Home Health & Wellness New Study Reveals Clinical Instability Predicts Psychiatric Hospitalisation

New Study Reveals Clinical Instability Predicts Psychiatric Hospitalisation

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For patients with psychiatric conditions, illness severity and instability are key factors in predicting future risk of hospitalisation, according to a new study by Holmusk, a leading behavioural health real-world evidence and data analytics company, that has been published in The Lancet Psychiatry. The study, which was enabled by Holmusk’s NeuroBlu Database, could prove useful across the behavioural health care and research ecosystem, from facilitating early intervention to supporting the development of more targeted treatments.

The research was conducted using Holmusk’s NeuroBlu Database, a real-world data source that contains longitudinal, de-identified data from the electronic health records of more than 1.4 million patients receiving care for behavioural health conditions at over 30 health systems across the U.S. Researchers examined the associations among clinical severity, instability, and psychiatric hospitalisation, producing the first study to explore whether early clinical trajectory serves as a predictor of psychiatric hospitalisation across diagnoses. In addition to publication in the peer-reviewed The Lancet Psychiatry, findings from the study also have been presented in recent weeks at the Academy of Managed Care Pharmacy’s annual meeting, held this year in San Antonio, and at the annual European Congress of Psychiatry, held this year in Paris. 

“Our vision in creating the NeuroBlu Database was to provide the gold standard for behavioural health real-world data,” said Nawal Roy, Founder and CEO of Holmusk. “Findings like these validate that this vision is being realised. Even more importantly, they underscore the importance of developing standardised measures across behavioural health care in order to improve patient care and outcomes.”

The research team, led by clinical psychiatrist and the University of Oxford senior research fellow Maxime Taquet, found that patients who experienced severe illness and instability within the first two months of their clinical encounters had a significantly higher risk of psychiatric hospitalisation within six months. In fact, patients ranking in the top half for clinical severity and instability had a 45% increased risk of hospitalisation. Researchers used scores from the Clinical Global Impression Severity scale (CGI-S) to measure both clinical severity and instability. 

The study, which included over 36,000 patients, also found that this association remained consistent across a wide range of diagnoses, including major depressive disorder, bipolar disorder, schizophrenia, post-traumatic stress disorder, and other behavioural health conditions. The association was seen in both adults and children and in both men and women. To further establish the strength of the association, researchers replicated the analysis with a different group of patients, this time using the patient-reported outcome measure Patient Health Questionnaire (PHQ-9). Their findings complemented results from the original analysis.

“Holmusk’s NeuroBlu Database is an invaluable resource whose real-world data made this analysis possible,” Taquet said. “Not only does the NeuroBlu Database include frequently collected psychometric scales, such as the CGI-S used in this study, but it also includes longitudinal data across diverse treatment centres, including emergency rooms and hospitals. 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.”

The research has implications for many areas across the behavioural health ecosystem. In clinical practice, providers could use severity and instability to aid in making decisions, such as which patients might benefit from intensive interventions. Hospitals and health systems could use this information in planning the provision of care, such as predicting the need for psychiatric hospital beds. Finally, researchers could use these findings to accelerate the development of new interventions that aim to prevent hospitalisation or to help recruit specific patient cohorts based on their hospitalisation risk.

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