Psychometric evaluation and linking of the PHQ-9, QIDS-C, and VQIDS-C in a real-world population with major depressive disorder

Abstract

Purpose

Major depressive disorder (MDD) is a leading cause of disability worldwide. An accurate assessment of depressive symptomology is crucial for clinical management and research. This study assessed the convergent validity, reliability, and total scale score interconversion across the 9-item Patient Health Questionnaire (PHQ-9) self-report, the 16-item Quick Inventory of Depressive Symptomatology-clinician report (QIDS-C) (two widely used clinical ratings) and the 5-item Very Brief Quick Inventory of Depressive Symptoms-clinician report (VQIDS-C), which evaluate the core features of MDD.

Patients and Methods

This study leveraged electronic health record (EHR)-derived, de-identified data from the NeuroBlu Database (Version 23R1), a longitudinal behavioural health real-world platform. Classical Test Theory (CTT) and Item Response Theory (IRT) analyses were used to evaluate the reliability, validity of, and conversions between the scales. The Test Information Function (TIF) was calculated for each scale, with greater test information reflecting higher precision and reliability in measuring depressive symptomology. IRT was also used to generate conversion tables so that total scores on each scale could be compared to the other.

Results

The study sample (n = 2,156) had an average age of 36.4 years (standard deviation [SD] = 13.0) and 59.7% were female. The mean depression scores for the PHQ-9, QIDS-C, and VQIDS-C were 12.9 (SD = 6.6), 12.0 (SD = 4.9), and 6.18 (SD = 3.2), respectively. The Cronbach’s alpha coefficients for PHQ-9, QIDS-C, and VQIDS-C were 0.9, 0.8, and 0.7, respectively, suggesting acceptable internal consistency. PHQ-9 (TIF = 30.3) demonstrated the best assessment of depressive symptomology, followed by QIDS-C (TIF = 25.8) and VQIDS-C (TIF = 17.7).

Conclusion

Overall, PHQ-9, QIDS-C, and VQIDS-C appear to be reliable and convertible measures of MDD symptomology within a US-based adult population in a real-world clinical setting.

Keywords:

• psychometrics
• linking
• patient health questionnaire
• quick inventory of depressive symptoms
• depression
• real-world data

Abbreviations

MDD, Major depressive disorder; PHQ-9, Patient Health Questionnaire-9; QIDS-C - Quick Inventory of Depressive Symptomatology-Clinician Report; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders; ICD-10, International Classification of Disease – Version 10; VQIDS-C, 5-item Very Quick Inventory of Depressive Symptomatology- Clinician Report; EHR, Electronic Health Record; CTT, Classical Test Theory; IRT, Item Response Theory; IQR, Interquartile Range; SD, Standard Deviation; RMSE, Root Mean Square Error; IRCCC, Item Response Category Characteristic Curve; MÅDRS, Montgomery-Åsberg Depression Rating Scale; SRS, Self Rated Scale; BDI-II), The Beck Depression Inventory-II; HAM-D, Hamilton Rating Scale for Depression.

Data Sharing Statement

The data supporting this study originate with Holmusk Technologies, Inc. These de-identified data may be made available upon request and are subject to license agreement with Holmusk. Interested parties should contact [email protected] to determine licensing terms.

Ethics Approval

This study was conducted in accordance with the 1964 Declaration of Helsinki and its subsequent amendments. Institutional review board (IRB) approval of the study protocol, including a waiver of HIPAA authorization, was obtained prior to study conduct, and covers data originating from all sites represented. Approval was granted by the WCG IRB. (The Holmusk Real-World Evidence Parent Protocol; IRB registration number 1-1470336-1; Protocol ID HolmuskRWE_1.0).

Acknowledgments

Cody Patton (Holmusk Technologies, Inc) provided editorial support.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Emily OC Palmer reports a relationship with Holmusk Europe, Ltd. that includes: employment. Sheryl Ker reports a relationship with KKT Technologies, Pte. Ltd. that includes: employment. At the time the study was conducted Miguel E Rentería reports a relationship with KKT Technologies, Pte. Ltd. that includes: employment. A. John Rush has received consulting fees from Compass Inc., Curbstone Consultant LLC, Emmes Corp., Evecxia Therapeutics, Inc., Holmusk Technologies, Inc., ICON, PLC, Johnson and Johnson (Janssen), John Peter Smith Foundation, Liva-Nova, MindStreet, Inc., Neurocrine Biosciences Inc., Otsuka-US, Singapore Ministry of Health; speaking fees from Liva-Nova, Johnson and Johnson (Janssen); and royalties from Wolters Kluwer Health, Guilford Press and the University of Texas Southwestern Medical Center, Dallas, TX (for the Inventory of Depressive Symptoms and its derivatives). He is also named co-inventor on two patents: US Patent No. 7,795,033: Methods to Predict the Outcome of Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S, Wilson AS; and US Patent No. 7,906,283: Methods to Identify Patients at Risk of Developing Adverse Events During Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S. Thomas Carmody has received consulting fees from Alkermes, Inc. and Holmusk Technologies, Inc. The authors report no other conflicts of interest in this work.

Additional information

Funding

This work was funded by Holmusk Technologies, Inc.