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Alie MS, Negesse Y, Kindie K, Merawi DS. Machine learning algorithms for predicting COVID-19 mortality in Ethiopia. BMC Public Health 2024; 24:1728. [PMID: 38943093 PMCID: PMC11212371 DOI: 10.1186/s12889-024-19196-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 06/19/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia. METHODS Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC). RESULTS The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features. CONCLUSION Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.
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Affiliation(s)
- Melsew Setegn Alie
- Department Public Health, School of Public Health, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia.
| | - Yilkal Negesse
- Department of Public Health, College of Medicine and Health Science, Debre Markos University, Gojjam, Ethiopia
| | - Kassa Kindie
- Department Nursing, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia
| | - Dereje Senay Merawi
- Department of Information Technology, Faculty of Technology, Debre Tabor University, Gonder, Ethiopia
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Dos Santos L, Silva LL, Pelloso FC, Maia V, Pujals C, Borghesan DH, Carvalho MD, Pedroso RB, Pelloso SM. Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study. PeerJ 2024; 12:e17428. [PMID: 38881861 PMCID: PMC11179634 DOI: 10.7717/peerj.17428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 04/29/2024] [Indexed: 06/18/2024] Open
Abstract
Background Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19. Methods In this study, 126 variables were collected with information on demography, hospital length stay and outcome, chronic diseases and tumors, comorbidities and risk factors, complications and adverse events, health care, and vital indicators of patients admitted to an ICU in southern Brazil. These variables were filtered and then selected by a ML algorithm known as decision trees to identify the optimal set of variables for predicting patient discharge using logistic regression. Finally, a confusion matrix was performed to evaluate the model's performance for the selected variables. Results Of the 532 patients evaluated, 180 were discharged: female (16.92%), with a central venous catheter (23.68%), with a bladder catheter (26.13%), and with an average of 8.46- and 23.65-days using bladder catheter and submitted to mechanical ventilation, respectively. In addition, the chances of discharge increase by 14% for each additional day in the hospital, by 136% for female patients, 716% when there is no bladder catheter, and 737% when no central venous catheter is used. However, the chances of discharge decrease by 3% for each additional year of age and by 9% for each other day of mechanical ventilation. The performance of the training data presented a balanced accuracy of 0.81, sensitivity of 0.74, specificity of 0.88, and the kappa value was 0.64. The test performance had a balanced accuracy of 0.85, sensitivity 0.75, specificity 0.95, and kappa value of 0.73. The McNemar test found that there were no significant differences in the error rates in the training and test data, suggesting good classification. This work showed that female, the absence of a central venous catheter and bladder catheter, shorter mechanical ventilation, and bladder catheter duration were associated with a greater chance of hospital discharge. These results may help develop measures that lead to a good prognosis for the patient.
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Affiliation(s)
- Lander Dos Santos
- State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil
| | - Lincoln Luis Silva
- Department of Emergency Medicine, Duke University School of Medicine, Durham, NC, United States of America
| | | | | | - Constanza Pujals
- State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil
| | | | - Maria Dalva Carvalho
- State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil
| | - Raíssa Bocchi Pedroso
- State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil
| | - Sandra Marisa Pelloso
- State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil
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Yang Y, Zhou L, Luo J, Xue J, Liu J, Zhang J, Wang Z, Gong P, Chen T. Prediction analysis of TBI 24-h survival outcome based on machine learning. Heliyon 2024; 10:e30198. [PMID: 38707345 PMCID: PMC11066620 DOI: 10.1016/j.heliyon.2024.e30198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024] Open
Abstract
Background Traumatic brain injury (TBI) is the major reason for the death of young people and is well known for its high mortality and morbidity. This paper aim to predict the 24h survival of patients with TBI. Methods A total of 1224 samples were involved in this analysis, and the clinical indicators involved included age, gender, blood pressure, MGAP and other fields, among which the target variable was "outcome", which was a binary variable. The methods mainly involved in this paper include data visualization analysis, single factor analysis, feature engineering analysis, random forest model (RF), K-Nearst Neighbors (KNN) model, and so on. Logistic regression model (LR) and deep neural network model (DNN). We will oversample the training set using the SMOTE method because of the very unbalanced labeling of the sample itself. Results Although the accuracy of all models is very high, the recall rate is relatively low. The DNN model with the best performance only reaches 0.17, and the corresponding AUC is 0.80. After resampling, we find that the recall rate of positive samples of all models has increased a lot, but the AUC of some models has decreased. Finally, the optimal model is LR, whose positive sample recall rate is 0.67 and AUC is 0.82. Conclusion Through resampling, we obtained that the best model is the RF model, whose recall rate and AUC are the best, and the AUC level is about 0.87, indicating that the accuracy performance of the model is still good.
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Affiliation(s)
- Yang Yang
- Department of Trauma Center, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
- Department of Chemistry, School of Science, China Pharmaceutical University, Nanjing, 211198, China
| | - Liulei Zhou
- Department of Trauma Center, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Jinhua Luo
- Department of Anesthesia Surgery, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Jianhua Xue
- Department of Trauma Center, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Jiajia Liu
- Department of Trauma Center, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Jiajia Zhang
- Department of Neurosurgery, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Ziheng Wang
- Department of Neurosurgery, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
- Clinical and Translational Research Center, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
- Suzhou Industrial Park Monash Research Institute of Science and Technology, Suzhou, China
- The School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau, China
| | - Peipei Gong
- Department of Neurosurgery, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Tianxi Chen
- Department of Emergency Medicine, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
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Cohen-Cohen S, Jabal MS, Rinaldo L, Savastano LE, Lanzino G, Cloft H, Brinjikji W. Middle meningeal artery embolization for chronic subdural hematoma: A single-center experience and predictive modeling of outcomes. Neuroradiol J 2024; 37:192-198. [PMID: 38147825 DOI: 10.1177/19714009231224431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND Remarkable interest is rising around middle meningeal artery embolization (MMAE) as an emerging alternative therapy for chronic subdural hematoma (cSDH). The study aims to highlight a large center experience and the variables associated with treatment failure and build experimental machine learning (ML) models for outcome prediction. MATERIAL AND METHODS A 2-year experience in MMAE for managing patients with chronic subdural hematoma was analyzed. Descriptive statistical analysis was conducted using imaging and clinical features of the patients and cSDH, which were subsequently used to build predictive models for the procedure outcome. The modeling evaluation metrics were the area under the ROC curve and F1-score. RESULTS A total of 100 cSDH of 76 patients who underwent MMAE were included with an average follow-up of 6 months. The intervention had a per procedure success rate of 92%. Thrombocytopenia had a highly significant association with treatment failure. Two patients suffered a complication related to the procedure. The best performing machine learning models in predicting MMAE failure achieved an ROC-AUC of 70%, and an F1-score of 67%, including all patients with or without surgical intervention prior to embolization, and an ROC-AUC of 82% and an F1-score of 69% when only patients who underwent upfront MMAE were included. CONCLUSION MMAE is a safe and minimally invasive procedure with great potential in transforming the management of cSDH and reducing the risk of surgical complications in selected patients. An ML approach with larger sample size might help better predict outcomes and highlight important predictors following MMAE in patients with cSDH.
