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. 2022 Aug 12;22(1):223.
doi: 10.1186/s12874-022-01700-y.

Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning

Gayatri Marathe  1   2 , Erica E M Moodie  1 , Marie-Josée Brouillette  2   3 , Joseph Cox  1   3 , Curtis Cooper  4 , Charlotte Lanièce Delaunay  1   3 , Brian Conway  5 , Mark Hull  6 , Valérie Martel-Laferrière  7 , Marie-Louise Vachon  8 , Sharon Walmsley  9 , Alexander Wong  10 , Marina B Klein  11   12   13 ; Canadian Co-Infection Cohort
Collaborators, Affiliations

Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning

Gayatri Marathe et al. BMC Med Res Methodol. .

Abstract

Background: Depression is common in the human immunodeficiency virus (HIV)-hepatitis C virus (HCV) co-infected population. Demographic, behavioural, and clinical data collected in research settings may be of help in identifying those at risk for clinical depression. We aimed to predict the presence of depressive symptoms indicative of a risk of depression and identify important classification predictors using supervised machine learning.

Methods: We used data from the Canadian Co-infection Cohort, a multicentre prospective cohort, and its associated sub-study on Food Security (FS). The Center for Epidemiologic Studies Depression Scale-10 (CES-D-10) was administered in the FS sub-study; participants were classified as being at risk for clinical depression if scores ≥ 10. We developed two random forest algorithms using the training data (80%) and tenfold cross validation to predict the CES-D-10 classes-1. Full algorithm with all candidate predictors (137 predictors) and 2. Reduced algorithm using a subset of predictors based on expert opinion (46 predictors). We evaluated the algorithm performances in the testing data using area under the receiver operating characteristic curves (AUC) and generated predictor importance plots.

Results: We included 1,934 FS sub-study visits from 717 participants who were predominantly male (73%), white (76%), unemployed (73%), and high school educated (52%). At the first visit, median age was 49 years (IQR:43-54) and 53% reported presence of depressive symptoms with CES-D-10 scores ≥ 10. The full algorithm had an AUC of 0.82 (95% CI:0.78-0.86) and the reduced algorithm of 0.76 (95% CI:0.71-0.81). Employment, HIV clinical stage, revenue source, body mass index, and education were the five most important predictors.

Conclusion: We developed a prediction algorithm that could be instrumental in identifying individuals at risk for depression in the HIV-HCV co-infected population in research settings. Development of such machine learning algorithms using research data with rich predictor information can be useful for retrospective analyses of unanswered questions regarding impact of depressive symptoms on clinical and patient-centred outcomes among vulnerable populations.

Keywords: Depressive symptoms; HIV-HCV co-infection; Random forests; Supervised machine learning.

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Conflict of interest statement

JC received grants and consulting fees from ViiV Healthcare, Merck, and Gilead and personal fees from Bristol-Myers Squibb. CC has received personal fees for being a member of the national advisory boards of Gilead, Merck, Janssen, and Bristol-Myers Squibb. BC is a board member, consultant, and has received grants and payment for lectures from AbbVie, Gilead, and Merck, and payment for educational presentations from AbbVie. MH has served as a consultant for Merck, Vertex Pharmaceuticals, Pfizer, Viiv Healthcare, and Ortho-Jansen. MH has also received grants from the National Institute on Drug Abuse, as well as payment for lectures from Merck and Ortho-Janssen. MLV reports personal fees from Abbvie, personal fees from Merck, personal fees from Gilead, outside the submitted work. SW received grants, consulting fees, lecture fees, nonfinancial support, and fees for the development of educational presentations from Merck, ViiV Healthcare, GlaxoSmithKline, Pfizer, Gilead, AbbVie, Bristol-Myers Squibb, and Janssen. MBK reports grants for investigator-initiated studies from ViiV Healthcare, AbbVie, Merck, and Gilead; and consulting fees from ViiV Healthcare, Merck, AbbVie, and Gilead. GM, EEMM, MJB, CLD, VML, and AW have no conflicts of interest to disclose.

Figures

Fig. 1
Fig. 1
Receiver Operating Characteristic (ROC) curve for the A Full algorithm (x = 137) and B Reduced algorithm (x = 46)
Fig. 2
Fig. 2
Predictor importance plots: A Full algorithm and B Reduced algorithm. Abbreviations: BMI: Body Mass Index; P6M: In the past 6 months; CD4: Cluster of differentiation 4 receptor; EQ-5D-3L: EuroQoL-5Dimension-3Level; RNA: Ribonucleic acid; Hep B: Hepatitis B virus
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