Predictive Validity of Body Mass Index Cutoff Values Used in the Global Leadership Initiative on Malnutrition Criteria for Discriminating Severe and Moderate Malnutrition Based on In‐Patients With Pneumonia in Asians

Abstract

Background This study aimed to investigate the predictive validity of the previously reported body mass index (BMI) cutoff values of the Global Leadership Initiative on Malnutrition (GLIM) criteria for patients with pneumonia in Asians, using a nationwide registry database. Methods The study included 26,098 (2282 patients aged <70 years and 23,784 patients aged ≥70 years) patients with pneumonia aged ≥65 years who were enrolled in the Japan Medical Data Center database between April 2014 and December 2018. Malnutrition was diagnosed using the GLIM criteria, and the severity of malnutrition was diagnosed by using a BMI cutoff value of <17.0 and <17.8 for patients aged <70 and ≥70 years, respectively. Multivariate analysis was performed to determine whether the severity of malnutrition based on BMI was associated with poor clinical outcomes, such as 30‐day in‐hospital mortality, length of hospital stay (LOS), and 30‐day readmission. Results A total of 14.7% and 24.1% of patients aged <70 and ≥70 years, respectively, experienced severe malnutrition. Severe malnutrition was independently associated with 30‐day in‐hospital mortality (hazard ratio [HR], 1.19; 95% CI, 1.05–1.34), prolonged LOS (coefficient, 5.13; 95% CI, 4.31–5.94), and 30‐day readmission (HR, 2.28; 95% CI, 1.89–2.75) in patients aged ≥70 years; however, only prolonged LOS was independently associated with patients aged <70 years (coefficient, 3.27; 95% CI, −0.24–6.78). Conclusion The BMI cutoff values might be valid for patients with pneumonia aged ≥70 years in Asians. Further investigation is required to validate the cutoff value for older adults aged <70 years.

Utilization and validation of the Global Leadership Initiative on Malnutrition (GLIM): A scoping review

“…Within healthcare system outcomes, mortality was reported most frequently (n ¼ 33; 27%) [16,22,23,25,26,28e31, 34,37e42,44,45,53,55,57,60,61,65,67,68,73e75,80,83,85,89]. Other outcomes were hospitalization/readmissions/costs (n ¼ 11; 9%) [27,29,34,38,44,62,67,69,83,85,91], hospital and/or intensive care unit LOS (n ¼ 10; 8%) [27,34,44,53,65,67,77,83,91], complications (n ¼ 7; 6%) [53e55, 68,72,85], critical care admission (n ¼ 2; 2%) [42,67], and institutionalization (n ¼ 2; 2%) [16,29]. Patient outcomes included sarcopenia (n ¼ 6; 5%) [17,18,24,35,38,93], physical function (n ¼ 5; 4%) [38,57,75,80,93], quality of life (n ¼ 5; 4%) [56,59,64,74,75], falls/fractures (n ¼ 3; 2%) [29,38], frailty (n ¼ 2; 2%)…”

“…Fourteen (23%) studies were retrospective; of these, the types of validity reported were concurrent (n ¼ 1; 7%) [40], concurrent, predictive (n ¼ 2; 14%) [41,57], predictive (n ¼ 10; 71%) [16,26,27,31,34,39,53,68,77,84], and predictive, construct (n ¼ 1; 7%) [32]. Two (14%) studies applied SGA as the semi-gold standard for validation [40,41], and 1 (7%) applied the ESPEN criteria [57].…”

Accuracy of the Simplified Nutritional Appetite Questionnaire for Malnutrition and Sarcopenia Screening among Older Patients Requiring Rehabilitation

“…However, previous studies have not validated the accuracy of the SNAQ against the Global Leadership Initiative on Malnutrition (GLIM) criteria, used to diagnose undernutrition in various regions and ethnicities [ 7 ]. The GLIM criteria are highly sensitive in diagnosing undernutrition in older inpatients and have good predictive validity for poor prognosis [ 18 , 19 , 20 , 21 ]. Therefore, determining the sensitivity and specificity of the SNAQ to the GLIM criteria may provide opportunities for interventions for both poor appetite and undernutrition.…”

Risk of Weight Loss in Adult Patients and the Effect of Staffing Registered Dietitians in Kaifukuki (Convalescent) Rehabilitation Wards: A Retrospective Analysis of a Nationwide Survey

“…However, previous reports indicated a strong correlation between routinely collected data on body weight and the data on body weight measured by standardised methods [ 24 , 25 ]. Moreover, numerous studies that were published recently have reported on real-world (unstandardised) body weight [ 14 , 35 , 36 , 37 , 38 , 39 ]. Thus, the data on body weight reported in our study may be reliable and represent a ‘real-world situation’ that is not fully standardised for body weight measurements.…”