Diagnostic performance of shear wave measurement in the detection of hepatic fibrosis: A multicenter prospective study

AIM

This study aimed to establish the shear wave measurement (SWM) cut-off value for each fibrosis stage using magnetic resonance (MR) elastography values as a reference standard.

METHODS

We prospectively analyzed 594 patients with chronic liver disease who underwent SWM and MR elastography. Correlation coefficients (were analyzed, and the diagnostic value was evaluated by the area under the receiver operating characteristic curve. Liver stiffness was categorized by MR elastography as F0 (<2.61 kPa), F1 (≥2.61 kPa, <2.97 kPa, any fibrosis), F2 (≥2.97 kPa, <3.62 kPa, significant fibrosis), F3 (≥3.62 kPa, <4.62 kPa, advanced fibrosis), or F4 (≥4.62 kPa, cirrhosis).

RESULTS

The median SWM values increased significantly with increasing fibrosis stage (p < 0.001). The correlation coefficient between SWM and MR elastography values was 0.793 (95% confidence interval 0.761-0.821). The correlation coefficients between SWM and MR elastography values significantly decreased with increasing body mass index and skin-capsular distance; skin-capsular distance values were associated with significant differences in sensitivity, specificity, accuracy, or positive predictive value, whereas body mass index values were not. The best cut-off values for any fibrosis, significant fibrosis, advanced fibrosis, and cirrhosis were 6.18, 7.09, 8.05, and 10.89 kPa, respectively.

CONCLUSIONS

This multicenter study in a large number of patients established SWM cut-off values for different degrees of fibrosis in chronic liver diseases using MR elastography as a reference standard. It is expected that these cut-off values will be applied to liver diseases in the future.

A comparative study of hepatic steatosis using two different qualitative ultrasound techniques measured based on magnetic resonance imaging-derived proton density fat fraction

AIM

This study aimed to evaluate the diagnostic performance of attenuation measurement (ATT; dual-frequency method) and improved algorithm of ATT (iATT; reference method) for the assessment of hepatic steatosis using magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) as the reference standard.

METHODS

We prospectively analyzed 427 patients with chronic liver disease who underwent ATT, iATT, or MRI-derived PDFF. Correlation coefficients were analyzed, and diagnostic values were evaluated by area under the receiver operating characteristic curve (AUROC). The steatosis grade was categorized as S0 (<5.2%), S1 (≥5.2%, <11.3%), S2 (≥11.3%, <17.1%), and S3 (≥17.1%) according to MRI-derived PDFF values.

RESULTS

The median ATT and iATT values were 0.61 dB/cm/MHz (interquartile range 0.55-0.67 dB/cm/MHz) and 0.66 dB/cm/MHz (interquartile range 0.57-0.77 dB/cm/MHz). ATT and iATT values increased significantly as the steatosis grade increased in the order S0, S1, S2, and S3 (p < 0.001). The correlation coefficients between ATT or iATT values and MRI-derived PDFF values were 0.533 (95% confidence interval [CI] 0.477-0.610) and 0.803 (95% CI 0.766-0.834), with a significant difference between them (p < 0.001). For the detection of hepatic steatosis of ≥S1, ≥S2, and ≥S3, iATT yielded AUROCs of 0.926 (95% CI 0.901-0.951), 0.913 (95% CI 0.885-0.941), and 0.902 (95% CI 0.869-0.935), with significantly higher AUROC values than for ATT (p < 0.001, p < 0.001, p = 0.001).

CONCLUSION

iATT showed excellent diagnostic performance for hepatic steatosis, and was strongly correlated with MRI-derived PDFF, with AUROCs of ≥0.900.

Diagnostic accuracy of community optometrists for age-related macular degeneration using colour fundus photographs: A pilot evaluation

PURPOSE

The accurate diagnosis of age-related macular degeneration (AMD) represents an important step in delaying and preventing vision loss and achieving optimal patient care. Therefore, this pilot study aimed to estimate the diagnostic accuracy of community optometrists for identifying AMD using colour fundus photographs (CFPs) to support sample size calculations for subsequent definitive studies.

