medicina-60-01718-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/1dc3eec3f80a/medicina-60-01718-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/0697cb4e9d4e/medicina-60-01718-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/b0252a85fc2c/medicina-60-01718-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9009/11509229/b334cc2f5aeb/medicina-60-01718-g005.jpg

相似文献

[1]
Machine Learning Algorithm-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa During the COVID-19 Pandemic.

Medicina (Kaunas). 2024-10-20

[2]
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[3]
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[9]
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[10]
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引用本文的文献

[1]
Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.

Front Public Health. 2025-7-18

[2]
Artificial Intelligence and Machine Learning in Diabetic Foot Ulcer Care: Advances in Diagnosis, Treatment, Prognosis, and Novel Therapeutic Strategies.

J Diabetes Sci Technol. 2025-8-3

[3]
Analysis of risk factors and predictive value of a nomogram model for sepsis in patients with diabetic foot.

World J Diabetes. 2025-4-15

[4]
Artificial intelligence applied to diabetes complications: a bibliometric analysis.

Front Artif Intell. 2025-1-31

[5]
Clinical Reasoning and Knowledge Assessment of Rheumatology Residents Compared to AI Models: A Pilot Study.

J Clin Med. 2024-12-5

本文引用的文献

[1]
A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life.

Sensors (Basel). 2024-6-27

[2]
The role of machine learning in advancing diabetic foot: a review.

Front Endocrinol (Lausanne). 2024

[3]
Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study.

Biosensors (Basel). 2024-3-29

[4]
A Review of Machine Learning Algorithms for Biomedical Applications.

Ann Biomed Eng. 2024-5

[5]
Life expectancy of patients with diabetic foot sepsis post-lower extremity amputation at a regional hospital in a South African setting. A retrospective cohort study.

Surgeon. 2024-4

[6]
Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach.

J Clin Med. 2023-9-7

[7]
An Overview of Risk Factors for Diabetic Foot Amputation: An Observational, Single-centre, Retrospective Cohort Study.

touchREV Endocrinol. 2023-5

[8]
A Machine Learning Model for Prediction of Amputation in Diabetics.

J Diabetes Sci Technol. 2024-7

[9]
Foot Osteomyelitis Location and Rates of Primary or Secondary Major Amputations in Patients With Diabetes.

Foot Ankle Int. 2022-7

[10]
Exploration of machine algorithms based on deep learning model and feature extraction.

Math Biosci Eng. 2021-9-3

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