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Zhou HH, Tang YL, Xu TH, Cheng B. C-reactive protein: structure, function, regulation, and role in clinical diseases. Front Immunol 2024; 15:1425168. [PMID: 38947332 PMCID: PMC11211361 DOI: 10.3389/fimmu.2024.1425168] [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: 04/29/2024] [Accepted: 06/03/2024] [Indexed: 07/02/2024] Open
Abstract
C-reactive protein (CRP) is a plasma protein that is evolutionarily conserved, found in both vertebrates and many invertebrates. It is a member of the pentraxin superfamily, characterized by its pentameric structure and calcium-dependent binding to ligands like phosphocholine (PC). In humans and various other species, the plasma concentration of this protein is markedly elevated during inflammatory conditions, establishing it as a prototypical acute phase protein that plays a role in innate immune responses. This feature can also be used clinically to evaluate the severity of inflammation in the organism. Human CRP (huCRP) can exhibit contrasting biological functions due to conformational transitions, while CRP in various species retains conserved protective functions in vivo. The focus of this review will be on the structural traits of CRP, the regulation of its expression, activate complement, and its function in related diseases in vivo.
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Affiliation(s)
- Hai-Hong Zhou
- Centre for Translational Medicine, Gansu Provincial Academic Institute for Medical Research, Lanzhou, China
- Centre for Translational Medicine, Gansu Provincial Cancer Hospital, Lanzhou, China
- Centre for Translational Medicine, Sun Yat-sen University Cancer Center Gansu Hospital, Lanzhou, China
| | - Yu-Long Tang
- Ministry of Education (MOE), Key Laboratory of Cell Activities and Stress Adaptations, School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Tian-Hao Xu
- Ministry of Education (MOE), Key Laboratory of Cell Activities and Stress Adaptations, School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Bin Cheng
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Research Unit of Peptide Science, Chinese Academy of Medical Sciences, Lanzhou University, Lanzhou, China
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2
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Zhang L, Liu Y, Zou J, Wang T, Hu H, Zhou Y, Lu Y, Qiu T, Zhou J, Liu X. The Development and Evaluation of a Prediction Model for Kidney Transplant-Based Pneumocystis carinii Pneumonia Patients Based on Hematological Indicators. Biomedicines 2024; 12:366. [PMID: 38397968 PMCID: PMC10886538 DOI: 10.3390/biomedicines12020366] [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: 01/07/2024] [Revised: 01/21/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND This study aimed to develop a simple predictive model for early identification of the risk of adverse outcomes in kidney transplant-associated Pneumocystis carinii pneumonia (PCP) patients. METHODS This study encompassed 103 patients diagnosed with PCP, who received treatment at our hospital between 2018 and 2023. Among these participants, 20 were categorized as suffering from severe PCP, and, regrettably, 13 among them succumbed. Through the application of machine learning techniques and multivariate logistic regression analysis, two pivotal variables were discerned and subsequently integrated into a nomogram. The efficacy of the model was assessed via receiver operating characteristic (ROC) curves and calibration curves. Additionally, decision curve analysis (DCA) and a clinical impact curve (CIC) were employed to evaluate the clinical utility of the model. The Kaplan-Meier (KM) survival curves were utilized to ascertain the model's aptitude for risk stratification. RESULTS Hematological markers, namely Procalcitonin (PCT) and C-reactive protein (CRP)-to-albumin ratio (CAR), were identified through machine learning and multivariate logistic regression. These variables were subsequently utilized to formulate a predictive model, presented in the form of a nomogram. The ROC curve exhibited commendable predictive accuracy in both internal validation (AUC = 0.861) and external validation (AUC = 0.896). Within a specific threshold probability range, both DCA and CIC demonstrated notable performance. Moreover, the KM survival curve further substantiated the nomogram's efficacy in risk stratification. CONCLUSIONS Based on hematological parameters, especially CAR and PCT, a simple nomogram was established to stratify prognostic risk in patients with renal transplant-related PCP.
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Affiliation(s)
- Long Zhang
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yiting Liu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jilin Zou
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Tianyu Wang
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Haochong Hu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yujie Zhou
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yifan Lu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Tao Qiu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiangqiao Zhou
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xiuheng Liu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.Z.); (Y.L.); (J.Z.); (T.W.); (H.H.); (Y.Z.); (Y.L.); (T.Q.)
