Malnutrition risk and severity: Impact on patient outcomes and financial hospital reimbursement in a tertiary teaching hospital

Sophie Marchand; Bruno Lapauw; Kristof Eeckloo; Mieke Deschepper

doi:10.1016/j.clnesp.2022.01.013

Malnutrition risk and severity: Impact on patient outcomes and financial hospital reimbursement in a tertiary teaching hospital

Clin Nutr ESPEN. 2022 Apr:48:386-392. doi: 10.1016/j.clnesp.2022.01.013. Epub 2022 Jan 17.

Authors

Sophie Marchand¹, Bruno Lapauw², Kristof Eeckloo³, Mieke Deschepper⁴

Affiliations

¹ Centre for Healthy Food and Dietetics, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium. Electronic address: Sophie.marchand@uzgent.be.
² Department of Endocrinology, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Internal Medicine and Pediatrics, Ghent University, C. Heymanslaan 10, 9000, Ghent, Belgium.
³ Strategic Policy Cell, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Public Health and Primary Care, Ghent University, C. Heymanslaan 10, Ghent, Belgium.
⁴ Strategic Policy Cell, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium.

PMID: 35331518
DOI: 10.1016/j.clnesp.2022.01.013

Abstract

Background & aims: Despite its negative impact on patients and health care expenditures, malnutrition remains an under-recognized problem in hospitals. The objectives were thus: 1) to study the prevalence of malnutrition risk, protein-calorie malnutrition and cachexia in a Belgian tertiary care hospital, 2) to evaluate the impact thereof on patient outcomes, and 3) to evaluate the impact of optimizing malnutrition screening, diagnosing, registration and coding on hospital reimbursement.

Methods: Data was included from all multi-day admissions between January 1, 2017 and December 31, 2019. The NRS2002 was used as screening tool. Patient outcomes were modeled using (generalized) linear mixed models, with pathology and severity of illness as random effects. The financial impact of the screening, diagnosing and registration process was evaluated comparing net revenues related to a malnutrition diagnosis in the year before (2017) and the year after (2019) the optimization process.

Results: 55,345 patients were evaluated for malnutrition risk at admission of whom 23.6% are considered malnourished or at risk for malnutrition, 0.6% have cachexia and 4.6% protein-calorie malnutrition. Overall length of stay is 2.2 days (p < 0.001) longer in the at-risk population, and 6.2 and 5.0 days longer in patients with cachexia and protein-calorie malnutrition as compared to patients not at risk. Odds ratio for in-hospital mortality is 2.9 (p < 0.001) for the at-risk patients and 3.0 (p < 0.001) for patients with cachexia. Optimization of dietetic workflow and registration, specifying malnutrition severity and facilitating malnutrition coding can increase hospital reimbursement, with approximately 0.4% of all justified beds.

Conclusions: Malnutrition still affects both patients and health care finances. Patients at risk for, or having malnutrition at admission have worse outcomes than those without. Importantly, hospital reimbursement for these patients can effectively be increased by implementing an automated nutritional screening and diagnosing protocol with optimized dietetic registration and enhanced nutritional coding.

Keywords: Hospital reimbursement; Malnutrition; NRS2002; Protein-calorie malnutrition; Risk assessment.

MeSH terms

Hospitals, Teaching
Humans
Malnutrition* / diagnosis
Malnutrition* / epidemiology
Nutrition Assessment*
Nutritional Status
Tertiary Care Centers