Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve ...risk prediction models.
We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case-control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone-binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting.
Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor-positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.
Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification.
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Objectives:
To identify the differences of clinical characteristics and outcomes of severe pneumonia in children under 5 years old with and without adenovirus infection.
Methods:
A retrospective ...cohort study was conducted in three pediatric hospitals in Guangzhou, China. In total, 1,595 children under the age of 5 with WHO-defined severe pneumonia had adenovirus testing performed between January 1, 2009 and December 31, 2019. Demographics, complications, the first routine laboratory findings, therapeutic records, and clinical outcome were collected from electronic medical records. We compared characteristics of children with and without adenovirus infection.
Results:
Adenovirus was detected in 75 (4.7%) out of 1,595 children with severe pneumonia. Cases with adenovirus infection were more likely to be boys (74.7 vs. 63.0%), older than 1 year old (78.7 vs. 25.1%), but less likely to have mixed virus infections (25.3 vs. 92.9%) and combined with cardiovascular disease (12.0 vs. 39.7%), and had more abnormal laboratory results than cases without adenovirus infection. Antiviral therapy (4.9%) was rarely used in children with severe pneumonia, but antibiotic therapy (65.3%) was commonly used, especially in cases with adenovirus infection (91.9%). Children infected with adenovirus (9.3 vs. 2.5%) were also hospitalized longer and had a higher mortality within 30 days of hospitalization.
Conclusions:
Children with severe pneumonia under 5 years old with adenovirus infection had more abnormal laboratory findings and more severe clinical outcomes than cases without adenovirus infection. More attention should be focused on the harm caused by adenovirus infection.
Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large ...cohorts, we examined whether modeling this heterogeneity could improve prediction.
We built two models, for ER+ (Model
) and ER- tumors (Model
), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare Model
and the Gail model (Model
) regarding their applicability in risk assessment for chemoprevention.
Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for Model
and 0.59 for Model
. External validation reduced the C-statistic of Model
(0.59) and Model
(0.57). In external evaluation of calibration, Model
outperformed the Model
: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, Model
produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while Model
did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10
for Model
and 3.0 × 10
for Model
.
Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
Whether early lumbar puncture (LP) and blood indicators are suitable as diagnostic criteria and helpful to treatment strategies for newborns remains to be solved. The study was to evaluate the value ...of cerebrospinal fluid (CSF) at the first LP and blood indicators at the similar time in the early diagnosis and the drug therapy of neonatal bacterial meningitis.
We conducted a retrospective observational study of 997 infants with suspected bacterial meningitis between June 2012 and June 2018. CSF and blood parameters were evaluated by three stepwise logistic models to assess their ability: to distinguish bacterial meningitis from nonbacterial meningitis, to distinguish positive CSF culture from negative, and to distinguish Gram-positive bacteria from negative.
Of the 997 neonates, 236 (23.67%) were later diagnosed as bacterial meningitis. Of the neonates with meningitis, 54 (22.88%) had positive CSF culture results. And of neonates with positive CSF culture, 27 (50%) had Gram-positive results. One or more CSF indicators were added to the three models. Only blood hypersensitive C-reactive protein and blood lactate dehydrogenase were added to the first model, while no blood parameters was added to the other two models. The areas under the effect-time curves of the three models were 0.91 (95% confidence interval CI: 0.89-0.92,
< 0.001), 0.69 (95% CI: 0.63-0.75,
< 0.001), and 0.86 (95% CI: 0.74-0.94,
< 0.001), respectively.
LP was irreplaceable predictor of bacterial meningitis, and comprehensive analysis of CSF indicators can predict the offending organism, which enables refinement of therapy.
Aims/hypothesis
Thus far, it is unclear whether lifestyle recommendations for people with diabetes should be different from those for the general public. We investigated whether the associations ...between lifestyle factors and mortality risk differ between individuals with and without diabetes.
Methods
Within the European Prospective Investigation into Cancer and Nutrition (EPIC), a cohort was formed of 6,384 persons with diabetes and 258,911 EPIC participants without known diabetes. Joint Cox proportional hazard regression models of people with and without diabetes were built for the following lifestyle factors in relation to overall mortality risk: BMI, waist/height ratio, 26 food groups, alcohol consumption, leisure-time physical activity, smoking. Likelihood ratio tests for heterogeneity assessed statistical differences in regression coefficients.
