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

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.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.