Background: Accurate prediction of neurorehabilitation outcomes following Spinal Cord Injury (SCI) is crucial for optimizing healthcare resource allocation and improving rehabilitation strategies. Artificial Neural Networks (ANNs) may identify complex prognostic factors in patients with SCI. However, the influence of psychological variables on rehabilitation outcomes remains underexplored despite their potential impact on recovery success. Methods: A cohort of 303 patients with SCI was analyzed with an ANN model that employed 17 input variables, structured into two hidden layers and a single output node. Clinical and psychological data were integrated to predict functional outcomes, which were measured by the Spinal Cord Independence Measure (SCIM) at discharge. Paired Wilcoxon tests were used to evaluate pre-post differences and linear regression was used to assess correlations, with Pearson's coefficient and the Root Mean Square Error calculated. Results: Significant improvements in SCIM scores were observed (21.8 +/- 15.8 at admission vs. 57.4 +/- 22.5 at discharge, p < 0.001). The model assigned the highest predictive weight to SCIM at admission (10.3%), while psychological factors accounted for 36.3%, increasing to 40.9% in traumatic SCI cases. Anxiety and depression were the most influential psychological predictors. The correlation between the predicted and actual SCIM scores was R = 0.794 for the entire sample and R = 0.940 for traumatic cases. Conclusions: The ANN model demonstrated the strong impact, especially for traumatic SCI, of psychological factors on functional outcomes. Anxiety and depression emerged as dominant negative predictors. Conversely, self-esteem and emotional regulation functioned as protective factors increasing functional outcomes. These findings support the integration of psychological assessments into predictive models to enhance accuracy in SCI rehabilitation outcomes.

The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study / Mascanzoni, Marta; Luciani, Alessia; Tamburella, Federica; Iosa, Marco; Lena, Emanuela; Di Fonzo, Sergio; Pisani, Valerio; Di Lucente, Maria Carmela; Caretti, Vincenzo; Sideli, Lucia; Cuzzocrea, Gaia; Scivoletto, Giorgio. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 13:23(2024). [10.3390/jcm13237114]

The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study

Iosa, Marco;
2024

Abstract

Background: Accurate prediction of neurorehabilitation outcomes following Spinal Cord Injury (SCI) is crucial for optimizing healthcare resource allocation and improving rehabilitation strategies. Artificial Neural Networks (ANNs) may identify complex prognostic factors in patients with SCI. However, the influence of psychological variables on rehabilitation outcomes remains underexplored despite their potential impact on recovery success. Methods: A cohort of 303 patients with SCI was analyzed with an ANN model that employed 17 input variables, structured into two hidden layers and a single output node. Clinical and psychological data were integrated to predict functional outcomes, which were measured by the Spinal Cord Independence Measure (SCIM) at discharge. Paired Wilcoxon tests were used to evaluate pre-post differences and linear regression was used to assess correlations, with Pearson's coefficient and the Root Mean Square Error calculated. Results: Significant improvements in SCIM scores were observed (21.8 +/- 15.8 at admission vs. 57.4 +/- 22.5 at discharge, p < 0.001). The model assigned the highest predictive weight to SCIM at admission (10.3%), while psychological factors accounted for 36.3%, increasing to 40.9% in traumatic SCI cases. Anxiety and depression were the most influential psychological predictors. The correlation between the predicted and actual SCIM scores was R = 0.794 for the entire sample and R = 0.940 for traumatic cases. Conclusions: The ANN model demonstrated the strong impact, especially for traumatic SCI, of psychological factors on functional outcomes. Anxiety and depression emerged as dominant negative predictors. Conversely, self-esteem and emotional regulation functioned as protective factors increasing functional outcomes. These findings support the integration of psychological assessments into predictive models to enhance accuracy in SCI rehabilitation outcomes.
2024
artificial neural networks; functional outcome; psychological variables; spinal cord injury
01 Pubblicazione su rivista::01a Articolo in rivista
The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study / Mascanzoni, Marta; Luciani, Alessia; Tamburella, Federica; Iosa, Marco; Lena, Emanuela; Di Fonzo, Sergio; Pisani, Valerio; Di Lucente, Maria Carmela; Caretti, Vincenzo; Sideli, Lucia; Cuzzocrea, Gaia; Scivoletto, Giorgio. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 13:23(2024). [10.3390/jcm13237114]
File allegati a questo prodotto
File Dimensione Formato  
Mascanzoni_Role_psychological_variables_2024.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 640.92 kB
Formato Adobe PDF
640.92 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1737707
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact