Combination Of Clinical Risk Factors Research Articles

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Development and validation of nomogram of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumor markers

BackgroundPeritoneal metastasis (PM) is not uncommon in patients with gastric cancer(GC), which affects clinical treatment decisions, but the relevant examination measures are not efficiently detected. Our goal was to develop a clinical radiomics nomogram to better predict peritoneal metastases.MethodsA total of 3480 patients from 2 centers were divided into 1 training, 1 internal validation, and 1 external validation cohort(1949 in the internal training set, 704 in the validation set, and 827 in the external validation cohort) with clinicopathologically confirmed GC. We recruited 11 clinical factors, including age, sex, smoking status, tumor size, differentiation, Borrmann type, location, clinical T stage, and serum tumor markers (STMs) comprising carbohydrate antigen 19–9 (CA19-9), carbohydrate antigen 72–4 (CA72-4), and carcinoembryonic antigen (CEA), to develop the radiomics nomogram. For clinical predictive feature selection and the establishment of clinical models, statistical methods of analysis of variance (ANOVA), relief and recursive feature elimination (RFE) and logistic regression analysis were used. To develop combined predictive models, tumor diameter, type, and location, clinical T stage and STMs were finally selected. The discriminatory ability of the nomogram to predict PM was evaluated by the area under the receiver operating characteristic curve(AUC), and decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of the nomogram.ResultsThe AUC of the clinical models was 0.762 in the training cohorts, 0.772 in the internal validation cohort, and 0.758 in the external validation cohort. However, when combined with STMs, the AUC was improved to 0.806, 0.839 and 0.801, respectively. DCA showed that the combined nomogram was of good clinical evaluation value to predict PM in GC.ConclusionsThe present study proposed a clinical nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of PM in GC patients.

Prospective Investigation of 18FDG-PET/MRI with Intravoxel Incoherent Motion Diffusion-Weighted Imaging to Assess Survival in Patients with Oropharyngeal or Hypopharyngeal Carcinoma.

To prospectively investigate the prognostic value of 18F-FDG PET/MRI in patients with oropharyngeal or hypopharyngeal squamous cell carcinomas (OHSCC) treated by chemoradiotherapy. The study cohort consisted of patients with OHSCC who had undergone integrated PET/MRI prior to chemoradiotherapy or radiotherapy. Imaging parameters derived from intravoxel incoherent motion (IVIM), dynamic contrast-enhanced MRI (DCE-MRI), and 18F-FDG PET were analyzed in relation to overall survival (OS) and recurrence-free survival (RFS). In multivariable analysis, T classification (p < 0.001), metabolic tumor volume (p = 0.013), and pseudo-diffusion coefficient (p = 0.008) were identified as independent risk factors for OS. The volume transfer rate constant (p = 0.015), initial area under the curve (p = 0.043), T classification (p = 0.018), and N classification (p = 0.018) were significant predictors for RFS. The Harrell's c-indices of OS and RFS obtained from prognostic models incorporating clinical and PET/MRI predictors were significantly higher than those derived from the traditional TNM staging system (p = 0.001). The combination of clinical risk factors with functional parameters derived from IVIM and DCE-MRI plus metabolic PET parameters derived from 18F-FDG PET in integrated PET/MRI outperformed the information provided by traditional TNM staging in predicting the survival of patients with OHSCC.

Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer

BackgroundPreoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images.MethodsA total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities.ResultsTwelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81–0.93) and 0.90 (95% CI, 0.83–0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients.ConclusionThe proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.

Radiomics Nomogram for Predicting Stroke Recurrence in Symptomatic Intracranial Atherosclerotic Stenosis.

ObjectiveTo develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS).MethodsThe data of 156 patients with SICAS were obtained from the hospital database. Those with and without stroke recurrence were identified. The 156 patients were separated into a training cohort (n = 110) and a validation cohort (n = 46). Baseline clinical data were collected from our medical records, and plaque radiological features were extracted from vascular wall high-resolution imaging (VW-HRMRI). The imaging sequences included 3D-T1WI-VISTA, T2WI, and 3D-T1WI-VISTA-enhanced imaging. Least absolute shrinkage and selection operator (LASSO) analysis were used to select the radiomics features associated with stroke recurrence. Then, multiple logistic regression analysis of clinical risk factors, radiological features, and radiomics signatures were performed, and a predictive nomogram was constructed to predict the probability of stroke recurrence in SICAS. The performance of the nomogram was evaluated.ResultsDiabetes mellitus, plaque burden, and enhancement ratio were independent risk factors for stroke recurrence [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.04–3.79, p = 0.018; OR = 1.76, per 10% increase, 95% CI, 1.28–2.41, p < 0.001; and OR = 1.94, 95% CI: 1.27–3.09, p < 0.001]. Five features of 3D-T1WI-VISTA, six features of T2WI, and nine features of 3D-T1WI-VISTA-enhanced images were associated with stroke recurrence. The radiomics signature in 3D-T1WI-VISTA-enhanced images was superior to the radiomics signature of the other two sequences for predicting stroke recurrence in both the training cohort [area under the curve (AUC), 0.790, 95% CI: 0.669–0.894] and the validation cohort (AUC, 0.779, 95% CI: 0.620–0.853). The combination of clinical risk factors, radiological features, and radiomics signature had the best predictive value (AUC, 0.899, 95% CI: 0.844–0.936 in the training cohort; AUC, 0.803, 95% CI: 0.761–0.897 in the validation cohort). The C-index of the nomogram was 0.880 (95% CI: 0.805–0.934) and 0.817 (95% CI: 0.795–0.948), respectively, in the training and validation cohorts. The decision curve analysis further confirmed that the radiomics nomogram had good clinical applicability with a net benefit of 0.458.ConclusionThe radiomics features were helpful to predict stroke recurrence in patients with SICAS. The nomogram constructed by combining clinical high-risk factors, plaque radiological features, and radiomics features is a reliable tool for the individualized risk assessment of predicting the recurrence of SICAS stroke.

Prediction of twin pregnancy preeclampsia based on clinical risk factors, early pregnancy serum markers, and uterine artery pulsatility index.

Objectives:To investigate whether a combination of clinical risk factors, early pregnancy serum markers, and uterine artery pulsatility index (UTPI) can be used to predict twin preeclampsia (PE).Methods:This case control study included women with twin pregnancies who had undergone obstetrics treatments and gave birth at the Huzhou Maternity and Child Health Care Hospital from October 2018 to November 2020. Patients with PE comprised study group, and patients without PE comprised control group based on selection criteria and a 1:1 ratio. Statistical analysis was performed using clinical risk factors, early pregnancy serum markers, and UTPIs, and the area under the receiver operating curve (AUC. Sensitivity, and the specificity of different combinations of these variables were calculated to predict PE in women with twin pregnancy.Results:Logistic regression analysis revealed four independent predictors for the onset of PE during twin pregnancies: first delivery (OR, 7.51; P=0.045), conception method (OR, 7.11; P=0.036), β-HCG level (per SD OR, 2.73; P=0.026), and UTPI (OR, 0.17; P=0.043). First-delivery and IVF pregnancy methods both lead to a 7-fold increase in the PE risk during twin pregnancies. Every one sigma (standard deviation) increase in the β-HCG level led to a 2.73-fold increase in the PE risk. Every UTPI increment by 1.0 reduces the risk of PE by 83%. The prediction efficiencies were based on an AUC of 0.837, a sensitivity of 69%, and a specificity of 92% for the clinical risk factors; an AUC of 0.800, a sensitivity of 81%, and specificity of 78% for the β-HCG level, and an AUC of 0.814, a sensitivity of 88%, and a specificity of 65% for the UTPI. AUC was 0.928, sensitivity 85%, and a specificity 88% after applying the three types of indicators together for prediction.Conclusions:By combining early pregnancy serum markers (β-HCG), and UTPI, the predictive value for PE during twin pregnancy is improved together with its sensitivity and specificity.

Prognostic Value of Estimated Glomerular Filtration Rate 3–6 Months after Percutaneous Coronary Intervention

Background: The prognostic significance of follow-up (f/u) renal function for patients undergoing percutaneous coronary intervention (PCI) remains unknown. This study sought to investigate the prognostic implications of f/u renal function in patients undergoing PCI. Methods: A drug-eluting stent registry was used. We divided patients into 4 groups according to the change in the estimated glomerular filtration rate (eGFR) before PCI and 3–6 months after PCI. Patients with normal pre-PCI eGFR and f/u eGFR were assigned to group 1. Those with normal pre-PCI eGFR and abnormal f/u eGFR were assigned to group 2. Patients with abnormal pre-PCI eGFR and normal f/u eGFR were assigned to group 3. Patients with abnormal pre-PCI eGFR and f/u eGFR were allocated into group 4. Results: A total of 4,899 PCI patients were enrolled. The death rate in group 1, 2, 3, and 4 at 3 years was 2, 11, 4, and 9%, respectively. This showed significant differences between groups, except between groups 2 and 4. The prognosis of a group with aggravation from normal renal function was worse than that of a group with recovery from abnormal renal function. A prediction model that combines clinical risk factors and f/u eGFR has more power for predicting clinical outcomes than a combination of clinical risk factors and pre-PCI eGFR. Conclusion: Post-PCI eGFR was more accurate for predicting patient outcomes than pre-PCI eGFR.

A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer

BackgroundAccurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinical-radiomics nomogram for the individual preoperative prediction of LNM in CRC patients.MethodsWe enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors (age, sex, preoperative carbohydrate antigen 19-9 (CA19-9) level, preoperative carcinoembryonic antigen (CEA) level, tumor size, tumor location, histotype, differentiation and M stage) to build the clinical model. We used analysis of variance (ANOVA), relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors and the imaging features of primary lesions and peripheral lymph nodes), established classification models with logistic regression analysis and selected the respective candidate models by fivefold cross-validation. Then, we combined the clinical risk factors, primary lesion radiomics features and peripheral lymph node radiomics features of the candidate models to establish combined predictive models. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, decision curve analysis (DCA) and a nomogram were used to evaluate the clinical usefulness of the model.ResultsThe clinical-primary lesion radiomics-peripheral lymph node radiomics model, with the highest AUC value (0.7606), was regarded as the candidate model and had good discrimination and calibration in both the training and validation sets. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in the clinical environment.ConclusionThe present study proposed a clinical-radiomics nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of LNM in CRC patients.

Early prediction of spontaneous Patent Ductus Arteriosus (PDA) closure and PDA-associated outcomes: a prospective cohort investigation

BackgroundPatent ductus arteriosus (PDA), the most commonly diagnosed cardiovascular condition in preterm infants, is associated with increased mortality and harmful long-term outcomes (chronic lung disease, neurodevelopmental delay). Although pharmacologic and/or interventional treatments to close PDA likely benefit some infants, widespread routine treatment of all preterm infants with PDA may not improve outcomes. Most PDAs close spontaneously by 44-weeks postmenstrual age; treatment is increasingly controversial, varying markedly between institutions and providers. Because treatment detriments may outweigh benefits, especially in infants destined for early, spontaneous PDA closure, the relevant unanswered clinical question is not whether to treat all preterm infants with PDA, but whom to treat (and when). Clinicians cannot currently predict in the first month which infants are at highest risk for persistent PDA, nor which combination of clinical risk factors, echocardiographic measurements, and biomarkers best predict PDA-associated harm.MethodsProspective cohort of untreated infants with PDA (n=450) will be used to predict spontaneous ductal closure timing. Clinical measures, serum (brain natriuretic peptide, N-terminal pro-brain natriuretic peptide) and urine (neutrophil gelatinase-associated lipocalin, heart-type fatty acid-binding protein) biomarkers, and echocardiographic variables collected during each of first 4 postnatal weeks will be analyzed to identify those associated with long-term impairment. Myocardial deformation imaging and tissue Doppler imaging, innovative echocardiographic techniques, will facilitate quantitative evaluation of myocardial performance. Aim1 will estimate probability of spontaneous PDA closure and predict timing of ductal closure using echocardiographic, biomarker, and clinical predictors. Aim2 will specify which echocardiographic predictors and biomarkers are associated with mortality and respiratory illness severity at 36-weeks postmenstrual age. Aim3 will identify which echocardiographic predictors and biomarkers are associated with 22 to 26-month neurodevelopmental delay. Models will be validated in a separate cohort of infants (n=225) enrolled subsequent to primary study cohort.DiscussionThe current study will make significant contributions to scientific knowledge and effective PDA management. Study results will reduce unnecessary and harmful overtreatment of infants with a high probability of early spontaneous PDA closure and facilitate development of outcomes-focused trials to examine effectiveness of PDA closure in “high-risk” infants most likely to receive benefit.Trial registrationClinicalTrials.gov NCT03782610. Registered 20 December 2018.

