Reward – and threat – related neural function associated with depression

2021 ◽  

The focus of this podcast is on the recently published JCPP paper ‘Reward- and threat-related neural function associated with risk and presence of depression in adolescents: a study using a composite risk score in Brazil’, co-authored by Dr. Johnna Swartz.

2019 ◽  
Vol 29 (5) ◽  
pp. 861-868 ◽  
Author(s):  
Douglas Hamilton ◽  
John Cullinan

Abstract Background Haemolytic Uraemic Syndrome (HUS) is a serious complication of Shiga toxin-producing Escherichia coli (STEC) infection and the key reason why intensive health protection against STEC is required. However, although many potential risk factors have been identified, accurate estimation of risk of HUS from STEC remains challenging. Therefore, we aimed to develop a practical composite score to promptly estimate the risk of developing HUS from STEC. Methods This was a retrospective cohort study where data for all confirmed STEC infections in Ireland during 2013–15 were subjected to statistical analysis with respect to predicting HUS. Multivariable logistic regression was used to develop a composite risk score, segregating risk of HUS into ‘very low risk’ (0–0.4%), ‘low risk’ (0.5–0.9%), ‘medium risk’ (1.0–4.4%), ‘high risk’ (4.5–9.9%) and ‘very high risk’ (10.0% and over). Results There were 1397 STEC notifications with complete information regarding HUS, of whom 5.1% developed HUS. Young age, vomiting, bloody diarrhoea, Shiga toxin 2, infection during April to November, and infection in Eastern and North-Eastern regions of Ireland, were all statistically significant independent predictors of HUS. Demonstration of a risk gradient provided internal validity to the risk score: 0.2% in the cohort with ‘very low risk’ (1/430), 1.1% with ‘low risk’ (2/182), 2.3% with ‘medium risk’ (8/345), 3.1% with ‘high risk’ (3/98) and 22.2% with ‘very high risk’ (43/194) scores, respectively, developed HUS. Conclusion We have developed a composite risk score which may be of practical value, once externally validated, in prompt estimation of risk of HUS from STEC infection.


BMJ Open ◽  
2012 ◽  
Vol 2 (4) ◽  
pp. e000856 ◽  
Author(s):  
Christian Bjurman ◽  
Ulrika Snygg-Martin ◽  
Lars Olaison ◽  
Michael L X Fu ◽  
Ola Hammarsten

2019 ◽  
Vol 6 (1) ◽  
pp. e000318 ◽  
Author(s):  
Michelle A Petri ◽  
John Conklin ◽  
Tyler O'Malley ◽  
Thierry Dervieux

BackgroundLow C3 and lupus anticoagulant (LAC) are known risk factors for thrombosis in SLE. We evaluated the association between C4d products deposited on platelets (PC4d) and thrombosis in SLE. Antiphosphatidyl serine/prothrombin (PS/PT) complex antibody was also evaluated as an alternative to LAC.MethodsThis was a cross-sectional analysis of 149 consented patients with SLE (mean age: 47±1 years, 86% female) classified with (n=16) or without (n=133) thrombotic events in the past 5 years. Abnormal PC4d (≥20 units) was measured using flow cytometry. LAC and C3 were measured using dilute Russell’s viper venom time (>37 s) and immunoturbidimetry, respectively. Anti-PS/PT antibody status (IgG) was measured by immunoassay. Statistical analysis consisted of logistic regression and calculation of OR estimates with 95% CI.ResultsAbnormal PC4d (OR=8.4, 95% CI 2.8 to 24.8), low C3 (OR=9.5, 95% CI 3.0 to 30.3), LAC (OR=5.4, 95% CI 1.3 to 22.3) and anti-PS/PT IgG (OR=3.4, 95% CI 1.2 to 9.7) status associated with thrombosis (p<0.05). Cumulatively, the presence of PC4d, low C3 and LAC abnormalities as a composite risk score was higher in the presence of thrombosis (1.93±0.25) than in its absence (0.81±0.06) (p<0.01). Each unit of this composite risk score yielded an OR of 5.2 (95% CI 2.5 to 10.7) to have thrombosis (p<0.01). The composite risk score with anti-PS/PT antibody status instead of LAC also associated with thrombosis (p<0.01).ConclusionA composite risk score including PC4d, low C3 and LAC was associated with recent thrombosis and acknowledges the multifactorial nature of thrombosis in SLE.


2013 ◽  
Vol 42 (s1) ◽  
pp. 26-26
Author(s):  
T. Prior ◽  
E. Mullins ◽  
P. Bennett ◽  
S. Kumar

2018 ◽  
Vol 101 ◽  
pp. 128-145 ◽  
Author(s):  
Elisa Cuadrado-Godia ◽  
Md Maniruzzaman ◽  
Tadashi Araki ◽  
Anudeep Puvvula ◽  
Md Jahanur Rahman ◽  
...  

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Vanessa Xanthakis ◽  
Michael J Pencina ◽  
Lisa M Sullivan

Given the rapid growth of new prognostic biomarkers, it is critical to assess their incremental utility for risk prediction while considering standard risk factors. This assessment may be influenced by the approach used to model new biomarkers. We hypothesized that the performance of a putative biomarker is best assessed by adding it to a model that includes standard risk factors as individual variables, as compared to adding it to a composite risk score (based on standard risk factors) estimated from the current study or to a composite risk score from a published study. We also compared 3 approaches of adjusting the prior absolute risk of an event using the information from a new biomarker, when data regarding prior risk are limited, hypothesizing that conditioning the biomarker residuals on prior risk (Improved Bayes approach) or adjusting the intercept of a model that includes the prior risk estimate are superior to the Naïve Bayes approach. Incremental performance was evaluated by comparing measures of improvement in discrimination. Using 1000 simulated datasets, similar incremental performance was observed when a putative biomarker was added to a model with the individual risk factors as compared to adding it to a model with a risk score estimated from the current study. Including a biomarker in a model with a published risk score resulted in an overestimation of its contribution ( Table ).These findings were supported by Framingham Heart Study data predicting incident atrial fibrillation using CRP and BNP.The Improved Bayes approach was a better strategy for updating the prior risk estimate as compared to the Naïve Bayes approach, using information from a new biomarker (Table). Our theoretical and empirical results identified that adding a new biomarker into a multivariable prediction model that includes the individual risk factors is the preferred strategy for assessing the incremental yield of a novel biomarker, and using the Naive Bayes approach (when information on the prior absolute risk of an event is scarce) is suboptimal.


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