Dataset Information

The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS).

ABSTRACT: Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on clinical decision-making with or without tools developed for other purposes. We aimed to develop and validate a predictive model for suicide using data from linked population-based registers in individuals with severe mental illness. A national cohort of 75,158 Swedish individuals aged 15-65 with a diagnosis of severe mental illness (schizophrenia-spectrum disorders, and bipolar disorder) with 574,018 clinical patient episodes between 2001 and 2008, split into development (58,771 patients, 494 suicides) and external validation (16,387 patients, 139 suicides) samples. A multivariable derivation model was developed to determine the strength of pre-specified routinely collected socio-demographic and clinical risk factors, and then tested in external validation. We measured discrimination and calibration for prediction of suicide at 1 year using specified risk cut-offs. A 17-item clinical risk prediction model for suicide was developed and showed moderately good measures of discrimination (c-index 0.71) and calibration. For risk of suicide at 1 year, using a pre-specified 1% cut-off, sensitivity was 55% (95% confidence interval [CI] 47-63%) and specificity was 75% (95% CI 74-75%). Positive and negative predictive values were 2% and 99%, respectively. The model was used to generate a simple freely available web-based probability-based risk calculator (Oxford Mental Illness and Suicide tool or OxMIS) without categorical cut-offs. A scalable prediction score for suicide in individuals with severe mental illness is feasible. If validated in other samples and linked to effective interventions, using a probability score may assist clinical decision-making.

SUBMITTER: Fazel S

PROVIDER: S-EPMC6389890 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS).

Fazel Seena S Wolf Achim A Larsson Henrik H Mallett Susan S Fanshawe Thomas R TR

Translational psychiatry 20190225 1

Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on clinical decision-making with or without tools developed for other purposes. We aimed to develop and validate a predictive model for suicide using data from linked population-based registers in individuals with severe mental illness. A national cohort of 75,158 Swedish individuals aged 15-65 with a diagnosis of severe mental illness (schizophrenia-spectrum disorders, and bipolar disorder) ...[more]

PMID: 30804323

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Project description:BackgroundOxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.MethodsIn study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).ResultsIn study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).ConclusionsIt is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

Project description:BackgroundCurrent approaches to stratify patients with psychiatric disorders into groups on the basis of violence risk are limited by inconsistency, variable accuracy, and unscalability. To address the need for a scalable and valid tool to assess violence risk in patients with schizophrenia spectrum or bipolar disorder, we describe the derivation of a score based on routinely collected factors and present findings from external validation.MethodsOn the basis of a national cohort of 75 158 Swedish individuals aged 15-65 years with a diagnosis of severe mental illness (schizophrenia spectrum or bipolar disorder) with 574 018 patient episodes between Jan 1, 2001, and Dec 31, 2008, we developed predictive models for violent offending (primary outcome) within 1 year of hospital discharge for inpatients or clinical contact with psychiatric services for outpatients (patient episode) through linkage of population-based registers. We developed a derivation model to determine the relative influence of prespecified criminal history and sociodemographic and clinical risk factors, which are mostly routinely collected, and then tested it in an external validation. We measured discrimination and calibration for prediction of violent offending at 1 year using specified risk cutoffs.FindingsOf the cohort of 75 158 patients with schizophrenia spectrum or bipolar disorder, we assigned 58 771 (78%) to the derivation sample and 16 387 (22%) to the validation sample. In the derivation sample, 830 (1%) individuals committed a violent offence within 12 months of their patient episode. We developed a 16-item model. The strongest predictors of violent offending within 12 months were conviction for previous violent crime (adjusted odds ratio 5·03 [95% CI 4·23-5·98]; p<0·0001), male sex (2·32 [1·91-2·81]; p<0·0001), and age (0·63 per 10 years of age [0·58-0·67]; p<0·0001). In external validation, the model showed good measures of discrimination (c-index 0·89 [0·85-0·93]) and calibration. For risk of violent offending at 1 year, with a 5% cutoff, sensitivity was 62% (95% CI 55-68) and specificity was 94% (93-94). The positive predictive value was 11% and the negative predictive value was more than 99%. We used the model to generate a simple web-based risk calculator (Oxford Mental Illness and Violence tool [OxMIV]).InterpretationWe have developed a prediction score in a national cohort of patients with schizophrenia spectrum or bipolar disorder, which can be used as an adjunct to decision making in clinical practice by identifying those who are at low risk of violent offending. The low positive predictive value suggests that further clinical assessment in individuals at high risk of violent offending is required to establish who might benefit from additional risk management. Further validation in other countries is needed.FundingWellcome Trust and Swedish Research Council.