Results
Multivariable adjusted mortality risk among individuals with diabetes compared with those without was increased, with an HR of 1.62 (95% CI 1.51, 1.75). Intake of fruit, legumes, nuts, seeds, pasta, poultry and vegetable oil was related to a lower mortality risk, and intake of butter and margarine was related to an increased mortality risk. These associations were significantly different in magnitude from those in diabetes-free individuals, but directions were similar. No differences between people with and without diabetes were detected for the other lifestyle factors.
Conclusions/interpretation
Diabetes status did not substantially influence the associations between lifestyle and mortality risk. People with diabetes may benefit more from a healthy diet, but the directions of association were similar. Thus, our study suggests that lifestyle advice with respect to mortality for patients with diabetes should not differ from recommendations for the general population.
Artificial Intelligence (AI) has achieved state-of-the-art performance in medical imaging. However, most algorithms focused exclusively on improving the accuracy of classification while neglecting ...the major challenges in a real-world application. The opacity of algorithms prevents users from knowing when the algorithms might fail. And the natural gap between training datasets and the in-reality data may lead to unexpected AI system malfunction. Knowing the underlying uncertainty is essential for improving system reliability. Therefore, we developed a COVID-19 AI system, utilizing a Bayesian neural network to calculate uncertainties in classification and reliability intervals of datasets. Validated with four multi-region datasets simulating different scenarios, our approach was proved to be effective to suggest the system failing possibility and give the decision power to human experts in time. Leveraging on the complementary strengths of AI and health professionals, our present method has the potential to improve the practicability of AI systems in clinical application.
Display omitted
•A COVID-19 artificial intelligence diagnosis system with uncertainty estimation•Reliability and optional reliability intervals at dataset level as references•A proposed workflow that could be expanded to other diseases in practice
Bioinformatics; Neural networks; Artificial intelligence
The aim of this study was to assess the preventive potential of major lifestyle risk factors for acute myocardial infarction (AMI) in middle-aged men. Among 10,981 men in the Heidelberg cohort of the ...European Prospective Investigation into Cancer and Nutrition, aged 40.2-65.8 years when recruited, 378 developed first-ever AMI during a median follow-up period of 11.4 years. Current smoking, excess body weight, being physically inactive, but not high alcohol consumption, were identified as the major lifestyle risk factors for AMI using Cox regression analysis. A competing AMI risk model built from cause-specific Cox regression models and considering the risk of death predicted 353 AMI cases, 182 (51.6 %) of which were estimated as preventable through adherence to a healthy lifestyle profile (never smoking, normal body weight, physically active, and moderate alcohol consumption). The calculated age-specific 5-year incidence rates for AMI in the actual cohort and in a hypothetical, comparable cohort with all men following the healthy lifestyle profile were 128 and 39, respectively, per 100,000 person-years for the age group 40-44, increasing to 468 and 307 per 100,000 person-years for the age group 65-69. The estimated AMI incidence rates for men with the healthy lifestyle profile are still somewhat higher than the average rates reported for documented low-incidence regions, such as parts of Japan. Our analysis confirms the strong primary preventive potential for AMI based on avoidance of smoking and excess body weight, and on regular physical activity.
Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a ...risk score to identify these individuals and validate it in an external population.
We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥ 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample).
Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥ 200 points were 9% and 96%, respectively.
The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.
IntroductionSuccessful surgical treatment of congenital heart disease improves neonates’ long-term survival and leads to catch-up growth, which however does not occur in part of the patient ...population for largely undetermined reasons.Methods and analysisA multicentre, prospective cohort study is being conducted in four paediatric medical institutions in China to collect detailed nutritional, anthropometric and clinical data at perioperative phases and during a 1-year period of follow-up after surgery. The study is expected to recruit approximately 5000 patients by the year of 2023 when the cohort is fully established. The primary endpoint of this study is the occurrence of postoperative catch-up growth, which will be determined in both absolute and relative terms (ie, reduced anthropometric deficits from the reference measures and improved z-scores that have passed the −2 SD cut-offs). Multivariable regression analyses will be performed to identify factors that are statistically significantly associated with the absence of postoperative catch-up growth.Ethics and disseminationThe protocol of this study has been approved by the individual ethics committees of the participating centres (Guangzhou Women and Children’s Medical Centre (2008071601), the Children’s Hospital of Zhejiang University School of Medicine (2018-IRB-094), Gansu Provincial Maternity and Child-Care Hospital (2019-IRB-01) and Zhengzhou Cardiovascular Hospital (2019012001)). Written informed consent from parents will be obtained before study entry. Findings of this study will be disseminated through publications in international peer-reviewed journals and will be presented in academic conferences.
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