METHODS

Five practising community optometrists were invited to classify a total of 1023 CFPs for the (1) presence of AMD, and, if applicable, (2) stage of AMD (early/intermediate/late geographic atrophy/late neovascular AMD). Diagnosis by referral centre clinicians formed the reference standard. Diagnostic accuracy was assessed by the area under the receiver operating characteristic curve (aROC). Sensitivity, specificity, positive and negative predictive values were also calculated.

RESULTS

Of the 1023 CFPs included in the study, 226 images were of AMD and 797 images were of other ocular conditions or no abnormal findings. Participating community optometrists had a mean (SD) age of 30.2 (8.9) years, 60.0% (3/5) were female and the mean number of years practising in primary eye care was 5.4 (5.4) years. Community optometrists demonstrated excellent performance for diagnosing AMD, with an aROC of 0.86 (95% CI 0.83 to 0.89), sensitivity of 84.5% (95% CI 79.1 to 89.0) and specificity of 88.0% (95% CI 85.5 to 90.1). The aROC (95% CI) for diagnosing early, intermediate, late geographic atrophy and late neovascular AMD was 0.82 (0.73 to 0.91), 0.76 (0.72 to 0.81), 0.69 (0.49 to 0.90) and 0.55 (0.34 to 0.75), respectively.

CONCLUSIONS

These results justify the need for an appropriately powered definitive study to assess community clinicians' diagnostic accuracy for AMD.

Estimating transformations for evaluating diagnostic tests with covariate adjustment

Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference while handling the complexities associated with medical data. Such complexities might include non-normal data, covariates that influence the diagnostic potential of a test, ordinal biomarkers or censored data due to instrument detection limits. We propose a regression model for the transformed test results which exploits the invariance of receiver operating characteristic curves to monotonic transformations and accommodates these features. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the tram add-on package to the R system for statistical computing and graphics.

Carbapenem-resistant gram-negative bacterial infection in intensive care unit patients: Antibiotic resistance analysis and predictive model development

In this study, we analyzed the antibiotic resistance of carbapenem-resistant gram-negative bacteria (CR-GNB) in intensive care unit (ICU) patients and developed a predictive model. We retrospectively collected the data of patients with GNB infection admitted to the ICU of the First Affiliated Hospital of Fujian Medical University, who were then divided into a CR and a carbapenem-susceptible (CS) group for CR-GNB infection analysis. Patients admitted between December 1, 2017, and July 31, 2019, were assigned to the experimental cohort (n = 205), and their data were subjected to multivariate logistic regression analysis to identify independent risk factors for constructing the nomogram-based predictive model. Patients admitted between August 1, 2019, and September 1, 2020, were assigned to the validation cohort for validating the predictive model (n = 104). The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve analysis were used to validate the model's performance. Overall, 309 patients with GNB infection were recruited. Of them, 97 and 212 were infected with CS-GNB and CR-GNB, respectively. Carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were the most prevalent CR-GNB. The multivariate logistic regression analysis results of the experimental cohort revealed that a history of combination antibiotic treatments (OR: 3.197, 95% CI: 1.561-6.549), hospital-acquired infection (OR: 3.563, 95% CI: 1.062-11.959) and mechanical ventilation ≥ 7 days (OR: 5.096, 95% CI: 1.865-13.923) were independent risk factors for CR-GNB infection, which were then used for nomogram construction. The model demonstrated a good fit of observed data (p = 0.999), with an area under the ROC curve (AUC) of 0.753 (95% CI: 0.685-0.820) and 0.718 (95% CI: 0.619-0.816) for the experimental and validation cohort, respectively. The decision curve analysis results suggested that the model has a high practical value for clinical practice. The Hosmer-Lemeshow test indicated a good fit of the model in the validation cohort (p-value, 0.278). Overall, our proposed predictive model exhibited a good predictive value in identifying patients at high risk of developing CR-GNB infection in the ICU and could be used to guide preventive and treatment measures.

Validity of a combination of periodontal pathogens and salivary biomarkers as predictors of periodontitis

OBJECTIVE

Chronic periodontitis is caused by multiple risk factors. To predict chronic periodontitis in older people, we evaluated the association between a combination of major periodontal pathogens and salivary biomarkers and the presence of periodontitis.

