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. 2019 Jul;31(7):993-999.
doi: 10.1007/s40520-018-1033-7. Epub 2018 Sep 6.

Predictive model of length of stay in hospital among older patients

Radcliffe Lisk  1 , Mahir Uddin  2 , Anita Parbhoo  1 , Keefai Yeong  1 , David Fluck  3 , Pankaj Sharma  2 , Michael E J Lean  4 , Thang S Han  5   6
Affiliations

Predictive model of length of stay in hospital among older patients

Radcliffe Lisk et al. Aging Clin Exp Res. 2019 Jul.

Abstract

Background: Most National Health Service (NHS) hospital bed occupants are older patients because of their frequent admissions and prolonged length of stay (LOS). We evaluated demographic and clinical factors as predictors of LOS in a single NHS Trust and derived an equation to estimate LOS.

Methods: Stepwise logistic and linear regressions were used to predict prolonged LOS (upper-quintile LOS > 17 days) and LOS respectively, from demographic factors and acute and pre-existing conditions.

Results: Of 374 (men:women = 127:247) admitted patients (20% to orthogeriatric, 69% to general medical and 11% to surgical wards), median age of 85 years (IQR = 78-90), 77 had acute first hip fracture; 297 had previous hip fracture (median time since previous fracture = 2.4 years) and 21 (7.1%) had recurrent hip fracture, with median time since first fracture = 2.4 years. Median LOS was 6.5 days (IQR = 1.8-14.8), and 38 (10.2%) died after 4.8 days (IQR = 1.6-14.3). Prolonged LOS was associated with discharge to places other than usual residence: OR = 3.1 (95% CI 1.7-5.7), acute stroke: OR = 10.1 (3.7-26.7), acute first hip fractures: OR = 6.8 (3.1-14.8), recurrent hip fractures: OR = 9.5 (3.2-28.7), urinary tract infection/pneumonia: OR = 4.0 (2.1-8.0), other acute fractures: OR = 9.8 (3.0-32.3) and malignancy: OR = 15.0 (3.1-71.8). Predictive equation showed estimated LOS was 11.6 days for discharge to places other than usual residence, 15 days for pre-existing or acute stroke, 9-14 days for acute and recurrent hip fractures, infections, other acute fractures and malignancy; these factors together explained 32% of variability in LOS.

Conclusions: A useful estimate of outcome and LOS can be made by constructing a predictive equation from information on hospital admission, to provide evidence-based guidance for resource requirements and discharge planning.

Keywords: Bed occupancy; Discharge destination; Frailty; Geriatrics; Health economics; NHS.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of patient cohort investigated in this study
Fig. 2
Fig. 2
Cumulative variances in LOS in hospital explained by discharge destination and a number of clinical factors
See this image and copyright information in PMC

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