Recognition of cognitive impairment and depressive symptoms in older patients with heart failure.

INTRODUCTION: Cognitive impairment and depression in patients with heart failure (HF) are common comorbidities and are associated with increased morbidity, readmissions and mortality. Timely recognition of cognitive impairment and depression is important for providing optimal care. The aim of our study was to determine if these disorders were recognised by clinicians and, secondly, if they were associated with hospital admissions and mortality within 6 months' follow-up. METHODS: Patients (aged ≥65 years) diagnosed with HF were included from the cardiology outpatient clinic of Gelre Hospitals. Cognitive status was evaluated with the Montreal Cognitive Assessment test (score ≤22). Depressive symptoms were assessed with the Geriatric Depression Scale (score >5). Patient characteristics were collected from electronic patient files. The clinician was blinded to the tests and asked to assess cognitive status and mood. RESULTS: We included 157 patients. Their median age was 79 years (65-92); 98 (62%) were male. The majority had New York Heart Association functional class II. Cognitive impairment was present in 56 (36%) patients. Depressive symptoms were present in 21 (13%) patients. In 27 of 56 patients (48%) cognitive impairment was not recognised by clinicians. Depressive symptoms were not recognised in 11 of 21 patients (52%). During 6 months' follow-up 24 (15%) patients were readmitted for HF-related reasons and 18 (11%) patients died. There was no difference in readmission and mortality rate between patients with or without cognitive impairment and patients with or without depressive symptoms. CONCLUSION: Cognitive impairment and depressive symptoms were infrequently recognised during outpatient clinic visits.

Risk of Four Geriatric Syndromes: A Comparison of Mental Health Care and General Hospital Inpatients.

An observational, cross-sectional study is conducted to compare elevated risk scores of four geriatric syndromes (falls, malnutrition, physical impairment, delirium) in older hospitalized psychiatric patients (n=178) with patients hospitalized in a general hospital (n=687). The median age of all patients was 78 years (IQR 73.3-83.3), 53% were female. After correction for age and gender, we found significantly more often an elevated risk in the mental health care group, compared to the general hospital group of falls (Odds Ratio (OR) = 1.75; 95% Confidence Interval (CI) 1.18-2.57), malnutrition (OR = 4.12; 95% CI 2.67-6.36) and delirium (OR = 6.45; 95% CI 4.23-9.85). The risk on physical impairment was not statistically significantly different in both groups (OR = 1.36; 95% CI .90-2.07). Older mental health care patients have a higher risk to develop geriatric syndromes compared to general hospital patients with the same age and gender, which might be explained by a higher level of frailty.

Interaction between geriatric syndromes in predicting three months mortality risk.

OBJECTIVES: Capturing frailty using a quick tool has proven to be challenging. We hypothesise that this is due to the complex interactions between frailty domains. We aimed to identify these interactions and assess whether adding interactions between domains improves mortality predictability. METHODS: In this retrospective cohort study, we selected all patients aged 70 or older who were admitted to one Dutch hospital between April 2015 and April 2016. Patient characteristics, frailty screening (using VMS (Safety Management System), a screening tool used in Dutch hospital care), length of stay, and mortality within three months were retrospectively collected from electronic medical records. To identify predictive interactions between the frailty domains, we constructed a classification tree with mortality as the outcome using five variables: the four VMS-domains (delirium risk, fall risk, malnutrition, physical impairment) and their sum. To determine if any domain interactions were predictive for three-month mortality, we performed a multivariable logistic regression analysis. RESULTS: We included 4,478 patients. (median age: 79 years; maximum age: 101 years; 44.8% male) The highest risk for three-month mortality included patients that were physically impaired and malnourished (23% (95%-CI 19.0-27.4%)). Subgroups had comparable three-month mortality risks based on different domains: malnutrition without physical impairment (15.2% (96%-CI 12.4-18.6%)) and physical impairment and delirium risk without malnutrition (16.3% (95%-CI 13.7-19.2%)). DISCUSSION: We showed that taking interactions between domains into account improves the predictability of three-month mortality risk. Therefore, when screening for frailty, simply adding up domains with a cut-off score results in loss of valuable information.

Does the Dutch Safety Management Program predict adverse outcomes for older patients in the emergency department?

PURPOSE: Frailty screening in the emergency department may identify frail patients at risk for adverse outcomes. This study investigated if the Dutch Safety Management Program (VMS) screener predicts outcomes in older patients in the emergency department. METHODS: In this prospective cohort study, patients aged 70 years or older presenting to the emergency department were recruited on workdays between 10:00 AM and 7:00 PM from May 2017 until August 2017. Patients were screened in four domains: activities of daily living, malnutrition, risk of delirium, and risk of falling. After 90 days of follow up, mortality, functional decline, living situation, falls, readmission to the emergency department, and readmission to the hospital were recorded. VMS was studied using the total VMS score as a predictor with ROC curve analysis, and using a cut-off point to divide patients into frail and non-frail groups to calculate positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 249 patients were included. Higher VMS score was associated with 90-day mortality (AUC 0.65, 95% CI 0.54-0.76) and falling (AUC 0.67, 95% CI 0.56-0.78). VMS frailty predicted mortality (PPV 0.15, NPV 0.94, p = 0.05) and falling (PPV 0.22, NPV 0.92, p = 0.02), but none of the other outcomes. CONCLUSION: In this selected group of patients, higher VMS score was associated with 90-day mortality and falls. The low positive predictive value shows that the VMS screener is unsuitable for identifying high-risk patients in the ED. The high negative predictive value indicates that the screener can identify patients not at risk for adverse medical outcomes. This could be useful to determine which patients should undergo additional screening.