Development of a Simplified Smell Test to Identify Patients with Typical Parkinson’s as Informed by Multiple Cohorts, Machine Learning and External Validation
Notice bibliographique
Résumé
ABSTRACT Background Reduced olfaction is a common feature of patients with typical Parkinson disease (PD). We sought to develop and validate a simplified smell test as a screening tool to help identify PD patients and explore its differentiation from other forms of parkinsonism. Methods We used the Sniffin’ Sticks Identification Test (SST-ID) and the University of Pennsylvania Smell Identification Test (UPSIT), together with data from three case-control studies, to compare olfaction in 301 patients with PD or dementia with Lewy bodies (DLB) to 36 subjects with multiple system atrophy (MSA), 32 individuals with progressive supranuclear palsy (PSP) and 281 neurologically healthy controls. Individual SST-ID and UPSIT scents were ranked by area under the receiver operating characteristic curve (AUC) values for group classification, with 10-fold cross-validation. Additional rankings were generated by leveraging results from eight published studies, collectively including 5,853 unique participants. Lead combinations were further validated using (semi-)independent datasets. An abbreviated list of scents was generated based on those shared by SST-ID and UPSIT. Findings We made the following five observations: (i) PD and DLB patients generally had worse olfaction than healthy controls, as published, with scores for MSA and PSP patients ranking as intermediate. (ii) SST-ID and UPSIT scents showed distinct discriminative performances, with the top odorants (licorice, banana, clove, rose, mint, pineapple and cinnamon) confirmed by external evidence. (iii) A subset of only seven scents demonstrated a similar performance to that of the complete 16-scent SST-ID and 40-scent UPSIT kits, in both discovery and validation steps. Seven scents distinguished PD/DLB subjects from healthy controls with an AUC of 0.87 (95%CI 0.85-0.9) and PD/DLB from PSP/MSA patients with an AUC of 0.73 (95%CI 0.65-0.8) within the three cohorts (n=650). (iv) Increased age was associated with a decline in olfaction. (v) Males generally scored lower than females, although this finding was not significant across all cohorts. Interpretation Screening of subjects for typical Parkinson’s-associated hyposmia can be carried out with a simplified scent identification test that relies on as few as seven specific odorants. There, the discrimination of PD/DLB subjects vs. age-matched controls is more accurate than that of PD/DLB vs. PSP/MSA patients. Funding This work was supported by: Parkinson Research Consortium; uOttawa Brain & Mind Research Institute; and the Aligning Science Across Parkinson’s Collaborative Research Network. Research in context Evidence before this study Chronic hyposmia is a common feature of Parkinson disease (PD) and dementia with Lewy bodies (DLB), which often precedes motor impairment and cognitive dysfunction by several years; it is also frequently associated with α-synuclein aggregate formation in the bulb. The presence of hyposmia increases an individual’s likelihood of having -what has recently been proposed as- a neuronal synucleinopathy disease, by >24-fold. Despite the strong association of PD with reduced olfaction, little is understood about it clinically, such as whether it is affected by sex and age, and whether hyposmia of PD is associated with the same scent identification difficulty seen in other conditions that present with parkinsonism. Moreover, due to its time-consuming nature and traditional administration by healthcare workers, extensive olfactory testing is not routinely performed during neurological assessments in movement disorder clinics. Added value of this study We analyzed the performance of both the Sniffin’ Sticks Test kit and UPSIT battery to discriminate between healthy controls, patients with PD/DLB and those with MSA or PSP. Comparison to and juxtaposition with eight other published studies allowed for the generation of a markedly abbreviated smell identification test that unified both tests, as described. Group classification performance by each scent and its distractors was further analyzed using machine learning and advanced Item Response Theory methods. Relations between each scent tested, sex and age were analyzed for the first time. Our findings suggest concrete steps to be implemented that would allow for simplified, routine olfaction testing in the future. Implications of all the available evidence Olfaction testing has emerged as an important neurological assessment part when examining subjects with Parkinson’s and those at risk of it. A simple, validated smell test containing fewer scents than current options could facilitate rapid testing of olfaction in clinic settings and at home, without supervision by healthcare workers. The usefulness of such a non-invasive test in population health screening efforts could be further enhanced when coupled to a self-administered survey that includes questions related to other risk factors associated with PD. As such, large-scale community screening and applications to routine practice in family doctors’ offices as well as in specialty clinics could be made operationally feasible and cost-effective.
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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 | 0,020 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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 ».