Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

  1. Araceli Rodríguez Vico 1
  2. Fernando Sánchez Hernández 2
  3. L. López Mesonero 3
  4. María Begoña García Cenador 3
  5. María Navelonga Moreno García 4
  1. 1 Facultad de Enfermería y Fisioterapia. Universidad de Salamanca.
  2. 2 Profesor en la Facultad de Enfermería y Fisioterapia. Universidad de Salamanca.
  3. 3 Facultad de Medicina. Universidad de Salamanca
  4. 4 Facultad de Ciencias (Informática y Automática). Universidad de Salamanca
Enfermería global: revista electrónica trimestral de enfermería

ISSN: 1695-6141

Year of publication: 2023

Issue Title: #69 Enero

Volume: 22

Issue: 1

Pages: 1-37

Type: Article

DOI: 10.6018/EGLOBAL.530591 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

More publications in: Enfermería global: revista electrónica trimestral de enfermería


Cited by

  • Scopus Cited by: 0 (08-11-2023)
  • Dimensions Cited by: 0 (23-03-2023)

SCImago Journal Rank

(Indicator corresponding to the last year available on this portal, year 2022)
  • Year 2022
  • SJR Journal Impact: 0.155
  • Best Quartile: Q4
  • Area: Nursing (miscellaneous) Quartile: Q4 Rank in area: 130/161

Índice Dialnet de Revistas

(Indicator corresponding to the last year available on this portal, year 2022)
  • Year 2022
  • Journal Impact: 0.580
  • Field: ENFERMERÍA Quartile: C1 Rank in field: 3/43

Scopus CiteScore

(Indicator corresponding to the last year available on this portal, year 2022)
  • Year 2022
  • CiteScore of the Journal : 0.8
  • Area: Nursing (all) Percentile: 25


(Data updated as of 23-03-2023)
  • Total citations: 0
  • Recent citations: 0


Nurses are often asked to predict factors that influence post-stroke outcome by the patient and family. Many studies have been carried out in order to determine the factors that influence the neurological status of the post-stroke patient at the moment of the discharge from the hospital. However, machine learning techniques have not been used for this purpose. Therefore, with the objective of obtaining association rules of neurological prognosis, a double analysis, both clinical and with machine learning techniques of the possible associations of factors that influence the neurological status of the post-stroke patients has been carried out. The Apriori algorithm detected several association rules with high confidence (≥ 95%), from which the following pattern: In patients in the age range of 50-80 years, the association of a NIHSS between 11 and 15 points (intermediate/low NIHSS), along with thrombectomy, leads to recovery ad integrum at discharge. With the SMOTE resampling technique, the 100% confidence was reached for the association of high NIHSS (>20) and involvement of the carotid and basilar arteries, with a dire prognosis (exitus). These rules confirm, for the first time with machine learning, the importance of the association of some predictors, in the post-stroke prognosis. The knowledge by the nurses of these association rules can successfully improve stroke outcome. In addition, the role of nurses in education programs that teach knowledge of risk factors and stroke prognosis becomes essential.

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