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Wu Z, Geng N, Liu Z, Pan W, Zhu Y, Shan J, Shi H, Han Y, Ma Y, Liu B. Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction. Virol J 2024; 21:96. [PMID: 38671532 PMCID: PMC11046891 DOI: 10.1186/s12985-024-02367-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking. METHODS A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed. RESULTS A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis. CONCLUSION Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.
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Affiliation(s)
- Zhipeng Wu
- Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing City, 100069, People's Republic of China
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, People's Republic of China
| | - Nan Geng
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Zhao Liu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Wen Pan
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Yueke Zhu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Jing Shan
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China
| | - Hongbo Shi
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Ying Han
- Department of Gastroenterology and Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Yingmin Ma
- Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing City, 100069, People's Republic of China.
- Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, People's Republic of China.
| | - Bo Liu
- Department of Emergency Medicine, Beijing Youan Hospital, Capital Medical University, Beijing City, 100069, People's Republic of China.
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Appel KS, Geisler R, Maier D, Miljukov O, Hopff SM, Vehreschild JJ. A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores. Clin Infect Dis 2024; 78:889-899. [PMID: 37879096 PMCID: PMC11006104 DOI: 10.1093/cid/ciad618] [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: 07/25/2023] [Revised: 09/22/2023] [Accepted: 10/04/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19). METHODS We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system). RESULTS Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far. CONCLUSIONS The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future.
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Affiliation(s)
- Katharina S Appel
- Department II of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ramsia Geisler
- Department II of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Daniel Maier
- Department II of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Olga Miljukov
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Sina M Hopff
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Cologne, Germany, University of Cologne
| | - J Janne Vehreschild
- Department II of Internal Medicine, Hematology/Oncology, Goethe University Frankfurt, Frankfurt am Main, Germany
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department I of Internal Medicine, Cologne, Germany
- German Centre for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
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Zahra A, van Smeden M, Abbink EJ, van den Berg JM, Blom MT, van den Dries CJ, Gussekloo J, Wouters F, Joling KJ, Melis R, Mooijaart SP, Peters JB, Polinder-Bos HA, van Raaij BFM, Appelman B, la Roi-Teeuw HM, Moons KGM, Luijken K. External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting. J Clin Epidemiol 2024; 168:111270. [PMID: 38311188 DOI: 10.1016/j.jclinepi.2024.111270] [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: 11/15/2023] [Revised: 01/26/2024] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVES To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes. STUDY DESIGN AND SETTING This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. MAIN OUTCOME MEASURE In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting. RESULTS All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24-0.81, and C-statistic 0.55-0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24-0.81 indicating signs of overfitting, and C-statistic of 0.55-0.71. CONCLUSION Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.
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Affiliation(s)
- Anum Zahra
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Evertine J Abbink
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jesse M van den Berg
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands; PHARMO Institute for Drug Outcomes Research, Utrecht, The Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands
| | - Carline J van den Dries
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jacobijn Gussekloo
- Section Gerontology and Geriatrics, LUMC Center for Medicine for Older People & Department of Public Health and Primary Care & Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Fenne Wouters
- Department of Medicine for Older People, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Aging & Later Life, Amsterdam, The Netherlands
| | - Karlijn J Joling
- Department of Medicine for Older People, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Aging & Later Life, Amsterdam, The Netherlands
| | - René Melis
- Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Simon P Mooijaart
- LUMC Center for Medicine for Older People, LUMC, Leiden, The Netherlands
| | - Jeannette B Peters
- Department of Pulmonary Diseases, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Harmke A Polinder-Bos
- Section of Geriatric Medicine, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Bas F M van Raaij
- LUMC Center for Medicine for Older People, LUMC, Leiden, The Netherlands
| | - Brent Appelman
- Amsterdam UMC Location University of Amsterdam, Center for Experimental and Molecular Medicine, Amsterdam, The Netherlands
| | - Hannah M la Roi-Teeuw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kim Luijken
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Baek S, Jeong YJ, Kim YH, Kim JY, Kim JH, Kim EY, Lim JK, Kim J, Kim Z, Kim K, Chung MJ. Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study. J Med Internet Res 2024; 26:e52134. [PMID: 38206673 PMCID: PMC10811577 DOI: 10.2196/52134] [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: 08/24/2023] [Revised: 11/03/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.
