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Moatar AI, Chis AR, Nitusca D, Oancea C, Marian C, Sirbu IO. HB-EGF Plasmatic Level Contributes to the Development of Early Risk Prediction Nomogram for Severe COVID-19 Cases. Biomedicines 2024; 12:373. [PMID: 38397975 PMCID: PMC10886796 DOI: 10.3390/biomedicines12020373] [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: 12/27/2023] [Revised: 01/27/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Heparin-Binding Epidermal Growth Factor-like Growth Factor (HB-EGF) is involved in wound healing, cardiac hypertrophy, and heart development processes. Recently, circulant HB-EGF was reported upregulated in severely hospitalized COVID-19 patients. However, the clinical correlations of HB-EGF plasma levels with COVID-19 patients' characteristics have not been defined yet. In this study, we assessed the plasma HB-EGF correlations with the clinical and paraclinical patients' data, evaluated its predictive clinical value, and built a risk prediction model for severe COVID-19 cases based on the resulting significant prognostic markers. (2) Methods: Our retrospective study enrolled 75 COVID-19 patients and 17 control cases from May 2020 to September 2020. We quantified plasma HB-EGF levels using the sandwich ELISA technique. Correlations between HB-EGF plasma levels with clinical and paraclinical patients' data were calculated using two-tailed Spearman and Point-Biserial tests. Significantly upregulated parameters for severe COVID-19 cases were identified and selected to build a multivariate logistic regression prediction model. The clinical significance of the prediction model was assessed by risk prediction nomogram and decision curve analyses. (3) Results: HB-EGF plasma levels were significantly higher in the severe COVID-19 subgroup compared to the controls (p = 0.004) and moderate cases (p = 0.037). In the severe COVID-19 group, HB-EGF correlated with age (p = 0.028), pulse (p = 0.016), dyspnea (p = 0.014) and prothrombin time (PT) (p = 0.04). The multivariate risk prediction model built on seven identified risk parameters (age p = 0.043, HB-EGF p = 0.0374, Fibrinogen p = 0.009, PT p = 0.008, Creatinine p = 0.026, D-Dimers p = 0.024 and delta miR-195 p < 0.0001) identifies severe COVID-19 with AUC = 0.9556 (p < 0.0001). The decision curve analysis revealed that the nomogram model is clinically relevant throughout a wide threshold probability range. (4) Conclusions: Upregulated HB-EGF plasma levels might serve as a prognostic factor for severe COVID-19 and help build a reliable risk prediction nomogram that improves the identification of high-risk patients at an early stage of COVID-19.
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Affiliation(s)
- Alexandra Ioana Moatar
- Doctoral School, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania; (A.I.M.); (D.N.)
- Department of Biochemistry, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania (I.-O.S.)
- Center for Complex Network Science, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania
| | - Aimee Rodica Chis
- Department of Biochemistry, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania (I.-O.S.)
- Center for Complex Network Science, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania
| | - Diana Nitusca
- Doctoral School, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania; (A.I.M.); (D.N.)
- Department of Biochemistry, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania (I.-O.S.)
- Center for Complex Network Science, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania
| | - Cristian Oancea
- Department of Pneumology, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania
| | - Catalin Marian
- Department of Biochemistry, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania (I.-O.S.)
- Center for Complex Network Science, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania
| | - Ioan-Ovidiu Sirbu
- Department of Biochemistry, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania (I.-O.S.)
- Center for Complex Network Science, University of Medicine and Pharmacy “Victor Babes”, 300041 Timisoara, Romania
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Wang Y, Liu S, Zhang W, Zheng L, Li E, Zhu M, Yan D, Shi J, Bao J, Yu J. Development and Evaluation of a Nomogram for Predicting the Outcome of Immune Reconstitution Among HIV/AIDS Patients Receiving Antiretroviral Therapy in China. Adv Biol (Weinh) 2024; 8:e2300378. [PMID: 37937390 DOI: 10.1002/adbi.202300378] [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: 07/26/2023] [Revised: 10/12/2023] [Indexed: 11/09/2023]
Abstract
This study aims to develop and evaluate a model to predict the immune reconstitution among HIV/AIDS patients after antiretroviral therapy (ART). A total of 502 HIV/AIDS patients are randomized to the training cohort and evaluation cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis are performed to identify the indicators and establish the nomogram for predicting the immune reconstitution. Decision curve analysis (DCA) and clinical impact curve (CIC) are used to evaluate the clinical effectiveness of the nomogram. Predictive factors included white blood cells (WBC), baseline CD4+ T-cell counts (baseline CD4), ratio of effector regulatory T cells to resting regulatory T cells (eTreg/rTreg) and low-density lipoprotein cholesterol (LDL-C) and are incorporated into the nomogram. The area under the curve (AUC) is 0.812 (95% CI, 0.767∼0.851) and 0.794 (95%CI, 0.719∼0.857) in the training cohort and evaluation cohort, respectively. The calibration curve shows a high consistency between the predicted and actual observations. Moreover, DCA and CIC indicate that the nomogram has a superior net benefit in predicting poor immune reconstitution. A simple-to-use nomogram containing four routinely collected variables is developed and internally evaluated and can be used to predict the poor immune reconstitution in HIV/AIDS patients after ART.