Project description:ObjectivesICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission.DesignProspective cohort study in seven Dutch ICUs.Setting/patientsICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020.InterventionsNone.Measurements and main resultsOutcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80).ConclusionsThe developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

Project description:ImportancePenicillin allergy is a significant public health issue for patients, antimicrobial stewardship programs, and health services. Validated clinical decision rules are urgently needed to identify low-risk penicillin allergies that potentially do not require penicillin skin testing by a specialist.ObjectiveTo develop and validate a penicillin allergy clinical decision rule that enables point-of-care risk assessment of patient-reported penicillin allergies.Design, setting, and participantsIn this diagnostic study, a multicenter prospective antibiotic allergy-tested cohort of 622 patients from 2 tertiary care sites in Melbourne, Australia (Austin Health and Peter MacCallum Cancer Centre) was used for derivation and internal validation of a penicillin allergy decision rule. Backward stepwise logistic regression was used to derive the model, including clinical variables predictive of a positive penicillin allergy test result. Internal validation of the final model used bootstrapped samples and the model scoring derived from the coefficients. External validation was performed in retrospective penicillin allergy-tested cohorts consisting of 945 patients from Sydney and Perth, Australia, and Nashville, Tennessee. Patients who reported a penicillin allergy underwent penicillin allergy testing using skin prick, intradermal, or patch testing and/or oral challenge (direct or after skin testing). Data were collected from June 26, 2008, to June 3, 2019, and analyzed from January 9 to 12, 2019.Main outcomes and measuresThe primary outcome for the model was any positive result of penicillin allergy testing performed during outpatient or inpatient assessment.ResultsFrom an internal derivation and validation cohort of 622 patients (367 female [59.0%]; median age, 60 [interquartile range{IQR}, 48-71] years) and an external validation cohort of 945 patients (662 female [70.1%]; median age, 55 [IQR, 38-68] years), the 4 features associated with a positive penicillin allergy test result on multivariable analysis were summarized in the mnemonic PEN-FAST: penicillin allergy, five or fewer years ago, anaphylaxis/angioedema, severe cutaneous adverse reaction (SCAR), and treatment required for allergy episode. The major criteria included an allergy event occurring 5 or fewer years ago (2 points) and anaphylaxis/angioedema or SCAR (2 points); the minor criterion (1 point), treatment required for an allergy episode. Internal validation showed minimal mean optimism of 0.003 with internally validated area under the curve of 0.805. A cutoff of less than 3 points for PEN-FAST was chosen to classify a low risk of penicillin allergy, for which only 17 of 460 patients (3.7%) had positive results of allergy testing, with a negative predictive value of 96.3% (95% CI, 94.1%-97.8%). External validation resulted in similar findings.Conclusions and relevanceIn this study, PEN-FAST was found to be a simple rule that accurately identified low-risk penicillin allergies that do not require formal allergy testing. The results suggest that a PEN-FAST score of less than 3, associated with a high negative predictive value, could be used by clinicians and antimicrobial stewardship programs to identify low-risk penicillin allergies at the point of care.