METHODS

Stimulated saliva samples were collected to analyze the prevalence of Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Prevotella intermedia, as well as four biomarkers: interleukin (IL)-1β, IL-6, tumor necrosis factor-α (TNF-α), and prostaglandin E2 (PGE2). A total of 201 Japanese patients were recruited. Oral examinations ware performed to determine chronic periodontitis as measured by Community Periodontal Index. The sociodemographic and behavioral characteristics were also obtained, and the parameters were adjusted as potential confounders to employ statistical models.

RESULTS

The odds ratio (OR) for the presence of P. gingivalis and the third tertile level of IL-1β as compared with the absence of P. gingivalis and the lowest tertile of IL-1β was highest in individuals with periodontitis (OR = 13.98; 95% confidence interval [CI] 3.87-50.52) with the best level (0.79) of area under the curve (AUC) based on the receiver operating characteristic curve. The OR for the presence of P. gingivalis and the third tertile of PGE2 was 7.76 (CI 1.89-31.91) with an AUC of 0.78. The coexistence of more than two periodontal bacteria and the third tertile of PGE2 was also strongly associated with chronic periodontitis (OR = 9.23, 95% CI 2.38-35.79) with an AUC of 0.76.

CONCLUSIONS

The combined information of the presence of P. gingivalis in stimulated saliva, and higher levels of salivary IL-1β may play a vital role in the detection and prediction of chronic periodontitis in older adults.

A family of estimators to diagnostic accuracy when candidate tests are subject to detection limits-Application to diagnosing early stage Alzheimer disease

In disease diagnosis, individuals are usually assumed to be one of the two basic types, healthy or diseased, as typically based on an established gold standard. Candidate markers for diagnosing a disease often are much cheaper and less invasive than the gold standard but must be evaluated against the gold standard for their sensitivity and specificity to accurately diagnose the disease. When candidate diagnostic markers are fully measured, receiver operating characteristic curves have been the standard approaches for assessing diagnostic accuracy. However, full measurements of diagnostic markers may not be available above or below certain limits due to various practical and technical limitations. For example, in the diagnosis of Alzheimer disease using cerebrospinal fluid biomarkers, the Roche Elecsys® immunoassays have a measuring range for multiple cerebrospinal fluid molecular concentrations. Many cognitive tests used in diagnosing dementia due to Alzheimer disease are also subject to detection limits, often referred to as the floor and ceiling effects in the neuropsychological literature. We propose a new statistical methodology for estimating the diagnostic accuracy when a diagnostic marker is subject to detection limits by dividing the entire study sample into two sub-samples by a threshold of the diagnostic marker. We then propose a family of estimators to the area under the receiver operating characteristic curve by combining a conditional nonparametric estimator and another conditional semi-parametric estimator derived from Cox's proportional hazards model. We derive the variance to the proposed estimators, and further, assess the performance of the proposed estimators as a function of possible thresholds through an extensive simulation study, and recommend the optimum thresholds. Finally, we apply the proposed methodology to assess the ability of several cerebrospinal fluid biomarkers and cognitive tests in diagnosing early stage Alzheimer disease dementia.

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning

ABSTRACT

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.

Predicting the Need for Phototherapy After Discharge

BACKGROUND AND OBJECTIVES

Bilirubin screening before discharge is performed to identify neonates at risk for future hyperbilirubinemia. The American Academy of Pediatrics recommends using a graph of bilirubin levels by age (the Bhutani Nomogram) to guide follow-up and a different graph to determine phototherapy recommendations. Our objective was to evaluate predictive models that incorporate the difference between the last total serum bilirubin (TSB) before discharge and the American Academy of Pediatrics phototherapy threshold (Δ-TSB) to predict a postdischarge TSB above the phototherapy threshold by using a single graph.

METHODS

We studied 148 162 infants born at ≥35 weeks' gestation at 11 Kaiser Permanente Northern California facilities from 2012 to 2017 whose TSB did not exceed phototherapy levels and who did not receive phototherapy during the birth hospitalization. We compared 3 logistic models (Δ-TSB; Δ-TSB-Plus, which included additional variables; and the Bhutani Nomogram) by using the area under the receiver operating characteristic curve (AUC) in a 20% validation subset.

RESULTS

A total of 2623 infants (1.8%) exceeded the phototherapy threshold postdischarge. The predicted probability of exceeding the phototherapy threshold after discharge ranged from 56% for a predischarge Δ-TSB 0 to 1 mg/dL below the threshold to 0.008% for Δ-TSB >7 mg/dL below the threshold. Discrimination was better for the Δ-TSB model (AUC 0.93) and the Δ-TSB-Plus model (AUC 0.95) than for the Bhutani Nomogram (AUC 0.88).

