The goal of this observational study is to train a machine learning system based on data from patients affected by spontaneous Intracranial Hemorrage. The main question it aims to answer is whether there is a correlation between actual clinical pratice, reached outcomes and favorable or unfavorable predictive factors, and anamnesis.

Participants will be treated as per standard clinical practice.

In NeuroICU, treatments typically adhere to guidelines based on scientific consensus. Despite this, the prognosis for patients with intracranial hemorrhages has not significantly improved over recent decades, resulting in generally unsatisfactory outcomes. While randomized controlled trials (RCTs) are considered the gold standard for clinical research, they can be expensive and ethically challenging to conduct. Observational studies provide an alternative method, offering larger datasets covering longer periods, which can be more beneficial and feasible for certain research endeavors.

Machine Learning (ML) algorithms, unlike classical statistical methods, have the capability to process a vast number of variables, offering a personalized and dependable approach for healthcare providers in patient management. Recognizing the necessity for well-designed studies to identify optimal therapeutic strategies for neurocritical patients and acknowledging the limitations of existing guidelines, we aim to leverage ML programs to develop an advanced system capable of uncovering data patterns and linking them to potential outcomes.

The BLANDISH project focuses on patients with spontaneous intracerebral hemorrhage (sICH), a condition lacking proven beneficial treatment. By collecting and analyzing data from sICH patients admitted to neuroICU, the project aims to develop a supervised ML algorithm named BLANDISH.

This algorithm will stratify patients based on prognosis, identifying those at highest risk of death and secondary brain injuries. By guiding each patient towards the most targeted therapeutic strategy, the algorithm could help improve patient outcomes and assess the effectiveness of current clinical practices. Additionally, it may enable healthcare staff to better allocate resources and introduce individualized therapeutic programs based on precision medicine, potentially reducing hospitalization times and healthcare costs.

The initial step in developing the BLANDISH algorithm involves collecting clinical data, stored in a structured datalake, which serves as a data repository. This platform will gather information on the clinical course of sICH patients admitted to neuroICU. After data collection, the next steps include preprocessing, variable selection correlated with patient mortality, and algorithm training with input data and internal validation to control its behavior. External validation will follow, involving data collection from various clinical centers to assess the algorithm's reliability and generalization capacity. A multicenter observational clinical study will then be conducted to validate the BLANDISH algorithm, aiming to determine its impact on sICH patient outcomes. This final phase includes a survival study comparing patients in the experimental group (whose treatment is guided by BLANDISH) with those following standard clinical practice. The project aims to demonstrate the superiority of the ML approach over current guidelines, evaluating the accuracy and potential improvements in patient management across different care settings.

Inclusion Criteria:

• Patients who enter the NICU for acute spontaneous intracranial hemorrhage
• Adult patients (≥ 18 years)
• Sex: female, male, intersex

Exclusion Criteria:

• All patients affected by non-spontaneous ICH
• Patients with sICH determined by brain tumor or brain metastases
• All patients affected by chronic ICH
• Pregnant and puerperal women
• Refusal to participate in the protocol

All patients affected by spontaneous Intracranial Hemorrhage.