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Affiliation(s)
- Yi Wang
- Institute of Hepatology and Epidemiology, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Shourong Liu
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Wenhui Zhang
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Liping Zheng
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Er Li
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Mingli Zhu
- Medical Laboratory, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, 310023, China
| | - Dingyan Yan
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Jinchuan Shi
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Jianfeng Bao
- Institute of Hepatology and Epidemiology, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Jianhua Yu
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
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Ng DCE, Liew CH, Tan KK, Chin L, Ting GSS, Fadzilah NF, Lim HY, Zailanalhuddin NE, Tan SF, Affan MA, Nasir FFWA, Subramaniam T, Ali MM, Rashid MFA, Ong SQ, Ch'ng CC. Risk factors for disease severity among children with Covid-19: a clinical prediction model. BMC Infect Dis 2023; 23:398. [PMID: 37308825 DOI: 10.1186/s12879-023-08357-y] [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: 10/12/2022] [Accepted: 05/30/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19. METHODS We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively. CONCLUSION Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.
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Affiliation(s)
- David Chun-Ern Ng
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia.
| | - Chuin-Hen Liew
- Hospital Tuanku Ampuan Najihah, Negeri Sembilan, Ministry of Health, Jalan Melang, 72000, Kuala Pilah, Malaysia
| | - Kah Kee Tan
- Perdana University Seremban Clinical Academic Center, Negeri Sembilan, Jalan Rasah, 70300, Seremban, Malaysia
| | - Ling Chin
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Grace Sieng Sing Ting
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Nur Fadzreena Fadzilah
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Hui Yi Lim
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Nur Emylia Zailanalhuddin
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Shir Fong Tan
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Muhamad Akmal Affan
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | | | - Thayasheri Subramaniam
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Marlindawati Mohd Ali
- Hospital Tuanku Ja'afar, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Mohammad Faid Abd Rashid
- Negeri Sembilan State Health Department, Negeri Sembilan, Ministry of Health, Jalan Rasah, 70300, Seremban, Malaysia
| | - Song-Quan Ong
- Institute for Tropical Biology and Conservation, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Chin Chin Ch'ng
- Clinical Research Centre Hospital Pulau Pinang, Ministry of Health, Jalan Residensi, 10450, Pulau Pinang, Malaysia
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Ramos-Rincón JM, Ventura PS, Casas-Rojo JM, Mauri M, Bermejo CL, de Latierro AO, Rubio-Rivas M, Mérida-Rodrigo L, Pérez-Casado L, Barrientos-Guerrero M, Giner-Galvañ V, Gallego-Lezaun C, Milián AH, Manzano L, Blázquez-Encinar JC, Solís-Marquínez MN, García MG, Lobo-García J, Valente VAR, Roig-Martí C, León-Téllez M, Tellería-Gómez P, González-Juárez MJ, Gómez-Huelgas R, López-Escobar A, Bermejo CL, Núñez-Cortés JM, Santos JMA, Huelgas RG, Corbella X, Pérez FF, Homs N, Montero A, Mora-Luján JM, Rubio-Rivas M, Bandera VA, Alegría JG, Jiménez-García N, del Pino JL, Escalante MDM, Romero FN, Rodriguez VN, Sierra JO, de Blas PA, Cañas CA, Ayuso B, Morejón JB, Escudero SC, Frías MC, Tejido SC, de Miguel Campo B, Pedroche CD, Simon RD, Reyne AG, Veganzones LI, Huerta LJ, Blanco AL, Gonzalo JL, Lora-Tamayo J, Bermejo CL, de la Calle GM, Godoy RM, Perpiña BO, Ruiz DP, Fernández MS, Montes JT, Suárez AMÁ, Vergés CD, Martínez RFM, Aizpuru EMF, Carrasco AG, Amezua CH, Caleya JFL, Martínez DL, del Mar Martínez López M, Zapico AM, Iscar CO, Casado LP, Martínez MLT, Chamorro LMT, Casas LA, de Oña ÁA, Beato RA, Gonzalo LA, Muñoz JA, Oblitas CMA, García CA, Cebrián MB, Corral JB, Guerrero MB, Estrada ADB, Moreno MC, Fernández PC, Carrillo R, Pérez SC, Muñoz EC, Moreno ADC, Carvajal