Publication: Estimación Bayesiana del área bajo la curva ROC del dímero D y la Escala de Ginebra para el diagnóstico de tromboembolismo pulmonar en pacientes con Covid-19 en la urgencia hospitalaria
Loading...
Official URL
Full text at PDC
Publication Date
2022-06-18
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Exportar
Abstract
El tromboembolismo pulmonar (TEP) es una posible complicación de los pacientes con COVID-19, cuya frecuencia de aparición está aumentada en ellos respecto a población general. Su diagnóstico no es sencillo y existen recursos como la escala de Ginebra y el dímero D que pueden ayudar al mismo, pero no están validados en pacientes infectados por SARS-CoV-2. Para el diagnóstico de TEP se utiliza la angiografía pulmonar mediante tomografía computarizada, que constituye un gold standard imperfecto. La estadística bayesiana proporciona el marco teórico necesario para poder evaluar la validez de pruebas diagnósticas en ausencia de una prueba perfecta de confirmación de referencia. En el presente estudio se explora la aplicación de éstas y se comparan sus resultados con los proporcionados por la estadística frecuentista. Para su aplicación fue necesario aplicar una transformación de Box-Cox para que la distribución muestral se ajustara a una distribución gamma. Los resultados más precisos los proporcionó el modelo con gold standard y distribución de probabilidad a priori informativa, en tanto que los modelos con distribución de probabilidad a priori no informativa y/o sin gold standard presentaron problemas de convergencia y un peor ajuste a los datos. Los resultados obtenidos fueron consistentes entre las diferentes técnicas estadísticas utilizadas y se puede concluir que la escala de Ginebra no es útil y el dímero D sí lo es. Con esta prueba diagnóstica, un umbral en torno a 700 ng/mL parece apropiado como punto de corte de cribado para descartar TEP en estos pacientes.
Pulmonary thromboembolism (PE) is a possible complication of patients with COVID-19, whose frequency of appearance is increased in them compared to the general population. Its diagnosis is not easy and there are resources such as the Geneva scale and D-dimer that can help to achieve it, but they have not been validated in patients infected with SARS-CoV-2. Computed tomography pulmonary angiography is used for the diagnosis of PE, which is an imperfect gold standard. Bayesian statistics provide the necessary theoretical framework to be able to assess the validity of diagnostic tests in the absence of a reference perfect confirmation test. In the present study, the application of it is explored and its results are compared with those provided by frequentist statistics. For its application, it was necessary to apply a Box-Cox transformation to reach a sample distribution adjusted to a gamma distribution. The most accurate results were provided by the model with a gold standard and informative a priori probability distribution, while the models with non-informative a priori probability distribution and/or without a gold standard presented convergence problems and a worse fit to the data. The results obtained were consistent between the different statistical techniques used and it can be concluded that the Geneva scale is not useful, and the D-dimer already is. With this diagnostic test, a threshold around 700 ng/mL seems appropriate as a screening cut-off point to rule-out PE in these patients.
Pulmonary thromboembolism (PE) is a possible complication of patients with COVID-19, whose frequency of appearance is increased in them compared to the general population. Its diagnosis is not easy and there are resources such as the Geneva scale and D-dimer that can help to achieve it, but they have not been validated in patients infected with SARS-CoV-2. Computed tomography pulmonary angiography is used for the diagnosis of PE, which is an imperfect gold standard. Bayesian statistics provide the necessary theoretical framework to be able to assess the validity of diagnostic tests in the absence of a reference perfect confirmation test. In the present study, the application of it is explored and its results are compared with those provided by frequentist statistics. For its application, it was necessary to apply a Box-Cox transformation to reach a sample distribution adjusted to a gamma distribution. The most accurate results were provided by the model with a gold standard and informative a priori probability distribution, while the models with non-informative a priori probability distribution and/or without a gold standard presented convergence problems and a worse fit to the data. The results obtained were consistent between the different statistical techniques used and it can be concluded that the Geneva scale is not useful, and the D-dimer already is. With this diagnostic test, a threshold around 700 ng/mL seems appropriate as a screening cut-off point to rule-out PE in these patients.
Description
UCM subjects
Unesco subjects
Keywords
Citation
Ministerio de Sanidad (Gobierno de España). Información microbiológica acerca de SARS-CoV-2. [Internet]. [citado 13 de mayo de 2022]. Disponible en: https://www.sanidad.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov/documentos/20220113_MICROBIOLOGIA.pdf
Hu B, Guo H, Zhou P, Shi Z-L. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. 2021;19(3):141-54.
