1
|
Wu Z, Geng N, Liu Z, Pan W, Zhu Y, Shan J, Shi H, Han Y, Ma Y, Liu B. Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction. Virol J 2024; 21:96. [PMID: 38671532 PMCID: PMC11046891 DOI: 10.1186/s12985-024-02367-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking. METHODS A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed. RESULTS A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis. CONCLUSION Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.
Collapse
Affiliation(s)
- Zhipeng Wu
- Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing City, 100069, People's Republic of China
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, People's Republic of China
| | - Nan Geng
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Zhao Liu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Wen Pan
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Yueke Zhu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Jing Shan
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Hongbo Shi
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Ying Han
- Department of Gastroenterology and Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Yingmin Ma
- Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing City, 100069, People's Republic of China.
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, People's Republic of China.
| | - Bo Liu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China.
| |
Collapse
|
2
|
Zhang Z, Tang L, Guo Y, Guo X, Pan Z, Ji X, Gao C. Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19. J Inflamm Res 2024; 17:2445-2457. [PMID: 38681069 PMCID: PMC11048291 DOI: 10.2147/jir.s449497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 04/04/2024] [Indexed: 05/01/2024] Open
Abstract
Background As of 30 April 2023, the COVID-19 pandemic has resulted in over 6.9 million deaths worldwide. The virus continues to spread and mutate, leading to continuously evolving pathological and physiological processes. It is imperative to reevaluate predictive factors for identifying the risk of early disease progression. Methods A retrospective study was conducted on a cohort of 1379 COVID-19 patients who were discharged from Xin Hua Hospital affiliated with Shanghai Jiao Tong University School of Medicine between 15 December 2022 and 15 February 2023. Patient symptoms, comorbidities, demographics, vital signs, and laboratory test results were systematically documented. The dataset was split into testing and training sets, and 15 different machine learning algorithms were employed to construct prediction models. These models were assessed for accuracy and area under the receiver operating characteristic curve (AUROC), and the best-performing model was selected for further analysis. Results AUROC for models generated by 15 machine learning algorithms all exceeded 90%, and the accuracy of 10 of them also surpassed 90%. Light Gradient Boosting model emerged as the optimal choice, with accuracy of 0.928 ± 0.0006 and an AUROC of 0.976 ± 0.0028. Notably, the factors with the greatest impact on in-hospital mortality were growth stimulation expressed gene 2 (ST2,19.3%), interleukin-8 (IL-8,17.2%), interleukin-6 (IL-6,6.4%), age (6.1%), NT-proBNP (5.1%), interleukin-2 receptor (IL-2R, 5%), troponin I (TNI,4.6%), congestive heart failure (3.3%) in Light Gradient Boosting model. Conclusion ST-2, IL-8, IL-6, NT-proBNP, IL-2R, TNI, age and congestive heart failure were significant predictors of in-hospital mortality among COVID-19 patients.
Collapse
Affiliation(s)
- Zhishuo Zhang
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Lujia Tang
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Yiran Guo
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Xin Guo
- School of Information Science and Technology, Sanda University, Shanghai, Pudong District, 201209, China
| | - Zhiying Pan
- School of Information Science and Technology, Sanda University, Shanghai, Pudong District, 201209, China
| | - Xiaojing Ji
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Chengjin Gao
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| |
Collapse
|
3
|
Wu S, Liao G, Mao J, Yan H, Chen J, Peng J. Factors Associated with Mortality Among Severe Omicron Patients for COVID-19. Infect Drug Resist 2024; 17:1309-1319. [PMID: 38585415 PMCID: PMC10999197 DOI: 10.2147/idr.s450504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/13/2024] [Indexed: 04/09/2024] Open
Abstract
Purpose The purpose of the study was to explore the potential risk factors of mortality in patients with severe pneumonia during the omicron pandemic in South China in 2022. Methods Clinical data was collected from patients hospitalized with omicron COVID-19. Then, patients were categorized into the non-survival and survival groups. A comprehensive analysis was conducted to analyze the factors associated with negative outcome in individuals suffering from severe omicron COVID-19. Results In this study, 155 severe COVID-19 patients were included, comprising 55 non-survivors and 100 survivors. Non-survivors, in comparison to survivors, exhibited elevated levels of various biomarkers including neutrophil count, hypersensitive troponin T, urea, creatinine, C-reactive protein, procalcitonin, interleukin-6, plasma D-dimer, and derived neutrophil-to-lymphocyte ratio (dNLR) (P < 0.05). They also displayed reduced lymphocyte count, platelet count, and albumin levels (P < 0.05) and were more prone to developing comorbidities, including shock, acute cardiac and renal injury, acute respiratory distress syndrome, coagulation disorders, and secondary infections. Platelet count (PLT) <100 × 10^/L, interleukin-6 (IL-6) >100 pg/mL, and dNLR >5.0 independently contributed to the risk of death in patients suffering from severe COVID-19. Conclusion PLT, IL-6, and dNRL independently contributed to the risk of mortality in patients with severe pneumonia during the 2022 omicron pandemic in South China.
