Hanna M van Loo1, Tim B Bigdeli2, Yuri Milaneschi3, Steven H Aggen4, Kenneth S Kendler5. 1. Department of Psychiatry, University of Groningen, University Medical Center Groningen, Hanzeplein 1 (PO Box 30.001), 9700 RB Groningen, the Netherlands. Electronic address: h.van.loo@umcg.nl. 2. Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States; Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, United States. 3. Department of Psychiatry, Amsterdam Public Health and Neuroscience Amsterdam research institutes, Amsterdam UMC and GGZ inGeest Amsterdam, Amsterdam, the Netherlands. 4. Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States. 5. Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background. CONCLUSIONS: 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.
Authors: Andrew S Moriarty; Nicholas Meader; Kym Ie Snell; Richard D Riley; Lewis W Paton; Carolyn A Chew-Graham; Simon Gilbody; Rachel Churchill; Robert S Phillips; Shehzad Ali; Dean McMillan Journal: Cochrane Database Syst Rev Date: 2021-05-06
Authors: Andrew S Moriarty; Lewis W Paton; Kym I E Snell; Richard D Riley; Joshua E J Buckman; Simon Gilbody; Carolyn A Chew-Graham; Shehzad Ali; Stephen Pilling; Nick Meader; Bob Phillips; Peter A Coventry; Jaime Delgadillo; David A Richards; Chris Salisbury; Dean McMillan Journal: Diagn Progn Res Date: 2021-07-02