Background and aims the enlargement of the spleen and liver as the presenting sign of a paediatric patient, is of immediate importance and must alert the clinical team about the importance of the disease they are facing.

Furthermore, epidemiology is constantly changing. The diseases that impact our population today are not the same that clinicians encountered 20 years ago. We must continue investigating our own epidemiology at the same rate that it is changing in order to determine the diseases that have the most impact in each of our individual environments.

Our team conducted research to further understand the change in the epidemiology of the paediatric patients that arrive to our institution with hepatosplenomegaly, the goal is to know the epidemiology and later create a new algorithm that could diminish the costs in diagnostic procedures and make a diagnosis in a timely manner.

Material and methods A retrospective, observational and descriptive investigation through the exhaustive review of files from all the patients with hepatosplenomegaly arriving to our institution from the year 2004 to 2016, without clear aetiology. We developed a database that allowed us to sort the diagnoses by frequency in order to understand which diagnosis required more focus and, therefore, more time and resources.

Results 81 files were reviewed and classified into six categories upon final diagnosis: metabolic disease (22%), anatomic disease (21%), infectious disease (18.5%), rheumatologic disease (17.3%), oncologic disease (10%), and hematologic disease (8.6%). Patients with a diagnosis of leukaemia upon arrival to our institution were excluded.

Conclusion Infectious and tropical diseases have diminished as the leading cause of hepatosplenomegaly in our hospital; other causes have resurfaced as a consequence of the change in our epidemiology following better public health policies and universal vaccination. With this information we concluded that there is a need for better diagnostic tools, justifying the development of a new algorithm, built from the information found in our study.