Illness Severity And Clinical Instability Are Critical For Predicting Risk Of Psychiatric Hospitalization Across A Wide Range Of Diagnoses, New Research Finds
According to a new study published in The Lancet Psychiatry, illness severity and clinical instability are two critical factors for predicting future hospitalization risk among patients with psychiatric conditions.
This finding could be integral for behavioral health care and research, impacting how early interventions are facilitated to the development of treatments that are more targeted.
The study was conducted by researchers affiliated with Holmusk, a behavioral health and data analytics company. So, they utilized Holmusk’s NeuroBlu Database– a data source containing the electronic health records of over 1.4 million U.S. patients who received care for behavioral health conditions.
The team used this data to examine relationships between instability, clinical severity, and psychiatric hospitalization– ultimately resulting in the first-ever study to explore if early clinical trajectory might predict psychiatric hospitalization across various diagnoses.
Following the study’s publication in the medical journal, the researchers’ findings were also presented to the European Congress of Psychiatry in Paris and the Academy of Managed Care Pharmacy in San Antonio.
“Our vision in creating the NeuroBlu Database was to provide the gold standard for behavioral health real-world data,” said Founder and CEO of Holmusk, Nawal Roy.
“Findings like these validate that this vision is being realized. Even more importantly, they underscore the importance of developing standardized measures across behavioral health care in order to improve patient care and outcomes.”
Led by Maxime Taquet, a clinical psychiatrist and senior research fellow at the University of Oxford, the team discovered that if patients experienced instability and severe illness during the first two months of clinical encounters, then they faced a drastically higher risk of undergoing psychiatric hospitalization within six months.
Moreover, the patients who ranked in the top half for instability and clinical severity had an increased risk of hospitalization at 45%.
Jacob Lund – stock.adobe.com – illustrative purposes only, not the actual people
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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