MCC, de Santos S, Gómez AE, Carracedo EF, Jenaro MMFM, Valle FG, Garcia A, Fernandez-Bravo IG, Leoni MEG, Antúnez MG, Narciso CGS, Gurjian AA, Ibáñez LJ, Olleros CL, Mendo CL, García SL, Jimeno VM, Nohales CM, Núñez-Cortés JM, Ledesma SM, Míguez AM, Delgado CM, Ortega LO, Sánchez SP, Virto AP, Sanz MTP, Llorente BP, Ruiz SP, Fernández-Llamazares GS, Macías MT, Samaniego NT, do Rego AT, Garcia MVV, Villarreal G, Etayo MZ, Lara RA, Fernandez IC, García JCC, García García GM, Granados JG, Sánchez BG, Periáñez FJM, Perez MJP, Pérez JLB, Méndez MLS, Rivera NA, Vieitez AC, del Corral Beamonte E, Manglano JD, Mera IF, del Mar Garcia Andreu M, Aseguinolaza MG, Lezaun CG, Laorden CJ, Murgui RM, Sanz MTM, Ayala-Gutiérrez MM, López RB, Fonseca JB, Buonaiuto VA, Martínez LFC, Palacios LC, Muriel CC, de Windt F, Christophel ATFT, Ocaña PG, Huelgas RG, García JG, Oliver JAH, Jansen-Chaparro S, López-Carmona MD, Quirantes PL, Sampalo AL, Lorenzo-Hernández E, Sevilla JJM, Carmona JM, Pérez-Belmonte LM, de Pedro IP, Pineda-Cantero A, Gómez CR, Ricci M, Cánovas JS, Troncoso JÁ, Fernández FA, Quintana FB, Arenzana CB, Molina SC, Candalija AC, Bengoa GD, de Gea Grela A, de Lorenzo Hernández A, Vidal AD, Capitán CF, Iglesias MFG, Muñoz BG, Gil CRH, Martínez JMH, Hontañón V, Hernández MJJ, Lahoz C, Calvo CM, Gutiérrez JCM, Prieto MM, Robles EM, Saldaña AM, Fernández AM, Prieto JMM, Mozo AN, López CMO, Peláez EP, Pampyn MP, Simón MAQ, Ramos Ramos JC, Ruperto LR, Purificación AS, Bueso TS, Torre RS, Abanedes CIS, Tabares YU, Mayoral MV, Manau JV, del Carmen Beceiro Abad M, Romero MAF, Castro SM, Guillan EMP, Nuñez MP, Fontan PMP, de Larriva APA, Espinal PC, Lista JD, Fuentes-Jiménez F, del Carmen Guerrero Martínez M, Vázquez MJG, Torres JJ, Pérez LL, López-Miranda J, Piedra LM, Orge MM, Vinagre JP, Pérez-Martinez P, Vílchez MER, Martínez AR, Cabrera JLR, Torres-Peña JD, Tomás MA, Balaz D, Tur DB, Navarro RC, Pérez PC, Redondo JC, White ED, Espínola ME, Del Barrio LE, Atiénzar PJE, Cervera CG, Núñez DFG, Navarro FG, Galvañ VG, Uranga AG, Martínez JG, Isasi IH, Villar LL, Sempere VM, Cruz JMN, Fernández SP, García JJP, Pleguezuelos RP, Pérez AR, Ripoll JMS, Mira AS, Wikman-Jorgensen P, Ayllón JAA, Artero A, del Mar Carmona Martín M, Valls MJF, de Mar Fernández Garcés M, Belda ABG, Cruz IL, López MM, Sanchis EM, Gandia JM, Roger LP, Belmonte AMP, García AV, Eisenhofer AA, Milla AA, Pérez IB, Gutiérrez LB, Garay JB, Parra JC, Díaz AC, Da Silva EC, Hernández MC, Díaz RC, Sánchez MJC, Gozalo CC, Martínez VCM, Doblado LD, de la Fuente Moral S, de Santiago AD, Yagüe ID, Velasco ID, Duca AM, del Campo PD, López GE, Palomo EE, Cruz AF, Gómez AG, Prieto SG, Revilla BG, Viejo MÁG, Irusta JG, Merino PG, Abreu EVG, Martín IG, Rojas ÁG, Villanueva AG, Jiménez JH, Estéllez FI, del Estal PL, Sáiz MCM, de Mendoza Fernández C, Urbistondo MM, Vera FM, Seirul-lo MM, Pita SM, Sánchez PAM, Hernández EM, Vargas AM, Concha VMT, De La Torre IM, Rubio EM, de Benito RM, Serrano AM, Palomo PN, Pascual IP, Martín-Vegue AJR, Martínez AR, Olleros CR, Montaud AR, Pizarro YR, García SR, de Domingo DR, Ortiz DS, Chica ES, Almena IS, Martin ES, Chen YT, de Ureta PT, Alijo ÁV, Comendador JMV, Núñez JAV, Yeguas IA, Gómez JA, Cuchillo JB, López IB, Clotet NC, Elías AEC, Manuel EC, de Luque CMC, Benbunan CC, Vilan LD, Hernández CD, Peralta EED, Pérez VE, Fernandez-Castelao S, Saavedra MOF, Klepzig JLG, del Rosario Iguarán Bermúdez M, Ferrer EJ, Rodríguez AM, de Pedro AM, Sánchez RÁM, Bailón MM, Álvarez SM, Orantos MJN, Mata CO, García EO, Mata DO, González CO, Perez-Somarriba J, Mateos PP, Muñoz MER, Regaira XR, Gallardo LMR, Fornie IS, Botrán AS, Robles MS, Urbano ME, González AMV, Martínez MV, Monge Monge D, Pasos EMF, García AV, Comet LS, Giménez LL, Samper UA, Repiso GA, Bruñén JMG, Barrio ML, Martínez MAC, Igual JJG, Fenoll RG, García MA, Monge EA, Rodríguez JÁ, Varela CA, Gòdia MB, Molina MB, Vega MB, Curbelo J, de las Heras Moreno A, Godoy ID, Alvarez ACE, Martín-Caro IF, López-Mosteiro AF, Marquez GG, Blanco MJG, del Álamo Hernández YG, Encina CGR, González NG, Rodríguez CG, Martín NLS, Báez MM, Delgado CM, Caballero PP, Serrano JP, Rodríguez LR, Cortés PR, Franco CR, Roy-Vallejo E, Vega MR, Lloret AS, Moreno BS, Alba MS, Ballesteros JS, Somovilla A, Fernández CS, Tirado MV, Marti AV, Pareja JFP, Fraile IP, Blanco AM, del