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Abdulnazar A, Kugic A, Schulz S, Stadlbauer V, Kreuzthaler M. O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies. BMC Med Inform Decis Mak 2024; 24:29. [PMID: 38297364 PMCID: PMC10829265 DOI: 10.1186/s12911-024-02425-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Oxygen saturation, a key indicator of COVID-19 severity, poses challenges, especially in cases of silent hypoxemia. Electronic health records (EHRs) often contain supplemental oxygen information within clinical narratives. Streamlining patient identification based on oxygen levels is crucial for COVID-19 research, underscoring the need for automated classifiers in discharge summaries to ease the manual review burden on physicians. METHOD We analysed text lines extracted from anonymised COVID-19 patient discharge summaries in German to perform a binary classification task, differentiating patients who received oxygen supplementation and those who did not. Various machine learning (ML) algorithms, including classical ML to deep learning (DL) models, were compared. Classifier decisions were explained using Local Interpretable Model-agnostic Explanations (LIME), which visualize the model decisions. RESULT Classical ML to DL models achieved comparable performance in classification, with an F-measure varying between 0.942 and 0.955, whereas the classical ML approaches were faster. Visualisation of embedding representation of input data reveals notable variations in the encoding patterns between classic and DL encoders. Furthermore, LIME explanations provide insights into the most relevant features at token level that contribute to these observed differences. CONCLUSION Despite a general tendency towards deep learning, these use cases show that classical approaches yield comparable results at lower computational cost. Model prediction explanations using LIME in textual and visual layouts provided a qualitative explanation for the model performance.
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Affiliation(s)
- Akhila Abdulnazar
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
- CBmed GmbH - Center for Biomarker Research in Medicine, Graz, Austria
| | - Amila Kugic
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Vanessa Stadlbauer
- CBmed GmbH - Center for Biomarker Research in Medicine, Graz, Austria
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Markus Kreuzthaler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
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Huang Y, Li J, Li M, Aparasu RR. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review. BMC Med Res Methodol 2023; 23:268. [PMID: 37957593 PMCID: PMC10641971 DOI: 10.1186/s12874-023-02078-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare. METHODS PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC). RESULTS Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%). CONCLUSIONS The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.
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Affiliation(s)
- Yinan Huang
- Department of Pharmacy Administration, School of Pharmacy, University of Mississippi, University, MS, 38677, USA
| | - Jieni Li
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, 77204, USA
| | - Mai Li
- Department of Industrial Engineering, Cullen College of Engineering, University of Houston, Houston, TX, USA
| | - Rajender R Aparasu
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, 77204, USA.
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Giuste FO, He L, Lais P, Shi W, Zhu Y, Hornback A, Tsai C, Isgut M, Anderson B, Wang MD. Early and fair COVID-19 outcome risk assessment using robust feature selection. Sci Rep 2023; 13:18981. [PMID: 37923795 PMCID: PMC10624921 DOI: 10.1038/s41598-023-36175-4] [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: 11/07/2022] [Accepted: 05/29/2023] [Indexed: 11/06/2023] Open
Abstract
Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.
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Affiliation(s)
- Felipe O Giuste
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Lawrence He
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Peter Lais
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Wenqi Shi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Yuanda Zhu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Andrew Hornback
- School of Computer Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Chiche Tsai
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Monica Isgut
- School of Biology, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Blake Anderson
- Department of Medicine, Emory University, Atlanta, GA, 30322, USA
| | - May D Wang
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA.
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Natanov D, Avihai B, McDonnell E, Lee E, Cook B, Altomare N, Ko T, Chaia A, Munoz C, Ouellette S, Nyalakonda S, Cederbaum V, Parikh PD, Blaser MJ. Predicting COVID-19 prognosis in hospitalized patients based on early status. mBio 2023; 14:e0150823. [PMID: 37681966 PMCID: PMC10653946 DOI: 10.1128/mbio.01508-23] [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: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 09/09/2023] Open
Abstract
IMPORTANCE COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help efficiently allocate resources, including ventilators and intensive care unit beds, particularly when hospital systems are strained. Our PLABAC and PRABLE models are unique because they accurately assess a COVID-19 patient's risk of death from only age and five commonly ordered laboratory tests. This simple design is important because it allows these models to be used by clinicians to rapidly assess a patient's risk of decompensation and serve as a real-time aid when discussing difficult, life-altering decisions for patients. Our models have also shown generalizability to external populations across the United States. In short, these models are practical, efficient tools to assess and communicate COVID-19 prognosis.
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Affiliation(s)
- David Natanov
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Byron Avihai
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Erin McDonnell
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Eileen Lee
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Brennan Cook
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Nicole Altomare
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Tomohiro Ko
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Angelo Chaia
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Carolayn Munoz
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | | | - Suraj Nyalakonda
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Vanessa Cederbaum
- Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Payal D. Parikh
- Department of Medicine, Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Martin J. Blaser
- Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, New Jersey, USA
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Chimbunde E, Sigwadhi LN, Tamuzi JL, Okango EL, Daramola O, Ngah VD, Nyasulu PS. Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa. Front Artif Intell 2023; 6:1171256. [PMID: 37899965 PMCID: PMC10600470 DOI: 10.3389/frai.2023.1171256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/15/2023] [Indexed: 10/31/2023] Open
Abstract
Background COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms. Methods Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics. Results From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC. Conclusion Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.
