Machine learning-based stratification of mild cognitive impairment in Parkinson’s disease: a multicenter cross-sectional analysis
Notice bibliographique
Résumé
BACKGROUND: Cognitive impairment is a prominent non-motor manifestation of Parkinson's disease (PD) and is associated with reduced quality of life, increased mortality, and higher healthcare utilization. We aimed to develop and externally validate a machine-learning model, trained on Montreal Cognitive Assessment (MoCA)-based Movement Disorder Society (MDS) Level I labels, that estimates the contemporaneous probability of mild cognitive impairment in PD (PD-MCI) from routinely collected clinical variables, enabling clinicians to prioritize MoCA-normal patients with higher model-estimated probability for MDS Level II neuropsychological evaluation and closer follow-up. METHODS: We analyzed 799 participants with PD from the Parkinson's Progression Markers Initiative (PPMI), randomly assigning them to training (n = 559) and internal validation (n = 240) cohorts. An independent external cohort comprised 70 consecutive patients recruited at The Affiliated Hospital of Guilin Medical University between February 2024 and March 2025. The reference outcome was MoCA-based PD-MCI (21-25) versus cognitively normal PD (26-30). Candidate predictors were screened by LASSO (1-SE criterion). To handle class imbalance, SMOTE was applied only during model fitting; both validation cohorts retained native class distributions. Five machine-learning models (logistic regression [LR], support vector machine, XGBoost, neural network, LightGBM) were evaluated on non-resampled data for discrimination (area under the receiver operating characteristic curve, AUC), calibration, and clinical utility (decision-curve analysis, DCA). Interpretability combined a nomogram with Shapley additive explanations (SHAP); a bilingual web calculator was also implemented. RESULTS: Of 799 PPMI participants, 169 (21.2%) met the MoCA-based PD-MCI definition. Seven routinely collected predictors were retained (sex, age, education, age at disease onset, MDS-UPDRS Part III, GDS, UPSIT). LR showed the most balanced performance: AUC 0.789 (training), 0.778 (internal), and 0.772 (external). At a fixed threshold of 0.50 in the external cohort, LR's sensitivity was 89.7%, specificity 43.9%, and F1-score 66.7%. Calibration and DCA favored LR. SHAP indicated education and motor severity as dominant contributors, followed by sex and age at onset; depressive burden (GDS) and hyposmia (UPSIT) increased risk, whereas chronological age had a smaller marginal effect. CONCLUSIONS: We developed and externally validated a probability-based, clinic-ready risk-stratification tool for PD-MCI using routinely available variables and MoCA-based MDS Level I labels. Implemented as a nomogram and bilingual calculator, it supports sensitivity-oriented triage-especially among MoCA-normal patients-by prioritizing timely MDS Level II evaluation and closer follow-up. The tool complements, rather than replaces, formal diagnostic assessment and does not predict long-term conversion. CLINICAL TRIAL NUMBER: Not applicable. The PPMI study is registered with ClinicalTrials.gov (NCT01141023) and the registration date is June 8, 2010.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».