Prevalence and impact of malnutrition on length of stay, readmission, and discharge destination

Abstract The Nottingham Hip Fracture Score (NHFS) has been developed for predicting 30-day and 1-year mortality after hip fracture. We hypothesise that NHFS may also predict other adverse events. Data from 666 patients (190 men, 476 women), aged 60.2–103.4 years, admitted with a hip fracture to a single centre from 1/10/2015 and 7/12/2017 were analysed. The ability of NHFS to predict mobility within 1 day after surgery, length of stay (LOS) find mortality, and discharge destination was evaluated by receiver operating characteristic curves and two-graph plots. The area under the curve (95% confidence interval [CI]) for predicting mortality was 67.4% (58.4–76.4%), prolonged LOS was 59.0% (54.0–64.0%), discharge to residential/nursing care was 62.3% (54.0–71.5%), and any two of failure to mobilise, prolonged LOS or discharge to residential/nursing care was 64.8% (59.0–70.6%). NHFS thresholds at 4 and 7 corresponding to the lower and upper limits of intermediate range where sensitivity and specificity equal 90% were identified for mortality and prolonged LOS, and 4 and 6 for discharge to residential/nursing care, which were used to create three risk categories. Compared with the low risk group (NHFS = 0–4), the high risk group (NHFS = 7–10 or 6–10) had increased risk of in-patient mortality: rates = 2.0% versus 7.1%, OR (95% CI) = 3.8 (1.5–9.9), failure to mobilise within 1 day of surgery: rates = 18.9% versus 28.3%, OR = 1.7 (1.0–2.8), prolonged LOS (> 17 days): rates = 20.3% versus 33.9%, OR = 2.2 (1.3–3.3), discharge to residential/nursing care: rates = 4.5% vs 12.3%, OR = 3.0 (1.4–6.4), and any two of failure to mobilise, prolonged LOS or discharge to residential/nursing care: rates = 10.5% versus 28.6%, 3.4 (95% CI 1.9–6.0), and stayed 4.1 days (1.5–6.7 days) longer in hospital. High NHFS associates with increased risk of mortality, prolonged LOS and discharge to residential/nursing care, lending further support for its use to identify adverse events.

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074DOES “FORM” PREDICT FUNCTION; USING SCORES TO PREDICT LENGTH OF STAY AND DISCHARGE DESTINATION

Age and Ageing ◽

10.1093/ageing/afw159.96 ◽

2016 ◽

Vol 45 (suppl 2) ◽

pp. ii13.204-ii56

Author(s):

Sarah Mc Nally ◽

Roisin Howlin ◽

Annette Keogh ◽

Emer Mc Inerney ◽

Sarah Murphy

Keyword(s):

Length Of Stay ◽

Discharge Destination

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Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

Australian Health Review ◽

10.1071/ah09806 ◽

2011 ◽

Vol 35 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Friedbert Kohler ◽

Roger Renton ◽

Hugh G. Dickson ◽

John Estell ◽

Carol E. Connolly

Keyword(s):

Length Of Stay ◽

Principal Components Analysis ◽

Principal Components ◽

Explanatory Variable ◽

Inpatient Rehabilitation ◽

Medical Rehabilitation ◽

Functional Improvement ◽

Discharge Destination ◽

Components Analysis ◽

Classification Tool

Objective. We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. Method. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. Results. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. Conclusion. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes. What is known about the topic? AN-SNAP v2, a major classification tool for inpatient rehabilitation units has been described and used in a small number of published studies. The ability to predict variance by AN-SNAP v2 has not been previously described. What does this paper add? This paper indicates that AN-SNAP v2 is not a good predictor of outcomes in patients in medical rehabilitation units, challenging its utility as a classification tool. What are the implications for practitioners? Practitioners will have a broader understanding of the strengths and weaknesses of the AN-SNAP v2 classification.

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