Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.
AI
evaluation
implementation
machine learning
malnutrition
usability/acceptance
Journal
Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
ISSN: 1365-277X
Titre abrégé: J Hum Nutr Diet
Pays: England
ID NLM: 8904840
Informations de publication
Date de publication:
13 Feb 2024
13 Feb 2024
Historique:
revised:
05
01
2024
received:
21
08
2023
accepted:
09
01
2024
medline:
13
2
2024
pubmed:
13
2
2024
entrez:
13
2
2024
Statut:
aheadofprint
Résumé
Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.
Sections du résumé
BACKGROUND
BACKGROUND
Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition.
METHODS
METHODS
This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days.
RESULTS
RESULTS
Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement.
CONCLUSION
CONCLUSIONS
MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : None
Informations de copyright
© 2024 The British Dietetic Association Ltd.
Références
Corkins MR, Guenter P, DiMaria-Ghalili RA, Jensen GL, Malone A, Miller S, et al. Malnutrition diagnoses in hospitalized patients: United States, 2010. J Parenter Enteral Nutr. 2014;38(2):186-195.
Agency for Healthcare Research and Quality. Malnutrition in hospitalized adults. Research Protocol October 30, 2020 [cited 2020 Dec]. Available from: https://effectivehealthcare.ahrq.gov/products/malnutrition-hospitalized-adults/protocol#6
Weiss AJ, Fingar KR, Barrett ML, Elixhauser A, Steiner CA, Guenter P, et al. Characteristics of hospital stays involving malnutrition, 2013: Statistical Brief #210. Rockville, MD: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs; 2006.
Schuetz P, Seres D, Lobo DN, Gomes F, Kaegi-Braun N, Stanga Z. Management of disease-related malnutrition for patients being treated in hospital. Lancet. 2021;398(10314):1927-1938.
Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition-a consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1-9.
Chao PC, Chuang HJ, Tsao LY, Chen PY, Hsu CF, Lin HC, et al. The Malnutrition Universal Screening Tool (MUST) and a nutrition education program for high risk cancer patients: strategies to improve dietary intake in cancer patients. Biomedicine. 2015;5(3):17.
Stratton RJ, Hackston A, Longmore D, Dixon R, Price S, Stroud M, et al. Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the ‘malnutrition universal screening tool’ (‘MUST’) for adults. Br J Nutr. 2004;92(5):799-808.
Tobert CM, Mott SL, Nepple KG. Malnutrition diagnosis during adult inpatient hospitalizations: analysis of a multi-institutional collaborative database of academic medical centers. J Acad Nutr Diet. 2018;118(1):125-131.
Yin L, Song C, Cui J, Lin X, Li N, Fan Y, et al. A fusion decision system to identify and grade malnutrition in cancer patients: machine learning reveals feasible workflow from representative real-world data. Clin Nutr. 2021;40(8):4958-4970.
Sharma V, Sharma V, Khan A, Wassmer DJ, Schoenholtz MD, Hontecillas R, et al. Malnutrition, health and the role of machine learning in clinical setting. Front Nutr. 2020;7:44.
Timsina P, Joshi HN, Cheng FY, Kersch I, Wilson S, Colgan C, et al. MUST-Plus: a machine learning classifier that improves malnutrition screening in acute care facilities. J Am Coll Nutr. 2020;40(1):3-12.
ASPEN Criteria. Available from: https://www.hcpro.com/HIM-282409-3288/New-malnutrition-criteria-could-help-ensure-consistent-coding.html. Accessed September, 2021.
White JV, Guenter P, Jensen G, Malone A, Schofield M, Academy Malnutrition Work Group. Consensus statement: academy of nutrition and dietetics and american society for parenteral and enteral nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). JPEN J Parenter Enteral Nutr. 2012;36(3):275-283. https://doi.org/10.1177/0148607112440285
Malone A, Hamilton C. The Academy of Nutrition and Dietetics/the American Society for Parenteral and Enteral Nutrition consensus malnutrition characteristics: application in practice. Nutr Clin Pract. 2013;28(6):639-650. https://doi.org/10.1177/0884533613508435
Flury BK, Riedwyl H. Standard distance in univariate and multivariate-analysis. Am Stat. 1986;40(3):249-251.
Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput. 2009;38(6):1228-1234.
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: L. Erlbaum Associates; 1988.
Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305-307. https://doi.org/10.1038/d41586-019-00857-9
Faraone SV. Interpreting estimates of treatment effects: implications for managed care. PT. 2008;33(12):700-711.
Keilwagen J, Grosse I, Grau J. Area under precision-recall curves for weighted and unweighted data. PLoS One. 2014;9(3):e92209.
Kuhn M. caret: Classification and regression training. 2021 [cited 2024 Jan]. Available from: https://cran.r-project.org/web/packages/caret/caret.pdf
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77.