Fracture prediction, imaging and screening in osteoporosis.

Osteoporosis is associated with increased fragility of bone and a subsequent increased risk of fracture. The diagnosis of osteoporosis is intimately linked with the imaging and quantification of bone and BMD. Scanning modalities, such as dual-energy X-ray absorptiometry or quantitative CT, have been developed and honed over the past half century to provide measures of BMD and bone microarchitecture for the purposes of clinical practice and research. Combined with fracture prediction tools such as Fracture Risk Assessment Tool (FRAX) (which use a combination of clinical risk factors for fracture to provide a measure of risk), these elements have led to a paradigm shift in the ability to diagnose osteoporosis and predict individuals who are at risk of fragility fracture. Despite these developments, a treatment gap exists between individuals who are at risk of osteoporotic fracture and those who are receiving therapy. In this Review, we summarize the epidemiology of osteoporosis, the history of scanning modalities, fracture prediction tools and future directions, including the most recent developments in prediction of fractures.

Clinical significance of evaluating coronary atherosclerosis in adult patients with hypertrophic cardiomyopathy who have chest pain.

Chest pain is a common symptom in patients with hypertrophic cardiomyopathy (HCM), causing difficulty determining whether there is coexistent coronary artery disease (CAD). We investigated whether coronary computed tomography angiography (CCTA) can assess the prevalence and clinical significance of CAD in adult patients with HCM showing chest pain through longitudinal follow-up. In 238 adult patients with HCM, who underwent CCTA for chest pain, we analyzed the degree of stenosis and adverse plaque characteristics (APCs) as CCTA variables. Three prediction models for adverse cardiovascular events (ACEs: all-cause mortality, myocardial infarction, unstable angina, heart failure, implantable cardioverter-defibrillator implantation, and stroke) were assessed using the combination of clinical risk factors, echocardiographic parameters, and CCTA variables. The prevalence of obstructive CAD (≥ 50% in luminal stenosis) and APC was 14.7% and 18.9%, respectively. During the follow-up period (median, 37months; range, 2-108months), there were 31 occurrences of ACEs (13.0%). Using multivariate Cox regression analysis, age, atrial fibrillation, low ejection fraction, obstructive CAD, and APCs were associated with ACEs (all p < 0.05). Among the prediction models for ACEs, the area under the curve (AUC) was higher (AUC = 0.92) when CCTA variables were added to the clinical (AUC = 0.84) and echocardiographic factors (AUC = 0.88) (p < 0.001). Using CCTA, about 20% of symptomatic HCM patients were associated with clinically significant atherosclerosis. Adding these CCTA variables to the clinical and echocardiographic variables may increase the predictions of ACEs; therefore, evaluating coronary atherosclerosis using CCTA may be helpful for symptomatic HCM patients. • Chest pain in adult patients with hypertrophic cardiomyopathy (HCM) remains challenging to distinguish from coronary artery disease. • Coronary computed tomography angiography (CCTA) can assess the severity and characteristics of coronary atherosclerosis in symptomatic HCM patients. • Adding CCTA variables to clinical and echocardiographic factors may increase the predictions of adverse cardiac events in HCM patients, and thus evaluating coronary atherosclerosis using CCTA may be helpful for HCM patients with chest pain.

