Bermúdez YGJ, Barreto FEE, Chaljub BE, López CY, Naranjo UAM, Rabassa López-Calleja MA, Lagomasino HÁL, Mirabal RR, Lastayo CRG, Iturralde EAV, Allende GA, Quintero FYF

Language: Spanish
References: 20
Page: 392-401
PDF size: 951.29 Kb.

ABSTRACT

Introduction: Phenomena prediction through prognostic scales is a valuable tool in medical sciences nowadays and it should be included in the decision-making process. Predicting postoperative mediastinitis allows to count on resources for its prevention.
Objective: To build a prognostic scale to stratify the risk of suffering from postop-erative mediastinitis.
Methods: A case-control study for the risk factors of postoperative mediastinitis was carried out at the Cardiocentro Ernesto Guevara from Santa Clara, Cuba. After the logistic regression, the model was obtained and from it, the predictors to ob-tain the Cuban prognostic scale of postoperative mediastinitis PREDICMED were included and weighted, which was validated through several methods.
Results: This scale was obtained, counting on six predictors and two risk strata. Its performance was analyzed through adjustment, calibration and determination of its discriminating capacity, showing good results. Internal validation was carried out through the data division method and its capacity was compared in both sub-sets (development and validation) showing no differences. Its good construct va-lidity was demonstrated, since there were no differences between the predicted and the observed probabilities. Its contents validity was also analyzed by experts. Finally, its criteria validity was determined when compared with another similar scale (Medscore). PREDICMED showed a very good discriminatory capacity (area under the curve 0.962) as well as high values of sensitivity (84.62%) and specificity (92.31%).
Conclusions: The Cuban prognostic scale PREDICMED, to stratify the risk of post-operative mediastinitis showed good validation parameters and it was able to stratify the risk in not high and high.

REFERENCES

1. Molina JE, Nelson EC, Smith RR. Treatment of postoperative sternal dehiscence with mediastinitis: twenty-four-year use of a single method. J Thorac Cardiovasc Surg. 2006;132(4):782-7. [DOI]

2. González R, Raffo M, Vera M, Alarcón E, Saldías R, Gyhra A, et al. Mediastinitis postquirúrgica en cirugía cardíaca. Rev Chil Cir. 2005;57(3):203-8.

3. Barthelemy A. Post-sternotomy mediastinitis. En: Mathieu D, editor. Handbook on Hyperbaric Medicine. Netherlands: Springer; 2006. p. 567-76.

4. van Wingerden JJ, Ubbink DT, van der Horst CM, de Mol BA. Poststernotomy mediastinitis: a classification to initiate and evaluate reconstructive management based on evidence from a structured review. J Cardiothorac Surg [Internet]. 2014 [citado 18/04/2020];9:179. Disponible en: https://doi. org/10. 1186/s13019-014-0179-4

5. Borracci RA, Arribalzaga EB. Modelo basado en lógica borrosa para estratificar el riesgo de la cirugía cardíaca. Rev Argent Cardiol 2015;83(4):305-13. (DOI)

6. Bermúdez-Yera GJ, Naranjo-Ugalde AM, Rabassa MA, Lagomasino-Hidalgo AL, Chaljub-Bravo E, Barreto-Fiu EE. Modelo predictivo de mediastinitis postoperatoria en cirugía cardiovascular. Cir Cardiov. 2019;26(6):277-82. (DOI)

7. Nogues I, Piccininni RA, Donato MM, Gambarte MJ, Ciambrone GM, Giorgini J, et al. Desarrollo y validación externa de un puntaje predictivo de mediastinitis posoperatoria en cirugía cardíaca derivado del registro multicéntrico CONAREC XVI. Rev Argent Cardiol. 2019;87(4):290-5. (DOI)

8. Nieto Cabrera M. Diseño y validación de un modelo predictivo de mediastinitis en cirugía cardiaca [Tesis Doctoral]. Madrid: Universidad Complutense de Madrid [Internet]; 2014 [citado 22/04/2020]. Disponible en: https://eprints. ucm. es/id/eprint/29771/1/T35996. pdf

