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- Brikell, Isabell, et al.
(författare)
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ADHD medication discontinuation and persistence across the lifespan : a retrospective observational study using population-based databases
- 2024
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Ingår i: Lancet psychiatry. - : Elsevier. - 2215-0374 .- 2215-0366. ; 11:1, s. 16-26
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Although often intended for long-term treatment, discontinuation of medication for ADHD is common. However, cross-national estimates of discontinuation are missing due to the absence of standardised measures. The aim of this study was to determine the rate of ADHD treatment discontinuation across the lifespan and to describe similarities and differences across countries to guide clinical practice.METHODS: We did a retrospective, observational study using population-based databases from eight countries and one Special Administrative Region (Australia, Denmark, Hong Kong, Iceland, the Netherlands, Norway, Sweden, the UK, and the USA). We used a common analytical protocol approach and extracted prescription data to identify new users of ADHD medication. Eligible individuals were aged 3 years or older who had initiated ADHD medication between 2010 and 2020. We estimated treatment discontinuation and persistence in the 5 years after treatment initiation, stratified by age at initiation (children [age 4-11 years], adolescents [age 12-17 years], young adults [age 18-24 years], and adults [age ≥25 years]) and sex. Ethnicity data were not available.FINDINGS: 1 229 972 individuals (735 503 [60%] males, 494 469 females [40%]; median age 8-21 years) were included in the study. Across countries, treatment discontinuation 1-5 years after initiation was lowest in children, and highest in young adults and adolescents. Within 1 year of initiation, 65% (95% CI 60-70) of children, 47% (43-51) of adolescents, 39% (36-42) of young adults, and 48% (44-52) of adults remained on treatment. The proportion of patients discontinuing was highest between age 18 and 19 years. Treatment persistence for up to 5 years was higher across countries when accounting for reinitiation of medication; at 5 years of follow-up, 50-60% of children and 30-40% of adolescents and adults were covered by treatment in most countries. Patterns were similar across sex.INTERPRETATION: Early medication discontinuation is prevalent in ADHD treatment, particularly among young adults. Although reinitiation of medication is common, treatment persistence in adolescents and young adults is lower than expected based on previous estimates of ADHD symptom persistence in these age groups. This study highlights the scope of medication treatment discontinuation and persistence in ADHD across the lifespan and provides new knowledge about long-term ADHD medication use.FUNDING: European Union Horizon 2020 Research and Innovation Programme.
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| 2. |
- Chen, Qi, et al.
(författare)
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Predicting suicide attempt or suicide death following a visit to psychiatric specialty care : A machine learning study using Swedish national registry data
- 2020
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Ingår i: PLoS Medicine. - San Francisco : Public Library of Science (PLoS). - 1549-1277 .- 1549-1676. ; 17:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior.METHODS AND FINDINGS: The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87-0.89) and 0.89 (95% CI = 0.88-0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown.CONCLUSIONS: By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation.
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| 4. |
- Garcia-Argibay, Miguel, 1988-, et al.
(författare)
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Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset : a nationwide deep learning approach
- 2023
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Ingår i: Molecular Psychiatry. - : Springer Nature. - 1359-4184 .- 1476-5578. ; 28:3, s. 1232-1239
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Tidskriftsartikel (refereegranskat)abstract
- Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74-0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians' decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.
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| 5. |
- Zhang-James, Yanli, et al.
(författare)
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Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data
- 2020
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Ingår i: Journal of Child Psychology and Psychiatry. - : Blackwell Publishing. - 0021-9630 .- 1469-7610. ; 61:12, s. 1370-1379
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Tidskriftsartikel (refereegranskat)abstract
- Background: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.Methods: Psychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress, and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989 and 1993. We trained (a) a cross-sectional random forest (RF) model using data available by age 17 to predict SUD diagnosis between ages 18 and 19; and (b) a longitudinal recurrent neural network (RNN) model with the Long Short-Term Memory (LSTM) architecture to predict new diagnoses at each age.Results: The area under the receiver operating characteristic curve (AUC) was 0.73(95%CI 0.70-0.76) for the random forest model (RF). Removing prior diagnosis from the predictors, the RF model was still able to achieve significant AUCs when predicting all SUD diagnoses (0.69, 95%CI 0.66-0.72) or new diagnoses (0.67, 95%CI: 0.64, 0.71) during age 18-19. For the model predicting new diagnoses, model calibration was good with a low Brier score of 0.086. Longitudinal LSTM model was able to predict later SUD risks at as early as 2 years age, 10 years before the earliest diagnosis. The average AUC from longitudinal models predicting new diagnoses 1, 2, 5 and 10 years in the future was 0.63.Conclusions: Population registry data can be used to predict at-risk comorbid SUDs in individuals with ADHD. Such predictions can be made many years prior to age of the onset, and their SUD risks can be monitored using longitudinal models over years during child development. Nevertheless, more work is needed to create prediction models based on electronic health records or linked population registers that are sufficiently accurate for use in the clinic.
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