In-patient malnutrition leads to poor outcomes and mortality, and it is largely uninvestigated in non-urban populations. This study sought to: (1) retrospectively estimate the prevalence of malnutrition as diagnosed by dietetics in the rural Australian setting; (2) establish the proportion of all patients at “nutritional risk”; and (3) explore associations between demographic and clinical factors with malnutrition diagnosis and nutritional risk. A retrospective census was undertaken of medical files of all patients aged ≥18 years admitted to a rural hospital setting over a 12-month period. Logistic regression was used to explore associations between malnutrition diagnosis, nutritional risk and patient-related factors. In total, 711 admissions were screened during the 12-month period comprising 567 patients. Among the 125 patients seen by dietitians, 70.4% were diagnosed with malnutrition. Across the total sample, 77.0% had high levels of nutrition related symptoms warranting a need for further assessment by dietitians. Malnutrition diagnosis by dietitians was associated with being over the age of 65 years, and patients had higher odds of being admitted to a residential aged care facility following discharge. In this rural sample, the diagnosis rate of malnutrition appeared to be high, indicating that rural in-patients may be at a high risk of malnutrition. There was also a high proportion of patients who had documentation in their files that indicated they may have benefited from dietetic assessment and intervention, beyond current resourcing.

This study aimed to explore the diagnostic accuracy of the Patient-Generated Subjective Global Assessment (PG-SGA) malnutrition risk screening tool when used to score patients based on their electronic medical records (EMR), compared to bedside screening interviews. In-patients at a rural health service were screened at the bedside (n = 50) using the PG-SGA, generating a bedside score. Clinical notes within EMRs were then independently screened by blinded researchers. The accuracy of the EMR score was assessed against the bedside score using area under the receiver operating curve (AUC), sensitivity, and specificity. Participants were 62% female and 32% had conditions associated with malnutrition, with a mean age of 70.6 years (SD 14.9). The EMR score had moderate diagnostic accuracy relative to PG-SGA bedside screen, AUC 0.74 (95% CI: 0.59–0.89). The accuracy, specificity and sensitivity of the EMR score was highest for patients with a score of 7, indicating EMR screen is more likely to detect patients at risk of malnutrition. This exploratory study showed that applying the PG-SGA screening tool to EMRs had enough sensitivity and specificity for identifying patients at risk of malnutrition to warrant further exploration in low-resource settings.