Centro de Coordinación de Alertas y Emergencias Sanitarias. Valoración de la declaración del brote de nuevo coronavirus 2019 (n-CoV) una Emergencia de Salud Pública de Importancia Internacional (ESPII). [Internet]. [citado 13 de mayo de 2022]. Disponible en: https://www.sanidad.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov/documentos/Valoracion_declaracion_emergencia_OMS_2019_nCoV.pdf
Liu Y, Yan L-M, Wan L, Xiang T-X, Le A, Liu J-M, et al. Viral dynamics in mild and severe cases of COVID-19. Lancet Infect Dis. 2020;20(6):656-7.
Ministerio de Sanidad (Gobierno de España). Información clínica COVID-19. [Internet]. [citado 13 de mayo de 2022]. Disponible en: https://www.sanidad.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov/documentos/20211028_CLINICA.pdf
Miró Ò, Llorens P, Jiménez S, Piñera P, Burillo-Putze G, Martín A, et al. Frequency of five cardiovascular/hemostatic entities as primary manifestations of SARS-CoV-2 infection: Results of the UMC-19-S2. Int J Cardiol. 2021;330:268-72.
Konstantinides S V., Meyer G, Bueno H, Galié N, Gibbs JSR, Ageno W, et al. 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European respiratory society (ERS). Eur Heart J. 2020;41(4):543-603.
Miró Ò, Jiménez S, Mebazaa A, Freund Y, Burillo-Putze G, Martín A, et al. Pulmonary embolism in patients with COVID-19: incidence, risk factors, clinical characteristics, and outcome. Eur Heart J. 2021;42(33):3127-42.
Qanadli SD, Hajjam M El, Mesurolle B, Barré O, Bruckert F, Joseph T, et al. Pulmonary Embolism Detection: Prospective Evaluation of Dual-Section Helical CT versus Selective Pulmonary Arteriography in 157 Patients. Radiology.noviembre de 2000;217(2):447-55.
Covello B, McKeon B. Fluoroscopic Angiography Assessment, Protocols, and Interpretation [Internet]. StatPearls. 2022 [citado 15 de mayo de 2022]. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/33760526
Stein PD, Fowler SE, Goodman LR, Gottschalk A, Hales CA, Hull RD, et al. Multidetector Computed Tomography for Acute Pulmonary Embolism. N Engl J Med. 2006;354(22):2317-27.
Román Meza A, Alfaro Fernández P. Utilidad de la angiografía pulmonar por tomografía computarizada en las salas de emergencia de un hospital nacional de EsSalud. Rev Medica Hered. 2019;30(1):27.
Pollack C V., Schreiber D, Goldhaber SZ, Slattery D, Fanikos J, O’Neil BJ, et al. Clinical Characteristics, Management, and Outcomes of Patients Diagnosed With Acute Pulmonary Embolism in the Emergency Department. J Am Coll Cardiol. 2011;57(6):700-6.
Wells PS, Anderson DR, Rodger M, Ginsberg JS, Kearon C, Gent M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost. 2000;83(3):416-20.
Le Gal G, Righini M, Roy P-M, Sanchez O, Aujesky D, Bounameaux H, et al. Prediction of Pulmonary Embolism in the Emergency Department: The Revised Geneva Score. Ann Intern Med. 2006;144(3):165-71.
Ceriani E, Combescure C, Le gal G, Nendaz M, Perneger T, Bounameaux H, et al. Clinical prediction rules for pulmonary embolism: A systematic review and meta-analysis. J Thromb Haemost. 2010;8(5):957-70.
Kirsch B, Aziz M, Kumar S, Burke M, Webster T, Immadi A, et al. Wells Score to Predict Pulmonary Embolism in Patients with Coronavirus Disease 2019. Am J Med. 2021;134(5):688-90.
Perrier A, Roy P-M, Aujesky D, Chagnon I, Howarth N, Gourdier A-L, et al. Diagnosing pulmonary embolism in outpatients with clinical assessment, D-Dimer measurement, venous ultrasound, and helical computed tomography: a multicenter management study. Am J Med. 2004;116(5):291-9.
Crawford F, Andras A, Welch K, Sheares K, Keeling D, Chappell FM. D-dimer test for excluding the diagnosis of pulmonary embolism. Cochrane Database Syst Rev. 2016;2016(8).
Courtney DM, Steinberg JM, McCormick JC. Prospective diagnostic accuracy assessment of the HemosIL HS D-dimer to exclude pulmonary embolism in emergency department patients. Thromb Res. 2010;125(1):79-83.
Carrier M, Righini M, Djurabi RK, Huisman M V, Perrier A, Wells PS, et al. VIDAS D-dimer in combination with clinical pre-test probability to rule out pulmonary embolism. A systematic review of management outcome studies. Thromb Haemost. 2009;101(5):886-92.
Righini M, Van Es J, Den Exter PL, Roy P-M, Verschuren F, Ghuysen A, et al. Age-Adjusted D-Dimer Cutoff Levels to Rule Out Pulmonary Embolism. JAMA. 2014;311(11):1117.
