The validity of child and adolescent depression diagnoses in the Danish psychiatric central research register

Frederiksen, Louise; Bilenberg, Niels; Andersen, Lene; Henriksen, Natasha; Jørgensen, Jan Stener; Steinhausen, Hans Christoph; Wesselhoeft, Rikke

doi:10.1111/acps.13258

Cited by 10 publications

(9 citation statements)

References 27 publications

(56 reference statements)

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“…Several previous studies have reviewed detailed records and compared these to DNPR diagnoses. These studies have generally found good validity of diagnoses in the DNPR, for example for autism, 20 obsessive‐compulsive disorder (OCD), 21 ADHD, 22 schizophrenia 23 and depression, 24 although some concerns have been raised about diagnoses given in psychiatric emergency departments 24 (see Supporting Material for details).…”

Section: Methodsmentioning

confidence: 99%

Autism comorbidities show elevated female‐to‐male odds ratios and are associated with the age of first autism diagnosis

Rødgaard

Jensen

Miskowiak

et al. 2021

Acta Psychiatr Scand

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Objective To investigate the association between the comorbidity rates in autism and sex, birth year and the age at which autism was first diagnosed and compare the relative impact of each. Method Using the Danish National Patient Registry, cumulative incidences up to the age of 16 for 11 comorbid conditions (psychosis, affective disorders, anxiety disorders, conduct disorder, eating disorders, obsessive‐compulsive disorder, attention‐deficit hyperactivity disorder, epilepsy, tic disorders, sleep disorders or intellectual disability) were calculated for individuals with autism (N = 16,126) and non‐autism individuals (N = 654,977). Individuals were further stratified based on the age at the first autism diagnoses and comorbid diagnoses up to the age of 16 were compared. Results Most comorbidities were significantly associated with birth year and sex. Female/male odds ratios for 8 of 11 comorbid conditions were up to 67% higher than the corresponding odds ratios in the non‐autism population, including conditions that are generally more common in males than in females as well as conditions that are more common in females. All comorbidity rates were significantly associated with the age at the first autism diagnosis, which was a stronger predictor than sex and birth year for 8 conditions. Conclusions Comorbidity rates for females exceed what would be expected based on the sex ratios among non‐autistic individuals, indicating that the association between autism and comorbidity is stronger in females. Comorbidity rates are also highly dependent on the age at the first autism diagnosis, which may contribute to autism heterogeneity in research and clinical practice.

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Section: Methodsmentioning

confidence: 99%

Autism comorbidities show elevated female‐to‐male odds ratios and are associated with the age of first autism diagnosis

Rødgaard

Jensen

Miskowiak

et al. 2021

Acta Psychiatr Scand

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“…15 The overall validity of a single depression diagnosis (F32.0-3) identified in DNPR was found to be 72.8% for children and adolescents. 31 The overall validity of the same diagnoses was found to be similar for adults (overall validity of 75.4%). 31,32 We expect the validity to be similar for a recurrent depression diagnosis (F33.0-3).…”

Section: Research Ethicsmentioning

confidence: 83%

“…31 The overall validity of the same diagnoses was found to be similar for adults (overall validity of 75.4%). 31,32 We expect the validity to be similar for a recurrent depression diagnosis (F33.0-3). The eligibility criteria will influence the study population composition which may introduce false homogeneity, e.g., the criterion of not having certain psychiatric diagnoses 15 years prior to index date can exclude patients with psychiatric comorbidity.…”

Section: Research Ethicsmentioning

confidence: 83%

Treatment profiles and trajectories surrounding the diagnosis of major depressive disorder: a research protocol for a Danish register-based study.

et al. 2022

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Background: Major depressive disorder (MDD) is a prevalent illness that causes significant suffering and expenses at the personal and societal levels. The disorder is subject to heterogeneity reflected by diverse clinical phenotypes and assorted responses to treatment. Research on MDD treatments have focused on one treatment at a time, however many patients receive several different treatments. Considering the number of available treatment options, we hypothesize that it is possible to identify clinically meaningful groups of patients based on their psychiatric treatment. The objective of this study is therefore to identify psychiatric treatment profiles and trajectories of patients with major depressive disorder and, for the identified profiles and trajectories, to assess clinical and sociodemographic factors. Method: The study will be a population-based register study of patients with major depressive disorder in the Danish National Patient Register between 2011 and 2015. Using latent class analyses, we will identify homogenous groups of patients based on their psychiatric treatment patterns. These patterns constitute psychiatric treatment profiles which will be identified at six time-intervals, from 1.5 years before to 3 years after diagnosis of major depressive disorder. By cross-tabulating the identified treatment profiles, we will establish psychiatric treatment trajectories. Patients sharing profiles and trajectories will be characterized. Discussion: Identification of psychiatric treatment profiles and trajectories based on an unsupervised learning algorithm have the potential to reveal hidden patterns of psychiatric treatment. This will potentially pave the way for future studies of treatment combinations and a larger insight into the different courses of treatment. Furthermore, the assessment of clinical and sociodemographic factors may indicate different patient characteristics across treatment profiles and trajectories.

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“…The main strength of this study is the large-scale population-based data with high diagnostic validity that span multiple mental disorders and therefore provide a more accurate representation of a real clinical setting …”

Section: Discussionmentioning

confidence: 99%

“…The main strength of this study is the large-scale populationbased data with high diagnostic validity [37][38][39][40] that span multiple mental disorders and therefore provide a more accurate representation of a real clinical setting. 14,41,42 Additionally, access to large-scale genetic data provides a unique possibility to investigate the added value of genetics in diagnostic practice.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

et al. 2023

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ImportanceDiagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy.ObjectiveTo develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics.Design, Setting, and ParticipantsThis study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022.ExposureAt least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ.Main Outcomes and MeasuresThe predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance.ResultsA total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72.Conclusions and RelevanceResults of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.

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The validity of child and adolescent depression diagnoses in the Danish psychiatric central research register

Cited by 10 publications

References 27 publications

Autism comorbidities show elevated female‐to‐male odds ratios and are associated with the age of first autism diagnosis

Autism comorbidities show elevated female‐to‐male odds ratios and are associated with the age of first autism diagnosis

Treatment profiles and trajectories surrounding the diagnosis of major depressive disorder: a research protocol for a Danish register-based study.

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

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