Castillo Cantero R, López JLV, Lorite IR, Martínez RF, García IS, Rangel LS, Álvarez AA, Juarros OA, López AA, Castiñeira CC, Calviño AC, Sánchez MC, Varela RF, Castro SJF, Trigo AP, Jarel RP, Varea FR, Freán IR, Alonso LR, Pensado FJS, Porto DV, Saavedra CC, Gómez JF, López BG, Garrido MSH, Amorós AIL, Gil SL, de los Reyes Pascual Pérez M, Perea NR, García AT, Lobo JA, Casanovas LF, Amigo JL, Fernández MM, Bermúdez IO, Fernández MP, Rhyman N, Piqueras NV, Pedrajas JNA, García AM, Vargas I, Jiménez IA, González MC, Cobos-Siles M, Corral-Gudino L, Cubero-Morais P, Fernández MG, González JPM, Dehesa MP, Espinosa PS, Blanco SC, Gamboa JOM, Mosteiro CS, Asiain AS, Santos JMA, Barrera ABB, Vela BB, Muiño CB, Fernández CB, Hernáiz RC, López IC, Rojo JMC, Troncoso AC, Romano PC, Deodati F, Santiago AE, Sánchez GGC, Guijarro EG, Sánchez FJG, de la Torre PG, de Guzmán García-Monge M, Luordo D, González MM, Bermejo JAM, Valverde CP, Quero JLP, Rojas FR, García LR, Gonzalo ES, Muñoz FJT, de la Sota JV, Martínez JV, Gómez MG, Sánchez PR, Gonzalez GA, Iraurgi AL, Arostegui AA, Martínez PA, Fernández IMP, Becerro EM, Jiménez AI, Núñez CV, López MA, López EG, Losada MSA, Estévez BR, Muñoz AMA, Fernández MB, Cano V, Moreno RC, Garcia-Tenorio FC, Nájera BDT, González RE, Butenegro MPG, Díez AG, Caverzaschi VG, Pedraza PMG, Moraleja JG, Carvajal RH, Aranda PJ, González RL, Caparachini ÁL, Castañeyra PL, Ancin AL, Garcia JDM, Romero CM, Saiz MJM, Moríñigo HM, Nicolás GM, Platon EM, Oliveri F, Ortiz Ortiz E, Rafael RP, Galán PR, Berrocal MAS, de Ávila VSR, Sierra PT, Aranda YU, Clemente JV, Bergua CY, de la Peña Fernández A, Milián AH, Manrique MA, Erdozain AC, Ruiz ALI, Luque FJB, Carrasco-Sánchez FJ, de-Sousa-Baena M, Leal JD, Rubio AE, Huertas MF, Bravo JAG, Macías AG, Jiménez EG, Jiménez AH, Quintero CL, Reguera CM, Marcos FJM, Beamud FM, Pérez-Aguilar M, Jiménez AP, Castaño VR, dedel AlcazarRío AS, Ruiz LT, González DA, de Zabalza IAP, Hernández SA, Sáenz JC, Dendariena B, del Mazo MG, de Narvajas Urra IM, Hernández SM, Fernández EM, Somovilla JLP, Pejenaute ER, Rodríguez-Solís JB, Osorio LC, del Pilar Fidalgo Montero M, Soriano MIF, Rincón EEL, Hermida AM, Carrilero JM, Santiago JÁP, Robledo MS, Rojas PS, Yebes NJT, Vento V, Vaca LFA, Arnanz AA, García OA, González MB, Sanz PB, Llisto AC, de Pedro Baena S, Del Hoyo Cuenda B, Fabregate-Fuente M, Osorio MAG, Sánchez IG, García AG, Cisneros OAL, Manzano L, Martínez-Lacalzada M, Ortiz BM, Rey-García J, González ER, Díaz CS, Fajardo GS, Carantoña CS, Viteri-Noël A, Zhilina Zhilina S, Claudio GMA, Rodríguez VB, Muñoz CC, Pérez AC, Orbes MVC, Sánchez DE, Revuelta SI, Martín MM, González JIM, Oterino JÁM, Alonso LM, Balbuena SP, García MLP, Prados AR, Rodríguez-Alonso B, Alegría ÁR, Ledesma MS, Pérez RJT, Encinar JCB, Cilleros CM, Martínez IJ, Delange TG, González RF, Noya AG, Ceron CH, Avanzini II, Diez AL, Mato PL, Vizcaya AML, Benítez DP, Zemsch MMP, Expósito LP, Bar MP, González LR, Lara LR, Cabañero D, Ballester MC, Fernández PC, Sánchez RG, Escrig MJ, Amela CM, Gómez LP, Navarro CP, Parra JAT, de Almeida CT, Villarejo MEF, Calvo VP, Otero SP, López BG, Frías CA, Romero VM, Pérez LA, Velado EM, González RA, Boixeda R, Fernández Fernández J, Mármol CL, Navarro MP, Guzmán AR, Fustier AS, Castro JL, Reboiro MLL, González CS, Sala ER, Izuel JMP, Zamrani ZK, Diaz HA, Lopez TD, Pego EM, Pérez CM, Ferro AP, Trigo SS, Sambade DS, Ferrin MT, del Carmen Vázquez Friol M, Maneiro LV, Rodríguez BC, Espartero MEG, Rivas LM, de la Sierra Navas Alcántara M, Tirado-Miranda R, Marquínez MNS, García VA, Suárez DB, Arenas NG, García PM, Copa DC, García AÁ, Álvarez JC, Calderón MJM, Noriega RG, Rubia MC, García JL, Martínez LT, Celeiro JF, Aguilar DEO, Riesco IM, Bécares JV, Mateos AB, García AAT, Casamayor JD, Silvera DG, Díaz AA, Carballo CH, Tejera A, Prieto MJM, Muñoz MBM, Del Arco Delgado JM, Díaz DR, Feria MB, Herrera Herrera FJ, de la Luz Padilla Salazar M, Luis RH, Ledezma EMC, del Mar López Gámez M, Hernández LT, Pérez SC, García SGA, Gainett GC, Hidalgo AG, Daza JM, Peraza MH, Santos RA, Bernabeu-Wittel M, Suárez SR, Nieto M, Miranda LG, Mancera RMG, Torre FE, Quiles CH, Guzmán CC, de la Cuesta JD, Vega JET, del Carmen López Ríos M, Jiménez PD, Franco BB, de Juan CJ, Rivero SG, Tenllado JL, Lara VA, Estrada AG, Ena J, Segado JEG, Ferrer RG, Lorenzo