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
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Mohammedain SA, Badran S, Elzouki AY, Salim H, Chalaby A, Siddiqui MYA, Hussein YY, Rahim HA, Thalib L, Alam MF, Al-Badriyeh D, Al-Maadeed S, Doi SAR. Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study. Future Virol 2023:10.2217/fvl-2023-0036. [PMID: 37970094 PMCID: PMC10630949 DOI: 10.2217/fvl-2023-0036] [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: 02/14/2023] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.
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Affiliation(s)
- Shahd A Mohammedain
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Saif Badran
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Department of Plastic Surgery, Hamad Medical Corporation, Doha, Qatar
| | - AbdelNaser Y Elzouki
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Halla Salim
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Ayesha Chalaby
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - MYA Siddiqui
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Yehia Y Hussein
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Hanan Abdul Rahim
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Lukman Thalib
- Department of Biostatistics, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | | | - Sumaya Al-Maadeed
- Department of Computer Science, College of Engineering, Qatar University, Doha, Qatar
| | - Suhail AR Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
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Li QY, An ZY, Pan ZH, Wang ZZ, Wang YR, Zhang XG, Shen N. Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores. World J Clin Cases 2023; 11:2716-2728. [PMID: 37214568 PMCID: PMC10198108 DOI: 10.12998/wjcc.v11.i12.2716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/12/2023] [Accepted: 03/17/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND Early identification of severe/critical coronavirus disease 2019 (COVID-19) is crucial for timely treatment and intervention. Chest computed tomography (CT) score has been shown to be a significant factor in the diagnosis and treatment of pneumonia, however, there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.
AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels.
METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients. The study also took into consideration the general clinical indicators such as dyspnea, oxygen saturation, alternative lengthening of telomeres (ALT), and androgen suppression treatment (AST), which are commonly associated with severe/critical COVID-19 cases. The study employed lasso regression to evaluate and rank the significance of different disease characteristics.
RESULTS The results showed that blood oxygen saturation, ALT, IL-6/IL-10, combined score, ground glass opacity score, age, crazy paving mode score, qsofa, AST, and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases. The study established a COVID-19 severe/critical early warning system using various machine learning algorithms, including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier. The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.
CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.
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Affiliation(s)
- Qiu-Yu Li
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing 100191, China
| | - Zhuo-Yu An
- Department of Education, Peking University People’s Hospital, Beijing 100044, China
| | - Zi-Han Pan
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing 100191, China
| | - Zi-Zhen Wang
- Department of Education, China-Japan Friendship Hospital, Beijing 100029, China
| | - Yi-Ren Wang
- Department of Education, Peking University People’s Hospital, Beijing 100044, China
| | - Xi-Gong Zhang
- Department of Education, Beijing Jishuitan Hospital, Beijing 100096, China
| | - Ning Shen
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing 100191, China
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Esen SG, Basak C, Leyla Ö, Aslıhan A, Evrim Eylem A. The effect of ACE2 receptor, IFN-γ, and TNF-α polymorphisms on the severity and prognosis of the disease in SARS-CoV-2 infection. J Investig Med 2023; 71:526-535. [PMID: 36876951 PMCID: PMC9996099 DOI: 10.1177/10815589231158379] [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: 03/07/2023]
Abstract
To investigate the effect of genetic variations in the angiotensin converting enzyme (ACE), interferon (IFNG) and tumor necrosis factor (TNF-α) genes on the severity of coronavirus disease (COVID-19). Between September and December 2021, 33 patients with COVID-19 were included in this prospective study. The patients were classified and compared according to disease severity: mild&moderate (n = 26) vs severe&critical (n = 7). These groups were evaluated to assess possible relationships with ACE, TNF-α and IFNG gene variations using univariate and multivariable analyses. The median age of the mild&moderate group was 45.5 (22-73), and that of the severe&critical group was 58 (49-80) years (p = 0.014). Seventeen (65.4%) of the mild&moderate patients and 3 (42.9%) of severe&critical patients were female (p = 0.393). According to results of univariate analysis, the percentage of patients with the c.418-70C>G variant of the ACE gene was significantly higher in the mild&moderate group (p = 0.027). The ACE gene polymorphisms, c.2312C>T, c.3490G>A, c.3801C>T, and c.731A>G, were each only seen in separate patients with critical disease. The following variants were observed more frequently in the mild&moderate group: c.582C>T, c.3836G>A, c.511+66A>G, c.1488-58T>C, c.3281+25C>T, c.1710-90G>C, c.2193A> G, c.3387T>C for ACE; c.115-3delT for IFNG; and c.27C>T for TNF. It can be expected that patients carrying the ACE gene c.418-70C>G variant may present with a mild clinical manifestation of COVID-19. Several genetic polymorphisms may be associated with pathophysiology, as they appear to help predict COVID-19 severity and enable early identification of the patients requiring aggressive treatment.
