Illness Severity And Clinical Instability Are Critical For Predicting Risk Of Psychiatric Hospitalization Across A Wide Range Of Diagnoses, New Research Finds

Clinical severity and instability were measured using Clinical Global Impression Severity Scale (CGI-S) scores, and over 36,000 patients were ultimately included.

The researchers also found that this relationship was consistent across a broad range of diagnoses– such as bipolar disorder, major depressive disorder, post-traumatic stress disorder, and schizophrenia. Additionally, the association was observed among both children and adults, as well as in both women and men.

In order to confirm the strength of this relationship, the research team decided to replicate their analysis with a second group of patients using the Patient Health Questionnaire (PHQ-9)– which measures patient-reported outcomes. And interestingly, the findings from this second group still complemented the original analysis’ results. Taquet largely credited the NeuroBlu Database for making this study possible.

“Not only does the NeuroBlu Database include frequently collected psychometric scales, such as the CGI-S used in this study, but it also included longitudinal data across diverse treatment centers, including emergency rooms and hospitals,” Taquet explained.

“The completeness of this EHR-derived data enabled us to examine patients’ clinical journeys and determine whether they had been admitted to the hospital within the first six months after initiating care.”

And now, the findings could have widespread implications throughout the behavioral health landscape. Clinicians could use instability and severity to better inform decisions– for instance, deciding which patients may benefit from more intensive interventions.

On the other hand, hospitals and other health systems could rely on this information to plan care provisions, including predicting how many psychiatric hospital beds will be needed.

In the research realm, scientists can also benefit from the findings– using them to advance new intervention development aimed at preventing the risk of hospitalization.

To read the study’s complete findings, visit the link here.

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