Objective: Administrative data validation is essential for identifying biases and misclassification in research. The objective of this study was to determine the accuracy of diagnostic codes for acute stroke and transient ischemic attack (TIA) using the Ontario Stroke Registry (OSR) as the reference standard. Methods: We identified stroke and TIA events in inpatient and emergency department (ED) administrative data from eight regional stroke centres in Ontario, Canada, from April of 2006 through March of 2008 using ICD–10–CA codes for subarachnoid haemorrhage (I60, excluding I60.8), intracerebral haemorrhage (I61), ischemic (H34.1 and I63, excluding I63.6), unable to determine stroke (I64), and TIA (H34.0 and G45, excluding G45.4). We linked administrative data to the Ontario Stroke Registry and calculated sensitivity and positive predictive value (PPV). Results:: We identified 5,270 inpatient and 4,411 ED events from the administrative data. Inpatient administrative data had an overall sensitivity of 82.2% (95% confidence interval [CI95%]=81.0, 83.3) and a PPV of 68.8% (CI95%=67.5, 70.0) for the diagnosis of stroke, with notable differences observed by stroke type. Sensitivity for ischemic stroke increased from 66.5 to 79.6% with inclusion of I64. The sensitivity and PPV of ED administrative data for diagnosis of stroke were 56.8% (CI95%=54.8, 58.7) and 59.1% (CI95%=57.1, 61.1), respectively. For all stroke types, accuracy was greater in the inpatient data than in the ED data. Conclusion: The accuracy of stroke identification based on administrative data from stroke centres may be improved by including I64 in ischemic stroke type, and by considering only inpatient data.

This study had a twofold goal: to define differences in psychological aspects between cancer patients and a control group and to explore the predictive value of such aspects for the evolution of the disease two years later. Firstly, personality, anxiety, anger and depression were assessed in both groups. Results of t-analyses revealed significant group differences. In personality, cancer patients had higher levels of neuroticism and lower levels of extraversion, agreeableness and conscientiousness than the control group. In emotional variables, cancer patients had higher levels of anxiety and some aspects of anger, but there were no group differences in depression levels. Secondly, applying a quasi-prospective design, the predictive value of personality, emotions and coping styles for the evolution of cancer (favourable or unfavourable) was explored using generalized linear models and logistic regression. A four-predictor logistic model was fitted: Anger Expression-In, Resignation, Self-blame and Conscientiousness, indicating that the higher Anger Expression-in, Resignation, and Self-blame scores together with a lower Conscientiousness score, the more likely it is for patients' cancer to evolve unfavourably. These results indicate the crucial role of psychological aspects for the evolution of the disease and the need to include such aspects in the design of clinical interventions.

Background: Previous studies have found the Mini-mental State Examination (MMSE) predictive of capacity to consent to power of attorney and to enter into residential care. We aimed to assess the utility of the MMSE in predicting capacity to consent to research in elderly U.K. care home residents, to determine the optimal cut-point score, and to ascertain which items of the MMSE most predicted capacity.

Methods: Care home residents (n = 227) who had been recruited into a randomized controlled trial and assessed for capacity to consent were subsequently administered the MMSE and a measure of depression. A receiver operator characteristic (ROC) curve was generated and the maximal MMSE cut-point that best predicted capacity status determined. Multiple regression analyses were conducted to assess the effect of other variables on capacity status.

Results: The area under the ROC curve was 0.86 (p < 0.001, 95% CI 0.80–0.91). The optimal cut-point was 13/14, which provided a positive predictive value of 0.84 (0.79–0.89) and negative predictive value of 0.69 (0.62–0.75). The odds ratio of a MMSE score ≥14 predicting having capacity was 11.95 (6.03–23.94). Depression reduced the odds of having capacity (OR 0.32, 0.17–0.62, p = 0.001). All individual items of the MMSE apart from following a written command (“close your eyes”) had odds ratios significantly associated with capacity status. The item most associated with capacity status was being able to verbally repeat a sentence (“no ifs, ands, or buts”) correctly (OR 5.6, 95% CI 3.0–10.5).

