Cardiovascular Effects of Spinal Cord Stimulation: The Highs, the Lows, and the Don't Knows.

BACKGROUND AND OBJECTIVES: There are many potential etiologies of impaired cardiovascular control, from chronic stress to neurodegenerative conditions or central nervous system lesions. Since 1959, spinal cord stimulation (SCS) has been reported to modulate blood pressure (BP), heart rate (HR), and HR variability (HRV), yet the specific stimulation sites and parameters to induce a targeted cardiovascular (CV) change for mitigating abnormal hemodynamics remain unclear. To investigate the ability and parameters of SCS to modulate the CV, we reviewed clinical studies using SCS with reported HR, BP, or HRV findings. MATERIALS AND METHODS: A keyword-based electronic search was conducted through MEDLINE, Embase, and PubMed data bases, last searched on February 3, 2023. Inclusion criteria were studies with human participants receiving SCS with comparison with SCS turned off, with reporting of either HR, HRV, or BP findings. Non-English studies, conference abstracts, and studies not reporting standalone effects of SCS when comparing SCS with non-SCS interventions were excluded. Results were plotted for visual analysis. When available, participant-specific stimulation parameters and effects were extracted and quantitatively analyzed using ordinary least squares regression. RESULTS: A total of 59 studies were included in this review; 51 studies delivered SCS invasively through implanted/percutaneous leads. Eight studies used noninvasive, transcutaneous electrodes. We found numerous reports of cervical, high thoracic, and mid-to-low thoracolumbar SCS increasing resting BP, and cervical/mid-to-low thoracolumbar SCS decreasing BP. The effect of SCS location on HR and HRV was equivocal. We were unable to analyze stimulation parameters owing to inadequate parameter reporting in many publications. CONCLUSIONS: Our findings suggest CV neuromodulation, particularly BP modulation, with SCS to be a promising frontier. Further research with larger randomized controlled trials and detailed reporting of SCS parameters will be necessary for appropriate evaluation of SCS as a CV therapy.

Machine Learning Model for Chest Radiographs: Using Local Data to Enhance Performance.

PURPOSE: To develop and assess the performance of a machine learning model which screens chest radiographs for 14 labels, and to determine whether fine-tuning the model on local data improves its performance. Generalizability at different institutions has been an obstacle to machine learning model implementation. We hypothesized that the performance of a model trained on an open-source dataset will improve at our local institution after being fine-tuned on local data. METHODS: In this retrospective, institutional review board approved study, an ensemble of neural networks was trained on open-source datasets of chest radiographs for the detection of 14 labels. This model was then fine-tuned using 4510 local radiograph studies, using radiologists' reports as the gold standard to evaluate model performance. Both the open-source and fine-tuned models' accuracy were tested on 802 local radiographs. Receiver-operator characteristic curves were calculated, and statistical analysis was completed using DeLong's method and Wilcoxon signed-rank test. RESULTS: The fine-tuned model identified 12 of 14 pathology labels with area under the curves greater than .75. After fine-tuning with local data, the model performed statistically significantly better overall, and specifically in detecting six pathology labels (P < .01). CONCLUSIONS: A machine learning model able to accurately detect 14 labels simultaneously on chest radiographs was developed using open-source data, and its performance was improved after fine-tuning on local site data. This simple method of fine-tuning existing models on local data could improve the generalizability of existing models across different institutions to further improve their local performance.

A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia.

RATIONALE AND OBJECTIVES: Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden. MATERIALS AND METHODS: We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL. RESULTS: Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349). CONCLUSION: Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.

Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression.

BACKGROUND: Enhanced prediction of progression in secondary progressive multiple sclerosis (SPMS) could improve clinical trial design. Machine learning (ML) algorithms are methods for training predictive models with minimal human intervention. OBJECTIVE: To evaluate individual and ensemble model performance built using decision tree (DT)-based algorithms compared to logistic regression (LR) and support vector machines (SVMs) for predicting SPMS disability progression. METHODS: SPMS participants (n = 485) enrolled in a 2-year placebo-controlled (negative) trial assessing the efficacy of MBP8298 were classified as progressors if a 6-month sustained increase in Expanded Disability Status Scale (EDSS) (≥1.0 or ≥0.5 for a baseline of ≤5.5 or ≥6.0 respectively) was observed. Variables included EDSS, Multiple Sclerosis Functional Composite component scores, T2 lesion volume, brain parenchymal fraction, disease duration, age, and sex. Area under the receiver operating characteristic curve (AUC) was the primary outcome for model evaluation. RESULTS: Three DT-based models had greater AUCs (61.8%, 60.7%, and 60.2%) than independent and ensemble SVM (52.4% and 51.0%) and LR (49.5% and 51.1%). CONCLUSION: SPMS disability progression was best predicted by non-parametric ML. If confirmed, ML could select those with highest progression risk for inclusion in SPMS trial cohorts and reduce the number of low-risk individuals exposed to experimental therapies.