Integrated Care to Address the Physical Health Needs of People with Severe Mental Illness: A Mapping Review of the Recent Evidence on Barriers, Facilitators and Evaluations.

People with mental health conditions have a lower life expectancy and poorer physical health outcomes than the general population. Evidence suggests this is due to a combination of clinical risk factors, socioeconomic factors, and health system factors, notably a lack of integration when care is required across service settings.Several recent reports have looked at ways to better integrate physical and mental health care for people with severe mental illness (SMI). We built on these by conducting a mapping review that looked for the most recent evidence and service models in this area. This involved searching the published literature and speaking to people involved in providing or using current services.Few of the identified service models were described adequately and fewer still were evaluated, raising questions about the replicability and generalisability of much of the existing evidence. However, some common themes did emerge. Efforts to improve the physical health care of people with SMI should empower staff and service users and help remove everyday barriers to delivering and accessing integrated care. In particular, there is a need for improved communication among professionals and better information technology to support them, greater clarity about who is responsible and accountable for physical health care, and greater awareness of the effects of stigmatisation on the wider culture and environment in which services are delivered.

To Predict the Requirement of Pharmacotherapy by OGTT Glucose Levels in Women with GDM Classified by the IADPSG Criteria.

The aim of this study was to assess the association between OGTT glucose levels and requirement of pharmacotherapy in GDM patients classified by the IADPSG criteria. This study included 203 GDM patients (108 managed with lifestyle modification and 95 requiring pharmacotherapy). Clinical risk factors and OGTT glucose concentrations at 0 (G0), 60 (G60), and 120 min (G120) were collected. OGTT glucose levels were significantly associated with the later requirement of pharmacotherapy (ROC-AUC: 71.1, 95% CI: 63.8–78.3). Also, the combination of clinical risk factors (age, BMI, parity, and pharmacotherapy in previous gestation) showed an acceptable predictive accuracy (ROC-AUC: 72.1, 95% CI: 65.0–79.2), which was further improved when glycemic parameters were added (ROC-AUC: 77.5, 95% CI: 71.5–83.9). Random forest analysis revealed the highest variable importance for G0, G60, and age. OGTT glucose measures in addition to clinical risk factors showed promising properties for risk stratification in GDM patients classified by the recently established IADPSG criteria.

Prevalence and Trends of Metabolic Syndrome in Slovakia during the Period of 2003-2012.

Metabolic syndrome is a combination of clinical risk factors for cardiovascular disease as well as for diabetes. Metabolic syndrome arises from insulin resistance accompanied with abnormal adipose deposition. The aim of our cross-sectional time trends study was to characterize the prevalence of metabolic syndrome and its five risk determinants among the clients of Health Advice Centres of Regional Public Health Authorities in Slovakia. The study was stratified by gender and age groups during the 10 year period from 2003–2012. Prevalence data were estimated in adults and children (≥10 years, N=79,904) from the nationwide electronic database of Health Advice Centres of Regional Public Health Authorities in Slovak Republic "Test of healthy heart" from 2003 to 2012. The overall prevalence of metabolic syndrome was 30.2% in males and 26.6% in females, abdominal obesity was confirmed in 48.3% of males and 53.9% of females. Increased triglyceride level has higher prevalence among males (33.3%) compared to females (24.2%). Blood pressure (BP) values and fasting glucose values were significantly higher in males (58.2%) than females (41.9%). During the 10 year period from 2003 to 2012, we confirmed an increased trend in the age-adjusted prevalence of metabolic syndrome. Abdominal obesity and elevated triglycerides had also increased time trends prevalence in both sexes. The prevalence of people without risk determinants of metabolic syndrome had a time decreasing trend. A surprising finding is a decrease in the proportion of persons with suboptimal HDL-cholesterol. The proportion of people with elevated BP and glucose showed little change during the reporting period. The increasing prevalence of metabolic syndrome, abdominal obesity, and elevated triglycerides highlights the urgency of addressing these health problems as a healthcare priority to reduce cardiovascular mortality in the Slovak Republic.