Conclusions: The MMSE is predictive of capacity to consent to research and may have a role in guiding researchers in this domain. It should not, however, be used in lieu of a formal capacity assessment as even the optimal cut-point of 13/14 mislabeled 24% of capable subjects as lacking capacity, and 21% of incapable subjects as having capacity.

The Mini Nutritional Assessment (MNA) is recommended for grading nutritional status in the elderly. A new index for predicting the risk of nutrition-related complications, the Geriatric Nutritional Risk Index (GNRI), was recently proposed but little is known about its possible use in the assessment of nutritional status. Thus, we aimed to investigate its ability to assess the nutritional status and predict the outcome when compared with the MNA. Anthropometry and biochemical parameters were determined in 241 institutionalised elderly (ninety-four males and 147 females; aged 80·1 (sd 8·3) years). Nutritional risk and nutritional state were graded by the GNRI and MNA, respectively. At 6 months outcomes were: death; infections; bedsores. According to the GNRI and MNA, the prevalence of high risk (GNRI <  92)/malnutrition (MNA <  17), moderate risk (GNRI 92–98)/malnutrition at-risk (MNA 17–23·5) and no risk (GNRI > 98)/good status (MNA > 24) were 20·7/12·8 %, 36·1/39 % and 43·2/48·2 %, respectively, with poor agreement in scoring the patient (Cohen's kappa test: κ = 0·29; 95 % CI 0·19, 0·39). GNRI categories showed a stronger association (OR) with overall outcomes than MNA classes, although no difference (P>0·05) was found between malnutrition (v. ‘good status’, OR 6·4; 95 % CI 2·1, 71·9) and high nutritional risk (v. ‘no risk’, OR 9·7; 95 % CI 3·0, 130). Multivariate logistic regression revealed the GNRI as an independent predictor of complications. In overall-outcome prediction, a good sensitivity was found only for GNRI <  98 (0·86 (95 % CI 0·67, 0·96)). The combination of a GNRI > 98 with an MNA > 24 seemed to exclude adverse outcomes. The GNRI showed poor agreement with the MNA in nutritional assessment, but appeared to better predict outcome. In home-care resident elderly, outcome prediction should be performed by combining the suggestions from both these tools.

Background: Depression is common in elderly people but physicians may not screen for it because of the length of time required by current screening instruments. We have developed a short screening instrument for depression for use in elderly people with normal cognition, mild cognitive impairment or early dementia.

Methods: Participants were aged 55 years or more, had scored 20 or more on the standardized Mini-mental State Examination (SMMSE) and had been referred to a specialist geriatric outpatient memory clinic. Scores on the 30-item Geriatric Depression Scale (GDS) were analyzed. A composite GDS score, consisting of the top five individual question scores that correlated to depression (GDS ≥ 14), were analyzed using a receiver operating curve analysis.

Results: There were 810 patients with SMMSE scores of 20 or greater, of whom 202 (24.9%) scored 14 or more on the GDS, indicating depression. GDS question 16, “Do you often feel downhearted and blue?,” had the highest correlation with the overall scores of 14 or more on the 30-point instrument (r = 0.64, p < 0.001). The next four questions with the highest correlates were Q10, “Do you often feel helpless?” (r = 0.56, p < 0.001), Q3, “Do you feel that your life is empty?” (r = 0.54, p < 0.001), Q9, “Do you feel happy most of the time?” (r = 0.52, p < 0.001), and Q1, “Are you basically satisfied with your life?” (r = 0.50, p < 0.001). The negative predictive value of “Do you often feel downhearted and blue?” answered negatively for depression was 96%. These five questions were used as a short screening instrument. The positive predictive value of four or five positive responses was 97%. These data were not significantly different whether the patient's SMMSE score was 20–25 or 26–30.

Conclusions: The AB Clinician Depression Screen (ABCDS), comprising five questions, can rapidly identify patients with depression or eliminate that diagnosis. In this population, these five questions may be used instead of the longer 30-question GDS scale.

Emergency physicians use diagnostic tests extensively, and the ability to order and interpret test results appropriately is a critical skill. An understanding of sensitivity, specificity, predictive values and likelihood ratios, as well as an awareness of the importance of pre-test probability, is essential. The purpose of this article is to explain, in a straightforward and clinically applicable manner, the core concepts related to diagnostic testing.