Dataset

Dataset: The development and internal validation of a model to predict functional recovery after trauma

Graaf, de, M. (Creator), Reininga, I. (Creator), Heineman, E. (Creator), El Moumni, M. (Creator), University of Groningen, 3-Mar-2019

Dataset

Description

A prospective single-center longitudinal cohort study. Patients were assessed at 6 weeks and 12 months post-injury.
Patients that presented at the emergency department with an acute traumatic injury, were prompted for participation. Patients that completed the assessments at 6 weeks and 12 months post injury were included. Exclusion criteria: age < 18, age > 65, pathologic fractures, injuries that resulted in severe neurologic deficits. The predicted outcome, functional recovery, was defined as a Short Musculoskeletal Function Assessment (SMFA-NL) Problems with Daily Activities (PDA) subscale ≤ 12.2 points at 12 months post-injury (Dutch population norm). Predictors were: gender, age, living with partner, number of chronic health conditions, SMFA-NL PDA score 6 weeks post-injury, ICU admission, length of stay in hospital, injury severity score, occurrence of complications and treatment type. All predictors were obtained before 6 weeks post-injury. Missing data were multiply imputed. Predictor variables were selected using backward stepwise multivariable logistic regression. A total of 246 patients were included.
Date made available3-Mar-2019
PublisherUniversity of Groningen
Geographical coverageThe Netherlands
Access to the dataset Open
Contact researchdata@rug.nl

    Keywords on Datasets

  • Health, functional recovery, trauma, Dutch Short Musculoskeletal Function Assessment, surgery, social context, chronic health conditions
Related Publications
  1. The development and internal validation of a model to predict functional recovery after trauma

    de Graaf, M. W., Reininga, I. H. F., Heineman, E. & El Moumni, M., 14-Mar-2019, In : PLoS ONE. 14, 3, 16 p., 0213510.

    Research output: Contribution to journalArticleAcademicpeer-review

View all (1) »

ID: 94903051