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Affiliation(s)
- Emmanuel Chimbunde
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Lovemore N. Sigwadhi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jacques L. Tamuzi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | | | - Olawande Daramola
- Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Veranyuy D. Ngah
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Peter S. Nyasulu
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Chen R, Chen J, Yang S, Luo S, Xiao Z, Lu L, Liang B, Liu S, Shi H, Xu J. Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis. Int J Med Inform 2023; 177:105151. [PMID: 37473658 DOI: 10.1016/j.ijmedinf.2023.105151] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Accurate prediction of prognostic outcomes in patients with COVID-19 could facilitate clinical decision-making and medical resource allocation. However, little is known about the ability of machine learning (ML) to predict prognosis in COVID-19 patients. OBJECTIVE This study aimed to systematically examine the prognostic value of ML in patients with COVID-19. METHODS A systematic search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and IEEE Xplore up to December 15, 2021. Studies predicting the prognostic outcomes of COVID-19 patients using ML were eligible for inclusion. Risk of bias was evaluated by a tailored checklist based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pooled sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated to evaluate model performance. RESULTS A total of 33 studies that described 35 models were eligible for inclusion, with 27 models presenting mortality, four intensive care unit (ICU) admission, and four use of ventilation. For predicting mortality, ML gave a pooled sensitivity of 0.86 (95% CI, 0.79-0.90), a specificity of 0.87 (95% CI, 0.80-0.92), and an AUC of 0.93 (95% CI, 0.90-0.95). For the prediction of ICU admission, ML had a sensitivity of 0.86 (95% CI, 0.78-0.92), a specificity of 0.81 (95% CI, 0.66-0.91), and an AUC of 0.91 (95% CI, 0.88-0.93). For the prediction of ventilation, ML had a sensitivity of 0.81 (95% CI, 0.68-0.90), a specificity of 0.78 (95% CI, 0.66-0.87), and an AUC of 0.87 (95% CI, 0.83-0.89). Meta-regression analyses indicated that algorithm, population, study design, and source of dataset influenced the pooled estimate. CONCLUSION This meta-analysis demonstrated the satisfactory performance of ML in predicting prognostic outcomes in patients with COVID-19, suggesting the potential value of ML to support clinical decision-making. However, improvements to methodology and validation are still necessary before its application in routine clinical practice.
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Affiliation(s)
- Ruiyao Chen
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jiayuan Chen
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Sen Yang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Shuqing Luo
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Zhongzhou Xiao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Bilin Liang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | | | - Huwei Shi
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jie Xu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China.
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11
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Xin Y, Li H, Zhou Y, Yang Q, Mu W, Xiao H, Zhuo Z, Liu H, Wang H, Qu X, Wang C, Liu H, Yu K. The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2023; 23:155. [PMID: 37559062 PMCID: PMC10410953 DOI: 10.1186/s12911-023-02256-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/02/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. METHODS The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). FINDINGS Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05). INTERPRETATION Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.
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Affiliation(s)
- Yu Xin
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongxu Li
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Yuxin Zhou
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Qing Yang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Wenjing Mu
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Han Xiao
- Departments of Pharmacy and Cardiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Zipeng Zhuo
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongyu Liu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongying Wang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Xutong Qu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Changsong Wang
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
| | - Haitao Liu
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
| | - Kaijiang Yu
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
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12
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Pisano F, Cannas B, Fanni A, Pasella M, Canetto B, Giglio SR, Mocci S, Chessa L, Perra A, Littera R. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19. Front Med (Lausanne) 2023; 10:1230733. [PMID: 37601789 PMCID: PMC10433226 DOI: 10.3389/fmed.2023.1230733] [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: 05/29/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.
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Affiliation(s)
- Fabio Pisano
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Barbara Cannas
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Alessandra Fanni
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Manuela Pasella
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | | | - Sabrina Rita Giglio
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Stefano Mocci
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Luchino Chessa
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Liver Unit, Department of Internal Medicine, University Hospital of Cagliari, Cagliari, Italy
| | - Andrea Perra
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Roberto Littera
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
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Sharifi-Kia A, Nahvijou A, Sheikhtaheri A. Machine learning-based mortality prediction models for smoker COVID-19 patients. BMC Med Inform Decis Mak 2023; 23:129. [PMID: 37479990 PMCID: PMC10360290 DOI: 10.1186/s12911-023-02237-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population. METHODS A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated. RESULTS The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For "at admission" models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F1 score of 86.2%. For the "post-admission" models, XGBoost also outperformed the rest with an accuracy of 90.5% and F1 score of 89.9%. Active smoking was among the most important features in patients' mortality prediction. CONCLUSION Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients' chance of survival.