Righini M, Le Gal G, De Lucia S, Roy P-M, Meyer G, Aujesky D, et al. Clinical usefulness of D-dimer testing in cancer patients with suspected pulmonary embolism. Thromb Haemost. 2006;95(4):715-9.
Chabloz P, Reber G, Boehlen F, Hohlfeld P, De Moerloose P. TAFI antigen and D-dimer levels during normal pregnancy and at delivery. Br J Haematol. 2001;115(1):150-2.
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-62.
CSLI. Quantitative D-dimer for the exclusion of venous thromboembolic disease; approved guideline. CSLI document H69-A. 940 West Valley Road, Suite 1400, Wayne, PA 19087, USA; 2011.
Ventura-Díaz S, Quintana-Pérez J V., Gil-Boronat A, Herrero-Huertas M, Gorospe-Sarasúa L, Montilla J, et al. A higher D-dimer threshold for predicting pulmonary embolism in patients with COVID-19: a retrospective study. Emerg Radiol. 2020;27(6):679-89.
Korevaar DA, Es J Van. Pulmonary embolism in COVID-19: D-dimer threshold selection should not be based on maximising Youden s index. Eur Respir J. 2021;57(2):2020-1.
Suh YJ, Hong H, Ohana M, Bompard F, Revel MP, Valle C, et al. Pulmonary Embolism and Deep Vein Thrombosis in COVID-19: A Systematic Review and Meta-Analysis. Radiology. 2021;298(2):E70-80.
Pepe MS. The Statistical Evaluation of Medical Test for Clasification and Prediction. Primera ed. Oxford: Oxford Univ Press; 2003. 66-129 p.
Broemeling LD. Bayesian Biostatistics and Diagnostic Medicine. Primera ed. Houston, USA.: Chapman and Hall/CRC; 2007. 165-182 p.
Wagner C. White Paper: INNOVANCE D-Dimer Assay for exclusion of VTE and use of an age-dependent cutoff: a critical consideration. [Internet]. Siemens Healthcare Diagnostics Inc. 2020 [citado 15 de mayo de 2022]. Disponible en: https://cdn0.scrvt.com/39b415fb07de4d9656c7b516d8e2d907/1800000007329173/0732825165e1/INNOVANCE_D-DimerWP_Final_SNG_1800000007329173.pdf
Lesaffre E, Lawson AB. Bayesian Biostatistics. Segunda ed. Chichester: John Wiley and Sons; 2013. 3-19, 41, 104-223, 460-483 p.
Castro-Sandoval P, Barrós-González R, Galindo-Martín MA, Ruiz-Grinspan MS, Rodríguez Leal CM. Uso de escalas predictivas de tromboembolia pulmonar en un servicio de urgencias. Med Clin (Barc). 2022;(Aceptada para publicación con DOI: https://doi.org/10.1016/j.medcli.2022.03.016).
Asociación Médica Mundial. Declaración de Helsinki de la AMM - Principios éticos para las investigaciones médicas en seres humanos. [Internet]. 2013 [citado 15 de mayo de 2022]. Disponible en: https://www.wma.net/es/policies-post/declaracion-de-helsinki-de-la-amm-principios-eticos-para-las-investigaciones-medicas-en-seres-humanos/
Jefatura del Estado. Ley 14/2007, de 3 de julio, de Investigación biomédica. [Internet]. BOE-A-2007-12945. 2007 [citado 15 de mayo de 2022]. Disponible en: https://www.boe.es/eli/es/l/2007/07/03/14/con
Jefatura del Estado. Ley Orgánica 3/2018, de 5 de diciembre, de Protección de Datos Personales y garantía de los derechos digitales. [Internet]. BOE-A-2018-16673. 2018 [citado 15 de mayo de 2022]. Disponible en: https://www.boe.es/eli/es/lo/2018/12/05/3
Royston JP. An Extension of Shapiro and Wilk’s W Test for Normality to Large Samples. Appl Stat. 1982;31(2):115.
Evers M. How to draw a QQ plot in R? [Internet]. [citado 12 de mayo de 2022]. Disponible en: https://stackoverflow.com/questions/50092506/how-to-draw-a-qq-plot-in-r
Faraggi D, Reiser B, Schisterman EF. ROC curve analysis for biomarkers based on pooled assessments. Stat Med. 2003;22(15):2515-27.
Best DJ, Roberts DE. Algorithm AS 89: The Upper Tail Probabilities of Spearman’s Rho. Appl Stat. 1975;24(3):377.
Box GEP, Cox DR. An Analysis of Transformations. J R Stat Soc Ser B. 1964;26(2):211-43.
de la Guia González AV. La transformación Box-Cox [Internet]. E-prints Complutense. 2014. p. 1-42. Disponible en: https://eprints.ucm.es/id/eprint/47245/
R Core Team, Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria; 2020.
