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64 Making big data meaningful to the patient: advancing towards better care of CKD
  1. Augusto Cesar Soares dos Santos1,2,
  2. Carlos Roberto Santos1,
  3. Ana Carolina Aguiar Nascimento1
  1. 1Prefeitura Municipal, Contagem, Brazil
  2. 2Faculdade Ciencias Medicas de Minas Gerais, Belo Horizonte, Brazil


Objectives The use of pre-specified algorithms in the interpretation of big data is still an underexplored tool. Currently, many believe artificial intelligence (AI) could serve to improve clinical decision-making by correcting weakness in the care plan of patients with chronic diseases. In Brazil, the number of patients with chronic kidney disease (CKD) is rapidly increasing. Despite that, we still have few population-based studies on CKD especially regarding patients transitioning from conservative ambulatory care to renal replacement therapy (RRT). In general, outcomes in late-stage CKD are believed to be suboptimal, reflecting practices as late referral to nephrologists, fragmentation of care, inadequate patient education, low adherence to protocols and poor communication. In a scenario of scarce data, it´s difficult to define standards and therefore measure improvements. One major challenge in this filed is to transform data into relevant information capable to indicate the need for changes to improve the process of care.

Method This study aimed to investigate the characteristics of patients initiating renal replacement therapy at the city of Contagem, Minas Gerais, Brazil. All patients were assisted by the Brazilian Public Healthcare System, Sistema Unico de Saude, SUS. All patients initiating RRT from 2012 to 2018, were included. Data were collected prospectively, starting in 2016, with the help of a standardized template. A set of pre-defined questions were formulated aiming to assess patients for their characteristics at the time of initiation of RRT: creatinine clearance, definitive vascular access, primary modality RRT. All data were stored in a databank with the help of a mobile app. For a better assessment of the availability/usage of local healthcare resources, multiple layers of information were used (which included the georeferenced informed address). Privacy of subjects and the confidentiality of their personal information were handled following the ethical principles of the Declaration of Helsinki.

Results During the study period, 2.436 patients (mean age 53.6 SD 14.8 years; male 55.1%) initiated RRT. 87.7% initiated RRT while hospitalized (emergency care or intensive care unit). 95.1% had a double-lumen dialysis catheter as first vascular access. DM was the most prevalent isolated cause for CKD (35.1%). Peritoneal dialysis was the initial treatment modality for 4.4%. Most patients, despite living in the city of Contagem, initiated RRT in healthcare facilities in the city of Belo Horizonte, the capital of the State of Minas Gerais, and were later transferred.

Conclusions The practice of monitoring the delivered care allows clear opportunities for improvements, setting the stage to encourage change in our current clinical practices. Unquestionably, the use of big data and artificial intelligence is an exciting field of research allowing standards of care to be contextualized in the view of local practices and the availability of local healthcare resources. It is still a matter of debate whether such innovations will necessarily result in improved informed decision-making and how it will affect both the quality and the costs of the delivered healthcare. In our location, despite the well-known recommendations on how to best transition patients with advanced CKD to RRT, the results suggested the care for patients with CKD still pose important challenges. The importance of dialysis modality planning, definitive vascular access, and elective initiation RRT seems to be underestimated in real-life demanding immediate improvements in the current established plan of care.

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