VG, Arroyo RM, García MG, Hernández FJV, González ÁLM, Montes BV, Die RMG, Molinero AM, Regidor MM, Díez RR, Sierra BH, García LFD, Acedo IEA, Cano CMS, García VH, Bernal BR, Jiménez JC, Bazán EC, Reniu AC, Grabalosa JR, Solà JF, De Boulle IC, Xancó CG, Núñez OR, Ripper CJ, Gutiérrez AG, Trallero LER, Novo MFA, Lecumberri JJN, Ruiz NP, Riancho J, García IS, Baena PC, Sevilla JE, Padilla LG, Ronquillo PG, Bustos PG, Botías MN, Taboada JR, Rodríguez MR, Alvarez VA, Suárez NM, Suárez SR, Díaz SS, Pérez LS, Gómez MF, Castaño CM, Rodríguez LM, Vázquez C, Estévanez IC, Gutiérrez CY, Sela MM, Cosío SF, Álvaro CMG, García JL, Piñeiro AP, Viera YC, Rodríguez LC, de Juan Alvarez C, Benitez GF, Escudero LG, Torres JM, Escriche PM, Canteli SP, Pérez MCR, Soler JA, Remolar MB, Álvarez AC, Carlotti DD, Gimeno MJE, Juana SF, López PG, Soler MTG, de la Sota DP, Castellanos GP, Catalán IP, Martí CR, Monzó PR, Padilla JR, Gaya NT, Blasco JU, Pascual MAM, Vidal LJ, Conesa AA, Rivas MCA, Alsina MH, Romero JM, Diez-Canseco AMU, Martínez FA, Vásquez EA, Stablé JCE, Belmonte AH, Peiró AM, Goñi RM, Castellanos MCP, Belda BS, Navarro DV, Lombraña AS, Ugartondo JC, Plaza ABM, Asensio AN, Alves BP, López NV, Téllez ML, Epelde F, Torrente I, Vasco PG, Santacruz AR, Muñoz AV, Giner MJE, Calvo-Sotelo AE, Sardón EG, González JG, Salazar LG, Garcia AA, Días IM, Gomez AS, Matos MC, Gaspar SN, Nieto AG, Méndez RG, Álvarez AR, Hernández OP, Ramírez AP, González MCM, Lorite MNN, Navarrete LG, Negrin JCA, González JFA, Jiménez I, Toledo PO, Ponce EM, Torres XTE, González SG, Fernández CN, Gómez PT, Gisbert OA, Llistosella MB, Casanova PC, Flores AG, Hinojo AG, Martínez AIM, del Carmen Nogales Nieves M, Austrui AR, Cervantes AZ, Castro VA, Lomba AMB, Aparicio RB, Morales MF, Villar JMF, Monteagudo MTL, García CP, Ferreira LR, Llovo DS, Feijoo MBV, Romero JAM, de Albornoz JLSC, Pérez MJS, Martín ES, Astrua TC, Giraldo PTG, Juárez MJG, Fernandez VM, Echevarry AVR, Arche JFV, Rivero MGR, Martínez AM, Bernad RV, Limia C, Fernández CA, Fernández AT, Fajardo LP, de Vega Santos T, Ruiz AL, Míguez HM. Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry. Intern Emerg Med 2023; 18:907-915. [PMID: 36680737 PMCID: PMC9862219 DOI: 10.1007/s11739-023-03200-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023]
Abstract
The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.
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Affiliation(s)
| | - Paula Sol Ventura
- Fundacio Institut d’Investigacio en Ciències de La Salut Germans Trias I Pujol (IGTP), 08916 Badalona, Spain
| | - José-Manuel Casas-Rojo
- Internal Medicine Department, Infanta Cristina University Hospital, Parla, 28981 Madrid, Spain
| | - Marc Mauri
- Data Scientist, Kaizen AI, Barcelona, Spain
| | | | | | - Manuel Rubio-Rivas
- Department of Internal Medicine, Bellvitge University Hospital, Hospitalet de Llobregat, Barcelona, Spain
| | | | | | | | - Vicente Giner-Galvañ
- Internal Medicine Department. Hospital, Clínico Universitario de Sant Joan d’Alacant, Alicante, Spain
| | | | | | - Luis Manzano
- Internal Medicine Department, Ramón y Cajal University Hospital, Madrid, Spain
| | | | | | | | | | | | | | | | | | | | - Ricardo Gómez-Huelgas
- Internal Medicine Department, Regional University Hospital of Málaga, Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), Málaga, Spain
| | - Alejandro López-Escobar
- Pediatrics Department, Clinical Research Unit, Hospital Universitario Vithas Madrid La Milagrosa, Fundación Vithas. Madrid, Madrid, Spain
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Padilha DM, Garcia GR, Liveraro GS, Mendes MC, Takahashi ME, Lascala F, Silveira MN, Pozzuto L, Carrilho LA, Guerra LD, Moreira RC, Branbilla SR, Dertkigil SS, Takahashi J, Carvalheira JB. Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis. INFORMATICS IN MEDICINE UNLOCKED 2023; 36:101138. [PMID: 36474601 PMCID: PMC9715454 DOI: 10.1016/j.imu.2022.101138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/17/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
Background and objectives We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19. Methods A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test. Results Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04. Conclusion ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality.