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Affiliation(s)
- Sayın Gülensoy Esen
- Department of Chest Diseases, Ufuk University Faculty of Medicine, Ankara, Turkey
| | - Celtikci Basak
- Department of Biochemistry, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Özer Leyla
- Department of Medical Genetics, Yüksek İhtisas University Faculty of Medicine, Ankara, Turkey
| | - Alhan Aslıhan
- Department of Biostatistics, Ufuk University Faculty of Medicine, Ankara, Turkey
| | - Akpınar Evrim Eylem
- Department of Chest Diseases, Ufuk University Faculty of Medicine, Ankara, Turkey
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Deng J, Heybati K, Ramaraju HB, Zhou F, Rayner D, Heybati S. Differential efficacy and safety of anti-SARS-CoV-2 antibody therapies for the management of COVID-19: a systematic review and network meta-analysis. Infection 2023; 51:21-35. [PMID: 35438413 PMCID: PMC9016212 DOI: 10.1007/s15010-022-01825-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/01/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE To assess and compare the relative efficacy and safety of anti-SARS-CoV-2 antibody regimens for COVID-19. METHODS This systematic review and random-effects network meta-analysis was conducted according to PRISMA-NMA. Literature searches were conducted across MEDLINE, EMBASE, PubMed, Web of Science, CENTRAL, and CNKI up to February 20th, 2022. Interventions were ranked using P scores. RESULTS Fifty-five RCTs (N = 45,005) were included in the review. Bamlanivimab + etesevimab (OR 0.13, 95% CI 0.02-0.77) was associated with a significant reduction in mortality compared to standard of care/placebo. Casirivimab + imdevimab reduced mortality (OR 0.67, 95% CI 0.50-0.91) in baseline seronegative patients only. Four different regimens led to a significant decrease in the incidence of hospitalization compared to standard of care/placebo with sotrovimab ranking first in terms of efficacy (OR 0.20, 95% CI 0.08-0.48). No treatment improved incidence of mechanical ventilation, duration of hospital/ICU stay, and time to viral clearance. Convalescent plasma and anti-COVID IVIg both led to a significant increase in adverse events compared to standard of care/placebo, but no treatment increased the odds of serious adverse events. CONCLUSION Anti-SARS-CoV-2 mAbs are safe, and could be effective in improving mortality and incidence of hospitalization. Convalescent plasma and anti-COVID IVIg were not efficacious and could increase odds of adverse events. Future trials should further examine the effect of baseline seronegativity, disease severity, patient risk factors, and SARS-CoV-2 strain variation on the efficacy of these regimes. REGISTRATION PROSPERO-CRD42021289903.
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Affiliation(s)
- Jiawen Deng
- Faculty of Health Sciences, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada.
