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Scientific abstract

Suicide is a significant global public health concern, resulting in millions of years of life lost annually. Individuals experiencing self-harm face high risks, yet often receive inadequate care, emphasizing the need for improved management. Emergency departments play a crucial role in suicide prevention, but current approaches lack personalization. The PERMANENS project aims to develop a Clinical Decision Support System (CDSS) to assist clinicians in assessing and managing self-harm patients. This CDSS will provide personalized risk profiles and evidence-based treatment plans based on extensive data and advanced machine learning techniques, ultimately enhancing patient care and reducing suicide mortality.

The PERMANENS CDSS represents a groundbreaking approach to suicide prevention, addressing the challenges of self-harm management in emergency departments. By offering personalized risk assessments and tailored treatment plans, this user-friendly software enhances clinical decision-making, promotes standardized care, and improves patient satisfaction and treatment compliance. It utilizes comprehensive data from various countries and employs cutting-edge machine learning algorithms to predict suicide risk accurately. This innovative system has the potential to revolutionize suicide prevention efforts and significantly reduce suicide-related mortality on a large scale, offering hope for a brighter future in healthcare.

1. Understanding the Gravity of Suicide as a Public Health Issue

Suicide constitutes a major, yet preventable, public health issue, representing an annual loss of 34.6 million years of life worldwide. For each suicide death, there are at least 20 suicide attempts, amounting annually to 16 million attempts and 160 million individuals with suicidal ideation and associated risk for suicide globally, in addition to at least five close family members or friends bereaved through each suicide.

2. Addressing Challenges in Self-Harm Management and Treatment

People with self-harm are at elevated risk for repetition of self-harm, death by suicide, and for inadequate treatment delivery (i.e., care not consistent with clinical recommendations). Only 1 out of 6 patients with self-harm are directly referred to psychiatric services, and only half receive some psychiatric treatment in the months following self-harm. Those directly referred but without attendance have 3 times higher risk for suicide, highlighting the importance of increasing continuity of care among those patients.

3. Emergency Departments: Key Players in Suicide Prevention

To address this, emergency departments are key healthcare settings to detect high-risk patients and to intervene. They often represent the first medical contact after self-harm and can offer specialized risk assessment and referral to psychiatric intervention. Effective suicide prevention interventions exist (e.g., brief contact interventions following discharge after a suicide crisis, short-term psychotherapy), although it is unclear which interventions are most effective for particular suicide risk profiles, suggesting the need for personalised approaches.

4. PERMANENS Project: Objectives and Approach

The PERMANENS project aims to develop a Clinical Decision Support System software prototype that assists clinicians in the assessment and management of patients with self-harm at the emergency department. Trained on evidence accumulated in clinical settings and based on the patient’s particular clinical history, the CDSS will provide the clinician with personalized risk profiles for relevant adverse outcomes, including self-harm, method escalation, death by suicide and other causes, and not following up with proposed treatment. The CDSS will provide an overview of the most important risk factors, and propose an evidence-based treatment plan, tailored to the patient’s specific risk profile.

5. Enhancing Clinical Decision-Making with the PERMANENS Approach

To develop the risk prediction models, population-representative registry data from three countries (Ireland, Norway and Sweden) and one region (Catalonia, Spain) will be used. The OMOP Common Data Model will ensure data interoperability across sites, and a federated analysis approach will eliminate the need for remote access to individual-level data. Data preparation for predictive modelling will include the development of case validation algorithms, the delineation of adverse healthcare trajectories post-discharge, and the creation of a series of clinically relevant predictor variables. Machine learning-based algorithms will be used to develop clinically interpretable prediction models, including state-of-the-art techniques to deal with class imbalance, feature selection, and prediction bias.

The proposed CDSS will enable structured professional judgement, standardization of care, increased patient satisfaction, and higher treatment compliance among patients with self-harm. It will consist of user-friendly and fully functional software, scalable for implementation in entire healthcare systems, enabling the timely and tailored delivery of effective treatment among large populations of patients with suicide risk. Future routine implementation of Clinical Decision Support System for self-harm management at the emergency department has a high potential for effectively reducing suicide mortality in the population.

Gobierno de España Instituto de Salud Carlos III European Union ERA Per Med

The PERMANENS project is supported by Instituto de Salud Carlos III (ISCIII) and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia (AC22/00006; AC22/00045), the Swedish Innovation Agency (no. 2022-00549), the Research Council of Norway (project no. 342386) and the Health Research Board Ireland (ERAPERMED2022) under the frame of ERA PerMed.