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Affiliation(s)
- Daniela M.H. Padilha
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Gabriel R. Garcia
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - Gianni S.S. Liveraro
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - Maria C.S. Mendes
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil,Department of Internal Medicine, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Maria E.S. Takahashi
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - Fabiana Lascala
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Marina N. Silveira
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Lara Pozzuto
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Larissa A.O. Carrilho
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Lívia D. Guerra
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Rafaella C.L. Moreira
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Sandra R. Branbilla
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Sérgio S.J. Dertkigil
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Jun Takahashi
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - José B.C. Carvalheira
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil,Corresponding author. Division of Oncology, Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Rua Vital Brasil, 80, Cidade Universitária, ZIP Code: 13, 083-888, Campinas, SP, Brazil
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Hwangbo S, Kim Y, Lee C, Lee S, Oh B, Moon MK, Kim SW, Park T. Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record. Front Public Health 2022; 10:1007205. [PMID: 36518574 PMCID: PMC9742409 DOI: 10.3389/fpubh.2022.1007205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
Background As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient. Objective This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms. Methods The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups (i.e., 4-group classification) and using two groups after regrouping the four severity level into two (i.e., binary classification). Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE. Results Of the four maximum severity groups, the moderate group had the highest percentage (1,115 patients; 49.5%). As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (http://statgen.snu.ac.kr/software/nomogramDaeguCovid/). Conclusions We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field.
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Affiliation(s)
- Suhyun Hwangbo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Yoonjung Kim
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Chanhee Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Seoul, South Korea
| | - Bumjo Oh
- Department of Family Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Min Kyong Moon
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Shin-Woo Kim
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Statistics, Seoul National University, Seoul, South Korea
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Vaidya P, Alilou M, Hiremath A, Gupta A, Bera K, Furin J, Armitage K, Gilkeson R, Yuan L, Fu P, Lu C, Ji M, Madabhushi A. An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study. FRONTIERS IN RADIOLOGY 2022; 2:781536. [PMID: 36437821 PMCID: PMC9696643 DOI: 10.3389/fradi.2022.781536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (MRM), clinical (MCM), and combined clinical-radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. METHODS We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T . RESULTS The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The MRCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over MCM. CONCLUSION The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amogh Hiremath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, United States
| | - Jennifer Furin
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Keith Armitage
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Robert Gilkeson
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Lei Yuan
- Department of Information Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mengyao Ji
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
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Zhang H, Zhong F, Wang B, Liao M. A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics. Future Virol 2022. [PMID: 35371273 PMCID: PMC8862443 DOI: 10.2217/fvl-2020-0193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 01/24/2022] [Indexed: 12/15/2022]
Abstract
Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893–0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.
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Affiliation(s)
- Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Feiyang Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Binchen Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
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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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Kondakov A, Berdalin A, Lelyuk V, Gubskiy I, Golovin D. Risk Factors of In-Hospital Mortality in Non-Specialized Tertiary Center Repurposed for Medical Care to COVID-19 Patients in Russia. Diagnostics (Basel) 2021; 11:diagnostics11091687. [PMID: 34574028 PMCID: PMC8470792 DOI: 10.3390/diagnostics11091687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
The purpose of our study is to investigate the risk factors of in-hospital mortality among patients who were admitted in an emergency setting to a non-specialized tertiary center during the first peak of coronavirus disease in Moscow in 2020. The Federal Center of Brain and Neurotechnologies of the Federal Medical and Biological Agency of Russia was repurposed for medical care for COVID-19 patients from 6th of April to 16th of June 2020 and admitted the patients who were transported by an ambulance with severe disease. In our study, we analyzed the data of 635 hospitalized patients aged 59.1 ± 15.1 years. The data included epidemiologic and demographic characteristics, laboratory, echocardiographic and radiographic findings, comorbidities, and complications of the COVID-19, developed during the hospital stay. Results of our study support previous reports that risk factors of mortality among hospitalized patients are older age, male gender (OR 1.91, 95% CI 1.03–3.52), previous myocardial infarction (OR 3.15, 95% CI 1.47–6.73), previous acute cerebrovascular event (stroke, OR = 3.78, 95% CI 1.44–9.92), known oncological disease (OR = 3.39, 95% CI 1.39–8.26), and alcohol abuse (OR 6.98, 95% CI 1.62–30.13). According to the data collected, high body mass index and smoking did not influence the clinical outcome. Arterial hypertension was found to be protective against in-hospital mortality in patients with coronavirus pneumonia in the older age group. The neutrophil-to-lymphocyte ratio showed a significant increase in those patients who died during the hospitalization, and the borderline was found to be 2.5. CT pattern of “crazy paving” was more prevalent in those patients who died since their first CT scan, and it was a 4-fold increase in the risk of death in case of aortic and coronal calcinosis (4.22, 95% CI 2.13–8.40). Results largely support data from other studies and emphasize that some factors play a major role in patients’ stratification and medical care provided to them.
