Gender-Related Factors Influence the Subjective Perception of Deformity in Patients Undergoing Surgery for Idiopathic Scoliosis.
Davide BizzocaGiuseppe SolarinoAnna Maria MorettiLorenzo MorettiPasquale DramisinoAndrea PiazzollaBiagio MorettiPublished in: Journal of personalized medicine (2023)
The present study aims to depict the importance of gender-related factors in the subjective perception of spine deformity in adolescents undergoing posterior instrumented fusion for scoliosis. Patients undergoing posterior spinal instrumentation and fusion (PSF) for idiopathic adolescent scoliosis (AIS) were recruited. The following data were recorded: gender, age, parents' civil status, Tegner Activity Scale (TAS), body mass index (BMI), concomitant diseases, and history of neuropsychological disorders. Each patient underwent clinical and radiological evaluations according to the protocol used at our institution. All the patients were assessed before surgery using the following Patient-Reported Outcome Measures (PROMs): the Italian version of the revised Scoliosis Research Society-22 patient questionnaire (SRS-22R), the Quality-of-Life Profile for Spinal Deformities (QLPSDs) questionnaire, and the Spinal Appearance Questionnaire (SAQ). The present study recruited 80 patients (male: 19, female: 61). A significant correlation was observed between BMI, TAS, and subjective perception scores. A worse deformity perception was observed in female patients and patients with divorced parents. Gender-related factors impact the subjective perception of spine deformity in patients undergoing PSF for AIS. Specific assessment and correction are needed to improve postoperative outcomes in these patients.
Keyphrases
- patients undergoing
- end stage renal disease
- patient reported
- body mass index
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- mental health
- patient reported outcomes
- young adults
- peritoneal dialysis
- randomized controlled trial
- minimally invasive
- spinal cord
- type diabetes
- skeletal muscle
- machine learning
- atrial fibrillation
- cross sectional
- big data
- weight gain
- data analysis
- mild cognitive impairment
- childhood cancer