Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose: Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods: Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results: The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion: We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

Dataset to accompany the manuscript: Depressive symptoms, socially anxious symptoms, psychosocial maturity, and risk perception associations with risk-taking behaviour.

Dataset to accompany the manuscript: Depressive symptoms, socially anxious symptoms, psychosocial maturity, and risk perception associations with risk-taking behaviour

Depressive and socially anxious symptoms, psychosocial maturity, and risk perception: Associations with risk-taking behaviour.

Risk-taking behaviour and onset of mental illness peak in adolescence and young adulthood. This study evaluated the interconnectedness of the domains of risk-taking behaviour, mental health (symptoms of depression and social anxiety), psychosocial maturity, risk perception, age, and gender in a sample of 306 adolescents and young adults. Participants between the ages of 16 and 35 completed online self-report measures assessing risk-taking behaviour, depressive symptoms, socially anxious symptoms, psychosocial maturity and risk perception. Socially anxious symptoms, psychosocial maturity, and risk perception were directly associated with risk-taking behaviour. Correlations between depressive symptoms, socially anxious symptoms, and psychosocial maturity were found. Psychosocial maturity proved a better predictor of risk-taking behaviour than age in this cohort. The findings indicate that mental health impacts upon risk-taking behaviour and that consideration should be given to psychosocial maturity in attempts to reduce adolescent and young adult risk-taking behaviour

Identification of heart failure hospitalisation from NHS Digital data:comparison with expert adjudication

Background and Aims: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalisation for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF.Methods: Patients experiencing at least one HHF, as determined by NHS Digital data, and age and sex matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases (ICD)-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Results: 504 patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position (I50: 96·2% [95% confidence interval, CI: 94·1 – 97·7%]; NICOR: 93·3% [CI 90·8 – 95·4%]; OIS: 95·6% [CI 93·3 – 97·2%]), but decreased substantially as the number of diagnosis positions expanded. Sensitivity (40·0% [CI 12·2 – 73·8%]) and positive predictive value (PPV) (highest with I50: 17·4% [CI 8·1 – 33·6%]) were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly (36·4%; [16·6 – 62·2%]).Conclusions: NHS Digital data were not able to accurately identify HHF, and should not be used in isolation for this purpose. <br/

Identification of heart failure hospitalization from NHS Digital data:comparison with expert adjudication

Aims: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalization for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF. Methods and results: Patients experiencing at least one HHF, as determined by NHS Digital data, and age- and sex-matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; and NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Five hundred four patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position {I50: 96.2% [95% confidence interval (CI) 94.1–97.7%]; NICOR: 93.3% [CI 90.8–95.4%]; OIS: 95.6% [CI 93.3–97.2%]} but decreased substantially as the number of diagnosis positions expanded. Sensitivity [40.0% (CI 12.2–73.8%)] and positive predictive value (PPV) [highest with I50: 17.4% (CI 8.1–33.6%)] were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly [36.4% (CI 16.6–62.2%)]. Conclusions: NHS Digital data were not able to accurately identify HHF and should not be used in isolation for this purpose.</p