Clinical records anonymisation and text extraction (CRATE): an open-source software system.

BACKGROUND: Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information. Patient-identifiable information cannot in general be shared outside clinical care teams without explicit consent, but anonymisation/de-identification allows research uses of clinical data without explicit consent. RESULTS: This article presents CRATE (Clinical Records Anonymisation and Text Extraction), an open-source software system with separable functions: (1) it anonymises or de-identifies arbitrary relational databases, with sensitivity and precision similar to previous comparable systems; (2) it uses public secure cryptographic methods to map patient identifiers to research identifiers (pseudonyms); (3) it connects relational databases to external tools for natural language processing; (4) it provides a web front end for research and administrative functions; and (5) it supports a specific model through which patients may consent to be contacted about research. CONCLUSIONS: Creation and management of a research database from sensitive clinical records with secure pseudonym generation, full-text indexing, and a consent-to-contact process is possible and practical using entirely free and open-source software.The project was funded in part by the UK National Institute of Health Research Cambridge Biomedical Research Centre. The work was conducted within the Behavioural and Clinical Neuroscience Institute, University of Cambridge, supported by the Wellcome Trust and the UK Medical Research Council

Mental health, big data and research ethics : parity of esteem in mental health research from a UK perspective

Central to ethical debates in contemporary mental health research are the rhetoric of parity of esteem, challenges underpinned by the social construct of vulnerability, and the tendency to homogenise the population diagnosed with mental health conditions. Such ethical dimensions are further complicated by the contemporary endeavour to work with ‘big data’ which has led to ambitious claims for discovery and knowledge. Research in mental health is challenging due to the perceived constraints of ethical principles such as the protection of autonomy, consent, risk and harms. This article discusses how ethical considerations need to be reconceptualised when using big data sets. The argument is foregrounded with an appraisal of the prevailing political discourse of parity of esteem demonstrating that ongoing disparities in services and research should also be considered when inquiry uses big data

The Medium-Term Impact of COVID-19 Lockdown on Referrals to Secondary Care Mental Health Services: A Controlled Interrupted Time Series Study.

To date, there is a paucity of information regarding the effect of COVID-19 or lockdown on mental disorders. We aimed to quantify the medium-term impact of lockdown on referrals to secondary care mental health clinical services. We conducted a controlled interrupted time series study using data from Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), UK (catchment population ~0.86 million). The UK lockdown resulted in an instantaneous drop in mental health referrals but then a longer-term acceleration in the referral rate (by 1.21 referrals per day per day, 95% confidence interval [CI] 0.41-2.02). This acceleration was primarily for urgent or emergency referrals (acceleration 0.96, CI 0.39-1.54), including referrals to liaison psychiatry (0.68, CI 0.35-1.02) and mental health crisis teams (0.61, CI 0.20-1.02). The acceleration was significant for females (0.56, CI 0.04-1.08), males (0.64, CI 0.05-1.22), working-age adults (0.93, CI 0.42-1.43), people of White ethnicity (0.98, CI 0.32-1.65), those living alone (1.26, CI 0.52-2.00), and those who had pre-existing depression (0.78, CI 0.19-1.38), severe mental illness (0.67, CI 0.19-1.15), hypertension/cardiovascular/cerebrovascular disease (0.56, CI 0.24-0.89), personality disorders (0.32, CI 0.12-0.51), asthma/chronic obstructive pulmonary disease (0.28, CI 0.08-0.49), dyslipidemia (0.26, CI 0.04-0.47), anxiety (0.21, CI 0.08-0.34), substance misuse (0.21, CI 0.08-0.34), or reactions to severe stress (0.17, CI 0.01-0.32). No significant post-lockdown acceleration was observed for children/adolescents, older adults, people of ethnic minorities, married/cohabiting people, and those who had previous/pre-existing dementia, diabetes, cancer, eating disorder, a history of self-harm, or intellectual disability. This evidence may help service planning and policy-making, including preparation for any future lockdown in response to outbreaks

Clinical Presentation, Diagnostic Features, and Mortality in Dementia with Lewy Bodies.

BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common degenerative dementia in older people. However, rates of misdiagnosis are high, and little is known of its natural history and outcomes. Very few previous studies have been able to access routine clinical information for large, unbiased DLB cohorts in order to establish initial presentation, neuropsychological profile, and mortality. OBJECTIVE: To examine in detail, symptom patterns at presentation and their association with outcomes, including mortality, in a large naturalistic DLB cohort from a secondary care sample. METHODS: A retrospective cohort design was used to identify a DLB cohort (n = 251) from Cambridgeshire and Peterborough NHS Foundation Trust (CPFT). Information relating to first consultation, diagnosis, and DLB diagnostic features were extracted. RESULTS: A wide range of presenting complaints and differential initial diagnoses were identified for the cohort. Along with memory loss (27.1%) and hallucinations (25.4%), low mood (25.1%) was noted as a key presenting complaint among the DLB cohort. Rates of REM sleep disorder were considerably lower (8.4%) than would be expected. Deficits in non-amnestic cognitive domains were associated with reduced mortality compared with amnestic-only presentations. CONCLUSION: Individuals later diagnosed with DLB present initially to secondary care with a wide range of symptoms and complaints, some of which are not immediately suggestive of a DLB diagnosis. More examinations of large cohorts such as this are needed to further elucidate the complex presentation and clinical course of DLB, and to confirm whether amnestic-only presentation confers a worse outcome.Alzheimer's Society; CPFT; NIHR Cambridge Biomedical Research Centr

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis.

BACKGROUND: Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. FINDINGS: 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases. INTERPRETATION: We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. FUNDING: National Institute for Health Research and Wellcome Trust

Risk factors for excess deaths during lockdown among older users of secondary care mental health services without confirmed COVID‐19: A retrospective cohort study

Funder: UK National Institute for Health Research (NIHR) Cambridge Biomedical Research CentreFunder: NIHR Applied Research Collaboration East of EnglandAbstract: Objective: To investigate factors contributing to excess deaths of older patients during the initial 2020 lockdown beyond those attributable to confirmed COVID‐19. Methods: Retrospective cohort study comparing patients treated between 23 March 2020 and 14 June 2020, deemed exposed to the pandemic/lockdown, to patients treated between 18 December 2019 and 10 March 2020, deemed to be unexposed. Data came from electronic clinical records from secondary care mental health services in Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), UK (catchment area population ∼0.86 million). Eligible patients were aged 65 years or over at baseline with at least 14 days' follow‐up, excluding patients diagnosed with confirmed or suspected SARS‐CoV‐2 infection. The primary outcome was all‐cause mortality. Findings: In the two cohorts, 3,073 subjects were exposed to lockdown and 4,372 subjects were unexposed; the cohorts were followed up for an average of 74 and 78 days, respectively. After controlling for confounding by sociodemographic factors, smoking status, mental comorbidities, and physical comorbidities, patients with dementia suffered an additional 53% risk of death (HR = 1.53, 95% CI = 1.02–2.31), and patients with severe mental illness suffered an additional 123% risk of death (HR = 2.23, 95% CI = 1.42–3.49). No significant additional mortality risks were identified from physical comorbidities, potentially due to low statistical power in that respect. Conclusion: During lockdown people with dementia or severe mental illness had a higher risk of death without confirmed COVID‐19. These data could inform future health service responses and policymaking to help prevent avoidable excess death during future outbreaks of this or a similar infectious disease