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Affiliation(s)
- Ali Sharifi-Kia
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Azin Nahvijou
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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14
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Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 2023; 38:355-372. [PMID: 36840867 PMCID: PMC9958330 DOI: 10.1007/s10654-023-00973-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 01/28/2023] [Indexed: 02/26/2023]
Abstract
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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Affiliation(s)
- Chepkoech Buttia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
- Epistudia, Bern, Switzerland
| | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- ELKH-DE Public Health Research Group of the Hungarian Academy of Sciences, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Epistudia, Bern, Switzerland
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Hamidreza Raeisi-Dehkordi
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lum Kastrati
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mojgan Amiri
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renald Meçani
- Department of Pediatrics, “Mother Teresa” University Hospital Center, Tirana, University of Medicine, Tirana, Albania
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Petek Eylul Taneri
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- HRB-Trials Methodology Research Network College of Medicine, Nursing and Health Sciences University of Galway, Galway, Ireland
| | | | - Peter Francis Raguindin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
- Faculty of Health Sciences, University of Lucerne, Lucerne, Switzerland
| | - Faina Wehrli
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Farnaz Khatami
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Octavio Pano Espínola
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Preventive Medicine and Public Health, University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
| | - Lyda Z. Rojas
- Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia
| | | | | | - Fadi Alijla
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Beatrice Minder
- Public Health and Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Alexander B. Leichtle
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, and Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
| | - Nora Lüthi
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Simone Ehrhard
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Yok-Ai Que
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurenz Kopp Fernandes
- Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf Hautz
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Epistudia, Bern, Switzerland
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15
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Jaskolowska J, Balcerzyk-Barzdo E, Jozwik A, Gaszynski T, Ratajczyk P. Selected Predictors of COVID-19 Mortality in the Hospitalised Patient Population in a Single-Centre Study in Poland. Healthcare (Basel) 2023; 11:healthcare11050719. [PMID: 36900724 PMCID: PMC10001137 DOI: 10.3390/healthcare11050719] [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: 09/19/2022] [Revised: 02/17/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023] Open
Abstract
Background: The correct analysis of COVID-19 predictors could substantially improve the clinical decision-making process and enable emergency department patients at higher mortality risk to be identified. Methods: We retrospectively explored the relationship between some demographic and clinical factors, such as age and sex, as well as the levels of ten selected factors, namely, CRP, D-dimer, ferritin, LDH, RDW-CV, RDW-SD, procalcitonin, blood oxygen saturation, lymphocytes, and leukocytes, and COVID-19 mortality risk in 150 adult patients diagnosed with COVID-19 at Provincial Specialist Hospital in Zgierz, Poland (this hospital was transformed, in March 2020, into a hospital admitting COVID-19 cases only). All blood samples for testing were collected in the emergency room before admission. The length of stay in the intensive care unit and length of hospitalisation were also analysed. Results: The only factor that was not significantly related to mortality was the length of stay in the intensive care unit. The odds of dying were significantly lower in males, patients with a longer hospital stay, patients with higher lymphocyte levels, and patients with higher blood oxygen saturation, while the chances of dying were significantly higher in older patients; patients with higher RDW-CV and RDW-SD levels; and patients with higher levels of leukocytes, CRP, ferritin, procalcitonin, LDH, and D-dimers. Conclusions: Six potential predictors of mortality were included in the final model: age, RDW-CV, procalcitonin, and D-dimers level; blood oxygen saturation; and length of hospitalisation. The results obtained from this study suggest that a final predictive model with high accuracy in mortality prediction (over 90%) was successfully built. The suggested model could be used for therapy prioritization.
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Affiliation(s)
- Joanna Jaskolowska
- Maria Sklodowska-Curie Provincial Specialist Hospital, 95-102 Zgierz, Poland
- Correspondence:
| | | | - Agnieszka Jozwik
- Maria Sklodowska-Curie Provincial Specialist Hospital, 95-102 Zgierz, Poland
| | - Tomasz Gaszynski
- Department of Anaesthesiology and Intensive Care, Medical University of Lodz, 90-153 Łódź, Poland
| | - Pawel Ratajczyk
- Department of Anaesthesiology and Intensive Care, Medical University of Lodz, 90-153 Łódź, Poland
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16
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Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 9:100021. [PMID: 36643886 PMCID: PMC9826539 DOI: 10.1016/j.immuno.2023.100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
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Affiliation(s)
- Sonia Gazeau
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Xiaoyan Deng
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Fatima Mostefai
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Julie Hussin
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Jane Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
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17
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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. J Cardiovasc Dev Dis 2023; 10:jcdd10020039. [PMID: 36826535 PMCID: PMC9967447 DOI: 10.3390/jcdd10020039] [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: 11/28/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
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18
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Khadem H, Nemat H, Elliott J, Benaissa M. Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228757. [PMID: 36433354 PMCID: PMC9692305 DOI: 10.3390/s22228757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/07/2022] [Accepted: 11/11/2022] [Indexed: 05/13/2023]
Abstract
People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.
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Affiliation(s)
- Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
- Correspondence:
| | - Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
- Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield S5 7AU, UK
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
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Telemedicine to Expand Access to Critical Care Around the World. Crit Care Clin 2022; 38:809-826. [PMID: 36162912 DOI: 10.1016/j.ccc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
This multiauthored communication gives a state-of-the-art global perspective on the increasing adoption of tele-critical care. Exponentially increasing sophistication in the deployment of Computers, Information, and Communication Technology has ensured extending the reach of limited intensivists virtually and reaching the unreached. Natural disasters, COVID-19 pandemic, and wars have made tele-intensive care a reality. Concerns and regulatory issues are being sorted out, cross-border cost-effective tele-critical care is steadily increasing Components to set up a tele-intensive care unit, and overcoming barriers is discussed. Importance of developing best practice guidelines and retraining is emphasized.
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20
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Culture and COVID-19-related mortality: a cross-sectional study of 50 countries. J Public Health Policy 2022; 43:413-430. [PMID: 35995942 PMCID: PMC9395903 DOI: 10.1057/s41271-022-00363-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2022] [Indexed: 11/26/2022]
Abstract
Using a cross-sectional sample of 50 countries we investigate the influence of Hofstede’s six-dimensions of culture on COVID-19 related mortality. A multivariable regression model was fitted that controls for health-related, economic- and policy-related variables that have been found to be associated with mortality. We included the percentage of population aged 65 and above, the prevalence of relevant co-morbidities, and tobacco use as health-related variables. Economic variables were GDP, and the connectedness of a country. As policy variables, the Oxford Stringency Index as well as stringency speed, and the Global Health Security Index were used. We also describe the importance of the variables by means of a random forest model. The results suggest that individualistic societies are associated with lower COVID-19-related mortality rates. This finding contradicts previous studies that supported the popular narrative that collectivistic societies with an obedient population are better positioned to manage the pandemic.
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Characteristics and risk factors for mortality in critically ill patients with COVID-19 receiving invasive mechanical ventilation: the experience of a private network in Sao Paulo, Brazil. J Crit Care Med (Targu Mures) 2022; 8:165-175. [PMID: 36062038 PMCID: PMC9396953 DOI: 10.2478/jccm-2022-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 06/12/2022] [Indexed: 12/15/2022] Open
Abstract
Abstract
Introduction
The use of invasive mechanical ventilation (IMV) in COVID-19 represents in an incremental burden to healthcare systems.
Aim of the study
We aimed to characterize patients hospitalized for COVID-19 who received IMV and identify risk factors for mortality in this population.
Material and Methods
A retrospective cohort study including consecutive adult patients admitted to a private network in Brazil who received IMV from March to October, 2020. A bidirectional stepwise logistic regression analysis was used to determine the risk factors for mortality.
