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The Management and Supervision Tool (MaST) helps NHS mental health care professionals identify patients who are most likely to need psychiatric hospital admission or home treatment, due to severe mental illness, through a Risk of Crisis (RoC) algorithm driven by electronic health record (EHR) data analytics. We describe the derivation and validation of the MaST RoC algorithm, and its implementation to support preventative mental healthcare in the NHS.
The RoC algorithm was developed and evaluated with EHR data from six UK NHS trusts using Ordered Predictor List propensity scores informed by a priori weightings from pre-existing literature, as well as real-world evidence evaluating the associations of clinical risk factors with mental health crisis using NHS EHR data. Mental health crisis was defined as admission to a psychiatric hospital or acceptance to a community crisis service within a 28-day period. Predictor variables included age, gender, accommodation status, employment status, Mental Health Act (MHA) status (under section or Community Treatment Order), and previous mental health service contacts (including hospital admissions and crisis services). Data were analysed using Ordered Predictor List propensity scores. The algorithm was derived using structured EHR data from 2,620 patients in a single NHS trust and externally validated using data from 107,879 patients in five other NHS trusts. Qualitative and quantitative data on feasibility, acceptability and system efficiency impacts of MaST implementation were obtained through staff surveys and local audits.
The factors associated with greatest propensity for mental health crisis included recent previous crisis, multiple previous crises, higher number of mental health service contacts in recent weeks, MHA section, accommodation status and employment status. The RoC algorithm identified 64% and 80% crises in its top quintile. Sentiment analysis of staff surveys suggested that the use of MaST improved productivity by reducing time taken to access patient information to support caseload management that was previously difficult to obtain through manual review of EHRs. The systems efficiency audit revealed a reduction in duration of crisis and inpatient admissions following MaST implementation.
The MaST RoC algorithm supports the identification of people more likely to use crisis services in NHS mental health trusts, is feasible to implement, and improves systems efficiency. EHR-derived algorithms can support real-world clinical practice to improve outcomes in people receiving NHS mental healthcare.
Catatonia, a severe neuropsychiatric syndrome, has few studies of sufficient scale to clarify its epidemiology or pathophysiology. We aimed to characterise demographic associations, peripheral inflammatory markers and outcome of catatonia.
Electronic healthcare records were searched for validated clinical diagnoses of catatonia. In a case–control study, demographics and inflammatory markers were compared in psychiatric inpatients with and without catatonia. In a cohort study, the two groups were compared in terms of their duration of admission and mortality.
We identified 1456 patients with catatonia (of whom 25.1% had two or more episodes) and 24 956 psychiatric inpatients without catatonia. Incidence was 10.6 episodes of catatonia per 100 000 person-years. Patients with and without catatonia were similar in sex, younger and more likely to be of Black ethnicity. Serum iron was reduced in patients with catatonia [11.6 v. 14.2 μmol/L, odds ratio (OR) 0.65 (95% confidence interval (CI) 0.45–0.95), p = 0.03] and creatine kinase was raised [2545 v. 459 IU/L, OR 1.53 (95% CI 1.29–1.81), p < 0.001], but there was no difference in C-reactive protein or white cell count. N-Methyl-d-aspartate receptor antibodies were significantly associated with catatonia, but there were small numbers of positive results. Duration of hospitalisation was greater in the catatonia group (median: 43 v. 25 days), but there was no difference in mortality after adjustment.
In the largest clinical study of catatonia, we found catatonia occurred in approximately 1 per 10 000 person-years. Evidence for a proinflammatory state was mixed. Catatonia was associated with prolonged inpatient admission but not with increased mortality.
People with serious mental illness (SMI) have a significantly shorter life expectancy than the general population. This study investigates whether the mortality rate in this group has changed over the last decade.
Using Clinical Record Interactive Search software, we extracted data from a large electronic database of patients in South East London. All patients with schizophrenia, schizoaffective disorder or bipolar disorder from 2008 to 2012 and/or 2013 to 2017 were included. Estimates of life expectancy at birth, standardised mortality ratios and causes of death were obtained for each cohort according to diagnosis and gender. Comparisons were made between cohorts and with the general population using data obtained from the UK Office of National Statistics.
In total, 26 005 patients were included. In men, life expectancy was greater in 2013–2017 (64.9 years; 95% CI 63.6–66.3) than in 2008–2012 (63.2 years; 95% CI 61.5–64.9). Similarly, in women, life expectancy was greater in 2013–2017 (69.1 years; 95% CI 67.5–70.7) than in 2008–2012 (68.1 years; 95% CI 66.2–69.9). The difference with general population life expectancy fell by 0.9 years between cohorts in men, and 0.5 years in women. In the 2013–2017 cohorts, cancer accounted for a similar proportion of deaths as cardiovascular disease.
Relative to the general population, life expectancy for people with SMI is still much worse, though it appears to be improving. The increased cancer-related mortality suggests that physical health monitoring should consider including cancer as well.
How neighbourhood characteristics affect the physical safety of people with mental illness is unclear.
