Epidemiology and microbiology of recurrent UTI in women in the community in Oxfordshire, UK.

Background: Recurrent urinary tract infection (rUTI) contributes to significant morbidity and antibiotic usage. Objectives: To characterize the age of women experiencing rUTI, the microbiology of rUTIs, and the risk of further rUTIs in Oxfordshire, UK. Patients and methods: We retrospectively analysed de-identified linked microbiology and hospital admissions data (Infections in Oxfordshire Research Database), between 2008 and 2019, including positive urine cultures from women aged ≥16 years in community settings. We defined rUTI as ≥2 positive urine cultures within 6 months or ≥3 within 12 months. Results: Of 201 927 women with urine culture performed, 84 809 (42%) had ≥1 positive culture, and 15 617 (18%) of these experienced ≥1 rUTI over a median (IQR) follow-up of 6 (3-9) years. Women with rUTI were 17.0 (95% CI: 16.3-17.7) years older on average. rUTI was commonest (6204; 40%) in those aged 70-89 years. Post-rUTI, the risk of further UTI within 6 months was 29.4% (95% CI: 28.7-30.2). Escherichia coli was detected in 65% of positive cultures. Among rUTIs where the index UTI was E. coli associated, the second UTI was also E. coli associated in 81% of cases. Conclusions: rUTIs represent a substantial healthcare burden, particularly in women >60 years. One-third of women experiencing rUTI have a further microbiologically confirmed UTI within 6 months.

Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS).

BACKGROUND: Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes. OBJECTIVE: We developed and validated a predictive model for suicide following self-harm. METHODS: We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work. FINDINGS: An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS). CONCLUSIONS: OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility. CLINICAL IMPLICATIONS: Using a clinical prediction score may assist clinical decision-making and resource allocation.

UMBRELLA protocol: systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcomes in patients with heart failure.

BACKGROUND: Heart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present. METHODS: We will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will meta-analyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed. DISCUSSION: This project aims to appraise and summarise the methodological conduct and predictive performance of existing clinically homogeneous HF prognostic models in separate systematic reviews.Registration: PROSPERO registration number CRD42019086990.

Prediction of violent reoffending in prisoners and individuals on probation: a Dutch validation study (OxRec).

Scalable and transparent methods for risk assessment are increasingly required in criminal justice to inform decisions about sentencing, release, parole, and probation. However, few such approaches exist and their validation in external settings is typically lacking. A total national sample of all offenders (9072 released from prisoners and 6329 individuals on probation) from 2011-2012 in the Netherlands were followed up for violent and any reoffending over 2 years. The sample was mostly male (n = 574 [6%] were female prisoners and n = 784 [12%] were female probationers), and median ages were 30 in the prison sample and 34 in those on probation. Predictors for a scalable risk assessment tool (OxRec) were extracted from a routinely collected dataset used by criminal justice agencies, and outcomes from official criminal registers. OxRec's predictive performance in terms of discrimination and calibration was tested. Reoffending rates in the Dutch prisoner cohort were 16% for 2-year violent reoffending and 44% for 2-year any reoffending, with lower rates in the probation sample. Discrimination as measured by the c-index was moderate, at 0.68 (95% CI: 0.66-0.70) for 2-year violent reoffending in prisoners and between 0.65 and 0.68 for other outcomes and the probation sample. The model required recalibration, after which calibration performance was adequate (e.g. calibration in the large was 1.0 for all scenarios). A recalibrated model for OxRec can be used in the Netherlands for individuals released from prison and individuals on probation to stratify their risk of future violent and any reoffending. The approach that we outline can be considered for external validations of criminal justice and clinical risk models.

Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder.

BACKGROUND: Recurrent mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilization is an important clinical goal. This study investigates the ability of control chart methodology to predict manic and/or depressive episodes by applying Shewhart's control rules to weekly self-reported scores from mania and depression questionnaires. METHODS: Shewhart's control rules were applied to weekly self-reported scores from the Altman Self-Rating Mania Scale (ASRM) and the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS) collected from 2001 to 2012 as part of the OXTEXT programme. Manic and depressive episodes were defined as an ASRM score ≥ 10 or a QIDS score ≥ 15, respectively. An episode-free run-in period of eight consecutive weeks without an episode of either type was used to calibrate control charts. Shewhart's rules were then applied to follow-up data. Their sensitivity and positive predictive value for predicting manic or depressive episodes within the next 4 weeks were calculated focusing on the first episode. Secondary analyses varying control chart type, length of episode-free run-in period, time frames to evaluate diagnostic accuracy, thresholds defining either manic or depressive episodes, and missing data methods were performed. RESULTS: Data from 146 participants (37% men) were included. The mean age was 43.4 (SD = 13.3) years. The median follow-up was 10 (IQR 5-40) weeks for mania and 10 (IQR 5-23) weeks for depression. A total of 53 (36%) participants had a manic episode and 67 (46%) had a depressive episode. For manic episodes, the sensitivity and positive predictive value of Shewhart's control rules were 30% (95% CI 19-45%) and 7% (95% CI 5-9%), and for depressive episodes, 33% (95% CI 22-46%) and 9% (95% CI 6-12%), respectively. Results from secondary analyses were similar to these. CONCLUSIONS: Tele-monitoring with control rules has the potential to predict about one-third of manic or depressive episodes before they occur, at the cost of a high false positive rate. Given the severe consequences of manic and depressive episodes, this trade-off may be desirable.