Related Experiment Video
Updated: Mar 27, 2026
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
Published on: September 30, 2020
Modeling Long-Term Depression Trajectories After Spinal Cord Injury Using Irregularly Sampled Assessments and
Alejandro García-Rudolph1,2,3, Maria Dolors Soler1,2,3, Anna Gilabert1,2,3
1Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.
Abstract:
PurposeTo identify and characterize latent trajectories of depression using growth mixture modeling (GMM) applied to irregularly timed assessments spanning up to 20 years post-injury, examine baseline rehabilitation variables significantly associated with trajectory membership, and determine predictors of sustained depressive burden.MethodsThis retrospective observational cohort study included adults with traumatic or non-traumatic spinal cord injury admitted for inpatient rehabilitation within 3 months post-injury (2005-2023), with follow-up extending until May 2025. Depressive symptoms were assessed using the Hospital Anxiety and Depression Scale (HADS-D) at admission, discharge, and follow-up, totaling 3,258 assessments (n = 679 patients). GMM identified latent trajectories while accommodating irregularly spaced assessments. Predictors of trajectory membership were analyzed through multivariable regression with quantified model discrimination.ResultsA 3-class GMM solution provided optimal fit (entropy=0.75). Most participants (81.6%) followed a stable low-depression trajectory (Class 1). A borderline depression trajectory (Class 2; 8.4%) remained persistently elevated, while a probable depression trajectory (Class 3; 10.0%) displayed delayed worsening peaking at 5-7 years post-injury. Class 3 included a significantly higher proportion of non-traumatic injuries (63%) and females (44.1%), with most patients (85.3%) showing no depressive symptoms during rehabilitation, but later exhibiting a marked increase in depressive burden. Logistic regression predicting Class 2 achieved good discrimination (AUC=0.81; 95% CI, 0.65-0.97), identifying baseline depressive symptoms, tetraplegia, female sex, and primary level of education as significant predictors.ConclusionsIrregularly sampled follow-ups revealed distinct depression trajectories, including delayed-onset risk. Findings emphasize early rehabilitation-based screening and long-term monitoring to target follow-up and psychological support.