Results
We included 215 patients, of which 96 died and 119 were discharged from ICU. The mean age was 62.7 ± 15.4 years and the most important comorbidities were hypertension (62.8%), obesity (50.7%) and diabetes (40%). Non-survivors had lower body mass index (BMI) (28.3 [25.5; 31.6] vs. 31.2 [28.3; 35], p<0.001, and a shorter duration from symptom onset to intubation (8.5 [6.0; 12] days vs. 10 [8.0; 12.5] days, p = 0.005). Multivariable regression analysis showed that the risk factors for mortality were age (OR: 1.07, 95% CI: 1.03 to 1.1, p < 0.001), creatinine level at the intubation date (OR: 3.28, 95% CI: 1.47 to 7.33, p = 0.004), BMI (OR: 0.91, 95% CI: 0.84 to 0.99, p = 0.033), lowest PF ratio within 48 hours post-intubation (OR: 0.988, 95% CI: 0.979 to 0.997, p = 0.011), barotrauma (OR: 5.18, 95% CI: 1.14 to 23.65, p = 0.034) and duration from symptom onset to intubation (OR: 0.76, 95% CI: 0.76 to 0.95, p = 0.006).
Conclusion
In our retrospective cohort we identified the main risk factors for mortality in COVID-19 patients receiving IMV: age, creatinine at the day of intubation, BMI, lowest PF ratio 48-hours post-intubation, barotrauma and duration from symptom onset to intubation.
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Elshennawy NM, Ibrahim DM, Sarhan AM, Arafa M. Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19. Diagnostics (Basel) 2022; 12:1847. [PMID: 36010198 PMCID: PMC9406405 DOI: 10.3390/diagnostics12081847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022] Open
Abstract
The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.
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Affiliation(s)
- Nada M. Elshennawy
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Dina M. Ibrahim
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Amany M. Sarhan
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Mohamed Arafa
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
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Kim KM, Evans DS, Jacobson J, Jiang X, Browner W, Cummings SR. Rapid prediction of in-hospital mortality among adults with COVID-19 disease. PLoS One 2022; 17:e0269813. [PMID: 35905072 PMCID: PMC9337639 DOI: 10.1371/journal.pone.0269813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/29/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission. METHODS This retrospective study included 13,190 racially and ethnically diverse adults admitted to one of the New York City Health + Hospitals (NYC H+H) system for COVID-19 illness between March 1 and June 30, 2020. Demographic characteristics, simple vital signs and routine clinical laboratory tests were collected from the electronic medical records. A clinical prediction model to estimate the risk of dying during the hospitalization were developed. RESULTS Mean age (interquartile range) was 58 (45-72) years; 5421 (41%) were women, 5258 were Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 were (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables-oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine-that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5-1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5-98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/. CONCLUSIONS In a diverse population of hospitalized patients with COVID-19 illness, a clinical prediction model using a few readily available vital signs reflecting the severity of disease may precisely predict in-hospital mortality in diverse populations and can rapidly assist decisions to prioritize admissions and intensive care.
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Affiliation(s)
- Kyoung Min Kim
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
- Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea
| | - Daniel S. Evans
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
| | - Jessica Jacobson
- New York City Health + Hospitals/Bellevue-NYU Grossman School of Medicine, New York, New York, United States of America
| | - Xiaqing Jiang
- Orthopedic Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Warren Browner
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
| | - Steven R. Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
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24
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Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/3783058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic has greatly affected populations worldwide and has posed a significant challenge to medical systems. With the constant increase in the number of severe COVID-19 infections, an essential area of research has been directed towards predicting the mortality rate of these patients, in order to make informed medical decisions about the necessary healthcare priorities. Although a large amount of research has attempted to predict the mortality rate of COVID-19 patients, the association between the mortality rate of COVID-19 patients and their underlying health conditions has been given significantly less attention. Meanwhile, patients with underlying conditions often face a worse COVID-19 prognosis. Therefore, the goal of this study was to classify the mortality rate of patients diagnosed with COVID-19, who also suffer from underlying health conditions or comorbidities. To achieve our goal, we applied machine learning (ML) models on a new publicly available dataset, not investigated by any existing literature. The dataset provides detailed information on 582 COVID-19 patients and facilitates a robust forecasting model of the mortality rate. The dataset was analysed using seven ML classifiers, namely, Bagging, J48, logistic regression (LR), random forest (RF), support vector machine (SVM), naïve Bayes (NB), and threshold selector. A comparative analysis was performed across the seven ML techniques, and their performance was assessed based on evaluation parameters including classification accuracy, true-positive rate, and false-positive rate. The best performance was demonstrated by the Bagging algorithm with an accuracy of 83.55% when using all the dataset features. The findings are intended to assist researchers and physicians in the early identification of at-risk COVID-19 patients and to make the appropriate intensive care decisions.
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25
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Akter S, Das D, Haque RU, Quadery Tonmoy MI, Hasan MR, Mahjabeen S, Ahmed M. AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19. Comput Biol Med 2022; 146:105657. [DOI: 10.1016/j.compbiomed.2022.105657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/14/2022] [Accepted: 05/18/2022] [Indexed: 11/30/2022]
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26
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Gustafson D, Ngai M, Wu R, Hou H, Schoffel AC, Erice C, Mandla S, Billia F, Wilson MD, Radisic M, Fan E, Trahtemberg U, Baker A, McIntosh C, Fan CPS, Dos Santos CC, Kain KC, Hanneman K, Thavendiranathan P, Fish JE, Howe KL. Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies. EBioMedicine 2022; 78:103982. [PMID: 35405523 PMCID: PMC8989492 DOI: 10.1016/j.ebiom.2022.103982] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/15/2022] [Accepted: 03/22/2022] [Indexed: 02/07/2023] Open
Abstract
Background Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe coronavirus disease 2019 (COVID-19), indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, are unique to COVID-19, and if vascular permeability can be therapeutically targeted. Methods Prospectively evaluating the prevalence of circulating inflammatory, cardiac, and EC activation markers as well as developing a microRNA atlas in 241 unvaccinated patients with suspected SARS-CoV-2 infection allowed for prognostic value assessment using a Random Forest model machine learning approach. Subsequent ex vivo experiments assessed EC permeability responses to patient plasma and were used to uncover modulated gene regulatory networks from which rational therapeutic design was inferred. Findings Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients, while dysregulation of specific microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating the datasets using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction, which was ameliorated using angiopoietin-1 mimetic or recombinant Slit2-N. Interpretation Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis. Funding Information This work was directly supported by grant funding from the Ted Rogers Center for Heart Research, Toronto, Ontario, Canada and the Peter Munk Cardiac Center, Toronto, Ontario, Canada.