To examine neighbourhood effects on physical victimisation towards people using mental health services.
We developed and evaluated a machine-learning-derived free-text-based natural language processing (NLP) algorithm to ascertain clinical text referring to physical victimisation. This was applied to records on all patients attending National Health Service mental health services in Southeast London. Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital Episode Statistics, linked to Clinical Record Interactive Search), were collected in this group, defined as ‘cases’ and concurrently sampled controls. Multilevel logistic regression models estimated associations (odds ratios, ORs) between neighbourhood-level fragmentation, crime, income deprivation, and population density and physical victimisation.
Based on a human-rated gold standard, the NLP algorithm had a positive predictive value of 0.92 and sensitivity of 0.98 for (clinically recorded) physical victimisation. A 1 s.d. increase in neighbourhood crime was accompanied by a 7% increase in odds of physical victimisation in women and an 13% increase in men (adjusted OR (aOR) for women: 1.07, 95% CI 1.01–1.14, aOR for men: 1.13, 95% CI 1.06–1.21, P for gender interaction, 0.218). Although small, adjusted associations for neighbourhood fragmentation appeared greater in magnitude for women (aOR = 1.05, 95% CI 1.01–1.11) than men, where this association was not statistically significant (aOR = 1.00, 95% CI 0.95–1.04, P for gender interaction, 0.096). Neighbourhood income deprivation was associated with victimisation in men and women with similar magnitudes of association.
Neighbourhood factors influencing safety, as well as individual characteristics including gender, may be relevant to understanding pathways to physical victimisation towards people with mental illness.
Mood instability and sleep disturbance are common symptoms in people with mental illness. Both features are clinically important and associated with poorer illness trajectories. We compared clinical outcomes in people presenting to secondary mental health care with mood instability and/or sleep disturbance with outcomes in people without either mood instability or sleep disturbance.
Data were from electronic health records of 31,391 patients ages 16–65 years presenting to secondary mental health services between 2008 and 2016. Mood instability and sleep disturbance were identified using natural language processing. Prevalence of mood instability and sleep disturbance were estimated at baseline. Incidence rate ratios were estimates for clinical outcomes including psychiatric diagnoses, prescribed medication, and hospitalization within 2-years of presentation in persons with mood instability and/or sleep disturbance compared to individuals without either symptom.
Mood instability was present in 9.58%, and sleep disturbance in 26.26% of patients within 1-month of presenting to secondary mental health services. Compared with individuals without either symptom, those with mood instability and sleep disturbance showed significantly increased incidence of prescription of any psychotropic medication (incidence rate ratios [IRR] = 7.04, 95% confidence intervals [CI] 6.53–7.59), and hospitalization (IRR = 5.32, 95% CI 5.32, 4.67–6.07) within 2-years of presentation. Incidence rates of most clinical outcomes were considerably increased among persons with both mood instability and sleep disturbance, relative to persons with only one symptom.
Mood instability and sleep disturbance are present in a wide range of mental disorders, beyond those in which they are conventionally considered to be symptoms. They are associated with poor outcomes, particularly when they occur together. The poor prognosis associated with mood instability and sleep disorder may be, in part, because they are often treated as secondary symptoms. Mood instability and sleep disturbance need better recognition as clinical targets for treatment in their own right.
The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.
We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation.
We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves.
In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%).
EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.
Acute and transient psychotic disorders (ATPD) are characterized by an acute onset and a remitting course, and overlap with subgroups of the clinical high-risk state for psychosis. The long-term course and outcomes of ATPD are not completely clear.
Electronic health record-based retrospective cohort study, including all patients who received a first index diagnosis of ATPD (F23, ICD-10) within the South London and Maudsley (SLaM) National Health Service Trust, between 1 st April 2006 and 15th June 2017. The primary outcome was risk of developing persistent psychotic disorders, defined as the development of any ICD-10 diagnoses of non-organic psychotic disorders. Cumulative risk of psychosis onset was estimated through Kaplan-Meier failure functions (non-competing risks) and Greenwood confidence intervals.
A total of 3074 patients receiving a first index diagnosis of ATPD (F23, ICD-10) within SLaM were included. The mean follow-up was 1495 days. After 8-year, 1883 cases (61.26%) retained the index diagnosis of ATPD; the remaining developed psychosis. The cumulative incidence (Kaplan-Meier failure function) of risk of developing any ICD-10 non-organic psychotic disorder was 16.10% at 1-year (95%CI 14.83–17.47%), 28.41% at 2-year (95%CI 26.80–30.09%), 33.96% at 3-year (95% CI 32.25–35.75%), 36.85% at 4-year (95%CI 35.07–38.69%), 40.99% at 5-year (95% CI 39.12–42.92%), 42.58% at 6-year (95%CI 40.67–44.55%), 44.65% at 7-year (95% CI 42.66–46.69%), and 46.25% at 8-year (95% CI 44.17–48.37%). The cumulative risk of schizophrenia-spectrum disorder at 8-year was 36.14% (95% CI 34.09–38.27%).
