BackgroundDifferentiating mild cognitive impairment (MCI) from dementia is important, as treatment options differ. There are few short (<5 min) but accurate screening tools that discriminate between MCI, normal cognition (NC) and dementia, in the Dutch language. The Quick Mild Cognitive Impairment (Qmci) screen is sensitive and specific in differentiating MCI from NC and mild dementia. Given this, we adapted the Qmci for use in Dutch-language countries and validated the Dutch version, the Qmci-D, against the Dutch translation of the Standardised Mini-Mental State Examination (SMMSE-D).MethodThe Qmci was translated into Dutch with a combined qualitative and quantitative approach. In all, 90 participants were recruited from a hospital geriatric clinic (25 with dementia, 30 with MCI, 35 with NC). The Qmci-D and SMMSE-D were administered sequentially but randomly by the same trained rater, blind to the diagnosis.ResultsThe Qmci-D was more sensitive than the SMMSE-D in discriminating MCI from dementia, with a significant difference in the area under the curve (AUC), 0.73 compared to 0.60 (p = 0.024), respectively, and in discriminating dementia from NC, with an AUC of 0.95 compared to 0.89 (p = 0.006). Both screening instruments discriminated MCI from NC with an AUC of 0.86 (Qmci-D) and 0.84 (SMMSE-D).ConclusionThe Qmci-D shows similar,(good) accuracy as the SMMSE-D in separating NC from MCI; greater,(albeit fair), accuracy differentiating MCI from dementia, and significantly greater accuracy in separating dementia from NC. Given its brevity and ease of administration, the Qmci-D seems a useful cognitive screen in a Dutch population. Further study with a suitably powered sample against more sensitive screens is now required.

BackgroundDifferentiating mild cognitive impairment (MCI) from dementia is important, as treatment options differ. There are few short (<5 min) but accurate screening tools that discriminate between MCI, normal cognition (NC) and dementia, in the Dutch language. The Quick Mild Cognitive Impairment (Qmci) screen is sensitive and specific in differentiating MCI from NC and mild dementia. Given this, we adapted the Qmci for use in Dutch-language countries and validated the Dutch version, the Qmci-D, against the Dutch translation of the Standardised Mini-Mental State Examination (SMMSE-D).MethodThe Qmci was translated into Dutch with a combined qualitative and quantitative approach. In all, 90 participants were recruited from a hospital geriatric clinic (25 with dementia, 30 with MCI, 35 with NC). The Qmci-D and SMMSE-D were administered sequentially but randomly by the same trained rater, blind to the diagnosis.ResultsThe Qmci-D was more sensitive than the SMMSE-D in discriminating MCI from dementia, with a significant difference in the area under the curve (AUC), 0.73 compared to 0.60 (p = 0.024), respectively, and in discriminating dementia from NC, with an AUC of 0.95 compared to 0.89 (p = 0.006). Both screening instruments discriminated MCI from NC with an AUC of 0.86 (Qmci-D) and 0.84 (SMMSE-D).ConclusionThe Qmci-D shows similar,(good) accuracy as the SMMSE-D in separating NC from MCI; greater,(albeit fair), accuracy differentiating MCI from dementia, and significantly greater accuracy in separating dementia from NC. Given its brevity and ease of administration, the Qmci-D seems a useful cognitive screen in a Dutch population. Further study with a suitably powered sample against more sensitive screens is now required.

BackgroundPredicting risk of adverse healthcare outcomes, among community dwelling older adults, is difficult. The Risk Instrument for Screening in the Community (RISC) is a short (2–5 min), global subjective assessment of risk created to identify patients’ 1-year risk of three outcomes:institutionalisation, hospitalisation and death.MethodsWe compared the accuracy and predictive ability of the RISC, scored by Public Health Nurses (PHN), to the Clinical Frailty Scale (CFS) in a prospective cohort study of community dwelling older adults (n = 803), in two Irish PHN sectors. The area under the curve (AUC), from receiver operating characteristic curves and binary logistic regression models, with odds ratios (OR), compared the discriminatory characteristics of the RISC and CFS.ResultsFollow-up data were available for 801 patients. The 1-year incidence of institutionalisation, hospitalisation and death were 10.2, 17.7 and 15.6 % respectively. Patients scored maximum-risk (RISC score 3,4 or 5/5) at baseline had a significantly greater rate of institutionalisation (31.3 and 7.1 %, p < 0.001), hospitalisation (25.4 and 13.2 %, p < 0.001) and death (33.5 and 10.8 %, p < 0.001), than those scored minimum-risk (score 1 or 2/5). The RISC had comparable accuracy for 1-year risk of institutionalisation (AUC of 0.70 versus 0.63), hospitalisation (AUC 0.61 versus 0.55), and death (AUC 0.70 versus 0.67), to the CFS. The RISC significantly added to the predictive accuracy of the regression model for institutionalisation (OR 1.43, p = 0.01), hospitalisation (OR 1.28, p = 0.01), and death (OR 1.58, p = 0.001).ConclusionFollow-up outcomes matched well with baseline risk. The RISC, a short global subjective assessment, demonstrated satisfactory validity compared with the CFS.

BackgroundPredicting risk of adverse healthcare outcomes, among community dwelling older adults, is difficult. The Risk Instrument for Screening in the Community (RISC) is a short (2–5 min), global subjective assessment of risk created to identify patients’ 1-year risk of three outcomes:institutionalisation, hospitalisation and death.MethodsWe compared the accuracy and predictive ability of the RISC, scored by Public Health Nurses (PHN), to the Clinical Frailty Scale (CFS) in a prospective cohort study of community dwelling older adults (n = 803), in two Irish PHN sectors. The area under the curve (AUC), from receiver operating characteristic curves and binary logistic regression models, with odds ratios (OR), compared the discriminatory characteristics of the RISC and CFS.ResultsFollow-up data were available for 801 patients. The 1-year incidence of institutionalisation, hospitalisation and death were 10.2, 17.7 and 15.6 % respectively. Patients scored maximum-risk (RISC score 3,4 or 5/5) at baseline had a significantly greater rate of institutionalisation (31.3 and 7.1 %, p < 0.001), hospitalisation (25.4 and 13.2 %, p < 0.001) and death (33.5 and 10.8 %, p < 0.001), than those scored minimum-risk (score 1 or 2/5). The RISC had comparable accuracy for 1-year risk of institutionalisation (AUC of 0.70 versus 0.63), hospitalisation (AUC 0.61 versus 0.55), and death (AUC 0.70 versus 0.67), to the CFS. The RISC significantly added to the predictive accuracy of the regression model for institutionalisation (OR 1.43, p = 0.01), hospitalisation (OR 1.28, p = 0.01), and death (OR 1.58, p = 0.001).ConclusionFollow-up outcomes matched well with baseline risk. The RISC, a short global subjective assessment, demonstrated satisfactory validity compared with the CFS.