Clinical risk factors alone are inadequate for predicting significant coronary artery disease

ObjectiveWe sought to derive and validate a model for identifying suspected ACS patients harboring undiagnosed significant coronary artery disease (CAD). MethodsThis was a secondary analysis of data from a randomized control trial (RCT). Patients randomized to the CTA arm of an RCT examining a CTA-based strategy for ruling-out acute coronary syndrome (ACS) constitute the derivation cohort, which was randomly divided into a training dataset (2/3, used for model derivation) and a test dataset (1/3, used for internal validation (IV)). ED patients from a different center receiving CTA to evaluate for suspected ACS constitute the external validation (EV) cohort. Primary outcome was CTA-assessed significant CAD (stenosis of ≥50% in a major coronary artery). ResultsIn the derivation cohort, 11.2% (76/679) of subjects had CTA-assessed significant CAD, and in the EV cohort, 8.2% of subjects (87/1056) had CTA-assessed significant CAD. Age was the strongest predictor of significant CAD among the clinical risk factors examined. Predictor variables included in the derived logistic regression model were: age, sex, tobacco use, diabetes, and race. This model exhibited an area under the receiver operating characteristic curve (ROC AUC) of 0.72 (95% CI: 0.61–0.83) based on IV, and 0.76 (95% CI: 0.70, 0.82) based on EV. The derived random forest model based on clinical risk factors yielded improved but not sufficient discrimination of significant CAD (ROC AUC = 0.76 [95% CI: 0.67–0.85] based on IV). Coronary artery calcium score was a more accurate predictor of significant CAD than any combination of clinical risk factors (ROC AUC = 0.85 [95% CI: 0.76–0.94] based on IV; ROC AUC = 0.92 [95% CI: 0.88–0.95] based on EV). ConclusionsClinical risk factors, either individually or in combination, are insufficient for accurately identifying suspected ACS patients harboring undiagnosed significant coronary artery disease.

Integrated care to address the physical health needs of people with severe mental illness: a rapid review

BackgroundPeople with mental health conditions have a lower life expectancy and poorer physical health outcomes than the general population. Evidence suggests that this discrepancy is driven by a combination of clinical risk factors, socioeconomic factors and health system factors.Objective(s)To explore current service provision and map the recent evidence on models of integrated care addressing the physical health needs of people with severe mental illness (SMI) primarily within the mental health service setting. The research was designed as a rapid review of published evidence from 2013–15, including an update of a comprehensive 2013 review, together with further grey literature and insights from an expert advisory group.SynthesisWe conducted a narrative synthesis, using a guiding framework based on nine previously identified factors considered to be facilitators of good integrated care for people with mental health problems, supplemented by additional issues emerging from the evidence. Descriptive data were used to identify existing models, perceived facilitators and barriers to their implementation, and any areas for further research.Findings and discussionThe synthesis incorporated 45 publications describing 36 separate approaches to integrated care, along with further information from the advisory group. Most service models were multicomponent programmes incorporating two or more of the nine factors: (1) information sharing systems; (2) shared protocols; (3) joint funding/commissioning; (4) colocated services; (5) multidisciplinary teams; (6) liaison services; (7) navigators; (8) research; and (9) reduction of stigma. Few of the identified examples were described in detail and fewer still were evaluated, raising questions about the replicability and generalisability of much of the existing evidence. However, some common themes did emerge from the evidence. Efforts to improve the physical health care of people with SMI should empower people (staff and service users) and help remove everyday barriers to delivering and accessing integrated care. In particular, there is a need for improved communication between professionals and better information technology to support them, greater clarity about who is responsible and accountable for physical health care, and awareness of the effects of stigmatisation on the wider culture and environment in which services are delivered.Limitations and future workThe literature identified in the rapid review was limited in volume and often lacked the depth of description necessary to acquire new insights. All members of our advisory group were based in England, so this report has limited information on the NHS contexts specific to Scotland, Wales and Northern Ireland. A conventional systematic review of this topic would not appear to be appropriate in the immediate future, although a more interpretivist approach to exploring this literature might be feasible. Wherever possible, future evaluations should involve service users and be clear about which outcomes, facilitators and barriers are likely to be context-specific and which might be generalisable.FundingThe research reported here was commissioned and funded by the Health Services and Delivery Research programme as part of a series of evidence syntheses under project number 13/05/11. For more information visitwww.nets.nihr.ac.uk/projects/hsdr/130511.