CONCLUSIONS

The use of Δ-TSB models had excellent ability to predict postdischarge TSB above phototherapy thresholds and may be simpler to use than the Bhutani Nomogram.

Impact of prevalence and case distribution in lab-based diagnostic imaging studies

We investigated effects of prevalence and case distribution on radiologist diagnostic performance as measured by area under the receiver operating characteristic curve (AUC) and sensitivity-specificity in lab-based reader studies evaluating imaging devices. Our retrospective reader studies compared full-field digital mammography (FFDM) to screen-film mammography (SFM) for women with dense breasts. Mammograms were acquired from the prospective Digital Mammographic Imaging Screening Trial. We performed five reader studies that differed in terms of cancer prevalence and the distribution of noncancers. Twenty radiologists participated in each reader study. Using split-plot study designs, we collected recall decisions and multilevel scores from the radiologists for calculating sensitivity, specificity, and AUC. Differences in reader-averaged AUCs slightly favored SFM over FFDM (biggest AUC difference: 0.047, SE = 0.023 , p = 0.047 ), where standard error accounts for reader and case variability. The differences were not significant at a level of 0.01 (0.05/5 reader studies). The differences in sensitivities and specificities were also indeterminate. Prevalence had little effect on AUC (largest difference: 0.02), whereas sensitivity increased and specificity decreased as prevalence increased. We found that AUC is robust to changes in prevalence, while radiologists were more aggressive with recall decisions as prevalence increased.

Characteristics Associated With High-Impact Pain in People With Temporomandibular Disorder: A Cross-Sectional Study

High-impact (disabling) pain diminishes the quality of life and increases health care costs. The purpose of this study was to identify the variables that distinguish between high- and low-impact pain among individuals with painful temporomandibular disorder (TMD). Community-dwelling adults (N = 846) with chronic TMD completed standardized questionnaires that assessed the following: 1) sociodemographic characteristics, 2) psychological distress, 3) clinical pain, and 4) experimental pain. We used high-impact pain, classified using the Graded Chronic Pain Scale, as the dependent variable in logistic regression modeling to evaluate the contribution of variables from each domain. Cross-validated area under the receiver operating characteristic curve (AUC) quantified model discrimination. One-third of the participants had high-impact pain. Sociodemographic variables discriminated weakly between low- and high-impact pain (AUC = .61, 95% confidence interval [CI] = 0.57, 0.65), with the exception of race. An 18-variable model encompassing all 4 domains had good discrimination (AUC = 0.79, 95% CI = 0.75, 0.82), as did a simplified model (sociodemographic variables plus catastrophizing, jaw limitation, and number of painful body sites) (AUC = 0.79, 95% CI = 0.76, 0.82). Duration of pain, sex, and experimental pain testing results were not associated. The characteristics that discriminated most effectively between people with low- and high-impact TMD pain included clinical pain features and the ability to cope with pain. PERSPECTIVE: This article presents the results of a multivariable model designed to discriminate between people with high- and low-impact pain in a community-based sample of people with painful chronic TMD. The findings emphasize the importance of catastrophizing, jaw limitation, and painful body sites associated with pain-related impact.

Bootstrap-based testing approaches for the assessment of the diagnostic accuracy of biomarkers subject to a limit of detection

Assessment of the diagnostic accuracy of biomarkers through receiver operating characteristic curve analysis frequently involves a limit of detection imposed by the laboratory analytical system precision. As a consequence, measurements below a certain level are undetectable and ignoring these is known to lead to negatively biased estimates of the area under the receiver operating characteristic curve. In this article, we introduce two receiver operating characteristic curve-based parametric approaches that tackle the issue of correct assessment of diagnostic markers in the presence of a limit of detection. Proposed approaches are simulation-based utilising bootstrap methodology. Non-parametric alternatives that are naively used in the literature do not solve the inherent problem of limit of detection values which are treated as censored observations. However, the latter seems to perform adequately well in our simulation study. Nonparametric bootstrap was consistently used throughout, while other bootstrap alternatives performed similarly in our pilot simulation study. The simulation study involves the comparison of parametric and non-parametric options described here versus alternative strategies that are routinely used in the literature. We apply all methods to a study-setting resembling a chemical quasi-standard situation, where compound tumour biomarkers were searched within a multi-variable set of measurements to discriminate between two groups, namely colorectal cancer and controls. We focus in the assessment of glutamine and methionine.

Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

My Brain Reads Pain in Your Face, Before Knowing Your Gender

Humans are expert at recognizing facial features whether they are variable (emotions) or unchangeable (gender). Because of its huge communicative value, pain might be detected faster in faces than unchangeable features. Based on this assumption, we aimed to find a presentation time that enables subliminal discrimination of pain facial expression without permitting gender discrimination. For 80 individuals, we compared the time needed (50, 100, 150, or 200 milliseconds) to discriminate masked static pain faces among anger and neutral faces with the time needed to discriminate male from female faces. Whether these discriminations were associated with conscious reportability was tested with confidence measures on 40 other individuals. The results showed that, at 100 milliseconds, 75% of participants discriminated pain above chance level, whereas only 20% of participants discriminated the gender. Moreover, this pain discrimination appeared to be subliminal. This priority of pain over gender might exist because, even if pain faces are complex stimuli encoding both the sensory and the affective component of pain, they signal a danger. This supports the evolution theory relating to the necessity of quickly reading aversive emotions to ensure survival but might also be at the basis of altruistic behavior such as help and compassion.

PERSPECTIVE

This study shows that pain facial expression can be processed subliminally after brief presentation times, which might be helpful for critical emergency situations in clinical settings.

Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment

The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatment benefit. We show that ΔAUC and relative excess risk due to interaction (RERI) measure predictive values under two specific definitions of treatment benefit. We investigate the properties of LRT, ΔAUC, and RERI using simulations. We illustrate these approaches using published melanoma data to understand the benefits of possible intervention on sun exposure in relation to the MC1R gene. The goal is to evaluate possible interventions on sun exposure in relation to MC1R.

Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data

A cure rate model is a survival model incorporating the cure rate with the assumption that the population contains both uncured and cured individuals. It is a powerful statistical tool for prognostic studies, especially in cancer. The cure rate is important for making treatment decisions in clinical practice. The proportional hazards (PH) cure model can predict the cure rate for each patient. This contains a logistic regression component for the cure rate and a Cox regression component to estimate the hazard for uncured patients. A measure for quantifying the predictive accuracy of the cure rate estimated by the Cox PH cure model is required, as there has been a lack of previous research in this area. We used the Cox PH cure model for the breast cancer data; however, the area under the receiver operating characteristic curve (AUC) could not be estimated because many patients were censored. In this study, we used imputation-based AUCs to assess the predictive accuracy of the cure rate from the PH cure model. We examined the precision of these AUCs using simulation studies. The results demonstrated that the imputation-based AUCs were estimable and their biases were negligibly small in many cases, although ordinary AUC could not be estimated. Additionally, we introduced the bias-correction method of imputation-based AUCs and found that the bias-corrected estimate successfully compensated the overestimation in the simulation studies. We also illustrated the estimation of the imputation-based AUCs using breast cancer data.

Clinical milestones for the prediction of severe anemia by chronic hepatitis C patients receiving telaprevir-based triple therapy

BACKGROUND & AIMS

Anemia is a common adverse effect of telaprevir (TVR) in combination with pegylated interferon (PegIFN)α and ribavirin (RBV) therapy. It occurs at a higher incidence with the TVR relative to PegIFNα and RBV alone. We herein evaluate the baseline and on-treatment predictors of the development of severe anemia by chronic hepatitis C virus (HCV) patients receiving TVR-based triple therapy.

METHODS

This prospective, multicenter study consisted of 292 patients (median age: 62 years) infected with HCV genotype 1. All received 12 weeks of TVR in combination with 24 weeks of PegIFNα2b and RBV. The definition of severe anemia during antiviral treatment is hemoglobin (Hb)<85 g/L.

