Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool
Notice bibliographique
Résumé
Qun Ding,1– 4,* Congcong Yu,2,3,* Xiang Xu,1,3 Yinjiao Hou,1,3 Yingwen Miao,2,3 Sijue Yang,2,3 Shihua Chen,2,3 Xuelin Ma,2,3 Zhou Zhang,2,3 Yan Bi1– 3 1Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 3Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, People’s Republic of China; 4Department of Endocrinology, the Second People’s Hospital of Lianyungang, Lianyungang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhou Zhang, Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Branch of National Clinical Research Centre for Metabolic Diseases, No. 321, Zhongshan Road, Nanjing, 210008, People’s Republic of China, Tel + 86 25-83106666, Email zhangzhou@smail.nju.edu.cn Yan Bi, Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Branch of National Clinical Research Centre for Metabolic Diseases, No. 321, Zhongshan Road, Nanjing, 210008, People’s Republic of China, Tel + 86 25-83105313, Email biyan@nju.edu.cnAim: Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM).Methods: This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer’s Association Workgroup, and a MoCA score of 19– 25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors’ beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods.Results: Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4– 13, and high risk ≥ 14).Conclusion: The risk score based on age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.Keywords: mild cognitive impairment, type 2 diabetes mellitus, risk score, prescreening tool
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».