Detection of Rheumatoid Arthritis-Interstitial Lung Disease Is Enhanced by Serum Biomarkers.

Interstitial lung disease (ILD), a leading cause of morbidity and mortality in rheumatoid arthritis (RA), is highly prevalent, yet RA-ILD is underrecognized. To identify clinical risk factors, autoantibodies, and biomarkers associated with the presence of RA-ILD. Subjects enrolled in Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) and American College of Rheumatology (ACR) cohorts were evaluated for ILD. Regression models were used to assess the association between variables of interest and RA-ILD. Receiver operating characteristic curves were generated in BRASS to determine if a combination of clinical risk factors and autoantibodies can identify RA-ILD and if the addition of investigational biomarkers is informative. This combinatorial signature was subsequently tested in ACR. A total of 113 BRASS subjects with clinically indicated chest computed tomography scans (41% with a spectrum of clinically evident and subclinical RA-ILD) and 76 ACR subjects with research or clinical scans (51% with a spectrum of RA-ILD) were selected. A combination of age, sex, smoking, rheumatoid factor, and anticyclic citrullinated peptide antibodies was strongly associated with RA-ILD (areas under the curve, 0.88 for BRASS and 0.89 for ACR). Importantly, a combinatorial signature including matrix metalloproteinase 7, pulmonary and activation-regulated chemokine, and surfactant protein D significantly increased the areas under the curve to 0.97 (P = 0.002, BRASS) and 1.00 (P = 0.016, ACR). Similar trends were seen for both clinically evident and subclinical RA-ILD. Clinical risk factors and autoantibodies are strongly associated with the presence of clinically evident and subclinical RA-ILD on computed tomography scan in two independent RA cohorts. A biomarker signature composed of matrix metalloproteinase 7, pulmonary and activation-regulated chemokine, and surfactant protein D significantly strengthens this association. These findings may facilitate identification of RA-ILD at an earlier stage, potentially leading to decreased morbidity and mortality.

Cryptogenic Stroke: When and How Should You Look for Arrhythmias?

This article outlines current evidence and indications for electrocardiographic monitoring in documenting subclinical atrial fibrillation (AF) in patients with cryptogenic stroke. Longer monitoring improves detection rates of subclinical AF. Incorporation of risk factors predicting patients at higher risk of stroke can be used to target populations suitable for longer-term monitoring. Although longer duration of AF is expected to increase the risk of stroke, the exact cutoff for duration of clinical significance is not yet established. It seems probable that a combination of clinical risk factors and duration of AF will provide the best prediction of future clinical stroke.