| | - Kiyan Heybati
- Mayo Clinic Alix School of Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | | | - Fangwen Zhou
- Faculty of Health Sciences, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada
| | - Daniel Rayner
- Faculty of Health Sciences, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada
| | - Shayan Heybati
- Faculty of Health Sciences, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada
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Muacevic A, Adler JR. Predictivity of CRP, Albumin, and CRP to Albumin Ratio on the Development of Intensive Care Requirement, Mortality, and Disease Severity in COVID-19. Cureus 2023; 15:e33600. [PMID: 36788868 PMCID: PMC9910810 DOI: 10.7759/cureus.33600] [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] [Accepted: 01/10/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND The C-reactive protein (CRP) to albumin ratio (CAR) is a new index calculated by dividing CRP by the albumin level. It has been claimed to have predictive value in determining morbidity and mortality in many critical diseases Aim: In this research, we aimed to elucidate the importance of CRP, albumin, and CAR as parameters that can predict the clinical course in COVID-19 patients. MATERIALS & METHOD In this retrospective analysis, the clinical, laboratory, and radiological findings of patients over the age of 18 who were diagnosed with SARS-CoV-2 infection with a positive reverse transcription-polymerase chain reaction (RT-PCR) test were evaluated. Age, gender, laboratory examinations at admission, and CRP and albumin values at the time of diagnosis have been recorded. The relationship of these parameters with the requirement for intensive care, exitus, and serious illness in the clinical follow-up of the patients was investigated. The baseline hospitalization parameters of the patients were compared between the severe and non-severe groups. RESULTS Individuals with severe disease had a higher rate of additional disease than those with non-severe disease. It was observed that the mean laboratory values of patients with severe disease had a statistically higher level of D-dimer, CRP, aspartate aminotransferase (AST), platelet distribution width (PDW), CRP-albumin ratio, and ferritin, compared to mild to moderate cases (p<0.05). The rate of additional disease in deceased patients was higher than in patients who were alive (p<0.05). The CAR value was found to be moderately predictive in our study revealing the severity of the disease, and the possibility that the severity of the disease might be higher in patients with a CAR value above 21.47. CONCLUSION The results of this study revealed that CAR is a potential parameter in distinguishing critically ill COVID-19 patients in need of intensive care. Therefore, one can say that CAR is an important biomarker in clinically determining COVID-19.
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Nakagawara K, Chubachi S, Namkoong H, Tanaka H, Lee H, Azekawa S, Otake S, Fukushima T, Morita A, Watase M, Sakurai K, Kusumoto T, Asakura T, Masaki K, Kamata H, Ishii M, Hasegawa N, Harada N, Ueda T, Ueda S, Ishiguro T, Arimura K, Saito F, Yoshiyama T, Nakano Y, Mutoh Y, Suzuki Y, Edahiro R, Murakami K, Sato Y, Okada Y, Koike R, Kitagawa Y, Tokunaga K, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Impact of upper and lower respiratory symptoms on COVID-19 outcomes: a multicenter retrospective cohort study. Respir Res 2022; 23:315. [PMID: 36380316 PMCID: PMC9665023 DOI: 10.1186/s12931-022-02222-3] [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: 07/27/2022] [Accepted: 10/12/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Respiratory symptoms are associated with coronavirus disease 2019 (COVID-19) outcomes. However, the impacts of upper and lower respiratory symptoms on COVID-19 outcomes in the same population have not been compared. The objective of this study was to characterize upper and lower respiratory symptoms and compare their impacts on outcomes of hospitalized COVID-19 patients. METHODS This was a multicenter, retrospective cohort study; the database from the Japan COVID-19 Task Force was used. A total of 3314 COVID-19 patients were included in the study, and the data on respiratory symptoms were collected. The participants were classified according to their respiratory symptoms (Group 1: no respiratory symptoms, Group 2: only upper respiratory symptoms, Group 3: only lower respiratory symptoms, and Group 4: both upper and lower respiratory symptoms). The impacts of upper and lower respiratory symptoms on the clinical outcomes were compared. The primary outcome was the percentage of patients with poor clinical outcomes, including the need for oxygen supplementation via high-flow oxygen therapy, mechanical ventilation, and extracorporeal membrane oxygenation or death. RESULTS Of the 3314 COVID-19 patients, 605, 1331, 1229, and 1149 were classified as Group 1, Group 2, Group 3, and Group 4, respectively. In univariate analysis, patients in Group 2 had the best clinical outcomes among all groups (odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.11-0.39), while patients in Group 3 had the worst outcomes (OR: 3.27, 95% CI: 2.43-4.40). Group 3 patients had the highest incidence of pneumonia, other complications due to secondary infections, and thrombosis during the clinical course. CONCLUSIONS Upper and lower respiratory tract symptoms had vastly different impacts on the clinical outcomes of COVID-19.