Project description:Interventions: We are assessing the impact of the National Bowel Cancer Screening Program (NBCSP) by comparing participation rates of people with and without Severe Mental Illness (SMI) and looking at health outcomes. We will be using data collected from Aug 2006 - latest available. We are linking data using Commonwealth data (NBCSP, Medicare Benefits Schedule, Pharmaceutical Benefits Scheme, Australian Cancer Database & the National Death Index) to compare bowel cancer screening participation in people with SMI to those from the general population. We will additionally link these to the NSW cancer registry and hospital data to examine care pathways from diagnosis through treatment and end-of-life care. We will also conduct a consultation with people with experience of SMI and Colorectal cancer and people with SMI in peer support roles who can provide their perspective on the barriers faced by people with SMI to gaining timely screening, diagnosis and optimal care. These participants will be recruited from New South Wales consumer organisations and organisations with a strong focus on provision of peer support and advocacy. We will use a semi structured group interview schedule to facilitate discussion of a single focus group of 15 people. The group will consist of both people with lived experience who have direct experience of bowel cancer (n=10), as well as other people with lived experience who have supported people in this situation – as peer support workers or in other consumer advocacy/support roles (n=5). We will use this qualitative arm of the study to further develop our understanding of the quantitative findings. The consultation will also develop ideas on how to improve access to care. Primary outcome(s): Participation rates in the National Bowel Cancer Screening Program assessed using data from the NBCSP.[Person-years will be calculated from entry into the study (2006) (the date of commencement of the NBCSP) or from the year that people become eligible for screening (i.e. age 50), whichever occurs later.. This is a retrospective cohort study, so all of the outcomes have already occurred and were recorded in the administrative data. This will be assessed when we receive the data.];Colonoscopy rates assessed using data from the NBCSP [Person-years will be calculated from entry into the study (2006 the date of commencement of the NBCSP), or from the year that people become eligible for screening (i.e. age 50), whichever occurs later, until colonoscopy. This is a retrospective cohort study, so all of the outcomes have already occurred and have been recorded in the administrative data.];Cancer diagnosis using data from the Australian Cancer Database[Person-years will be calculated from entry into the study (2006 the date of commencement of the NBCSP), or from the year that people become eligible for screening (i.e. age 50), whichever occurs later, until cancer diagnosis. This is a retrospective cohort study, so all of the outcomes have already occurred and have been recorded in the administrative data.] Study Design: Purpose: Screening;Duration: Longitudinal;Selection: Case control;Timing: Both

Project description:BACKGROUND:Physical health among people with severe mental illness (SMI) is a global concern. However, many people with SMI do not receive regular comprehensive health checks. There is currently no validated physical health check instrument systematically used in Finnish mental health services. Therefore, this study aims to validate and establish the potential clinical utility of the translated Health Improvement Profile (HIP) tool for Finnish patients with SMI and compare differences in physical health risk items across genders. METHODS:The content validity of the two-way translated Finnish HIP (HIP-F) was evaluated by five nurses and four patients with SMI using cognitive debriefing (to assess the clarity and relevance of each item and the recommended actions of the HIP tool). The potential clinical utility was assessed using a pilot test involving 47 patients. The prevalence of red-flagged (risk) items in the whole sample, across female and male participants, and the frequencies of any type of missing item response were calculated and analysed using descriptive statistics. A chi-square test was used to determine differences in frequencies of red-flagged items across genders. RESULTS:Based on the cognitive debriefing, the HIP-F was found to have moderate content validity regarding the clarity and relevance of the items and recommended actions (the average scale level content validity index, S-CVI/Ave, 0.74). In the pilot test, some missing item responses were identified, but in the sample, nurses identified 399 areas of health and health behaviour risks (mean 8.6 per patient) using the HIP-F. The most frequently red-flagged items were body mass index (BMI) and waist circumference (83.0%), smoking status (48.9%) and lipid levels (46.8%). Female patients had a higher frequency of red-flagged items than males in BMI (92.6% vs. 70.0%, p?=?0.04) and waist circumference (96.3% vs. 65.0%, p?=?0.01). CONCLUSIONS:The results demonstrate that the Finnish HIP has moderate content validity and preliminary clinical utility for evaluating the physical health and health behaviours of people with SMI. The HIP-F findings help to sign-post evidence-based interventions for identified areas of concern. Additional nurse training may be necessary to realise the potential clinical utility of the tool in Finland.

Dataset Information

The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS).

Publications

The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS).

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