RESULTS

101 (34.6%) patients developed severe anemia during the treatment period. Multivariable logistic regression analysis of possible pretreatment predictors of the development of severe anemia extracted baseline Hb < 135 g/L (Hazard ratio [HR], 2.53; p = 0.0013), estimated glomerular filtration rate <80 ml/min/1.73 m(2) (HR, 1.83; p = 0.0265), and inosine triphosphatase (ITPA) CC genotype (rs1127354) (HR, 2.91; p = 0.0024). For patients with ITPA CC (n = 227), multivariable logistic regression analysis of possible pretreatment and on-treatment predictors of the development of severe anemia extracted Hb level at week 2 (HR, 0.96; p = 0.0085) and the initial four weeks of weight-adjusted TVR (HR, 1.05; p = 0.0281).

CONCLUSIONS

Anemia remains a risk for all patients treated with TVR-based triple therapy. However, ITPA polymorphism (rs1127354) is useful for predicting the development of severe anemia and will be helpful in the management of treatment.

Development and validation of a risk-stratification score for surgical site occurrence and surgical site infection after open ventral hernia repair

BACKGROUND

Current risk-assessment tools for surgical site occurrence (SSO) and surgical site infection (SSI) are based on expert opinion or are not specific to open ventral hernia repairs. We aimed to develop a risk-assessment tool for SSO and SSI and compare its performance against existing risk-assessment tools in patients with open ventral hernia repair.

STUDY DESIGN

A retrospective study of patients undergoing open ventral hernia repair (n = 888) was conducted at a single institution from 2000 through 2010. Rates of SSO and SSI were determined by chart review. Stepwise regression models were built to identify predictors of SSO and SSI and internally validated using bootstrapping. Odds ratios were converted to a point system and summed to create the Ventral Hernia Risk Score (VHRS) for SSO and SSI, respectively. Area under the receiver operating characteristic curve was used to compare the accuracy of the VHRS models against the National Nosocomial Infection Surveillance Risk Index, Ventral Hernia Working Group (VHWG) grade, and VHWG score.

RESULTS

The rates of SSO and SSI were 33% and 22%, respectively. Factors associated with SSO included mesh implant, concomitant hernia repair, dissection of skin flaps, and wound class 4. Predictors of SSI included concomitant repair, dissection of skin flaps, American Society of Anesthesiologists class ≥ 3, wound class 4, and body mass index ≥ 40. The accuracy of the VHRS in predicting SSO and SSI exceeded National Nosocomial Infection Surveillance and VHWG grade, but was not better than VHWG score.

CONCLUSIONS

The VHRS identified patients at increased risk for SSO/SSI more accurately than the National Nosocomial Infection Surveillance scores and VHWG grade, and can be used to guide clinical decisions and patient counseling.

A validated clinical tool for the prediction of varices in PBC: the Newcastle Varices in PBC Score

BACKGROUND & AIMS

Gastro-oesophageal varices (GOV) can occur in early stage primary biliary cirrhosis (PBC), making it difficult to identify the appropriate time to begin screening with oesophageo-gastro-duodenoscopy (OGD). Our aim was to develop and validate a clinical tool to predict the probability of finding GOV in PBC patients.

METHODS

A cross-sectional retrospective study analysing clinical data of 330 PBC patients who underwent an OGD at the Freeman Hospital, Newcastle was used to create a predictive tool, the Newcastle Varices in PBC (NVP) Score, that was externally validated in PBC patients from Cambridge (UK) and Toronto (Canada).

RESULTS

48% of the Newcastle, 31% of the Cambridge, and 22% of the Toronto cohorts of PBC patients had GOV. Twenty-five percent (95% CI 18-32%) of the Newcastle cohort had GOV diagnosed at an index variceal bleed. Of the others, 37% (95% CI 28-46%) bled after a median of 1.5 years (IQR 3.75). Transplant-free survival was significantly better in those without GOV than in those with GOV (p<0.001), but similar in patients with GOV that bled and those that did not (p=0.1). The NVP score (%Probability)=1/[1+exp^-(9.186+0.001*alkaline phosphatase in IU-0.178*albumin in g/L-0.015*platelet × 10(9)) was validated in 2 external cohorts and was highly discriminant (AUROC 0.86). Cost consequences analyses revealed the NVP score to be as accurate as, but more economical than using either OGD directly or other risk scores for screening PBC patients.

CONCLUSIONS

The NVP score is an inexpensive, non-invasive, externally validated tool that accurately predicts GOV in PBC.

