Development and Technical Validation of a Novel Digital Decision Support System for Automated CONUT Scoring and Anemia Risk Screening in Outpatient Care
Authors
- Sofía Villar Yáñez Universidad Católica del Maule, Facultad de Medicina, Escuela de Bioingeniería Médica, Talca 3480112, Chile.
- Miguel Alfonzo Gómez Hospital Regional de Talca, Departamento de Hematología, Talca 3480112, Chile.
- Noralvis Fleitas-Salazar Universidad Católica del Maule, Facultad de Medicina, Departamento de Medicina Traslacional, Laboratorio de Ingeniería con Biocomponentes, Talca 3480112, Chile.
-
Seidy Pedroso-Santana
Universidad Autónoma de Chile, Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Chile.
https://orcid.org/0000-0002-2060-1247
DOI:
https://doi.org/10.70099/BJ/2026.03.01.12Keywords:
Anemia, Nutritional Screening, Bioinformatics, Clinical Decision Support System, Automated Diagnosis, Primary Care, MalnutritionAbstract
Anemia represents a critical global public health challenge with high prevalence among women of reproductive age and children under five years. Despite its impact, early detection in outpatient settings remains limited due to the scarcity of objective tools that systematically integrate nutritional and hematological parameters for screening purposes. While the Controlling Nutritional Status (CONUT) index is a validated predictor of clinical outcomes, its application is often restricted to hospital settings and relies on manual calculation, which is prone to error and hinders adoption in primary care. In this study, we introduce CONUT-Digital, a novel digital tool designed not only to automate CONUT calculations but to explicitly integrate an automated anemia risk assessment directly into the nutritional screening workflow, addressing a critical gap in digital health tools for primary care.
A retrospective study was conducted to validate the system using 60 real, anonymized clinical records selected to cover the full spectrum of nutritional risk categories. The CONUT-Digital system was compared against manual reference methods, demonstrating 100% agreement in total scores and nutritional classification while successfully processing multiple lymphocyte input formats. These results confirm that digitalization through this tool standardizes the assessment process, minimizes human error, and functions as a strategic screening mechanism to facilitate early detection of anemia risk. By linking routine biochemical parameters to automated clinical risk stratification, CONUT-Digital provides a robust support system for prioritizing nutritional interventions and strengthening public health management.
References
1. Anemia - What Is Anemia? | NHLBI, NIH [Internet]. U.S. Department of Health and Human Services; 2022 [cited 2026 March 9]. Available from: https://www.nhlbi.nih.gov/health/anemia
2. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(12):e935–e950. doi:10.1016/S2352-3026(23)00160-6
3. Karakochuk CD, Hess SY, Moorthy D, Namaste S, Parker ME, Rappaport AI, et al. Measurement and interpretation of hemoglobin concentration in clinical and field settings: a narrative review. Ann N Y Acad Sci. 2019;1450(1):126–146. doi:10.1111/nyas.14003
4. World Health Organization, editor. Guideline on haemoglobin cutoffs to define anaemia in individuals and populations. Geneva: World Health Organization; 2024.
5. Miano N, Di Marco M, Alaimo S, Coppolino G, L’Episcopo G, Leggio S, et al. Controlling Nutritional Status (CONUT) Score as a Potential Prognostic Indicator of In-Hospital Mortality, Sepsis and Length of Stay in an Internal Medicine Department. Nutrients. 2023;15(7):1554. doi:10.3390/nu15071554
6. Ignacio de Ulíbarri J, González-Madroño A, de Villar NGP, González P, González B, Mancha A, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38–45. doi:10.3305/nh.2005.20.1.4758
7. Lo Buglio A, Bellanti F, Carmignano DFP, Serviddio G, Vendemiale G. Association between Controlling Nutritional Status (CONUT) Score and Body Composition, Inflammation and Frailty in Hospitalized Elderly Patients. Nutrients. 2024;16(5):576. doi:10.3390/nu16050576
8. Xu D, Shen R, Hu M, Fan Q, Wu J. Prognostic impact of CONUT score in older patients with chronic heart failure. BMC Geriatr. 2024;24(1):738. doi:10.1186/s12877-024-05330-5
9. Kato T, Yaku H, Morimoto T, Inuzuka Y, Tamaki Y, Yamamoto E, et al. Association with Controlling Nutritional Status (CONUT) Score and In-hospital Mortality and Infection in Acute Heart Failure. Sci Rep. 2020;10(1):3320. doi:10.1038/s41598-020-60404-9
10. Pagliaro R, Scalfi L, Di Fiore I, Leoni A, Masi U, D’Agnano V, et al. Controlling Nutritional Status (CONUT) Score as a Predictor of Prognosis in Non-Small Cell Lung Cancer. Nutrients. 2025;17(21):3416. doi:10.3390/nu17213416
11. World Bank Open Data [Internet]. Washington, DC: The World Bank Group; 2024 [cited 2026 March 9]. Available from: https://data.worldbank.org
12. Grechuta K, Shokouh P, Alhussein A, Müller-Wieland D, Meyerhoff J, Gilbert J, et al. Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review. Interact J Med Res. 2024;13:e58036. doi:10.2196/58036
13. HL7 FHIR. Overview - FHIR v5.0.0 [Internet]. Health Level Seven International; 2023 [cited 2026 March 9]. Available from: https://hl7.org/fhir/overview.html
14. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. Npj Digit Med. 2020;3(1):17. doi:10.1038/s41746-020-0221-y
15. Rinninella E, Borriello R, D'Angelo M, Galasso T, Cintoni M, Raoul P, et al. COntrolling NUTritional Status (CONUT) as Predictive Score of Hospital Length of Stay (LOS) and Mortality: A Prospective Cohort Study in an Internal Medicine and Gastroenterology Unit in Italy. Nutrients. 2023;15(6):1472. doi:10.3390/nu15061472
16. Cederholm T, Barazzoni R, Austin P, Ballmer P, Biolo G, Bischoff SC, et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr. 2017;36(1):49–64. doi:10.1016/j.clnu.2016.09.004
17. Carrillo-Mora P, García-Franco A, Soto-Lara M, Rodríguez-Vásquez G, Pérez-Villalobos J, Martínez-Torres D, et al. Cambios fisiológicos durante el embarazo normal. Rev Fac Med México. 2021;64(1):39–48. doi:10.22201/fm.24484865e.2021.64.1.07
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Copyright (c) 2026 Sofía Villar Yáñez, Miguel Alfonzo Gómez, Noralvis Fleitas-Salazar, Seidy Pedroso-Santana (Author)
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