Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology.

Background and objective: Automated healthcare databases (AHDB) are an important data source for real life drug and healthcare use. In the filed of depression, lack of detailed clinical data requires the use of binary proxies with important limitations. The study objective was to create a Depressive Health State Index (DHSI) as a continuous health state measure for depressed patients using available data in an AHDB. Methods: The study was based on historical cohort design using the UK Clinical Practice Research Datalink (CPRD). Depressive episodes (depression diagnosis with an antidepressant prescription) were used to create the DHSI through 6 successive steps: (1) Defining study design; (2) Identifying constituent parameters; (3) Assigning relative weights to the parameters; (4) Ranking based on the presence of parameters; (5) Standardizing the rank of the DHSI; (6) Developing a regression model to derive the DHSI in any other sample. Results: The DHSI ranged from 0 (worst) to 100 (best health state) comprising 29 parameters. The proportion of depressive episodes with a remission proxy increased with DHSI quartiles. Conclusion: A continuous outcome for depressed patients treated by antidepressants was created in an AHDB using several different variables and allowed more granularity than currently used proxies.

A tolerability burden index in schizophrenia: incorporating patient perspective in clinical trial adverse event reporting.

Background: Adverse event (AE) reporting in clinical trials does not fully capture the patient-level perspective and comparing tolerability across treatments or among studies is difficult. Objective: This study was designed to develop a treatment tolerability index algorithm that combines AE reporting with physician- and patient-level AE information into a global burden score to allow comparison of the overall tolerability of antipsychotic medications used in treating schizophrenia. Study design: Data from a 4-arm, placebo-controlled clinical trial were used in the proposed tolerability index algorithm. For each patient, AEs were adjusted by frequency, severity, duration, and patient-experienced importance, and average tolerability-related burden scores were calculated. Setting: Algorithm development analyses. Patients: This study analyzed data from a previously completed clinical trial that evaluated a potential antipsychotic medication; no patients were involved in the current study. Intervention: No interventions were administered in this study; the analyses described used data derived from a previously completed clinical trial in which patients received bifeprunox, risperidone, or placebo. Main outcome measure: Burden scores and tolerability index scores were compared for patients who did or did not discontinue treatment because of AEs. Results: The number of AEs varied widely among patients. Burden scores were significantly worse for patients who discontinued treatment because of AEs. Mean tolerability index scores, adjusted based on AE frequency, severity-adjusted duration, and patient-experienced impact, were poorer for active medications than placebo, and increased with dose. Conclusion: The treatment tolerability index will allow comparison of AE burden and tolerability between treatments using existing clinical trial information. This suggests that scoring is possible, is clinically relevant, and allows standardized comparison of antipsychotic tolerability.