9. Gatti G, Dell'Angela L, Barbati G, Benussi B, Forti G, Gabrielli M, et al. A predictive scoring system for deep sternal wound infection after bilateral internal thoracic artery grafting. Eur J Cardiothorac Surg. 2016;49(3):910-7. (DOI)

10. Oliveira Sá MPB, Ferraz PE, Freire Soares A, Albuquerque Miranda RG, Lopes Araújo M, Vasconcelos Silva F, et al. Development and validation of a stratification tool for predicting risk of deep sternal wound infection after coronary artery bypass grafting at a Brazilian hospital. Braz J Cardiovasc Surg. 2017;32(1):1-7. (DOI)

11. dos Santos Oliveira F, Oliveira de Freitas LD, Rabelo-Silva ER, da Costa LM, Karam Kalil RA, Pereira de Moraes MA. Predictors of Mediastinitis Risk after Coronary Artery Bypass Surgery: Applicability of Score in 1. 322 Cases. Arq Bras Cardiol. 2017;109(3):207-212. (DOI)

12. Carvajal A, Centeno C, Watson R, Martínez M, Sanz Rubiales A. ¿Cómo validar un instrumento de medida de la salud? An Sist Sanit Navar. 2011;34(1):63-72.

13. Fernández Félix BM. Validación interna de modelos predictivos de regresión logística. Comando Validation (Stata) (Tesis de Maestría). Madrid: Universidad Complutense de Madrid [Internet]; 2018 [citado 26/04/2020]. Disponible en: https://eprints. ucm. es/id/eprint/49486/1/TFM_Borja%20Fern%C3%A1ndez%20Felix. pdf

14. López-Roldán P, Fachelli S. Metodología de la investigación social cuantitativa. 1ª ed. Barcelona, Universitat Autònoma de Barcelona; 2015.

15. Machín Rodríguez JC. Factores predictores de mediastinitis aguda en cirugía cardiovascular, protocolo de prevención y algoritmos diagnóstico y terapéutico (Tesis Doctoral). Santiago de Cuba [Internet]: Universidad de Ciencias Médicas de Santiago de Cuba [citado 26/042020]; 2011. Disponible en: http://tesis. sld. cu/index. php?P=FullRecord&ID=277

16. Gatti G, Rochon M, Raja SG, Luzzati R, Dreas L, Pappalardo A. Predictive models of surgical site infections after coronary surgery: insights from a validation study on 7090 consecutive patients. J Hosp Infect. 2019;102(3):277-86. [DOI]

17. de Oliveira Sá MPB, Soares Figueira E, Andrade Santos C, Figueiredo OJ, Albuquerque Lima RO, Gonçalves de Rueda F, et al. Validation of MagedanzSCORE as a predictor of mediastinitis after coronary artery bypass graft surgery. Rev Bras Cir Cardiovasc. 2011;26(3):386-92. (DOI)

18. Ruiz Hidalgo D. Desarrollo y validación de un modelo predictivo de mortalidad a corto plazo en ancianos ingresados por patología médica (Tesis Doctoral). Barcelona: Universitat Autònoma de Barcelona [Internet]; 2016 [citado 30/04/2020]. Disponible en: https://hdl. handle. net/10803/399340

19. López Fernández R, Crespo Hurtado E, Crespo Borges TP, Fadul Franco JS, García Saltos MB, Juca Maldonado FX, et al (eds). Expertos y prospectiva en la investigación pedagógica. Cienfuegos: Universo Sur; 2016. p. 46-109.

20. Nieto-Cabrera M, Fernández-Pérez C, García-González I, Martin-Benítez JC, Ferrero J, Bringas M, et al. Med-Score 24: A multivariable prediction model for poststernotomy mediastinitis 24 hours after admission to the intensive care unit. J Thorac Cardiovasc Surg. 2018;155(3):1041-51. (DOI)