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Affiliation(s)
- Dakota Gustafson
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Michelle Ngai
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada
| | - Ruilin Wu
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Huayun Hou
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada
| | | | - Clara Erice
- Johns Hopkins School of Medicine, Baltimore, USA
| | - Serena Mandla
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Filio Billia
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada
| | - Michael D Wilson
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Milica Radisic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Eddy Fan
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Interdepartmental Division of Critical Care and Institute of Medical Sciences, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Uriel Trahtemberg
- Keenan Research Center for Biomedical Research, Unity Health Toronto, Toronto, Canada; Critical Care Department, Galilee Medical Center, Nahariya, Israel
| | - Andrew Baker
- Interdepartmental Division of Critical Care and Institute of Medical Sciences, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Critical Care Department, Galilee Medical Center, Nahariya, Israel
| | - Chris McIntosh
- Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada; Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada; Techna Institute, University Health Network, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Vector Institute, University of Toronto, Toronto, Canada
| | - Chun-Po S Fan
- Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada
| | - Claudia C Dos Santos
- Interdepartmental Division of Critical Care and Institute of Medical Sciences, University of Toronto, Toronto, Canada; Keenan Research Center for Biomedical Research, Unity Health Toronto, Toronto, Canada
| | - Kevin C Kain
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Kate Hanneman
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada; Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Paaladinesh Thavendiranathan
- Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada; Ted Rogers Program in Cardiotoxicity Prevention, Toronto General Hospital, Toronto, Canada
| | - Jason E Fish
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada.
| | - Kathryn L Howe
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada; Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, Canada.
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Alle S, Kanakan A, Siddiqui S, Garg A, Karthikeyan A, Mehta P, Mishra N, Chattopadhyay P, Devi P, Waghdhare S, Tyagi A, Tarai B, Hazarik PP, Das P, Budhiraja S, Nangia V, Dewan A, Sethuraman R, Subramanian C, Srivastava M, Chakravarthi A, Jacob J, Namagiri M, Konala V, Dash D, Sethi T, Jha S, Agrawal A, Pandey R, Vinod PK, Priyakumar UD. COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits. PLoS One 2022; 17:e0264785. [PMID: 35298502 PMCID: PMC8929610 DOI: 10.1371/journal.pone.0264785] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/16/2022] [Indexed: 12/15/2022] Open
Abstract
The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.
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Affiliation(s)
- Shanmukh Alle
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Samreen Siddiqui
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Akshit Garg
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Akshaya Karthikeyan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Priyanka Mehta
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Neha Mishra
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Intel Technology India Private Limited, Bangalore, Karnataka, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Intel Technology India Private Limited, Bangalore, Karnataka, India
| | - Swati Waghdhare
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Akansha Tyagi
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Bansidhar Tarai
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Pranjal Pratim Hazarik
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Poonam Das
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Sandeep Budhiraja
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Vivek Nangia
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Arun Dewan
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | | | - C. Subramanian
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Mashrin Srivastava
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | | | - Johnny Jacob
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Madhuri Namagiri
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Varma Konala
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Debasish Dash
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Tavpritesh Sethi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Sujeet Jha
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Anurag Agrawal
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
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Safdar B, Wang M, Guo X, Cha C, Chun HJ, Deng Y, Dziura J, El-Khoury JM, Gorelick F, Ko AI, Lee AI, Safirstein R, Simonov M, Zhou B, Desir GV. Association of renalase with clinical outcomes in hospitalized patients with COVID-19. PLoS One 2022; 17:e0264178. [PMID: 35259186 PMCID: PMC8903289 DOI: 10.1371/journal.pone.0264178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 02/04/2022] [Indexed: 12/27/2022] Open
Abstract
Renalase is a secreted flavoprotein with anti-inflammatory and pro-cell survival properties. COVID-19 is associated with disordered inflammation and apoptosis. We hypothesized that blood renalase levels would correspond to severe COVID-19 and survival. In this retrospective cohort study, clinicopathologic data and blood samples were collected from hospitalized COVID-19 subjects (March—June 2020) at a single institution tertiary hospital. Plasma renalase and cytokine levels were measured and clinical data abstracted from health records. Of 3,450 COVID-19 patients, 458 patients were enrolled. Patients were excluded if <18 years, or opted out of research. The primary composite outcome was intubation or death within 180 days. Secondary outcomes included mortality alone, intensive care unit admission, use of vasopressors, and CPR. Enrolled patients had mean age 64 years (SD±17), were 53% males, and 48% non-whites. Mean renalase levels was 14,108·4 ng/ml (SD±8,137 ng/ml). Compared to patients with high renalase, those with low renalase (< 8,922 ng/ml) were more likely to present with hypoxia, increased ICU admission (54% vs. 33%, p < 0.001), and cardiopulmonary resuscitation (10% vs. 4%, p = 0·023). In Cox proportional hazard model, every 1000 ng/ml increase in renalase decreased the risk of death or intubation by 5% (HR 0·95; 95% CI 0·91–0·98) and increased survival alone by 6% (HR 0·95; CI 0·90–0·98), after adjusting for socio-demographics, initial disease severity, comorbidities and inflammation. Patients with high renalase-low IL-6 levels had the best survival compared to other groups (p = 0·04). Renalase was independently associated with reduced intubation and mortality in hospitalized COVID-19 patients. Future studies should assess the pathophysiological relevance of renalase in COVID-19 disease.