Collapse
Affiliation(s)
- Shuting Wu
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Guichan Liao
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jingchun Mao
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Haiming Yan
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Juanjuan Chen
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jie Peng
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| |
Collapse
|
4
|
Izhari MA, Hadadi MAA, Alharbi RA, Gosady ARA, Sindi AAA, Dardari DMM, Alotaibi FE, Klufah F, Albanghali MA, Alharbi TH. Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia. Healthcare (Basel) 2024; 12:729. [PMID: 38610151 PMCID: PMC11012004 DOI: 10.3390/healthcare12070729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers. METHODS A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken. COVID-19 exposure was assessed by performing real-time PCR on nasopharyngeal (NP) swabs. Descriptive and inferential statistics were applied for both continuous and categorical variables using Rstudio-version-4.0.2. Pearson correlation and regression were executed with a cut-off of p < 0.05 for evaluating significance. Data representation by R-packages and ggplot2. RESULTS A significant variation in the mean ± SD (highly-sever (HS)/moderately severe (MS)) of CRP (HS/MS: 102.4 ± 22.9/21.3 ± 6.9, p-value < 0.001), D-dimer (HS/MS: 661.1 ± 80.6/348.7 ± 42.9, p-value < 0.001), and ferritin (HS/MS: 875.8 ± 126.8/593.4 ± 67.3, p-value < 0.001) were observed. Thrombocytopenia, high PT, and PTT exhibited an association with the HS individuals (p < 0.001). CRP was correlated with neutrophil (r = 0.77), ferritin (r = 0.74), and WBC (r = 0.8). D-dimer correlated with platelets (r = -0.82), PT (r = 0.22), and PTT (r = 0.37). The adjusted odds ratios (Ad-OR) of CRP, ferritin, D-dimer, platelet, PT, and PTT for HS compared to MS were 1.30 (95% CI -1.137, 1.50; p < 0.001), 1.048 (95% CI -1.03, 1.066; p < 0.001), 1.3 (95% CI -1.24, 1.49, p > 0.05), -0.813 (95% CI -0.734, 0.899, p < 0.001), 1.347 (95% CI -1.15, 1.57, p < 0.001), and 1.234 (95% CI -1.16, 1.314, p < 0.001), respectively. CONCLUSION SARS-CoV-2 caused alterations in vital laboratory parameters and raised ferritin, CRP, and D-dimer presented an association with disease severity at a significant level.
Collapse
Affiliation(s)
- Mohammad Asrar Izhari
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Mansoor A. A. Hadadi
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
- Laboratory Department, Qunfudhah Hospital, Al-Qunfudhah 28887, Saudi Arabia
| | - Raed A. Alharbi
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Ahmed R. A. Gosady
- Laboratory Department, Baish General Hospital, Jazan 87597, Saudi Arabia
| | | | | | - Foton E. Alotaibi
- Department of Genetic Counseling, Al-Faisal University, Riyadh 11533, Saudi Arabia
| | - Faisal Klufah
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Mohammad A Albanghali
- Department of Public Health, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| | - Tahani H Alharbi
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65528, Saudi Arabia
| |
Collapse
|
5
|
Huyut MT, Huyut Z. Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees. Heliyon 2023; 9:e14015. [PMID: 36919085 PMCID: PMC9985543 DOI: 10.1016/j.heliyon.2023.e14015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 01/25/2023] [Accepted: 02/17/2023] [Indexed: 03/07/2023] Open
Abstract
Background and objective A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.
Collapse
Affiliation(s)
- Mehmet Tahir Huyut
- Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, Turkey
- Corresponding author. Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, Turkey.