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Affiliation(s)
- Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Atsuho Morita
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Mayuko Watase
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Kaori Sakurai
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Tatsuya Kusumoto
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Clinical Medicine (Laboratory of Bioregulatory Medicine), Kitasato University School of Pharmacy, Tokyo, Japan
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Katsunori Masaki
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Hirofumi Kamata
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Tetsuya Ueda
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Soichiro Ueda
- Department of Internal Medicine, JCHO (Japan Community Health Care Organization) Saitama Medical Center, Saitama, Japan
| | - Takashi Ishiguro
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Ken Arimura
- Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Fukuki Saito
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Takashi Yoshiyama
- Respiratory Disease Center, Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan
| | - Yasushi Nakano
- Department of Internal Medicine, Kawasaki Municipal Ida Hospital, Kawasaki, Japan
| | - Yoshikazu Mutoh
- Department of Infectious Diseases, Tosei General Hospital, Seto, Japan
| | - Yusuke Suzuki
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Ryuya Edahiro
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Koji Murakami
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yasunori Sato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- The Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
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Speech as a Biomarker for COVID-19 Detection Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6093613. [PMID: 35444694 PMCID: PMC9014833 DOI: 10.1155/2022/6093613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 11/30/2022]
Abstract
The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.
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10
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Miller JL, Tada M, Goto M, Chen H, Dang E, Mohr NM, Lee S. Prediction models for severe manifestations and mortality due to COVID-19: A systematic review. Acad Emerg Med 2022; 29:206-216. [PMID: 35064988 DOI: 10.1111/acem.14447] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Affiliation(s)
- Jamie L. Miller
- University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Masafumi Tada
- Department of Health Promotion and Human Behavior School of Public Health, Kyoto University Graduate School of Medicine Kyoto Japan
| | - Michihiko Goto
- Division of Infectious Diseases, Department of Internal Medicine University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Hao Chen
- University of Iowa Iowa City Iowa USA
| | | | - Nicholas M. Mohr
- Department of Emergency Medicine, Department of Anesthesia, Department of Epidemiology University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Sangil Lee
- Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA
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11
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Guner R, Kayaaslan B, Hasanoglu I, Aypak A, Bodur H, Ates I, Akinci E, Erdem D, Eser F, Izdes S, Kalem AK, Bastug A, Karalezli A, Surel AA, Ayhan M, Karaahmetoglu S, Turan IO, Arguder E, Ozdemir B, Mutlu MN, Bilir YA, Sarıcaoglu EM, Gokcinar D, Gunay S, Dinc B, Gemcioglu E, Bilmez R, Aydos O, Asilturk D, Inan O, Buzgan T. Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases. BMC Infect Dis 2021; 21:1004. [PMID: 34563117 PMCID: PMC8467006 DOI: 10.1186/s12879-021-06656-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 09/03/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.
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Affiliation(s)
- Rahmet Guner
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Bircan Kayaaslan
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey.
| | - Imran Hasanoglu
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Adalet Aypak
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Hurrem Bodur
- Department of Infectious Disease and Clinical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Ihsan Ates
- Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey
| | - Esragul Akinci
- Department of Infectious Disease and Clinical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Deniz Erdem
- Department of Anesthesiology and Reanimation, Ankara City Hospital, Ankara, Turkey
| | - Fatma Eser
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Seval Izdes
- Department of Anesthesiology and Reanimation and Intensive Care Unıt, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Ayse Kaya Kalem
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Aliye Bastug
- Department of Infectious Disease and Clinical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Aysegul Karalezli
- Department of Pulmonary Diseases, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Aziz Ahmet Surel
- Department of General Surgery, Ankara City Hospital, Ankara, Turkey
| | - Muge Ayhan
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | | | - Isıl Ozkocak Turan
- Department of Anesthesiology and Reanimation, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Emine Arguder
- Department of Anesthesiology and Reanimation and Intensive Care Unıt, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Burcu Ozdemir
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Mehmet Nevzat Mutlu
- Department of Anesthesiology and Reanimation, Ankara City Hospital, Ankara, Turkey
| | - Yesim Aybar Bilir
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Elif Mukime Sarıcaoglu
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Derya Gokcinar
- Department of Anesthesiology and Reanimation, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Sibel Gunay
- Department of Pulmonary Diseases, Ankara City Hospital, Ankara, Turkey
| | - Bedia Dinc
- Department of Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Emin Gemcioglu
- Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey
| | - Ruveyda Bilmez
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Omer Aydos
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Dilek Asilturk
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Osman Inan
- Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey
| | - Turan Buzgan
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
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12
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Meng Z, Guo S, Zhou Y, Li M, Wang M, Ying B. Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19. Signal Transduct Target Ther 2021; 6:316. [PMID: 34433805 PMCID: PMC8386162 DOI: 10.1038/s41392-021-00731-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 07/21/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023] Open
Abstract
The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.
