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Identifying relapse predictors in individual participant data with decision trees.

Lucas Böttcher1,2, Josefien J F Breedvelt3,4, Fiona C Warren5

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Summary

Predicting depression relapse is crucial. Machine learning models using age, age of onset, and depression severity improved relapse prediction accuracy compared to using depression severity alone.

Keywords:
Decision treeDepressionGradient boostingIndividual participant dataLogistic regressionMachine learningMeta analysisRelapse

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Area of Science:

  • Psychiatry
  • Computational Psychiatry
  • Machine Learning in Healthcare

Background:

  • Depression is a common and recurring mental health condition.
  • Accurate prediction of relapse or recurrence is essential for effective clinical management.
  • Machine learning applied to Individual Participant Data (IPD) offers potential for improved risk prediction accuracy.

Purpose of the Study:

  • To identify predictors of relapse and/or recurrence in depression.
  • To evaluate the performance of machine learning models, specifically decision trees, in predicting depressive relapse.
  • To compare the predictive accuracy of models using multiple risk indicators versus single indicators.

Main Methods:

  • Utilized Individual Participant Data (IPD) from four Randomized Controlled Trials (RCTs) comparing antidepressant treatment with psychological interventions.
  • Assessed ten baseline predictors for relapse and/or recurrence.
  • Applied decision tree algorithms, with and without gradient boosting, and logistic regression for classification and robustness analysis.

Main Results:

  • The combination of age, age of onset of depression, and depression severity significantly improved relapse risk prediction compared to depression severity alone.
  • Decision tree models achieved approximately 55% (without gradient boosting) and 58% (with gradient boosting) accuracy, sensitivity, and specificity in identifying patients at risk of relapse at intake.
  • Decision tree classifiers slightly outperformed logistic regression models in predictive accuracy.

Conclusions:

  • Decision tree classifiers utilizing multiple risk indicators can aid in developing treatment stratification strategies for depression.
  • These models hold potential for prioritizing individuals most in need of intensive treatment.
  • The study highlights ongoing challenges and gaps in accurately predicting depressive relapse.