JMIR Publications

ABSTRACT

Background:

Empirical evidence supporting the superiority of the Patient Health Questionnaire-2 (PHQ-2), which queries the cardinal symptoms of depression (anhedonia and depressed mood), over alternative Patient Health Questionnaire-9 (PHQ-9) item pairings for major depression screening is limited. Our previous analysis indicated that alternative PHQ-9 item pairings were more effective than the PHQ-2 at screening for depressive symptomatology.

Objective:

To improve the effectiveness of ultra-brief major depression screening by identifying the most effective ultra-brief version of the PHQ-9, using the computerised Composite International Diagnostic Interview (CIDI-Auto) as the reference standard diagnosis, and inputting item pairings into machine learning (ML) models.

Methods:

A data-driven, machine learning (ML)-based framework was applied to the PHQ-9 to derive the most effective ultra-brief version for major depression screening on a primary care data set that had previously been used to validate the PHQ-2. A post-hoc statistical analysis investigated individual item performance and the impact of item interrelationships on pairing performance.

Results:

The phq2&3 (ML-based depressed mood and sleep disturbances item pairing) emerged as the most effective screening instrument in terms of area under the curve (AUC) during the training process (0.904), outperforming the PHQ-2 (0.883). The phq2&3 also had a higher Youden’s index than the PHQ-2 (0.670 vs. 0.637). As single-item screeners, phq2 and phq6 had the highest AUC and Youden’s index. The phq6 had the highest correlation to the CIDI-Auto diagnosis, with phq2 ranking fourth. The phq3 had the sixth highest AUC and Youden’s index, and the eight highest correlation to the CIDI-Auto diagnosis. Of the 36 item pairings, the highest inter-item correlation was between items phq2 and phq6, while items phq2 and phq3 had the seventh lowest. The phq2&3 achieved a higher AUC than the PHQ-2 on the withheld test data (0.898 vs. 0.882) but scored lower in terms of Youden’s index (0.646 vs. 0.653). Notably, the phq1&2 (ML version of the PHQ-2) had a higher AUC (0.890) than the PHQ-2. It also had the highest Youden’s index (0.677) of all instruments on the test data, with a higher specificity than the PHQ-2 (0.805 vs 0.781) and the same sensitivity (0.872).

Conclusions:

Evaluating individual item performance overlooks the inter-relationship between the item pairings in ultra-brief instruments. While highly inter-correlated items indicate good internal reliability, this often harms their predictiveness as a pairing. Though the phq3 item exhibited relatively weak predictive performance individually, it complemented and enhanced screening performance when combined with the phq2, and achieved the best pairing screening performance. The use of ML models with ultra-brief instruments enhanced depression screening performance compared to traditional sum score methods. This was attributed to the flexibility provided by ML models in mapping item pairings to CIDI-Auto diagnosis and the increased number of thresholds available with ML-based screening instruments.