Background C-reactive protein (CRP) is a non-specific acute phase reactant elevated in infection or inflammation. Higher levels indicate more severe infection and have been used as an indicator of COVID-19 disease severity. However, the evidence for CRP as a prognostic marker is yet to be determined. The aim of this study is to examine the CRP response in patients hospitalized with COVID-19 and to determine the utility of CRP on admission for predicting inpatient mortality. Methods Data were collected between 27 February and 10 June 2020, incorporating two cohorts: the COPE (COVID-19 in Older People) study of 1564 adult patients with a diagnosis of COVID-19 admitted to 11 hospital sites (test cohort) and a later validation cohort of 271 patients. Admission CRP was investigated, and finite mixture models were fit to assess the likely underlying distribution. Further, different prognostic thresholds of CRP were analysed in a time-to-mortality Cox regression to determine a cut-off. Bootstrapping was used to compare model performance [Harrell’s C statistic and Akaike information criterion (AIC)]. Results The test and validation cohort distribution of CRP was not affected by age, and mixture models indicated a bimodal distribution. A threshold cut-off of CRP ≥40 mg/L performed well to predict mortality (and performed similarly to treating CRP as a linear variable). Conclusions The distributional characteristics of CRP indicated an optimal cut-off of ≥40 mg/L was associated with mortality. This threshold may assist clinicians in using CRP as an early trigger for enhanced observation, treatment decisions and advanced care planning.

Little is known about their stability and the factors that influence their persistence or change over the life-course. To address this, we use data from 158 participants from the Special Needs and Autism Project cohort studied at three time-points from 12 to 23 years. We used latent growth models to study the role of child, family, and contextual characteristics on the conduct, emotional, and attention deficit hyperactivity disorder domains of the parent-reported Strengths and Difficulties Questionnaire. Symptoms decreased significantly over time for all three domains, but many participants still remained above the published cutoffs for likely disorder on at least one of the three domains. Individual trajectories showed high levels of persistence. Higher initial adaptive function and language levels predicted a greater decline in conduct and attention deficit hyperactivity disorder symptoms. In contrast, increased emotional symptoms were predicted by higher language functioning, lower levels of autism symptom severity and higher parental education. Greater neighborhood deprivation was associated with more conduct problems but also a greater decline over time. Our findings highlight that it may be possible to accurately predict mental health trajectories over this time period, which could help parents and carers in planning and help professionals target resources more efficiently. Lay Abstract Although mental health problems are common in autism, relatively little is known about their stability and the factors that influence their persistence or change over the life-course. To address this, we use data from the Special Needs and Autism Project (SNAP) cohort studied at three time-points from 12 to 23 years. Using the parent-reported Strengths and Difficulties Questionnaire (SDQ) domains of conduct, emotional, and ADHD symptoms, we evaluated the role of child, family, and contextual characteristics on these three trajectories. Symptoms decreased significantly over time for all three domains, but many participants still scored above the published disorder cutoffs. Individuals showed high levels of persistence. Higher initial adaptive function and language levels predicted a greater decline in conduct and ADHD symptoms. In contrast, higher language functioning was associated with higher levels of emotional symptoms, as was lower levels of autism symptom severity and higher parental education. Those with higher neighborhood deprivation had higher initial conduct problems but a steeper decline over time. Our findings highlight that it may be possible to accurately predict mental health trajectories over this time period, which could help parents and carers in planning and help professionals target resources more efficiently.

Objective: For the first time, we use a longitudinal population-based autism cohort to chart the trajectories of cognition and autism symptoms from childhood to early adulthood and identify features that predict the level of function and change with development. Method: Latent growth curve models were fitted to data from the Special Needs and Autism Project cohort at three time points: 12, 16, and 23 years. Outcome measures were IQ and parent-reported Social Responsiveness Scale autism symptoms. Of the 158 participants with an autism spectrum disorder at 12 years, 126 (80%) were reassessed at 23 years. Child, family, and contextual characteristics obtained at 12 years predicted intercept and slope of the trajectories. Results: Both trajectories showed considerable variability. IQ increased significantly by a mean of 7.48 points from 12 to 23 years, whereas autism symptoms remained unchanged. In multivariate analysis, full-scale IQ was predicted by initial language level and school type (mainstream/specialist). Participants with a history of early language regression showed significantly greater IQ gains. Autism symptoms were predicted by Social Communication Questionnaire scores (lifetime version) and emotional and behavioral problems. Participants attending mainstream schools showed significantly fewer autism disorder symptoms at 23 years than those in specialist settings; this finding was robust to propensity score analysis for confounding. Conclusion: Our findings suggest continued cognitive increments for many people with autism across the adolescent period, Q6 but a lack of improvement in autism symptoms. Our finding of school influences on autism symptoms requires replication in other cohorts and settings before drawing any implications for mechanisms or policy.