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11
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Moon HJ, Kim K, Kang EK, Yang HJ, Lee E. Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram. J Korean Med Sci 2021; 36:e248. [PMID: 34490756 PMCID: PMC8422041 DOI: 10.3346/jkms.2021.36.e248] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/22/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. METHODS This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. RESULTS Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. CONCLUSION The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.
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Affiliation(s)
- Hui Jeong Moon
- SCH Biomedical Informatics Research Unit, Soonchunhyang University Seoul Hospital, Seoul, Korea
- STAT Team, C&R Research Inc., Seoul, Korea
| | - Kyunghoon Kim
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Eun Kyeong Kang
- Department of Pediatrics, Dongguk University Ilsan Hospital, Goyang, Korea
| | - Hyeon-Jong Yang
- Department of Pediatrics, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea.
| | - Eun Lee
- Department of Pediatrics, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea.
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12
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Tu C, Wang G, Geng Y, Guo N, Cui N, Liu J. Establishment of a Clinical Nomogram Model to Predict the Progression of COVID-19 to Severe Disease. Ther Clin Risk Manag 2021; 17:553-561. [PMID: 34103920 PMCID: PMC8179801 DOI: 10.2147/tcrm.s308961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 05/17/2021] [Indexed: 01/10/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aims to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely and efficient manner. Methods This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed using the discrimination curve. Results Risk factors, including demographic data, symptoms, laboratory and image findings, were recorded for the 202 patients. Eight of the 53 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. AUC, sensitivity, and specificity were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset. Conclusion We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.
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Affiliation(s)
- Changli Tu
- Department of Pulmonary and Critical Care Medicine (PCCM), The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, People's Republic of China
| | - Guojie Wang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, People's Republic of China
| | - Yayuan Geng
- Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Beijing, People's Republic of China
| | - Na Guo
- Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Beijing, People's Republic of China
| | - Ning Cui
- Medical Imaging Center, Shiyan Taihe Hospital, Shiyan, People's Republic of China
| | - Jing Liu
- Department of Pulmonary and Critical Care Medicine (PCCM), The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, People's Republic of China
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13
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Angeli E, Dalto S, Marchese S, Setti L, Bonacina M, Galli F, Rulli E, Torri V, Monti C, Meroni R, Beretta GD, Castoldi M, Bombardieri E. Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome. Eur J Radiol 2021; 137:109612. [PMID: 33662842 PMCID: PMC7907738 DOI: 10.1016/j.ejrad.2021.109612] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/16/2021] [Accepted: 02/20/2021] [Indexed: 12/15/2022]
Abstract
Purpose To evaluate the prognostic role of chest computed tomography (CT), alone or in combination with clinical and laboratory parameters, in COVID-19 patients during the first peak of the pandemic. Methods A retrospective single-center study of 301 COVID-19 patients referred to our Emergency Department (ED) from February 25 to March 29, 2020. At presentation, patients underwent chest CT and clinical and laboratory examinations. Outcomes included discharge from the ED after improvement/recovery (positive outcome), or admission to the intensive care unit or death (poor prognosis). A visual quantitative analysis was formed using two scores: the Pulmonary Involvement (PI) score based on the extension of lung involvement, and the Pulmonary Consolidation (PC) score based on lung consolidation. The prognostic value of CT alone or integrated with other parameters was studied by logistic regression and ROC analysis. Results The impact of the CT PI score [≥15 vs. ≤ 6] on predicting poor prognosis (OR 5.71 95 % CI 1.93−16.92, P = 0.002) was demonstrated; no significant association was found for the PC score. Chest CT had a prognostic role considering the PI score alone (AUC 0.722) and when evaluated with demographic characteristics, comorbidities, and laboratory data (AUC 0.841). We, therefore, developed a nomogram as an easy tool for immediate clinical application. Conclusions Visual analysis of CT gives useful information to physicians for prognostic evaluations, even in conditions of COVID-19 emergency. The predictive value is increased by evaluating CT in combination with clinical and laboratory data.
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Affiliation(s)
- Enzo Angeli
- Department of Radiology, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | - Serena Dalto
- Department of Oncology, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | - Stefano Marchese
- Department of Radiology, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | - Lucia Setti
- Department of Nuclear Medicine, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | - Manuela Bonacina
- Department of Nuclear Medicine, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | - Francesca Galli
- Laboratory of Methodology for Clinical Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Eliana Rulli
- Laboratory of Methodology for Clinical Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Valter Torri
- Laboratory of Methodology for Clinical Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Cinzia Monti
- Department of Radiology, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | - Roberta Meroni
- Department of Radiology, Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
| | | | - Massimo Castoldi
- Humanitas Gavazzeni, Via Gavazzeni, 21, 24125, Bergamo, BG, Italy.
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Huang Y, Zhang Z, Liu S, Li X, Yang Y, Ma J, Li Z, Zhou J, Jiang Y, He B. CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia. BMC Med Imaging 2021; 21:31. [PMID: 33596844 PMCID: PMC7887546 DOI: 10.1186/s12880-021-00564-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 12/28/2020] [Indexed: 01/08/2023] Open
Abstract
Background In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. Methods A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. Results The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). Conclusions CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00564-w.
