Laura Bonnett

AIMS: Force-Time Integral (FTI) is commonly used as a marker of ablation lesion quality during pulmonary vein isolation (PVI), but does not incorporate power. Ablation Index (AI) is a novel lesion quality marker that utilizes contact force, time, and power in a weighted formula. Furthermore, only a single FTI target value has been suggested despite regional variation in left atrial wall thickness. We aimed to study AI's and FTI's relationships with PV reconnection at repeat electrophysiology study, and regional threshold values that predicted no reconnection. METHODS AND RESULTS: Forty paroxysmal atrial fibrillation patients underwent contact force-guided PVI, and the minimum and mean AI and FTI values for each segment were identified according to a 12-segment model. All patients underwent repeat electrophysiology study at 2 months, regardless of symptoms, to identify sites of PV reconnection. Late PV reconnection was seen in 53 (11%) segments in 25 (62%) patients. Reconnected segments had significantly lower minimum AI [308 (252-336) vs. 373 (323-423), P < 0.0001] and FTI [137 (92-182) vs. 228 (157-334), P < 0.0001] compared with non-reconnected segments. Minimum AI and FTI were both independently predictive, but AI had a smaller P value. Higher minimum AI and FTI values were required to avoid reconnection in anterior/roof segments than for posterior/inferior segments (P < 0.0001). No reconnection was seen where the minimum AI value was ≥370 for posterior/inferior segments and ≥480 for anterior/roof segments. CONCLUSION: The minimum AI value in a PVI segment is independently predictive of reconnection of that segment at repeat electrophysiology study. Higher AI and FTI values are required for anterior/roof segments than for posterior/inferior segments to prevent reconnection.

External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to evaluating a model’s predictive performance and clinical usefulness.

AIMS: To validate the European Heart Rhythm Association (EHRA) symptom classification in atrial fibrillation (AF) and test whether its discriminative ability could be improved by a simple modification. METHODS AND RESULTS: We compared the EHRA classification with three quality of life (QoL) measures: the AF-specific Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) questionnaire; two components of the EQ-5D instrument, a health-related utility which can be used to calculate cost-effectiveness, and the visual analogue scale (VAS) which demonstrates patients' own assessment of health status. We then proposed a simple modification [modified EHRA (mEHRA)] to improve discrimination at the point where major treatment decisions are made. quality of life data and clinician-allocated EHRA class were prospectively collected on 362 patients with AF. A step-wise, negative association was seen between the EHRA class and both the AFEQT and the VAS scores. Health-related utility was only significantly different between Classes 2 and 3 (P < 0.001). We developed and validated the mEHRA score separating Class 2 (symptomatic AF not limiting daily activities), based on whether the patients were 'troubled by their AF' (Class 2b) or not (Class 2a). This produced two distinct groups with lower AFEQT and VAS scores and, importantly, both clinically and statistically significant lower health utility (Δutility 0.9, P = 0.01) in Class 2b than Class 2a. CONCLUSION: Based on patients' own assessment of their health status and the disease-specific AFEQT, the EHRA score can be considered a useful semi-quantitative classification. The mEHRA score has a clearer separation in health utility to assess the cost efficacy of interventions such as ablation, where Class 2b symptoms appear to be the appropriate treatment threshold.

Clinical prediction models estimate the risk of existing disease or future outcome for an individual, which is conditional on the values of multiple predictors such as age, sex, and biomarkers. In this article, Bonnett and colleagues provide a guide to presenting clinical prediction models so that they can be implemented in practice, if appropriate. They describe how to create four presentation formats and discuss the advantages and disadvantages of each format. A key message is the need for stakeholder engagement to determine the best presentation option in relation to the clinical context of use and the intended users

BACKGROUND: Viral meningitis is increasingly recognised, but little is known about the frequency with which it occurs, or the causes and outcomes in the UK. We aimed to determine the incidence, causes, and sequelae in UK adults to improve the management of patients and assist in health service planning. METHODS: We did a multicentre prospective observational cohort study of adults with suspected meningitis at 42 hospitals across England. Nested within this study, in the National Health Service (NHS) northwest region (now part of NHS England North), was an epidemiological study. Patients were eligible if they were aged 16 years or older, had clinically suspected meningitis, and either underwent a lumbar puncture or, if lumbar puncture was contraindicated, had clinically suspected meningitis and an appropriate pathogen identified either in blood culture or on blood PCR. Individuals with ventricular devices were excluded. We calculated the incidence of viral meningitis using data from patients from the northwest region only and used these data to estimate the population-standardised number of cases in the UK. Patients self-reported quality-of-life and neuropsychological outcomes, using the EuroQol EQ-5D-3L, the 36-Item Short Form Health Survey (SF-36), and the Aldenkamp and Baker neuropsychological assessment schedule, for 1 year after admission. FINDINGS: 1126 patients were enrolled between Sept 30, 2011, and Sept 30, 2014. 638 (57%) patients had meningitis: 231 (36%) cases were viral, 99 (16%) were bacterial, and 267 (42%) had an unknown cause. 41 (6%) cases had other causes. The estimated annual incidence of viral meningitis was 2·73 per 100 000 and that of bacterial meningitis was 1·24 per 100 000. The median length of hospital stay for patients with viral meningitis was 4 days (IQR 3-7), increasing to 9 days (6-12) in those treated with antivirals. Earlier lumbar puncture resulted in more patients having a specific cause identified than did those who had a delayed lumbar puncture. Compared with the age-matched UK population, patients with viral meningitis had a mean loss of 0·2 quality-adjusted life-years (SD 0·04) in that first year. INTERPRETATION: Viruses are the most commonly identified cause of meningitis in UK adults, and lead to substantial long-term morbidity. Delays in getting a lumbar puncture and unnecessary treatment with antivirals were associated with longer hospital stays. Rapid diagnostics and rationalising treatments might reduce the burden of meningitis on health services. FUNDING: Meningitis Research Foundation and UK National Institute for Health Research.