The association of maternal ACE A11860G with small for gestational age babies is modulated by the environment and by fetal sex: a multicentre prospective case-control study

We aimed to determine whether the ACE A11860G genotype is associated with small for gestational age babies (SGA) and to determine whether the association is affected by environmental factors and fetal sex. Overall, 3234 healthy nulliparous women with singleton pregnancies, their partners and babies were prospectively recruited in Adelaide, Australia and Auckland, New Zealand. Data analyses were confined to 2121 Caucasian parent–infant trios, among which 216 were pregnancies with SGA infants and 1185 were uncomplicated pregnancies. Women with the ACE A11860G GG genotype in the combined and Adelaide cohorts had increased risk for SGA [odds ratios (OR) 1.5, 95% confidence interval (CI) 1.1–2.1 and OR 2.0, 95% CI 1.3–3.3, respectively) and delivered lighter babies (P = 0.02; P = 0.007, respectively) compared with those with AA/AG genotypes. The maternal ACE A11860G GG genotype was associated with higher maternal plasma ACE concentration at 15 weeks' gestation than AA/AG genotypes (P < 0.001). When the Adelaide cohort was stratified by maternal socio-economic index (SEI) and pre-pregnancy green leafy vegetable intake, the ACE A11860G GG genotype was only associated with an increased risk for SGA (OR 4.9, 95% CI 1.8–13.4 and OR 3.3, 95% CI 1.6–7.0, respectively) and a reduction in customized birthweight centile (P = 0.006 and P = 0.03) if superimposed on maternal SEI <34 or pre-pregnancy green leafy vegetable intake <1 serve/day. Furthermore, the associations of maternal ACE A11860G with customized birthweight centile observed among Adelaide women with SEI <34 or pre-pregnancy green leafy vegetable intake <1 serve/day were female specific. The current study identified a novel association of maternal ACE A11860G with SGA. More interestingly, this association was modified by environmental factors and fetal sex, suggesting ACE A11860G–environment–fetal sex interactions.Trial Registry Name: Screening nulliparous women to identify the combinations of clinical risk factors and/or biomarkers required to predict pre-eclampsia, SGA babies and spontaneous preterm birth.URL: http://www.anzctr.org.au.Registration number: ACTRN12607000551493.

The discriminatory capacity of BMD measurements by DXA and dual X-ray and laser (DXL) at the calcaneus including clinical risk factors for detecting patients with vertebral fractures

Osteoporotic fracture risk depends on bone mineral density (BMD) and clinical risk factors (CRF). Five hundred and eighty-eight untreated female and male outpatient subjects were evaluated, 160 with vertebral fractures. BMD was measured both by using calcaneal dual X-ray and laser (DXL) and dual-energy X-ray absorptiometry (DXA), and CRF were evaluated. Detection frequencies for different BMD methods with or without CRF are presented. Osteoporotic fracture risk depends on bone mineral density and clinical risk factors. DXA of the spine/hip is considered a gold standard for BMD assessment, but due to degenerative conditions, particularly among the older population, assessment of BMD at the lumbar spine has been shown to be of limited significance. Portable calcaneal dual X-ray technology and laser can be an easily obtainable alternative. Vertebral fractures were evaluated in a baseline analysis of 588 females and males (median age 64.4, range 17.6-93.1 years), comparing BMD measurements by using DXL and DXA and CRF with/without BMD. One hundred and sixty subjects had radiological verified vertebral fractures. Area under receiver-operating characteristic curves (AUROCC) and univariate and multiple logistic regressions were calculated. AUROCC for detection of vertebral fractures was comparable for DXL at calcaneus and DXA at femoral neck (DXL 0.665 and DXA 0.670). Odds ratio for prevalent vertebral fracture was generally weak for DXA femoral neck (0.613) and DXL (0.521). Univariate logistic regression among CRF without BMD revealed age, prevalent fragility fracture, and body mass index significantly associated with prevalent vertebral fracture (AUROCC = 0.805). Combining BMD and CRF, a prognostic improvement in case of DXA at femoral neck (AUROCC 0.869, p = 0.02), DXL at calcaneus (AUROCC 0.869, p = 0.059), and DXA at total hip (AUROCC 0.861, p = 0.06) was observed. DXL was similarly sensitive compared with DXA for identification of subjects with vertebral fragility fractures, and combination of CRF with BMD by DXL or DXA further increased the discriminatory capacity for detection of patients susceptible to vertebral fracture.