Individuals with ATPD have a very high risk of developing persistent psychotic disorders and may benefit from early detection and preventive treatments to improve their outcomes.
Patients with acute and transient psychotic disorders (ATPDs) are by definition remitting, but have a high risk of developing persistent psychoses, resembling a subgroup of individuals at Clinical High Risk for Psychosis (CHR-P). Their pathways to care, treatment offered and long-term clinical outcomes beyond risk to psychosis are unexplored. We conducted an electronic health record-based retrospective cohort study including patients with ATPDs within the SLaM NHS Trust and followed-up to 8 years.
A total of 2561 ATPDs were included in the study. A minority were detected (8%) and treated (18%) by Early Intervention services (EIS) and none by CHR-P services. Patients were offered a clinical follow-up of 350.40 ± 589.90 days. The cumulative incidence of discharges was 40% at 3 months, 60% at 1 year, 69% at 2 years, 77% at 4 years, and 82% at 8 years. Treatment was heterogeneous: the majority of patients received antipsychotics (up to 52%), only a tiny minority psychotherapy (up to 8%).
Over follow-up, 32.88% and 28.54% of ATPDS received at least one mental health hospitalization or one compulsory hospital admission under the Mental Health Act, respectively. The mean number of days spent in psychiatric hospital was 66.39 ± 239.44 days.
The majority of ATPDs are not detected/treated by EIS or CHR-P services, receive heterogeneous treatments and short-term clinical follow-up. ATPDs have a high risk of developing severe clinical outcomes beyond persistent psychotic disorders and unmet clinical needs that are not targeted by current mental health services.
People with serious mental illness have a reduced life expectancy that is partly attributable to increased cardiovascular disease. One approach to address this is regular physical health monitoring. However, physical health monitoring is poorly implemented in everyday clinical practice and there is little evidence to suggest that it improves physical health. We argue that greater emphasis should be placed on primary prevention strategies such as assertive smoking cessation, dietary and exercise interventions and more judicious psychotropic prescribing.
Studies indicate that risk of mortality is higher for patients admitted
to acute hospitals at the weekend. However, less is known about clinical
outcomes among patients admitted to psychiatric hospitals.
To investigate whether weekend admission to a psychiatric hospital is
associated with worse clinical outcomes.
Data were obtained from 45 264 consecutive psychiatric hospital
admissions. The association of weekend admission with in-patient
mortality, duration of hospital admission and risk of readmission was
investigated using multivariable regression analyses. Secondary analyses
were performed to investigate the distribution of admissions, discharges,
in-patient mortality, episodes of seclusion and violent incidents on
different days of the week.
There were 7303 weekend admissions (16.1%). Patients who were aged
between 26 and 35 years, female or from a minority ethnic group were more
likely to be admitted at the weekend. Patients admitted at the weekend
were more likely to present via acute hospital services, other
psychiatric hospitals and the criminal justice system than to be admitted
directly from their own home. Weekend admission was associated with a
shorter duration of admission (B coefficient –21.1 days,
95% CI –24.6 to –17.6, P<0.001) and an increased risk
of readmission in the 12 months following index admission (incidence rate
ratio 1.13, 95% CI 1.08 to 1.18, P<0.001), but
in-patient mortality (odds ratio (OR) = 0.79, 95% CI 0.51 to 1.23,
P = 0.30) was not greater than for weekday admission.
Fewer episodes of seclusion occurred at the weekend but there was no
significant variation in deaths during hospital admission or violent
incidents on different days of the week.
Being admitted at the weekend was not associated with an increased risk
of in-patient mortality. However, patients admitted at the weekend had
shorter admissions and were more likely to be readmitted, suggesting that
they may represent a different clinical population to those admitted
during the week. This is an important consideration if mental healthcare
services are to be implemented across a 7-day week.
Venous thromboembolism is an important cause of morbidity and mortality. In recent years, growing awareness has led to the development of strategies to prevent venous thromboembolism in individuals admitted to hospital who are deemed to be at high risk. However, there remains a considerable degree of uncertainty over whether these strategies are of overall benefit and there are few published studies on people who are admitted to psychiatric hospitals. In this editorial I review current clinical practice and areas of uncertainty with respect to venous thromboembolism prophylaxis and its implementation in mental healthcare settings.
The HEALTH Passport is a tool to help patients make lifestyle changes to reduce the future burden of chronic disease. This study assesses the potential of this behaviour change strategy in psychiatric patients. We introduced 50 psychiatric in-patients to the HEALTH Passport and asked them to complete a semi-qualitative questionnaire. Results were compared with those of 100 controls.
Psychiatric in-patients are exposed to almost twice as many modifiable risk factors of chronic disease compared with controls. Although psychiatric in-patients are less motivated to address their risk factors, the HEALTH Passport could almost halve the proportion of psychiatric patients at high risk of chronic disease.
The low level of health literacy among psychiatric patients must be addressed to reduce their risk exposure. Potentially, the HEALTH Passport provides a cost-effective tool for this purpose.
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