INTRODUCTIONThe aim of this study was to assess the usefulness of 4 clinical prediction rules, the neuroimaging guidelines from the Canadian Consensus Conference on Dementia (CCCAD) and the modified Hachinski's Ischaemic Score (HIS) in identifying patients with suspected dementia who will benefit from neuroimaging.

MATERIALS AND METHODSTwo hundred and ten consecutive patients were referred to the memory clinic in a geriatric unit for the evaluation of possible dementia. Sensitivity, specificity and likelihood ratios (LR) were calculated for each of the prediction rules and the CCCAD guidelines, in terms of their ability to identify patients with significant lesions [defined firstly as space-occupying lesions (SOL) alone and secondly as SOL or strokes] on neuroimaging. Similar analyses were applied for the HIS in the detection of strokes.

RESULTSWhen considering SOL alone, sensitivities ranged from 28.6% to 100% and specificities ranged from 21.7% to 88.4%. However, when strokes were included in the definition of significant lesions, sensitivities ranged from 16.2% to 79.0% and specificities ranged from 20.9% to 92.4%. The modified HIS had a similarly poor sensitivity and specificity (43.3% and 78.9% respectively). The LR for the clinical decision tools did not support the use of any particular instrument.

CONCLUSIONSClinical decision tools do not give satisfactory guidance for determining the need for neuroimaging patients with suspected dementia, when the detection of strokes, in addition to SOL, is regarded as important. We recommend therefore that neuroimaging be considered for all patients with suspected mild or moderate dementia in whom the potential benefits of any treatment outweigh the potential risks.

Aged ; Dementia ; diagnostic imaging ; Female ; Humans ; Male ; Predictive Value of Tests ; Sensitivity and Specificity ; Severity of Illness Index ; Tomography, X-Ray Computed

INTRODUCTIONThe objective of this study was to determine factors, other than the Diagnostic Related Grouping (DRG), that can explain the variation in the cost of hospitalisation and length of hospital stay (LOS) in older patients.

MATERIALS AND METHODSThis was a prospective, observational cohort study involving 397 patients, aged 65 years and above. Data collected include demographic information, admission functional and cognitive status, overall illness severity score, number of referral to therapists, referral to medical social worker, cost of hospitalisation, actual LOS, discharge DRG codes and their corresponding trimmed average length of stay (ALOS).

RESULTSThe mean age of the cohort was 80.2 years. The DRG's trimmed ALOS alone explained 21% of the variation in the cost of hospitalisation and actual LOS. Incorporation of an illness severity score, number of referral to therapists and referral to medical social worker into the trimmed ALOS explained 30% and 31% of the variation in the cost and actual LOS, respectively.

CONCLUSIONThe DRG model is able to explain 21% of the variation in the cost of hospitalisation and actual LOS in older patients. Other factors that determined the variation in the cost of hospitalisation and LOS include the degree of illness severity, the number of referral to therapists and referral to medical social worker.

Age Factors ; Aged ; Confidence Intervals ; Diagnosis-Related Groups ; Female ; Frail Elderly ; statistics & numerical data ; Health Resources ; economics ; statistics & numerical data ; Health Status Indicators ; Hospitalization ; economics ; statistics & numerical data ; Humans ; Length of Stay ; statistics & numerical data ; trends ; Linear Models ; Male ; Prospective Studies ; Psychometrics ; Referral and Consultation ; Reproducibility of Results ; Severity of Illness Index ; Singapore ; Statistics, Nonparametric