Creation and validation of a linear index to measure the health state of patients with depression in automated healthcare databases.

Background and objective: We previously built a weighted Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB). We now propose a linear DHSI (L-DHSI) which is easier to use and to replicate across AHDBs. Methods: A historical cohort of patients with ≥1 episode of depression was identified in the Clinical Practice Research Datalink (CPRD). The DHSI was calculated for each treated episode of depression. Validation was performed by using validated definitions of remission (proxy and Patient Health Questionnaire 9 or PHQ-9) and comparing the L-DHSI between subgroups. Reliability was assessed using Cronbach's alpha. Results: Between 1 January 2006 and 31 December 2012, 309,279 episodes of depression were identified in the CPRD. Remission was observed in 5% of the patients with lowest L-DHSI scores and in 78% of the patients with highest L-DHSI scores. Although less sensitive than the weighted DHSI, the L-DHSI was reliable and relatively easy of use. The L-DHSI was highly correlated to the weighted DHSI (Spearman coefficient 0.790, p < 0.001). Conclusion: The L-DHSI represents a good balance between reliability, usability, and reproducibility. In addition, the linearity of this index allows for an easier interpretation than the original weighted DHSI.

Results and validation of an index to measure health state of patients with depression in automated healthcare databases.

Background and objective: A Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB) was developed to evaluate the health state of depressive patients, and its evolution. The study objective was to describe and validate this DHSI. Methods: A historical cohort of patients with at least one episode of depression was identified in the Clinical Practice Research Datalink (CPRD). The DHSI was calculated for each episode of depression. Validation was performed by comparing the DHSI between subgroups and using validated definitions of remission (proxy and PHQ-9). Robustness was studied by assessing the impact of modifying parameters of the DHSI. Results: 309,279 episodes of depression were identified in the CPRD between 1 January 2006 and 31 December 2012. Remission was observed in 8% of the patients showing the lower DHSI scores and in 88% of the patients showing the higher DHSI scores. The DHSI was robust to a modification of the most frequent variables and to the removal of rare parameters. Conclusion: The DHSI is specific to depression severity (with remission rates in accordance with the expected variations of the DHSI) and robust. It represents a promising tool for the analysis of AHDBs.

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.