medigraphic.com
SPANISH

Medimay

ISSN 2520-9078 (Electronic)
  • Contents
  • View Archive
  • Information
    • General Information        
    • Directory
  • Publish
    • Instructions for authors        
  • medigraphic.com
    • Home
    • Journals index            
    • Register / Login
  • Mi perfil

2022, Number 3

<< Back Next >>

Revista de Ciencias Médicas de la Habana 2022; 29 (3)

Validation of a prognostic scale of risk of death in elderly adults with acute brain infarct

Mendieta PMD, Bender BJE, González LI
Full text How to cite this article

Language: Spanish
References: 17
Page: 396-405
PDF size: 394.00 Kb.


Key words:

ictus, brain infarct, cerebrovascular disease, scale, prognosis, estimator.

ABSTRACT

Introduction: Acute brain infarct is responsible of a high mortality in elderly adults. So far, there is not any predictive scale of risk of death which evaluates entirely the patient.
Objective: To validate a scale of risk of death, in elderly adult patients, with acute brain infarct.
Methods: A longitudinal and prospective study was carried out, from January 1st to December 31st, 2019. The research was performed in two stages. In a first moment, 145 patients who suffered from acute brain infarct were studied at ¨Leopoldito Martínez¨ General Teaching Hospital and in a second moment, the 120 patients from ¨Aleida Fernández Chardiet¨ Clinical Surgical Hospital. In the process the Receiver Operating Characteristics Curve was applied, a binary logistic regression test was performed and a s correlation intraclass model was applied.
Results: It was established 10 as cut point. The area under the curve was 0.94, the model achieved a sensitivity of 0.97; the square root of Cox and Snell, showed values of 0.74 the square root of Nagelkerke was of 0.98, the logarithm of verisimilitude was visualized that was of – 5.545, a significant fact in the three values. The unique measures of absolute agreement showed a value of 0.86, very significant value p=0.000.
Conclusions: The scale to estimate scale of risk of death, in elderly adult patients, with acute brail infarct is valid.


REFERENCES

  1. Wilkins E, Wilson L, Wickramasinghe K, Bhatnagar P, Leal J, Luengo-Fernandez R, et al. European Cardiovascular Disease Statistics 2017[Internet]. Brussels: European Heart Network; 2017. [citado 19 Ago 2022]. Disponible en:https://ehnheart. org/images/CVD-statistics-report-August-2017. pdf

  2. Norrving B, Barrick J, Davalos A, Dichgans M, Cordonnier C, Guekht A, et al. Action plan for stroke in Europe 2018-2030. Eur Stroke J[Internet]. 2018[citado 19 Ago 2022]; 3(4): 309-36. Disponible en: https://www. ncbi. nlm. nih. gov/pmc/articles/PMC6571507/

  3. Cerda J, Cifuentes L. Uso de curvas ROC en investigación clínica. Aspectos teórico-prácticos. Rev Chil Infect [Internet]. 2012 [citado 19 Ago 2022];29(2):138-41. Disponible en: https://scielo. conicyt. cl/scielo. php?script=sci_arttext&pid=S0716-10182012000200003

  4. Bacallao Gallestey J. Las curvas ROC y las medidas de detectabilidad para la validación de predictores del rendimiento docente. Educ Med Super [Internet]. 1996 dic [citado 31 Ene 2022];10(1):1-2. Disponible en: http://scielo. sld. cu/scielo. php?script=sci_arttext&pid=S086421411996000100001&lng=es

  5. Shengping Y, Gilbert B. The receiver operating characteristic (ROC) curve. The Southwest Respiratory and Critical Care Chronicles [Internet]. 2017[citado 31 Ene 2022];5(19):34–6. Disponible en:https://pulmonarychronicles. com/index. php/pulmonarychronicles/article/view/391/848

  6. Fernández AI, Llaugel FA. Evaluación del uso de modelos de regresión logística para el diagnóstico de instituciones financieras. Ciencia y Sociedad [Internet]. 2011[citado 19 Ago 2022];35(4):590-627. Disponible en: https://www. redalyc. org/articulo. oa?id=87022786002

