Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed. Patients were randomly allocated to training (75% of patients) and testing (25% of patients) groups. Feature score weights were derived by rounding up the regression coefficients from the multivariable logistic regression model. Variables in the final scoring calculator were automatically selected through the ML process to optimize predictive performance.
Results: Eight features were included in the scoring system: sagittal deformity angular ratio (sDAR) of ≥15 (score = 2), type-3 spinal cord shape (score = 2), conus level below L2 (score = 2), cervical upper instrumented vertebra (score = 2), preoperative upright largest thoracic Cobb angle of ≥75° (score = 2), preoperative lower-extremity motor deficit (score = 2), preoperative upright largest thoracic kyphosis of ≥80° (score = 1), and total deformity angular ratio (tDAR) of ≥25 (score = 1). Higher cumulative scores were associated with increased rates of cord-level IONM data loss: patients with a cumulative score of ≤2 had a cord-level IONM data loss rate of 0.9%, whereas those with a score of ≥7 had a loss rate of 86%. When evaluated in the testing group, the scoring system achieved an accuracy of 93%, a sensitivity of 75%, a specificity of 94%, and an AUC (area under the receiver operating characteristic curve) of 0.898.
Conclusions: This is the first study to provide an ML-derived preoperative scoring system that predicts cord-level IONM data loss during pediatric and adult spinal deformity surgery with >90% accuracy.
Level Of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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http://dx.doi.org/10.2106/JBJS.24.00386 | DOI Listing |
J Med Internet Res
January 2025
Commonwealth Scientific and Industrial Research Organisation, Adelaide, Australia.
Background: A greater understanding of the effectiveness of digital self-management programs and their ability to support longer-term weight loss is needed.
Objective: This study aimed to explore the total weight loss and patterns of weight loss of CSIRO (Commonwealth Scientific and Industrial Research Organisation) Total Wellbeing Diet Online members during their first 12 months of membership and examine the patterns of platform use associated with greater weight loss.
Methods: Participants were Australian adults who joined the program between October 2014 and June 2022 and were classified as longer-term members, meaning they completed at least 12 weeks of the program, had baseline and 12-week weight data, and had a paid membership of ≥1 year (N=24,035).
J Bone Joint Surg Am
November 2024
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed.
Rev Bras Enferm
January 2025
Universidade Federal de Santa Catarina, Colégio de Aplicação. Santa Catarina, Santa Catarina, Brazil.
Objective: To analyze the new roles of community health workers as outlined in the 2017 National Primary Care Policy (PNAB) from the perspectives of both nurses and community health workers.
Methods: This qualitative study involved nurses and community health workers from Family Health teams, conducted through semi-structured interviews via videoconference between August 2021 and April 2022. The data were analyzed using thematic content analysis.
Epidemiol Serv Saude
January 2025
Universidade de Brasília, Brasília, DF, Brazil.
Objective: To estimate measles-mumps-rubella vaccination coverage, delay and loss to follow-up in children up to 24 months old living in Brazilian cities.
Methods: Surveys and questionnaires with a retrospective cohort of live births in 2017-2018, analyzing vaccination coverage and sociodemographic data of children and families, based on vaccination card records and interviews.
Results: Valid coverage of first dose was 90.
Rev Bras Enferm
January 2025
Universidade Franciscana. Santa Maria, Rio Grande do Sul, Brazil.
Objectives: to compare the sociodemographic and clinical severity indicators of hospitalized people with HIV in relation to clinical outcomes and urgent hospital admission.
Methods: a retrospective cohort study was conducted with 102 medical records of HIV-infected individuals hospitalized in a hospital in southern Brazil. In addition to descriptive analysis, Fisher's exact test, Pearson's Chi-square, and logistic regression were used.
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