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Affiliation(s)
- Yilong Huang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Zhenguang Zhang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Siyun Liu
- Precision Health Institution, PDx, GE Healthcare (China), Beijing, 100176, China
| | - Xiang Li
- Department of Radiology, The 3rd Peoples' Hospital of Kunming, Kunming, 650000, China
| | - Yunhui Yang
- Department of Medical Imaging, People's Hospital of Xishuangbanna Dai Autonomous Prefecture, Xishuangbanna, 666100, China
| | - Jiyao Ma
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Zhipeng Li
- Medical Imaging Department, Yunnan Provincial Infectious Disease Hospital, Kunming, 650000, China
| | - Jialong Zhou
- MRI Department, The First People's Hospital of Yunnan Province, Kunming, 650000, China
| | - Yuanming Jiang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Bo He
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
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15
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Devie A, Kanagaratnam L, Perotin JM, Jolly D, Ravey JN, Djelouah M, Hoeffel C. COVID-19: A qualitative chest CT model to identify severe form of the disease. Diagn Interv Imaging 2020; 102:77-84. [PMID: 33419693 PMCID: PMC7746121 DOI: 10.1016/j.diii.2020.12.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 12/11/2020] [Indexed: 02/06/2023]
Abstract
Chest CT helps identify patients with severe COVID-19 using only three qualitative features. A qualitative model based on three qualitative variables can avoid calculating semi-quantitative total CT score. New Early Warning Score 2 is comparable to the CT score for identification of severe forms of COVID-19.
Purpose The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. Materials and methods A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68 ± 14 (SD) years (range: 24–96 years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. Results Central involvement of lung parenchyma (P < 0.001), area of consolidation (P < 0.008), air bronchogram sign (P < 0.001), bronchiectasis (P < 0.001), traction bronchiectasis (P < 0.011), pleural effusion (P < 0.026), large involvement of either one of the upper lobes or of the middle lobe (P < 0.001) and total CT score ≥ 15 (P < 0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR) = 2.473], central involvement [OR = 2.760], pleural effusion [OR = 2.699]) and the semi-quantitative model (total CT score ≥ 15 [OR = 3.342], central involvement [OR = 2.344], pleural effusion [OR = 2.754]) with AUC of 0.722 (95% CI: 0.638–0.806) vs. 0.739 (95% CI: 0.656–0.823), respectively (P = 0.209). Conclusion We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.
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Affiliation(s)
- Antoine Devie
- Department of Radiology, Reims University Hospital, 51092 Reims, France.
| | - Lukshe Kanagaratnam
- Clinical Research Department, Reims University Hospital, 51092 Reims, France
| | - Jeanne-Marie Perotin
- Department of Respiratory Diseases, INSERM UMRS 1250, Reims University Hospital, 51092 Reims, France
| | - Damien Jolly
- Clinical Research Department, Reims University Hospital, 51092 Reims, France
| | - Jean-Noël Ravey
- Department of Radiology, Grenoble University Hospital, 38700 Grenoble, France
| | - Manel Djelouah
- Department of Radiology, Reims University Hospital, 51092 Reims, France
| | - Christine Hoeffel
- Department of Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, University of Reims Champagne-Ardenne, 51100 Reims, France
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Chen X, Peng F, Zhou X, Zhu J, Chen X, Gong Y, Shupeng W, Niu W. Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China. Aging (Albany NY) 2020; 13:1608-1619. [PMID: 33318316 PMCID: PMC7880337 DOI: 10.18632/aging.202261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/08/2020] [Indexed: 01/08/2023]
Abstract
Objectives: We aimed to identify potential risk factors for severe or critical coronavirus disease 2019 (COVID-19) and establish a prediction model based on significant factors. Methods: A total of 370 patients with COVID-19 were consecutively enrolled at The Third People’s Hospital of Yichang from January to March 2020. COVID-19 was diagnosed according to the COVID-19 diagnosis and treatment plan released by the National Health and Health Committee of China. Effect-size estimates are summarized as odds ratio (OR) and 95% confidence interval (CI). Results: 326 patients were diagnosed with mild or ordinary COVID-19, and 44 with severe or critical COVID-19. After propensity score matching and statistical adjustment, eight factors were significantly associated with severe or critical COVID-19 (p <0.05) relative to mild or ordinary COVID-19. Due to strong pairwise correlations, only five factors, including diagnostic delay (OR, 95% CI, p: 1.08, 1.02 to 1.17, 0.048), albumin (0.82, 0.75 to 0.91, <0.001), lactate dehydrogenase (1.56, 1.14 to 2.13, 0.011), white blood cell (1.27, 1.08 to 1.50, 0.004), and neutrophil (1.40, 1.16 to 1.70, <0.001), were retained for model construction and performance assessment. The nomogram model based on the five factors had good prediction capability and accuracy (C-index: 90.6%). Conclusions: Our findings provide evidence for the significant contribution of five independent factors to the risk of severe or critical COVID-19, and their prediction was reinforced in a nomogram model.
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Affiliation(s)
- Xin Chen
- Department of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Peng
- Department of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaoni Zhou
- Department of Pulmonary Disease, The Third People's Hospital of Yichang, Yichang, Hubei, China
| | - Jiang Zhu
- Department of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xin Chen
- Department of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yingying Gong
- Department of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Wang Shupeng
- Department of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Wenquan Niu
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
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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: 1622] [Impact Index Per Article: 405.5] [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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