NeuralCipher Research overview
The text below is the source article’s original abstract under a verified reuse license. cc by
ObjectiveThis study aimed to develop and validate a predictive model for the decline in treatment efficacy among Parkinson's disease (PD) patients based on clinical characteristics and biological markers, providing a basis for early risk identification and personalized therapeutic strategies.MethodsA retrospective study was conducted on 500 PD patients admitted to our hospital between January 2021 and December 2024. The patients were randomly divided into a training set (n = 350) and a validation set (n = 150) at a 7:3 ratio. Demographic characteristics, clinical rating scales, and biological markers were collected for all patients. In the training set, univariate analysis was performed to screen variables associated with treatment efficacy decline. After variable selection using LASSO regression, multivariate logistic regression analysis was performed to identify independent predictors. Predictive models, including random forest (RF), support vector machine (SVM), and gradient boosting, were constructed using Python 3.8.5 and the scikit-learn library. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and the optimal model was selected based on key predictor importance.ResultsNo significant differences in baseline characteristics were observed between the training and validation sets (all p > 0.05). A multivariate logistic regression analysis identified the total MDS-UPDRS score, MDS-UPDRS II (activities of daily living), MDS-UPDRS IV (motor complications), PDQ-39 score, E. coli/Lactobacillus ratio, fecal lactoferrin, and fecal calprotectin as independent risk factors (all p p ConclusionThe RF model incorporating clinical and biological markers effectively predicts decline in treatment efficacy among PD patients, with fecal calprotectin, fecal lactoferrin, and the E. coli/Lactobacillus ratio serving as key predictors.
Scientific assessment
Research question
Assess the question within the original study’s scope.
Methods & sample
Check the source article for study design and sample details.
Key findings
An editorially approved findings summary is not yet published for this record.
Limitations
Generalizability, bias and clinical meaning require separate assessment.
Methods, samples and clinical conclusions are not inferred from bibliographic metadata.