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Ratheesh A, Speed M, Salagre E, Berk M, Rohde C, Østergaard SD. Prior psychiatric morbidity and differential psychopharmacological treatment patterns: Exploring the heterogeneity of bipolar disorder in a nationwide study of 9594 patients. Bipolar Disord 2024. [PMID: 38649302 DOI: 10.1111/bdi.13432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
OBJECTIVES Individuals with bipolar disorders (BD) have heterogenic pre-onset illness courses and responses to treatment. The pattern of illness preceding the diagnosis of BD may be a marker of future treatment response. Here, we examined associations between psychiatric morbidity preceding the diagnosis of BD and pharmacological treatment patterns in the 2 years following diagnosis. METHODS In this register-based study, we included all patients with a diagnosis of BD attending Danish Psychiatric Services between January 1, 2012 and December 31, 2016. We examined the association between a diagnosis of substance use disorder, psychosis (other than schizophrenia or schizoaffective disorder), unipolar depression, anxiety/OCD, PTSD, personality disorder, or ADHD preceding BD and pharmacological treatment patterns following the diagnosis of BD (lithium, valproate, lamotrigine, antidepressants, olanzapine, risperidone, and quetiapine) via multivariable Cox proportional hazards regression adjusted for age, sex, and year of BD diagnosis. RESULTS We included 9594 patients with a median age of 39 years, 58% of whom were female. Antidepressants, quetiapine, and lamotrigine were the most commonly used medications in BD and were all linked to prior depressive illness and female sex. Lithium was used among patients with less diagnostic heterogeneity preceding BD, while valproate was more likely to be used for patients with prior substance use disorder or ADHD. CONCLUSION The pharmacological treatment of BD is linked to psychiatric morbidity preceding its diagnosis. Assuming that these associations reflect well-informed clinical decisions, this knowledge may inform future clinical trials by taking participants' prior morbidity into account in treatment allocation.
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Affiliation(s)
- Aswin Ratheesh
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Maria Speed
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Estela Salagre
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michael Berk
- School of Medicine, Barwon Health, Deakin University, IMPACT-The Institute for Mental and Physical Health and Clinical Translation, Geelong, Victoria, Australia
| | - Christopher Rohde
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Søren Dinesen Østergaard
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Bernstorff M, Hansen L, Enevoldsen K, Damgaard J, Hæstrup F, Perfalk E, Danielsen AA, Østergaard SD. Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness. Acta Psychiatr Scand 2024. [PMID: 38575118 DOI: 10.1111/acps.13687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/08/2024] [Accepted: 03/28/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness. METHODS The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts. RESULTS The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%). CONCLUSION A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.
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Affiliation(s)
- Martin Bernstorff
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Lasse Hansen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Kenneth Enevoldsen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Jakob Damgaard
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Frida Hæstrup
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Erik Perfalk
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Andreas Aalkjær Danielsen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Søren Dinesen Østergaard
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Ben David G, Amir Y, Salalha R, Sharvit L, Richter-Levin G, Atzmon G. Can Epigenetics Predict Drug Efficiency in Mental Disorders? Cells 2023; 12:1173. [PMID: 37190082 PMCID: PMC10136455 DOI: 10.3390/cells12081173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/23/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Psychiatric disorders affect millions of individuals and their families worldwide, and the costs to society are substantial and are expected to rise due to a lack of effective treatments. Personalized medicine-customized treatment tailored to the individual-offers a solution. Although most mental diseases are influenced by genetic and environmental factors, finding genetic biomarkers that predict treatment efficacy has been challenging. This review highlights the potential of epigenetics as a tool for predicting treatment efficacy and personalizing medicine for psychiatric disorders. We examine previous studies that have attempted to predict treatment efficacy through epigenetics, provide an experimental model, and note the potential challenges at each stage. While the field is still in its infancy, epigenetics holds promise as a predictive tool by examining individual patients' epigenetic profiles in conjunction with other indicators. However, further research is needed, including additional studies, replication, validation, and application beyond clinical settings.
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Affiliation(s)
- Gil Ben David
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (G.B.D.); (R.S.)
| | - Yam Amir
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (Y.A.)
| | - Randa Salalha
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (G.B.D.); (R.S.)
| | - Lital Sharvit
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (Y.A.)
| | - Gal Richter-Levin
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (G.B.D.); (R.S.)
- Department of Psychology, Faculty of Social Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel
- Integrated Brain and Behavior Research Center (IBBR), University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel
| | - Gil Atzmon
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (Y.A.)
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Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. THE LANCET DIGITAL HEALTH 2022; 4:e829-e840. [DOI: 10.1016/s2589-7500(22)00153-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/14/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
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Bernstorff M, Hansen L, Perfalk E, Danielsen AA, Østergaard SD. Stability of diagnostic coding of psychiatric outpatient visits across the transition from the second to the third version of the Danish National Patient Registry. Acta Psychiatr Scand 2022; 146:272-283. [PMID: 35730386 PMCID: PMC9543445 DOI: 10.1111/acps.13463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/07/2022] [Accepted: 06/15/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE In Denmark, data on hospital contacts are reported to the Danish National Patient Registry (DNPR). The ICD-10 main diagnoses from the DNPR are often used as proxies for mental disorders in psychiatric research. With the transition from the second version of the DNPR (DNPR2) to the third (DNPR3) in February-March 2019, the way main diagnoses are coded in relation to outpatient treatment changed substantially. Specifically, in the DNPR2, each outpatient treatment course was labelled with only one main diagnosis. In the DNPR3, however, each visit during an outpatient treatment course is labelled with a main diagnosis. We assessed whether this change led to a break in the diagnostic time-series represented by the DNPR, which would pose a threat to the research relying on this source. METHODS All main diagnoses from outpatients attending the Psychiatric Services of the Central Denmark Region from 2013 to 2021 (n = 100,501 unique patients) were included in the analyses. The stability of the DNPR diagnostic time-series at the ICD-10 subchapter level was examined by comparing means across the transition from the DNPR2 to the DNPR3. RESULTS While the proportion of psychiatric outpatients with diagnoses from some ICD-10 subchapters changed statistically significantly from the DNPR2 to the DNPR3, the changes were small in absolute terms (e.g., +0.6% for F2-psychotic disorders and +0.6% for F3-mood disorders). CONCLUSION The change from the DNPR2 to the DNPR3 is unlikely to pose a substantial threat to the validity of most psychiatric research at the diagnostic subchapter level.
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Affiliation(s)
- Martin Bernstorff
- Department of Affective DisordersAarhus University Hospital – PsychiatryAarhusDenmark,Department of Clinical MedicineAarhus UniversityAarhusDenmark
| | - Lasse Hansen
- Department of Affective DisordersAarhus University Hospital – PsychiatryAarhusDenmark,Department of Clinical MedicineAarhus UniversityAarhusDenmark
| | - Erik Perfalk
- Department of Affective DisordersAarhus University Hospital – PsychiatryAarhusDenmark,Department of Clinical MedicineAarhus UniversityAarhusDenmark
| | - Andreas Aalkjær Danielsen
- Department of Affective DisordersAarhus University Hospital – PsychiatryAarhusDenmark,Department of Clinical MedicineAarhus UniversityAarhusDenmark
| | - Søren Dinesen Østergaard
- Department of Affective DisordersAarhus University Hospital – PsychiatryAarhusDenmark,Department of Clinical MedicineAarhus UniversityAarhusDenmark
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Abstract
The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright 'COVID-19-related psychopathology'. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.
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