  7. Sánchez-Catalejo Ramírez E. Regresión Logística en Salud Pública[Internet]. Granada: Escuela Andaluza de Salud Pública; 2000 [citado 19 Ago 2022]. Disponible en: https://www. easp. es/project/regresion-logistica-en-salud-publica/

  8. Roque Cruz MJ. Modelos de regresión logística multinomial de la calidad de fibra de alpaca huacaya en función de sus características: sexo y edad - Corani, Carabaya, Puno-2017[Tesis]. PUNO-Perú: Universidad Nacional del Altiplano; 2018[citado 19 Ago 2022]. Disponible en: http://repositorio. unap. edu. pe/bitstream/handle/UNAP/7755/Roque_Cruz_Maria_Juaquina. pdf?sequence=1&isAllowed=y

  9. Fuentes Fernández S. Regresión Logistica[Tesis]. Madrid: Universidad Autónomo; 2011. [citado 19 Ago 2022]. Disponible en: https://docplayer. es/21085069-Santiago-de-la-fuente-fernandez-regresion-logistica. html

  10. Papavasileiou V, Milionis H, Michel P et al. ASREAL Score Predicts 5 years Dependence and Mortality in Acute Isquemic Stroke. Stroke [Internet]. 2013 Dic [citado 19 Ago 2022]; 44(6):1616–20. Disponible en: https://www. ahajournals. org/doi/10. 1161/STROKEAHA. 113. 001047?url_ver=Z39. 88-2003&rfr_id=ori:rid:crossref. org&rfr_dat=cr_pub%20%200pubmed

  11. Russell J. Chander, Bonnie YK, Lam, Xuling L, Aloysius YN. Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke. Scientific Reports [Internet]. 2017[citado 19 Ago 2022];7(12441). Disponible en: https://www. nature. com/articles/s41598-017-12755-z. pdf

  12. Chaudhary D, Abedi V. Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event. Front Neurol [Internet]. 2019 [citado 19 Ago 2022]; 12:10-6. Disponible en: https://www. ncbi. nlm. nih. gov/pmc/articles/PMC6861423/

  13. Arévalo T, Bryan E. Comparación entre las escalas de coma de Glasgow, NIHSS y FOUR como predictoras de mortalidad a 30 días en pacientes adultos con ictus isquémico [Tesis]. Trujillo: Universidad Nacional de Trujillo; 2019 [citado 19 Ago 2022]. Disponible en: http://dspace. unitru. edu. pe/handle/UNITRU/15399

  14. Sagaró del Campo NM, Zamora Matamoros L. Técnicas estadísticas para identificar posibles relaciones bivariadas. Rev Cub Anest Rean [Internet]. 2020 [citado 19 Ago 2022]; 19(2): [aprox. 9 p. ]. Disponible en: http://revanestesia. sld. cu/index. php/anestRean/article/view/603

  15. Fernández P, Díaz S. La fiabilidad de las mediciones clínicas: El análisis de concordancia para variables numéricas [Internet]. Países Bajos: Fisterra; 2011. [citado 19 Ago 2022]. Disponible en: https://www. fisterra. com/formacion/metodologia-investigacion/la-fiabilidad-mediciones-clinicas-analisis-concordancia-para-variables-numericas/

  16. Martínez CG, Cortés ME, Pérez Fernández AC. Metodología para el análisis de correlación y concordancia en equipos de mediciones similares. Universidad y Sociedad [Internet]. 2016[citado 19 Ago 2022];8 (4):65-70. Disponible en: http://scielo. sld. cu/scielo. php?script=sci_arttext&pid=S2218-36202016000400008

  17. Akoglu H. User's guide to correlation coefficients. Turkish Journal of Emergency Medicine[Internet]. 2018[citado 19 Ago 2022];18(3):91-3. Disponible en: https://www. ncbi. nlm. nih. gov/pmc/articles/PMC6107969/




2020     |     www.medigraphic.com

Mi perfil

C?MO CITAR (Vancouver)

Revista de Ciencias Médicas de la Habana. 2022;29