Comparison of verbal and computerised months backwards tests in a hospitalized older population
Background
Delirium is extremely prevalent, yet underdiagnosed, in older patients and is associated with prolonged length of hospital stay and higher mortality rates. Impaired attention is the cardinal defcit in delirium and is a required feature in diagnostic criteria. The verbal months backwards test (MBT) is the most sensitive bedside test of attention, however, hospital staf occasionally have difculty with its administration and interpretation. We hypothesise that the MBT on an electronic tablet may be easier and more consistent to use for both experienced and unexperienced medical professionals and, if the diagnostic efcacy was similar, aid delirium diagnosis.
Aim
We aim to investigate the correlation of the verbal MBT with a computerised MBT application.
Methods
Participants recruited (age>65, n=75) were allocated to diferent cohorts (Dementia and Delirium (DMDL), Dementia (DM), Delirium (DL), No Neurocognitive Disorder (NNCD)) and were administered both the verbal and electronic versions.
Results
Correlation between measurements were: overall Spearman’s rho= 0.772 (p < 0.0001); DMDL rho = 0.666 (p<0.0001); DL rho=0.778 (p=0.039); DM rho=0.378 (p=0.203); NNCD rho=0.143 (p=0.559).
Discussion
Overall, and for the delirious subset, statistically signifcant agreement was present. Poor inter-test correlation existed in the groups without delirium (DM, NNCD).
Conclusions
The MBTc correlates well with the MBTv in patients who are clinically suspected to have delirium but has poor correlation in patients without delirium. Visuospatial cognition and psychomotor defcits in a dementia cohort and mechanical factors (such as tremor, poor fngernail hygiene and visual impairment) in a group with no neurocognitive disorder may limit the utility of the MBTc in a hospitalised older population.
History
Publication
Aging Clinical and Experimental Research, 34, 2713–2719Publisher
SpringerOther Funding information
Open Access funding provided by the IReL Consortium. This research was funded by the Wellcome Trust, 215 Euston Road, London, NW1 2BE, UK.External identifier
Department or School
- Computer Science & Information Systems
- School of Medicine