Abstract
Objectives
Hospitalization for major depressive disorder (MDD) places a heavy burden on patients and the healthcare system. This study examines predictors of hospitalization in patients with newly-diagnosed MDD.
Methods
A retrospective cohort study of electronic health record (EHR)-derived de-identified data from the NeuroBlu Database, a longitudinal behavioral health real-world database comprising structured and unstructured patient-level clinical data, was conducted in adults (≥18 years) with ≥2 MDD diagnoses (second diagnosis=index date) between 09/2000-06/2020. Insurance plan enrolment was required for ≥60 days pre-index and ≥30 days post-index. Patients with bipolar disorder/schizophrenia/schizoaffective disorder diagnosed before day 30 were excluded. Hospitalizations
Results
7,286 of 104,631 patients with MDD met study criteria. 43.5% of train (N=5,116) and 41.9% of test (N=2,170) patients were hospitalized; median time to hospitalization was 12.6 months and 12.9 months, respectively. Train patients were mostly single (39.9%), white (81.2%), female (68.2%) with non-severe MDD (85%), median age 42 years, and median CGIS 4. Test patients had similar characteristics. Age (HR=0.99), marital status (widowed HR=1.47, divorced/separated HR=1.23, single HR=1.32, reference=married), CGIS (HR=1.20), substance use (HR=1.70), family (HR=1.35), financial (HR=1.15), legal (HR=1.25), and occupational stressors (HR=1.16) were important predictors of hospitalization (all p
Conclusions
The 8-predictor model performed well in the test set. Stressors and CGIS are not routinely collected in EHR or claims data but are important in determining clinical outcomes.
Hospitalization for major depressive disorder (MDD) places a heavy burden on patients and the healthcare system. This study examines predictors of hospitalization in patients with newly-diagnosed MDD.
Methods
A retrospective cohort study of electronic health record (EHR)-derived de-identified data from the NeuroBlu Database, a longitudinal behavioral health real-world database comprising structured and unstructured patient-level clinical data, was conducted in adults (≥18 years) with ≥2 MDD diagnoses (second diagnosis=index date) between 09/2000-06/2020. Insurance plan enrolment was required for ≥60 days pre-index and ≥30 days post-index. Patients with bipolar disorder/schizophrenia/schizoaffective disorder diagnosed before day 30 were excluded. Hospitalizations
Results
7,286 of 104,631 patients with MDD met study criteria. 43.5% of train (N=5,116) and 41.9% of test (N=2,170) patients were hospitalized; median time to hospitalization was 12.6 months and 12.9 months, respectively. Train patients were mostly single (39.9%), white (81.2%), female (68.2%) with non-severe MDD (85%), median age 42 years, and median CGIS 4. Test patients had similar characteristics. Age (HR=0.99), marital status (widowed HR=1.47, divorced/separated HR=1.23, single HR=1.32, reference=married), CGIS (HR=1.20), substance use (HR=1.70), family (HR=1.35), financial (HR=1.15), legal (HR=1.25), and occupational stressors (HR=1.16) were important predictors of hospitalization (all p
Conclusions
The 8-predictor model performed well in the test set. Stressors and CGIS are not routinely collected in EHR or claims data but are important in determining clinical outcomes.
| Original language | English |
|---|---|
| Pages (from-to) | S359 |
| Number of pages | 1 |
| Journal | Value in Health |
| Volume | 25 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1 Dec 2022 |