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Enregistrement W4401450240 · doi:10.1101/2024.08.09.24311696

Development of a Simplified Smell Test to Identify Patients with Typical Parkinson’s as Informed by Multiple Cohorts, Machine Learning and External Validation

2024· preprint· en· W4401450240 sur OpenAlexaff
Juan Li, Kelsey Grimes, Joseph Saade, Julianna J. Tomlinson, Tiago Mestre, Sebastian Schade, Sandrina Weber, Mohammed Dakna, Tamara Wicke, Elisabeth Lang, Claudia Trenkwalder, Natalina Salmaso, Andrew Frank, Tim Ramsay, Douglas G. Manuel, Brit Mollenhauer, Michael G. Schlossmacher

Notice bibliographique

RevuemedRxiv · 2024
Typepreprint
Langueen
DomaineNeuroscience
ThématiqueOlfactory and Sensory Function Studies
Établissements canadiensCarleton UniversityOttawa HospitalBruyèreUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésOlfactionReceiver operating characteristicParkinson's diseaseParkinsonismPsychologyDementia with Lewy bodiesAudiologyProgressive supranuclear palsyAtrophyDementiaMedicineDiseaseInternal medicineNeuroscience

Résumé

récupéré en direct d'OpenAlex

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.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,027
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,104

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,027
Méta-épidémiologie (sens strict)0,0020,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,062
Tête enseignante GPT0,296
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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