| | - Zübeyir Huyut
- Van Yuzuncu Yıl University, Faculty of Medicine, Department of Biochemistry, Van, Turkey
| |
Collapse
|
6
|
Smail SW, Babaei E, Amin K. Hematological, Inflammatory, Coagulation, and Oxidative/Antioxidant Biomarkers as Predictors for Severity and Mortality in COVID-19: A Prospective Cohort-Study. Int J Gen Med 2023; 16:565-580. [PMID: 36824986 PMCID: PMC9942608 DOI: 10.2147/ijgm.s402206] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/10/2023] [Indexed: 02/19/2023] Open
Abstract
Purpose Oxidative stress (OS) and inflammation are pivotal points in the pathophysiology of coronavirus disease-2019 (COVID-19). This study aims to use routine laboratory and oxidative stress/antioxidative biomarkers as predictors for the mortality of the disease. Patients and Methods This prospective cohort study, made up of 120 COVID-19 patients from emergency units in Erbil, Duhok, Kirkuk, and Sulaymaniyah cities in Iraq, from May the 1st to May the 30th, 2021, and 60 healthy controls (HCs) (n = 60). The patients were re-categorized into mild (n = 54), severe (n = 40), and critical (n = 26) groups based on the clinical criteria. Following admission to the hospital, blood was directly collected for measuring routine laboratory biomarkers. Results Neutrophils and neutrophil/lymphocyte ratio (NLR) were higher in the critical group, while lymphocytes were lower in the severe and critical groups compared to the mild group. The CRP, ferritin, and D-dimer values were more elevated in severe and critical cases than in mild COVID-19 cases. The levels of malondialdehyde (MDA), nitric oxide (NO), and copper were elevated, while the superoxide dismutase (SOD) activity level and total antioxidant capacity (TAC) level were lower. However, vitamin C, glutathione peroxidase (GPx), and catalase activity levels were not changed in the COVID-19 groups compared to the HCs. NO and ferritin were predictors of ICU hospitalization; D-dimer, MDA, and NLR were predictors of mortality. NO, and NLR were predictors of SpO2 depression. Moreover, NO, and copper have both good diagnostic values, their cutoffs were 39.01 and 11.93, respectively. Conclusion There is an association between immune dysregulation and oxidative imbalance. The biomarkers, that could be considered as predictors for the severity and mortality of COVID-19, are the NLR, NO, ferritin, and D-dimer. The age equal to and older than 50 has a poor prognosis in the Kurdish population.
Collapse
Affiliation(s)
- Shukur Wasman Smail
- Department of Biology, College of Science, Salahaddin University, Erbil, Iraq,Correspondence: Shukur Wasman Smail, Salahaddin University, Erbil, Iraq, Tel +9647504491092, Email
| | - Esmaeil Babaei
- Department of Biology, School of Natural Sciences, University of Tabriz, Tabriz, Iran,Department of Pharmacognosy, College of Pharmacy, Hawler Medical University, Erbil, Kurdistan Region, Iraq
| | - Kawa Amin
- College of Medicine, University of Sulaimani, Sulaymaniyah, Iraq
| |
Collapse
|
7
|
Huyut MT, Velichko A. LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19. MethodsX 2023; 10:102194. [PMID: 37122366 PMCID: PMC10115593 DOI: 10.1016/j.mex.2023.102194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/17/2023] [Indexed: 05/02/2023] Open
Abstract
Rapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly, require an expert team and take time. This study offers a fast, economical and reliable approach for the early diagnosis and prognosis of infectious diseases, especially COVID-19. For this purpose, characteristics of a large population of COVID-19 patients were determined (51 different routine blood values) and calibrated. In order to determine the diagnosis and prognosis of the disease, the calibrated features were run with the LogNNet model. LogNNet has a simple algorithm and performance indicators comparable to the most efficient algorithms available.This approach pointed out that routine blood values contain important information, especially in the detection of COVID-19, and showed that the LogNNet model can be used as an economical, safe and fast alternative tool in the diagnosis of this disease.-In the LogNNet feedforward neural network, a feature vector is passed through a specially designed reservoir matrix and transformed into a new feature vector of a different size, increasing the classification accuracy.-The presented network architecture can achieve 80%-99% classification accuracy using a range of weightings on devices with a total memory size of 1 to 29 kB constrained.-Due to the chaotic mapping procedures, the RAM usage in the LogNNet neural network processing process is greatly reduced. Hence, optimization of chaotic map parameters has an important function in LogNNet neural network application.
Collapse
Affiliation(s)
- Mehmet Tahir Huyut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Turkey
- Corresponding author.
| | - Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Str., 185910 Petrozavodsk, Russia
| |
Collapse
|
8
|
Velichko A, Huyut MT, Belyaev M, Izotov Y, Korzun D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207886. [PMID: 36298235 PMCID: PMC9610709 DOI: 10.3390/s22207886] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 05/16/2023]
Abstract
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
Collapse
Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
- Correspondence: (A.V.); (M.T.H.)
| | - Mehmet Tahir Huyut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Türkiye
- Correspondence: (A.V.); (M.T.H.)
| | - Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Yuriy Izotov
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| |
Collapse
|
9
|
Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network. SENSORS 2022; 22:s22134820. [PMID: 35808317 PMCID: PMC9269123 DOI: 10.3390/s22134820] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/16/2022] [Accepted: 06/23/2022] [Indexed: 01/08/2023]
Abstract
Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.
Collapse
|