Data mining algorithm predicts a range of adverse outcomes in major depression
•The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used to predict the course of depression.•Replication in new samples is crucial to test these prediction models.•Prediction models may assist cli...
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| Vydáno v: | Journal of affective disorders Ročník 276; s. 945 - 953 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.11.2020
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| ISSN: | 0165-0327, 1573-2517, 1573-2517 |
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| Abstract | •The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used to predict the course of depression.•Replication in new samples is crucial to test these prediction models.•Prediction models may assist clinicians in treatment decisions.
Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data.
We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1–9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness.
Our model consistently predicted future episodes of MD in both test samples (AUC 0.68–0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65–0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas.
Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background.
Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice. |
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| AbstractList | Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data.
We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1-9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness.
Our model consistently predicted future episodes of MD in both test samples (AUC 0.68-0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65-0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas.
Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background.
Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice. •The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used to predict the course of depression.•Replication in new samples is crucial to test these prediction models.•Prediction models may assist clinicians in treatment decisions. Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data. We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1–9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness. Our model consistently predicted future episodes of MD in both test samples (AUC 0.68–0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65–0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas. Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background. Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice. Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data.BACKGROUNDCourse of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data.We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1-9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness.METHODSWe applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1-9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness.Our model consistently predicted future episodes of MD in both test samples (AUC 0.68-0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65-0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas.RESULTSOur model consistently predicted future episodes of MD in both test samples (AUC 0.68-0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65-0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas.Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background.LIMITATIONSPrediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background.Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice.CONCLUSIONSOur prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice. Highlights•The course of major depression is highly varied. •Genetic and environmental risk factors influence the course of depression. •Data mining techniques can be used to predict the course of depression. •Replication in new samples is crucial to test these prediction models. •Prediction models may assist clinicians in treatment decisions. |
| Author | Aggen, Steven H. Kendler, Kenneth S. van Loo, Hanna M. Milaneschi, Yuri Bigdeli, Tim B. |
| Author_xml | – sequence: 1 givenname: Hanna M. surname: van Loo fullname: van Loo, Hanna M. email: h.van.loo@umcg.nl organization: Department of Psychiatry, University of Groningen, University Medical Center Groningen, Hanzeplein 1 (PO Box 30.001), 9700 RB Groningen, the Netherlands – sequence: 2 givenname: Tim B. surname: Bigdeli fullname: Bigdeli, Tim B. organization: Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States – sequence: 3 givenname: Yuri surname: Milaneschi fullname: Milaneschi, Yuri organization: Department of Psychiatry, Amsterdam Public Health and Neuroscience Amsterdam research institutes, Amsterdam UMC and GGZ inGeest Amsterdam, Amsterdam, the Netherlands – sequence: 4 givenname: Steven H. surname: Aggen fullname: Aggen, Steven H. organization: Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States – sequence: 5 givenname: Kenneth S. surname: Kendler fullname: Kendler, Kenneth S. organization: Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32745831$$D View this record in MEDLINE/PubMed |
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| Keywords | Recurrence Replication Data mining, prediction Major depression Course of illness |
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| Snippet | •The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used... Highlights•The course of major depression is highly varied. •Genetic and environmental risk factors influence the course of depression. •Data mining techniques... Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all'... |
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| SubjectTerms | Algorithms Anxiety Disorders - diagnosis Anxiety Disorders - epidemiology Course of illness Data Mining Data mining, prediction Depression Depressive Disorder, Major - diagnosis Humans Major depression Psychiatric/Mental Health Recurrence Replication |
| Title | Data mining algorithm predicts a range of adverse outcomes in major depression |
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