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Affiliation(s)
- Basmah Safdar
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- * E-mail:
| | - Melinda Wang
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Xiaojia Guo
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA CT HealthCare, West Haven, Connecticut, United States of America
| | - Charles Cha
- Department of Surgery, Hartford HealthCare, Hartford, Connecticut, United States of America
| | - Hyung J. Chun
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Yanhong Deng
- Yale Center of Analytics Sciences, New Haven, Connecticut, United States of America
| | - James Dziura
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- Yale Center of Analytics Sciences, New Haven, Connecticut, United States of America
| | - Joe M. El-Khoury
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Fred Gorelick
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Yale Center of Analytics Sciences, New Haven, Connecticut, United States of America
| | - Albert I. Ko
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Alfred I. Lee
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Robert Safirstein
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA CT HealthCare, West Haven, Connecticut, United States of America
| | - Michael Simonov
- Yale Center of Analytics Sciences, New Haven, Connecticut, United States of America
| | - Bin Zhou
- Yale Center of Analytics Sciences, New Haven, Connecticut, United States of America
| | - Gary V. Desir
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA CT HealthCare, West Haven, Connecticut, United States of America
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Jamshidi E, Asgary A, Tavakoli N, Zali A, Setareh S, Esmaily H, Jamaldini SH, Daaee A, Babajani A, Sendani Kashi MA, Jamshidi M, Jamal Rahi S, Mansouri N. Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU. Front Digit Health 2022; 3:681608. [PMID: 35098205 PMCID: PMC8792458 DOI: 10.3389/fdgth.2021.681608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 12/22/2021] [Indexed: 01/28/2023] Open
Abstract
Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.
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Affiliation(s)
- Elham Jamshidi
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amirhossein Asgary
- Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran
| | - Nader Tavakoli
- Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Alireza Zali
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroush Setareh
- Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran
| | - Hadi Esmaily
- Department of Clinical Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Hamid Jamaldini
- Department of Genetic, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Amir Daaee
- School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | - Amirhesam Babajani
- Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Masoud Jamshidi
- Department of Exercise Physiology, Tehran University, Tehran, Iran
| | - Sahand Jamal Rahi
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Nahal Mansouri
- Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Giancotti M, Lopreite M, Mauro M, Puliga M. The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic. Sci Rep 2021; 11:23739. [PMID: 34887452 PMCID: PMC8660820 DOI: 10.1038/s41598-021-03120-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/26/2021] [Indexed: 12/21/2022] Open
Abstract
This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.
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Affiliation(s)
- Monica Giancotti
- Department of Clinical and Experimental Medicine, Magna Graecia University, Viale Europa, Catanzaro, Italy
| | - Milena Lopreite
- Department of Economics, Statistics and Finance, University of Calabria, Calabria, Italy.
| | - Marianna Mauro
- Department of Clinical and Experimental Medicine, Magna Graecia University, Catanzaro, Italy
| | - Michelangelo Puliga
- Institute of Management, Sant'Anna School of Advanced Studies, Pisa, Italy
- Linkalab Computational Laboratory, Cagliari, Italy
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Linden T, Hanses F, Domingo-Fernández D, DeLong LN, Kodamullil AT, Schneider J, Vehreschild MJGT, Lanznaster J, Ruethrich MM, Borgmann S, Hower M, Wille K, Feldt T, Rieg S, Hertenstein B, Wyen C, Roemmele C, Vehreschild JJ, Jakob CEM, Stecher M, Kuzikov M, Zaliani A, Fröhlich H. Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2021; 1:100020. [PMID: 34988543 PMCID: PMC8677630 DOI: 10.1016/j.ailsci.2021.100020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 02/08/2023]
Abstract
Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.
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Affiliation(s)
- Thomas Linden
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, 93053 Regensburg, Germany
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Daniel Domingo-Fernández
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Lauren Nicole DeLong
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Alpha Tom Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Jochen Schneider
- Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, 81675 Munich, Germany
| | - Maria J G T Vehreschild
- Department II of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Julia Lanznaster
- Department of Internal Medicine II, Hospital Passau, Innstraße 76, 94032 Passau, Germany
| | - Maria Madeleine Ruethrich
- Institute for Infection Medicine and Hospital Hygiene, University Hospital Jena, 07743 Jena, Germany
| | - Stefan Borgmann
- Department of Infectious Diseases and Infection Control, Hospital Ingolstadt, 85049 Ingolstadt, Germany
| | - Martin Hower
- Department of Pneumology, Infectious Diseases and Intensive Care, Klinikum Dortmund gGmbH, Hospital of University Witten / Herdecke, 44137 Dortmund, Germany
| | - Kai Wille
- University Clinic for Haematology, Oncology, Haemostaseology and Palliative Care, Johannes Wesling Medical Centre Minden, 32429 Minden, Germany
| | - Torsten Feldt
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany
| | - Siegbert Rieg
- Department of Medicine II, University Hospital Freiburg, 79110 Freiburg, Germany
| | - Bernd Hertenstein
- Department of Medicine II, University Hospital Freiburg, 79110 Freiburg, Germany
| | - Christoph Wyen
- Christoph Wyen, Praxis am Ebertplatz Cologne, 50668 Cologne, Germany
| | - Christoph Roemmele
- Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, 86156 Augsburg, Germany
| | - Jörg Janne Vehreschild
- Department II of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Carolin E M Jakob
- Department I for Internal Medicine, University Hospital of Cologne, University of Cologne, 50931 Cologne, Germany
| | - Melanie Stecher
- Fraunhofer Institute for Translational Medicine and Pharmacologie (ITMP), VolksparkLabs, Schnackenburgallee 114, 22535 Hamburg, Germany
| | - Maria Kuzikov
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Andrea Zaliani
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
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Campbell TW, Wilson MP, Roder H, MaWhinney S, Georgantas RW, Maguire LK, Roder J, Erlandson KM. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. Int J Med Inform 2021; 155:104594. [PMID: 34601240 PMCID: PMC8459591 DOI: 10.1016/j.ijmedinf.2021.104594] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 08/30/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Abstract
Rationale Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. The study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. Methods Hierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of acute respiratory distress syndrome, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk. Main results Test performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, C-reactive protein, lactate dehydrogenase, and D-dimer were often found to be important in the assignment of risk. Conclusions Risk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.