Marsaglia G, Tsang WW, Wang J. Evaluating Kolmogorov’s Distribution. J Stat Softw. 2003;8(18):1-4.
Dorfman DD, Berbaum KS, Metz CE, Lenth R V., Hanley JA, Dagga HA. Proper receiver operating characteristic analysis: The bigamma model. Acad Radiol. 1997;4(2):138-49.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12(1):77.
López-Ratón M, Rodríguez-Álvarez MX, Suárez CC, Sampedro FG. OptimalCutpoints : An R Package for Selecting Optimal Cutpoints in Diagnostic Tests. J Stat Softw. 2014;61(8).
Choi YK, Johnson WO, Collins MT, Gardner IA. Bayesian inferences for receiver operating characteristic curves in the absence of a gold standard. J Agric Biol Environ Stat. 2006;11(2):210-29.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837-45.
Gamerman D. MAS451: Principles of Statistics [Internet]. Markov Chain Monte Carlo. 1997 [citado 5 de diciembre de 2021]. p. 1-34. Disponible en: https://web.archive.org/web/20220121020215/https://www.staff.ncl.ac.uk/d.j.wilkinson/teaching/mas451/notes.pdf
Susi García R, Espínola Vílchez R. Apuntes de Cálculo de Probabilidades. Primera ed. Universidad Complutense de Madrid, editor. Madrid: Facultad de Estudios Estadísticos; 24-25 p.
Gelman A, Carlin JB, Stern HS, Dunson BD, Vehtari A, Rubin DB. Bayesian Data Analysis. Tercera ed. Boca Raton: CRC Press; 2014. 261-350 p.
Gilks WR, Richardson S, Spiegelhalter DJ. Introducing Markov chain Monte Carlo. Primera ed.
Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC; 1996. 1-17 p.
Members of OpenBUGS Project Management Group. OpenBUGS. 2014.
Sturtz S, Ligges U, Gelman A. R2WinBUGS : A Package for Running WinBUGS from R. J Stat Softw. 2005;12(3).
Jafarzadeh SR, Johnson WO, Utts JM, Gardner IA. Bayesian estimation of the receiver operating characteristic curve for a diagnostic test with a limit of detection in the absence of a gold standard. Stat Med. 2010;29(20):2090-106.
Albert J. Bayesian Computation with R. Segunda ed. Baltimore, USA: Springer; 2009. 19-39 p.
Manly BFJ. Randomization, Bootstrap and Monte Carlo Methods in Biology: Texts in Statistical Science. Tercera ed. Boca Raton: Chapman and Hall/CRC; 2007. 41-80 p.
Dorny P, Phiri I., Vercruysse J, Gabriel S, Willingham A., Brandt J, et al. A Bayesian approach for estimating values for prevalence and diagnostic test characteristics of porcine cysticercosis. Int J Parasitol. 2004;34(5):569-76.
Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527.
Lijmer JG. Empirical Evidence of Design-Related Bias in Studies of Diagnostic Tests. JAMA. 1999;282(11):1061.
Microsoft. Microsoft Office Excel. 2022.
Mouhat B, Besutti M, Bouiller K, Grillet F, Monnin C, Ecarnot F, et al. Elevated D-dimers and lack of anticoagulation predict PE in severe COVID-19 patients. Eur Respir J. 2020;56(4):1-11.
Silva BV, Jorge C, Plácido R, Mendonça C, Urbano ML, Rodrigues T, et al. Pulmonary embolism and COVID-19: A comparative analysis of different diagnostic models performance. Am J Emerg Med. 2021;50:526-31.
Korevaar DA, Aydemir I, Minnema MW, Azijli K, Beenen LF, Heijmans J, et al. Routine screening for pulmonary embolism in COVID-19 patients at the emergency department: impact of D-dimer testing followed by CTPA. J Thromb Thrombolysis. 2021;52(4):1068-73.
Mitra R, Müller P, Johnson WO, de Carvalho M, de Carvalho VI, Jara A. Nonparametric Bayesian Inference in Biostatistics. Primera ed. Mitra R, Müller P, editores. Cham: Springer International Publishing; 2015. 3-54, 327-344 p.
Goudie RJB, Turner RM, De Angelis D, Thomas A. MultiBUGS : A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference. J Stat Softw. 2020;95(7).
Erkanli A, Sung M, Jane Costello E, Angold A. Bayesian semi-parametric ROC analysis. Stat Med. 2006;25(22):3905-28.
Branscum AJ, Johnson WO, Hanson TE, Gardner IA. Bayesian semiparametric ROC curve estimation and disease diagnosis. Stat Med. 2008;27(13):2474-96.