Mindlines: a new cohort of real-world data that allows researchers to look beyond diagnoses

Databases are often used for scientific research. The Rob Giel Research centre, a collaboration of the department of Psychiatry at the UMCG and five mental health care institutes, hosts three databases, two with data of adult patients and one with data of elderly patients. In these three cohorts real-world data of patients with psychiatric diagnoses are collected and monitored. But what would be the benefits if the three databases were combined to one big database? Dr. Daniëlle Cath, psychiatrist at GGZ Drenthe and honorary professor mental health innovation at the UMCG, and dr. Edith Liemburg, senior researcher at the UMCG, both involved in the initiation of the new big cohort ‘Mindlines’, explain the benefits.

Databases to monitor patients

Mindlines is a new initiative in which the three existing cohorts will be combined to form one big database. Two of the already existing cohorts are called PHAMOUS and MOPHAR, in which patients who are diagnosed with psychiatric disorder are monitored. These databases are useful investigating somatic comorbidities and side effects of psychotropic drugs, but can also be used to look at symptoms, recovery and lifestyle of the patients. The third existing cohort is called ROM-GPS, in which psychological complaints in elderly are monitored. Cath: “We know that patients with a severe psychiatric disorder die 10 to 20 years earlier than the general population, because patients with a psychiatric disorder generally are at risk to develop physical complaints including cardiovascular disorder. Therefore, MOPHAR and PHAMOUS are initially set up to monitor patients physically. Over time we added other risk factors to the databases, such as childhood trauma and lifestyle factors, and we decided to follow them not only on psychiatric signs and symptoms, but also on quality of life and wellbeing.” 

Mindlines: a new big cohort

Mindlines is the result of the merging and harmonising of the three already existing cohorts. “Mindlines will be used to monitor patients in general on the same parameters, both at various ages in adulthood, because we have two cohorts with adult patients and one with elderly, and over time”, mentions Cath. Monitoring patients in a merged cohort, where these patients are not divided in groups based on their diagnosis, has multiple benefits. Liemburg explains: “We would like to start looking outside the borders of diagnoses and age and with this merged database we can look at a broad spectrum of psychiatric disorders and parameters. By charting the course of disorders, we can investigate ways to provide patients targeted treatment based on their own personal characteristics.” One of the goals of Mindlines is to develop an algorithm with machine learning to provide personalised care. “We search for individual algorithms in the database that help us to discover personal profiles over the course of the disease. Then, when we know that someone has a certain profile, we may predict which treatment options will provide the highest chances of recovery”, explains Cath. 

Added value for research

In Mindlines data of more than 10000 patients from healthcare practices in the northern region of the Netherlands are stored: these comprise real-world data. First, a benefit of real-world data in comparison to data specifically collected for research, is that real-world data are not biased based on specific requirements for research. Using real-world data can be beneficial for researchers who investigate questions that are close to the clinic. Second, Mindlines allows researchers to use each other’s expertise. Cath explains: “In elderly patients researchers look at frailty as a proxy of biological aging, and a reliable predictor for early death and other physical problems. Frailty is potentially useful as a concept in our adult population, which we should investigate in younger adults. For example, in my clinical practice I recently encountered a 30 years old patient with symptoms of frailty. This was apparently a personalised characteristic, apart from the diagnosis, that we have to consider for treatment to improve the patient’s wellbeing.” Developing databases across diagnoses and age can generate a lot of new knowledge and potentially facilitates the possibility to investigate certain themes, apart from diagnosis, such as the effect of trauma in different patient groups over time.