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Affiliation(s)
| | - Melissa P Wilson
- Department of Medicine, Division of Personalized Medicine and Bioinformatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States
| | | | - Samantha MaWhinney
- Department of Biostatistics and Informatics, University of Colorado, Colorado School of Public Health, United States
| | | | | | | | - Kristine M Erlandson
- Department of Medicine, Division of Infectious Diseases, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States
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COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty. MATHEMATICS 2021. [DOI: 10.3390/math9172043] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The abundance of type and quantity of available data in the healthcare field has led many to utilize machine learning approaches to keep up with this influx of data. Data pertaining to COVID-19 is an area of recent interest. The widespread influence of the virus across the United States creates an obvious need to identify groups of individuals that are at an increased risk of mortality from the virus. We propose a so-called clustered random forest approach to predict COVID-19 patient mortality. We use this approach to examine the hidden heterogeneity of patient frailty by examining demographic information for COVID-19 patients. We find that our clustered random forest approach attains predictive performance comparable to other published methods. We also find that follow-up analysis with neural network modeling and k-means clustering provide insight into the type and magnitude of mortality risks associated with COVID-19.
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Liu M, Jiang H, Li Y, Li C, Tan Z, Jin F, Zhang T, Nan Y. Independent Risk Factors for the Dynamic Development of COVID-19: A Retrospective Study. Int J Gen Med 2021; 14:4349-4367. [PMID: 34408476 PMCID: PMC8364400 DOI: 10.2147/ijgm.s325112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 07/23/2021] [Indexed: 01/08/2023] Open
Abstract
Objective To identify the risk factors for predicting the dynamic progression of COVID-19. Methods A total of 2321 eligible patients were included in this study from February 4 to April 15, 2020. Two illness conditions, including mild/moderate (M/M) subtype to severe/critical (S/C) and S/C to fatality, were classified. Clinical message was collected and compared, respectively. Kaplan–Meier method, Cox regression model and risk score system were used to predict disease progression in S/C COVID-19. Results A total of 112 of 1761 patients with M/M subtype were progressors (P) and 1649 non-progressors (NP). Increasing disease progression associated with higher levels of neutrophils count (HR=1.958, 95% CI=1.253–3.059, P=0.003), CK (HR=2.203, 95% CI=1.048–4.632, P=0.037), LDH (HR=3.309, 95% CI=2.083–5.256, P<0.001) and CRP (HR=2.575, 95% CI=1.638–4.049, P<0.001), and lower level of lymphocytes count (HR=1.549, 95% CI=1.018–2.355, P=0.041), as well as total lesion volume ratio greater than ≥10% (HR=2.286, 95% CI=1.451–3.601, P<0.001) on admission. In progression to fatality, 56 of the 672 S/C cases died and 616 survived. Increasing fatality associated with lower level of lymphocytes count (HR:2.060, 95% CI:1.000–4.242, P=0.050), higher levels of BUN (HR:2.715, 95% CI:1.539–4.790, P<0.001), CK-MB (HR:3.412, 95% CI:1.760–6.616, P<0.001), LDH (HR:5.578, 95% CI:2.317–13.427, P<0.001), and PT (HR:3.619, 95% CI:2.102–6.231, P<0.001). Furthermore, high risk of neutrophils count, lymphocytes count, CK, LDH, CRP, and total lesion volume ratio was powerfully correlated with the incidence of progression to S/C in patients with NS COVID-19 and high odds of lymphocytes count, BUN, CK-MB, LDH, and PT were significantly associated with death in patients with S/C COVID-19. In addition, the progression and mortality rates increased with increasing risk scores. Conclusion Elevated LDH level and lymphopenia were independent predictors for COVID-19 sustainable management in classifying non-severe patients who progressed to severe condition and identifying S/C patients who deteriorated to fatal outcomes as well. Total lesion volume ratio ≥10% may provide early predictive evidence with COVID-19 patients at high risk of developing into S/C to improve prognosis.
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Affiliation(s)
- Miaomiao Liu
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
| | - Hua Jiang
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
| | - Yujuan Li
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
| | - Chunmei Li
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
| | - Zhijun Tan
- Department of Health Statistics, Air Force Military Medical University, Xi'an, 710032, People's Republic of China
| | - Faguang Jin
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
| | - Tao Zhang
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
| | - Yandong Nan
- Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, People's Republic of China
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Girelli D, Marchi G, Busti F, Vianello A. Iron metabolism in infections: Focus on COVID-19. Semin Hematol 2021; 58:182-187. [PMID: 34389110 PMCID: PMC8305218 DOI: 10.1053/j.seminhematol.2021.07.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 07/12/2021] [Indexed: 12/15/2022]
Abstract
Iron is a micronutrient essential for a wide range of metabolic processes in virtually all living organisms. During infections, a battle for iron takes place between the human host and the invading pathogens. The liver peptide hepcidin, which is phylogenetically and structurally linked to defensins (antimicrobial peptides of the innate immunity), plays a pivotal role by subtracting iron to pathogens through its sequestration into host cells, mainly macrophages. While this phenomenon is well studied in certain bacterial infections, much less is known regarding viral infections. Iron metabolism also has implications on the functionality of cells of the immune system. Once primed by the contact with antigen presenting cells, lymphocytes need iron to sustain the metabolic burst required for mounting an effective cellular and humoral response. The COVID-19 pandemic has boosted an amount of clinical and translational research over the possible influences of nutrients on SARS-CoV-2 infection, in terms of either susceptibility or clinical course. Here we review the intersections between iron metabolism and COVID-19, belonging to the wider domain of the so-called “nutritional immunity”. A better understanding of such connections has potential broad implications, either from a mechanistic standpoint, or for the development of more effective strategies for managing COVID-19 and possible future pandemics.
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Affiliation(s)
- Domenico Girelli
- Department of Medicine, Section of Internal Medicine, University of Verona, Euro Blood Net Referral Center, Azienda Ospedaliera Universitaria Integrata Verona, Italy.
| | - Giacomo Marchi
- Department of Medicine, Section of Internal Medicine, University of Verona, Euro Blood Net Referral Center, Azienda Ospedaliera Universitaria Integrata Verona, Italy
| | - Fabiana Busti
- Department of Medicine, Section of Internal Medicine, University of Verona, Euro Blood Net Referral Center, Azienda Ospedaliera Universitaria Integrata Verona, Italy
| | - Alice Vianello
- Department of Medicine, Section of Internal Medicine, University of Verona, Euro Blood Net Referral Center, Azienda Ospedaliera Universitaria Integrata Verona, Italy
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36
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Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis. Processes (Basel) 2021. [DOI: 10.3390/pr9081267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.
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