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Enregistrement W4392757112 · doi:10.1111/bdi.13415

Advancing clinical practice and discovery research through revised taxonomy: Case in point bipolar disorder diagnosis

2024· article· en· W4392757112 sur OpenAlexaff
Anne Duffy, Paul Grof

Notice bibliographique

RevueBipolar Disorders · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueBipolar Disorder and Treatment
Établissements canadiensUniversity of TorontoQueen's University
Organismes subventionnairesnon disponible
Mots-clésBipolar disorderPsychologyClinical PracticeTaxonomy (biology)PsychotherapistPsychiatryMedicineMoodFamily medicineBiology

Résumé

récupéré en direct d'OpenAlex

In the well-articulated paper by Malhi et al.1 in this journal, several problems with diagnosing bipolar disorder in children are discussed and as rightly pointed out “impede our ability to conduct meaningful research and advance clinical practice”. In fact, one could argue that the diagnostic challenges outlined apply to the diagnoses of mood disorders more generally. That is, reliance on a diagnostic checklist that reflects largely non-specific symptoms that cross diagnostic boundaries and are open to interpretation yield a highly heterogeneous population of mood disordered patients that share the same diagnosis but little else—differing in clinical course, family history, prognosis, treatment response and genetic and neurobiological correlates. Malhi et al. offer a novel solution to the current diagnostic dilemma. The authors express hope that revising the current taxonomy so as to focus on developmentally sensitive symptom clusters, reflecting the evolution of the disorder over development, will advance the field past the current stalemate and improve diagnostic accuracy. While we agree that a developmental lens provides an informative perspective through which to view psychopathology, there is no evidence that a sole focus on symptoms, no matter how well developmentally nuanced, will improve diagnostic classification. Rather, substantive evidence supports the need to identify more homogenous subtypes from within the current heterogeneous bipolar diagnostic construct to advance risk prediction, pharmacotherapy, and discovery research. Three bipolar subtypes based on distinct clinical profiles have been described based on research extending over six decades, each with preferential response to stabilizing treatment with lithium, antipsychotics and antiepileptics, respectively (Figure 1).2 Therefore, an alternative evidence-based solution would be to include these bipolar subtypes in a revised taxonomy. Specifically, substantive evidence supports that a long-term response to lithium identifies a more homogeneous subtype of bipolar disorder characterized by a recurrent episodic course, complete remission, a history of episodic mood disorders in family members, and distinctive genetic correlates.3 This distinctive clinical profile, identified by multivariate analyses, was actually delineated by Kraepelin over a century ago. Further, prospective longitudinal studies of the offspring of lithium responsive (LiR) and lithium non-responsive (LiNR) bipolar parents have provided evidence that bipolar disorder debuts as a depressive episode in adolescence, years on average before emergence of the first manic episode.4 The developmental history and clinical course differ between subgroups, with offspring of LiRs having normal or gifted development and offspring of LiNRs manifesting neurodevelopmental disorders (ADHD, learning difficulties). Childhood clinical antecedents predicting major mood disorders include anxiety and sleep disorders, which in the offspring of LiRs follow an episodic course, while in offspring of LiNRs are chronic or fluctuating with incomplete remission (Figure 2).4 Further, the clinical course of mood disorders in offspring aligns with that of the parent; that is offspring of LiRs manifest episodic remitting mood disorders with stable functioning between episodes, while offspring of LiNRs manifest chronic or partial remitting mood disorders with lower global functioning over time.4 Self-reported manic symptoms did not differentiate high-risk from control offspring (of well parents)—in fact, controls endorsed higher hypomanic symptom levels; however, hypomanic symptoms identified on clinical assessment did predict onset of mood disorders in high-risk offspring, while no clinically meaningful hypomania was identified in controls.5 Taken together, longitudinal studies over decades of carefully prospectively studied adult bipolar patients and their relatives, including their children, have provided convergent evidence supporting bipolar subtypes that differ in characteristic developmental trajectories of emergent psychopathology, clinical course, prognosis, treatment response, and genetic and neurobiological correlates. This strongly suggests these clinical profiles index bipolar subtypes with shared genetic factors and pathophysiological mechanisms that differ meaningfully between subtypes. Therefore, we argue that a necessary revision to advance precision diagnosis that maps to preferential stabilizing treatment and reliably associated biomarkers rests on the incorporation of characteristic clinical profiles of bipolar subtypes into the diagnostic taxonomy. The clinical profiles, as illustrated briefly here, go beyond developmentally sensitive symptom clusters, which only have clinical meaning when considered in the context of a carefully detailed family history and clinical course. Had the longitudinal evidence identifying characteristic clinical profiles of bipolar subtypes (including the developmental trajectories) been applied and incorporated in routine clinical practice, the entire debate about the validity of a pediatric (i.e. pre-pubertal mania) bipolar disorder equivalent might have been avoided or at least put to rest much earlier on. That said, lessons learned from the pediatric bipolar debate include that symptoms alone are insufficient evidence on which to rest a stable and accurate diagnosis, especially early in the emergent course. Further, as in other areas of medicine, the importance of a thorough clinical assessment that considers all predictive clinical information in the diagnostic formulation and includes collateral history and a carefully collected family history is paramount. While structured and semi-structured interviews and symptom checklists (developmentally sensitive or not) may be useful in large epidemiological studies, advances in psychiatry clinical practice and discovery research will require selective focus on carefully clinically characterized patients in order to identify those of the same bipolar subtype associated with a predictable course, preferential response to stabilizing treatment and shared pathogenesis.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,954
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,061
Tête enseignante GPT0,405
Écart entre enseignants0,344 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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