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Affiliation(s)
- Sangwon Baek
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Data Science, New York University, New York, NY, United States
| | - Yeon Joo Jeong
- Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Yun-Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Jin Young Kim
- Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
| | - Jin Hwan Kim
- Department of Radiology, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Eun Young Kim
- Department of Radiology, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Jae-Kwang Lim
- Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Jungok Kim
- Department of Infectious Diseases, Chungnam National University Sejong Hospital, Sejong, Republic of Korea
| | - Zero Kim
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Myung Jin Chung
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Radiology, Samsung Medical Center, Seoul, Republic of Korea
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Hai CN, Duc TB, Minh TN, Quang LN, Tung SLC, Duc LT, Duong-Quy S. Predicting mortality risk in hospitalized COVID-19 patients: an early model utilizing clinical symptoms. BMC Pulm Med 2024; 24:24. [PMID: 38200490 PMCID: PMC10777603 DOI: 10.1186/s12890-023-02838-1] [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: 07/23/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Despite global efforts to control the COVID-19 pandemic, the emergence of new viral strains continues to pose a significant threat. Accurate patient stratification, optimized resource allocation, and appropriate treatment are crucial in managing COVID-19 cases. To address this, a simple and accurate prognostic tool capable of rapidly identifying individuals at high risk of mortality is urgently needed. Early prognosis facilitates predicting treatment outcomes and enables effective patient management. The aim of this study was to develop an early predictive model for assessing mortality risk in hospitalized COVID-19 patients, utilizing baseline clinical factors. METHODS We conducted a descriptive cross-sectional study involving a cohort of 375 COVID-19 patients admitted and treated at the COVID-19 Patient Treatment Center in Military Hospital 175 from October 2021 to December 2022. RESULTS Among the 375 patients, 246 and 129 patients were categorized into the survival and mortality groups, respectively. Our findings revealed six clinical factors that demonstrated independent predictive value for mortality in COVID-19 patients. These factors included age greater than 50 years, presence of multiple underlying diseases, dyspnea, acute confusion, saturation of peripheral oxygen below 94%, and oxygen demand exceeding 5 L per minute. We integrated these factors to develop the Military Hospital 175 scale (MH175), a prognostic scale demonstrating significant discriminatory ability with an area under the curve (AUC) of 0.87. The optimal cutoff value for predicting mortality risk using the MH175 score was determined to be ≥ 3 points, resulting in a sensitivity of 96.1%, specificity of 63.4%, positive predictive value of 58%, and negative predictive value of 96.9%. CONCLUSIONS The MH175 scale demonstrated a robust predictive capacity for assessing mortality risk in patients with COVID-19. Implementation of the MH175 scale in clinical settings can aid in patient stratification and facilitate the application of appropriate treatment strategies, ultimately reducing the risk of death. Therefore, the utilization of the MH175 scale holds significant potential to improve clinical outcomes in COVID-19 patients. TRIAL REGISTRATION An independent ethics committee approved the study (Research Ethics Committee of Military Hospital 175 (No. 3598GCN-HDDD; date: October 8, 2021), which was performed in accordance with the Declaration of Helsinki, Guidelines for Good Clinical Practice.
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Affiliation(s)
- Cong Nguyen Hai
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
| | | | - The Nguyen Minh
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Lich Ngo Quang
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Son Luong Cao Tung
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Loi Trinh Duc
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Sy Duong-Quy
- Clinical Research Unit, Lam Dong Medical College and Bio-Medical Research Centre, Dalat City, Vietnam
- Immuno-Allergology Division, Hershey Medical Center, Penn State College of Medicine, Hershey, Pennsylvania, USA
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Muharremi G, Meçani R, Muka T. The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice. J Pers Med 2023; 14:53. [PMID: 38248754 PMCID: PMC10820165 DOI: 10.3390/jpm14010053] [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: 10/24/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024] Open
Abstract
Precision medicine (PM), through the integration of omics and environmental data, aims to provide a more precise prevention, diagnosis, and treatment of disease. Currently, PM is one of the emerging approaches in modern healthcare and public health, with wide implications for health care delivery, public health policy making formulation, and entrepreneurial endeavors. In spite of its growing popularity and the buzz surrounding it, PM is still in its nascent phase, facing considerable challenges that need to be addressed and resolved for it to attain the acclaim for which it strives. In this article, we discuss some of the current methodological pitfalls of PM, including the use of big data, and provide a perspective on how these challenges can be overcome by bringing PM closer to evidence-based medicine (EBM). Furthermore, to maximize the potential of PM, we present real-world illustrations of how EBM principles can be integrated into a PM approach.
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Affiliation(s)
| | - Renald Meçani
- Epistudia, 3008 Bern, Switzerland; (G.M.); (R.M.)
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8010 Graz, Austria
| | - Taulant Muka
- Epistudia, 3008 Bern, Switzerland; (G.M.); (R.M.)
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Kang JY, Bae YS, Chie EK, Lee SB. Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. SENSORS (BASEL, SWITZERLAND) 2023; 23:9597. [PMID: 38067970 PMCID: PMC10708735 DOI: 10.3390/s23239597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023]
Abstract
Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.
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Affiliation(s)
- Jin-Yeong Kang
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
- Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
| | - Ye Seul Bae
- Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea;
- Department of Future Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
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Potamias G, Gkoublia P, Kanterakis A. The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest. Front Immunol 2023; 14:1251067. [PMID: 38077337 PMCID: PMC10699200 DOI: 10.3389/fimmu.2023.1251067] [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: 06/30/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes. Methods Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results. Results The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events. Discussion The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.
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Affiliation(s)
- George Potamias
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
| | - Polymnia Gkoublia
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
- Graduate Bioinformatics Program, School of Medicine, University of Crete, Heraklion, Greece
| | - Alexandros Kanterakis
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
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