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Transcript
What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis Eiko Fried University of Leuven Network Analysis Approach to Psychopathology and Comorbidity ABCT, November 14, 2015 Diagnosis of Major Depression (MD) • Reliable diagnosis is essential to study and treat mental disorders • Reliable diagnosis of MD is difficult: biomarkers have very limited explanatory power, and MD was among the least reliable diagnosis in DSM-5 field trials (kappa = 0.28) • Current state: we measure depression symptoms to indicate the presence of MD. We add them to a sum-score, and suppose this adequately represents depression severity 2 Common cause model s1 s2 M s3 s4 s5 3 Common cause model s1 Red eyes s2 M s3 s4 s5 4 Common cause model M s1 Red eyes s2 Fever s3 s4 s5 5 Common cause model M s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough 6 Common cause model s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough 7 Common cause model • There is a specific relationship between symptoms of a disorder and the disorder itself (common cause model) M s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough 8 Common cause model • There is a specific relationship between symptoms of a disorder and the disorder itself (common cause model) M 1. s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough Symptoms are somewhat interchangeable 9 Common cause model • There is a specific relationship between symptoms of a disorder and the disorder itself (common cause model) M 1. s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough Symptoms are somewhat interchangeable 10 Common cause model • There is a specific relationship between symptoms of a disorder and the disorder itself (common cause model) M 1. 2. s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough Symptoms are somewhat interchangeable Symptoms are unrelated beyond their common cause 11 Common cause model • There is a specific relationship between symptoms of a disorder and the disorder itself (common cause model) M 1. 2. 3. s1 Red eyes s2 Fever s3 Runny nose s4 Koplik's spots s5 Cough Symptoms are somewhat interchangeable Symptoms are unrelated beyond their common cause A 'good' symptom is one that indicates the latent disease well 12 Psychiatry • Common cause model ubiquitous in psychiatry s1 s2 D s3 s4 s5 13 Measuring Major Depression • Common cause model MD s1 Insomnia s2 Fatigue s3 Concentration problems s4 Psychomotor problems s5 Weight loss 14 Measuring Major Depression • Common cause model – We measure symptoms to indicate the disorder – Add symptoms to total-score to indicate severity MD s1 Insomnia s2 Fatigue s3 Concentration problems s4 Psychomotor problems s5 Weight loss 15 Measuring Major Depression • Common cause model – – – – We measure symptoms to indicate the disorder Add symptoms to total-score to indicate severity Symptoms roughly interchangeable We want to treat the disease so symptoms disappear MD s1 Insomnia s2 Fatigue s3 Concentration problems s4 Psychomotor problems s5 Weight loss 16 Measuring Major Depression • Common cause model (overly simplistic) – – – – We measure symptoms to indicate the disorder Add symptoms to total-score to indicate severity Symptoms roughly interchangeable We want to treat the disease so symptoms disappear MD s1 Insomnia s2 Fatigue s3 Concentration problems s4 Psychomotor problems s5 Weight loss 17 Measuring Major Depression • Problem: there is a dramatic lack of consensus what depression symptoms (or good depression symptoms) are. Different depression instruments measure very different things. MD s1 Insomnia s2 Fatigue s3 Concentration problems s4 Psychomotor problems s5 Weight loss 18 What are 'good' depression symptoms? • DSM-5: 9 symptoms • None of the common rating scales of depression measure all DSM symptoms; all of them measure a number of symptoms not featured in the DSM – BDI: irritability, pessimism, feelings of being punished, … – HRSD: anxiety, genital symptoms, hypochondriasis, insights into the depressive illness, paralysis, … – CESD: frequent crying, talking less, perceiving others as unfriendly, … • As a result, there is little consistency across depression studies because patients are enrolled based on very different criteria 19 20 Measurement of depression • "The measurement of depression of depression is as confused as the basic construct of the state itself." 21 Network model • Symptoms co-occur due to their common cause 22 Network model • Symptoms co-occur because they cause each other Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 23 Network model • Symptoms co-occur because they cause each other • Symptoms are roughly equally important indicators Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 24 Network model • Symptoms co-occur because they cause each other • Symptoms are distinct entities with different characteristics Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 25 Network model • Symptoms co-occur because they cause each other • Symptoms are distinct entities with different characteristics • Reinforcing feedback loops (attractor state) Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 26 Network model • Important new questions arise: what symptoms are most central to driving depressive processes? Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 27 Network model • Important new questions arise: what symptoms are most central to driving depressive processes? Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 28 Network model • Important new questions arise: what symptoms are most central to driving depressive processes? Concentration problems s5 Insomnia s1 s4 s2 Fatigue s3 Psychomotor problems Weight loss 29 What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis Journal of Affective Disorders Eiko I. Fried Sacha Epskamp Randolph M. Nesse Francis Tuerlinckx Denny Borsboom 30 Research questions • What is the network structure of depression? – DSM symptoms – A large number of symptoms above and beyond the DSM criteria • What symptoms are most central, i.e. most connected in the network? 31 Sample • 3463 depressed outpatients from the enrollment stage of the STAR*D study – Mean age 41 years (SD=13), 63% female • IDS-C: 28-item questionnaire that covers 15 disaggregated DSM symptoms and 13 common non-DSM symptoms (e.g., anxiety, irritability) • Network estimation – Gaussian graphical model (special case of the Pairwise Markov Random Field): edges are partial correlation coefficients – Regularization via least absolute shrinkage and selection operator (lasso); very small edges set exactly to 0, results in a conservative (sparse) network 32 Network structure of MD Estimation - Edges equal partial correlations - Sparse network Interpretation - Heterogeneous network - Some clusters emerge DOI | 10.1016/j.jad.2015.09.005 33 Symptom importance DOI | 10.1016/j.jad.2015.09.005 34 Symptom importance DOI | 10.1016/j.jad.2015.09.005 35 Full IDS symptom network • Permutation test to examine differences in centrality between DSM and non-DSM symptoms: – Betweenness centrality: p = 0.12 – Closeness centrality: p = 0.64 – Node strength: p = 0.03 (0.08) • Controlling for outliers: – Betweenness centrality: p = 0.28 – Closeness centrality: p = 1 – Node strength: p = 0.13 • DSM symptoms are not more central than non-DSM symptoms DOI | 10.1016/j.jad.2015.09.005 36 Robustness analysis 37 Conclusions • Core assumptions of the common cause model do not seem remotely tenable for depression • "Depression sum-scores don't add up: why analyzing specific depression symptoms is essential" (Fried & Nesse, 2015) • Centrality measures may provide new insights regarding the clinical significance of specific depression symptoms. These insights likely have major clinical implications and suggest new approaches that may better predict outcomes such as the course of illness, probability of relapse, and treatment response. 38 Limitations • • • • STAR*D population Cross-sectional (indegree vs outdegree centrality) Heterogeneity of depression Topological overlap 39 Thank you Eiko Fried University of Leuven University of Amsterdam eiko-fried.com [email protected] Discussion • Robustness: – DSM and non-DSM symptoms do not differ regarding means (W = 121, p = 0.30) or SD (W = 89, p = 0.72) – 10 disaggregated symptoms not more central than the other 18 symptoms (node strength: p = 0.86; betweenness and closeness: p = 1) 42 43
