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Searching for disease-related malnutrition using Big Data tools.

María D Ballesteros Pomar1, Begoña Pintor de la Maza1, David Barajas Galindo1

  • 1Servicio de Endocrinología y Nutrición, Hospital Universitario de León, León, España.

Endocrinologia, Diabetes Y Nutricion
|March 10, 2020
PubMed
Summary

Disease-related malnutrition (DRM) is underdiagnosed, affecting 2.47% of hospitalized patients. A Big Data tool, Savana Manager®, identified patient profiles and associated conditions, aiding in better understanding and detection of DRM.

Keywords:
Big dataDesnutriciónDesnutrición relacionada con la enfermedadDisease-related malnutritionElectronic medical historyHistoria clínica electrónicaMalnutrition

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Area of Science:

  • Medical Informatics
  • Clinical Nutrition
  • Health Services Research

Background:

  • Disease-related malnutrition (DRM) is frequently underdiagnosed and underreported, despite its significant negative impact on patient prognosis.
  • The advancement of Big Data and artificial intelligence (AI) in medicine offers new avenues for knowledge generation and clinical insights.

Purpose of the Study:

  • To evaluate the utility of a Big Data tool, Savana Manager®, in identifying and quantifying the prevalence of DRM within a hospital setting.
  • To characterize the demographic and clinical profile of hospitalized patients with DRM.

Main Methods:

  • A retrospective, descriptive study was conducted using the Savana Manager® tool to analyze electronic medical records.
  • The tool automatically extracted clinical information from free text, searching for the term "malnutrition" between January 2012 and December 2017.
  • Patient characteristics with DRM were compared to the general hospitalized population.

Main Results:

  • Only 2.47% (4,446 of 180,279) of hospitalization records included a diagnosis of malnutrition.
  • Patients with DRM were older (mean age 75 vs. 59 years) and had higher in-hospital mortality (7.08% vs. 2.98%) and longer mean hospital stays (8 vs. 5 days).
  • Common comorbidities associated with DRM included heart failure (35%), respiratory infection (23%), urinary infection (20%), and chronic kidney disease (15%).

Conclusions:

  • The underdiagnosis of DRM remains a significant clinical challenge.
  • The Savana Manager® Big Data tool effectively aids in understanding the patient profile associated with DRM and highlights the need for improved detection strategies.