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Affiliation(s)
- Zirui Meng
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Shuo Guo
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yanbing Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Mengjiao Li
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Minjin Wang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
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13
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The association between serum levels of micronutrients and the severity of disease in patients with COVID-19. Nutrition 2021; 91-92:111400. [PMID: 34388583 PMCID: PMC8223004 DOI: 10.1016/j.nut.2021.111400] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 06/13/2021] [Accepted: 06/16/2021] [Indexed: 12/23/2022]
Abstract
Objectives This study aimed to compare the serum level of micronutrients with normal amounts, and assess their association with the severity of disease and inflammatory cytokines in patients with coronavirus disease 2019 (COVID-19). Methods The present cross-sectional study included 60 patients admitted to the intensive care unit with COVID-19. We recorded data on demographic characteristics, anthropometric information, and medical history. Serum levels of inflammatory markers (erythrocyte sedimentation rate, C-reactive protein, interferon-gamma, tumor necrosis factor-alpha, interleukin-6), vitamins (A, B9, B12, C, D, E), and minerals (magnesium, zinc, iron) were measured. A radiologist assessed the severity of lung involvement according to patient computed tomography scans. The severity of illness was evaluated with the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) score, oxygen saturation, and body temperature. Independent associations among the serum levels of micronutrients with the severity of COVID-19 were measured. Results Median patient age was 53.50 years (interquartile range, 12.75 years). Except for vitamin A and zinc, serum levels of other micronutrients were lower than the minimum normal. Patients with APACHE score ≥25 had a higher body mass index (P = 0.044), body temperature (P = 0.003), erythrocyte sedimentation rate (P = 0.008), C-reactive protein (P = 0.003), and lower oxygen saturation (P = 0.005), serum levels of vitamin D (P = < 0.001), and zinc (P = < 0.001) compared with patients with APACHE score <25. We found that lower serum levels of vitamin D, magnesium, and zinc were significantly and independently associated with higher APACHE scores (P = 0.001, 0.028, and < 0.001, respectively) and higher lung involvement (P = 0.002, 0.045, and < 0.001, respectively). Conclusions Lower serum levels of vitamin D, zinc, and magnesium were involved in severe COVID-19.
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14
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Tretter F, Wolkenhauer O, Meyer-Hermann M, Dietrich JW, Green S, Marcum J, Weckwerth W. The Quest for System-Theoretical Medicine in the COVID-19 Era. Front Med (Lausanne) 2021; 8:640974. [PMID: 33855036 PMCID: PMC8039135 DOI: 10.3389/fmed.2021.640974] [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: 12/12/2020] [Accepted: 02/17/2021] [Indexed: 12/15/2022] Open
Abstract
Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the need for conceptual integration of these different kinds of knowledge we suggest the application of general system theory (GST). This approach endorses an organism-centered view on health and disease, which according to Ludwig von Bertalanffy who was the founder of GST, we call Organismal Systems Medicine (OSM). We argue that systems science offers wider applications in the field of pathology and can contribute to an integrative systems medicine by (i) integration of evidence across functional and structural differentially scaled subsystems, (ii) conceptualization of complex multilevel systems, and (iii) suggesting mechanisms and non-linear relationships underlying the observed phenomena. We underline these points with a proposal on multi-level systems pathology including neurophysiology, endocrinology, immune system, genetics, and general metabolism. An integration of these areas is necessary to understand excess mortality rates and polypharmacological treatments. In the pandemic era this multi-level systems pathology is most important to assess potential vaccines, their effectiveness, short-, and long-time adverse effects. We further argue that these conceptual frameworks are not only valid in the COVID-19 era but also important to be integrated in a medicinal curriculum.
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Affiliation(s)
- Felix Tretter
- Bertalanffy Center for the Study of Systems Science, Vienna, Austria
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Johannes W Dietrich
- Endocrine Research, Medical Hospital I, Bergmannsheil University Hospitals, Ruhr University of Bochum, Bochum, Germany.,Ruhr Center for Rare Diseases (CeSER), Ruhr University of Bochum, Witten/Herdecke University, Bochum, Germany
| | - Sara Green
- Section for History and Philosophy of Science, Department of Science Education, University of Copenhagen, Copenhagen, Denmark
| | - James Marcum
- Department of Philosophy, Baylor University, Waco, TX, United States
| | - Wolfram Weckwerth
- Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria.,Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
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15
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1651] [Impact Index Per Article: 412.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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