BackgroundThe first episode of psychosis is a critical period in the emergence of cardiometabolic risk.AimsWe set out to explore the influence of individual and lifestyle factors on cardiometabolic outcomes in early psychosis.MethodThis was a prospective cohort study of 293 UK adults presenting with first-episode psychosis investigating the influence of sociodemographics, lifestyle (physical activity, sedentary behaviour, nutrition, smoking, alcohol, substance use) and medication on cardiometabolic outcomes over the following 12 months.ResultsRates of obesity and glucose dysregulation rose from 17.8% and 12%, respectively, at baseline to 23.7% and 23.7% at 1 year. Little change was seen over time in the 76.8% tobacco smoking rate or the quarter who were sedentary for over 10 h daily. We found no association between lifestyle at baseline or type of antipsychotic medication prescribed with either baseline or 1-year cardiometabolic outcomes. Median haemoglobin A1c (HbA1c) rose by 3.3 mmol/mol in participants from Black and minority ethnic (BME) groups, with little change observed in their White counterparts. At 12 months, one-third of those with BME heritage exceeded the threshold for prediabetes (HbA1c >39 mmol/mol).ConclusionsUnhealthy lifestyle choices are prevalent in early psychosis and cardiometabolic risk worsens over the next year, creating an important window for prevention. We found no evidence, however, that preventative strategies should be preferentially directed based on lifestyle habits. Further work is needed to determine whether clinical strategies should allow for differential patterns of emergence of cardiometabolic risk in people of different ethnicities.

Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance was observed when machine-learning methods (LASSO and RIDGE) were applied—using the same predictors—to an individualised, transdiagnostic, clinically based, risk calculator previously developed on the basis of clinical-learning (predictors: age, gender, age by gender, ethnicity, ICD-10 diagnostic spectrum), and externally validated twice. In a second experiment, two refined versions of the published model which expanded the granularity of the ICD-10 diagnosis were introduced: ICD-10 diagnostic categories and ICD-10 diagnostic subdivisions. Although these refined versions showed an increase in apparent performance, their external performance was similar to the original model. In a third experiment, the three refined models were analysed under machine-learning and clinical-learning with a variable event per variable ratio (EPV). The best performing model under low EPVs was obtained through machine-learning approaches. The development of prognostic models on the basis of a priori clinical knowledge, large samples and adequate events per variable is a robust clinical prediction method to forecast psychosis onset in patients at-risk, and is comparable to machine-learning methods, which are more difficult to interpret and implement. Machine-learning methods should be preferred for high dimensional data when no a priori knowledge is available.

We identified a prospective association between higher baseline serum Vitamin D levels and lower total psychotic symptoms and negative symptoms of psychosis at 12 months after first contact for psychosis. The results of this study require replication in larger prospective studies, and highlight the need for large randomised trials to assess the effect of vitamin D supplementation on symptoms of psychosis in FEP.

Training nurses in MI and basic CBT to support self-management did not lead to improvements in glycaemic control or other secondary outcomes in people with T2D at 18 months. It was also unlikely to be cost-effective. Furthermore, the increased contact with standard-care nurses did not improve glycaemic control.

RituxiCAN-C4 combined an open-labeled randomized controlled trial (RCT) in 7 UK centers to assess whether rituximab could stabilize kidney function in patients with chronic rejection, with an exploratory analysis of how B cell-depletion influenced T cell anti-donor responses relative to outcome. Between January 2007 and March 2015, 59 recruits were enrolled after screening, 23 of whom consented to the embedded RCT. Recruitment was halted when in a pre-specified per protocol interim analysis, the RCT was discovered to be significantly underpowered. This report therefore focuses on the exploratory analysis, in which we confirmed that when B cells promoted CD4+ anti-donor IFNγ production assessed by ELISPOT, this associated with inferior clinical outcome; these patterns were inhibited by optimized immunosuppression but not rituximab. B cell suppression of IFNγ production, which associated with number of transitional B cells and correlated with slower declines in kidney function was abolished by rituximab, which depleted transitional B cells for prolonged periods. We conclude that in this patient population, optimized immunosuppression but not rituximab promotes anti-donor alloresponses associated with favorable outcomes. Clinical Trial Registration: Registered with EudraCT (2006-002330-38) and www. ClinicalTrials.gov, identifier: NCT00476164.