Multi-wavelength spectrophotometric analysis for detection of xanthochromia in cerebrospinal fluid and accuracy for the diagnosis of subarachnoid hemorrhage

BACKGROUND

Cerebrospinal fluid (CSF) was examined for bilirubin, an important indicator for diagnosis of subarachnoid hemorrhage (SAH).

METHODS

A multi-wavelength (340, 415, and 460 nm) spectrophotometric assay was developed for the quantitative measurement of bilirubin in CSF, enabling the mathematical correction for absorbance of hemoglobin and proteins. Bilirubin and hemoglobin results were correlated to HPLC and a standard colorimetric assay, respectively. A subset of samples was sent for an absorbance reading at 450 nm following baseline correction. The multi-wavelength bilirubin assay was validated on 70 patients with confirmed SAH and 70 patients with neurologic symptoms who ruled out for SAH.

RESULTS

The multi-wavelength spectrophometric assay demonstrated no interferences due to proteins (albumin) up to 30 g/l or oxyhemoglobin up to 260 mg/l. The assay limit of detection was 0.2 mg/l, linear to 20 mg/l, and CVs ranged from 1 to 6% at bilirubin concentrations of 0.84 and 2.1mg/l. The spectrophotometric assay correlated to HPLC and the colorimetric assay for bilirubin and hemoglobin, respectively. Results also correlated to the absorbance method (with removal of samples with high hemoglobin and proteins). The area under the ROC curve for diagnosis of SAH was 0.971 and 0.954 for the HPLC and spectrophotometric assay, respectively. At a cutoff of 0.2mg/l, the clinical specificity was 100% for both assays, and the clinical sensitivity was 94.3% and 88.6% for SAH for the HPLC and spectrophotometric asays, respectively.

CONCLUSIONS

The multi-wavelength spectrophotometric assay is an objective alternative to visual inspection, HPLC, and absorbance for CSF bilirubin.

Aggregate plaque volume by coronary computed tomography angiography is superior and incremental to luminal narrowing for diagnosis of ischemic lesions of intermediate stenosis severity

OBJECTIVES

This study examined the performance of percent aggregate plaque volume (%APV), which represents cumulative plaque volume as a function of total vessel volume, by coronary computed tomography angiography (CTA) for identification of ischemic lesions of intermediate stenosis severity.

BACKGROUND

Coronary lesions of intermediate stenosis demonstrate significant rates of ischemia. Coronary CTA enables quantification of luminal narrowing and %APV.

METHODS

We identified 58 patients with intermediate lesions (30% to 69% diameter stenosis) who underwent invasive angiography and fractional flow reserve. Coronary CTA measures included diameter stenosis, area stenosis, minimal lumen diameter (MLD), minimal lumen area (MLA) and %APV. %APV was defined as the sum of plaque volume divided by the sum of vessel volume from the ostium to the distal portion of the lesion. Fractional flow reserve ≤ 0.80 was considered diagnostic of lesion-specific ischemia. Area under the receiver operating characteristic curve and net reclassification improvement (NRI) were also evaluated.

RESULTS

Twenty-two of 58 lesions (38%) caused ischemia. Compared with nonischemic lesions, ischemic lesions had smaller MLD (1.3 vs. 1.7 mm, p = 0.01), smaller MLA (2.5 vs. 3.8 mm(2), p = 0.01), and greater %APV (48.9% vs. 39.3%, p < 0.0001). Area under the receiver operating characteristic curve was highest for %APV (0.85) compared with diameter stenosis (0.68), area stenosis (0.66), MLD (0.75), or MLA (0.78). Addition of %APV to other measures showed significant reclassification over diameter stenosis (NRI 0.77, p < 0.001), area stenosis (NRI 0.63, p = 0.002), MLD (NRI 0.62, p = 0.001), and MLA (NRI 0.43, p = 0.01).

CONCLUSIONS

Compared with diameter stenosis, area stenosis, MLD, and MLA, %APV by coronary CTA improves identification, discrimination, and reclassification of ischemic lesions of intermediate stenosis severity.

Linear combination methods to improve diagnostic/prognostic accuracy on future observations

Multiple diagnostic tests or biomarkers can be combined to improve diagnostic accuracy. The problem of finding the optimal linear combinations of biomarkers to maximise the area under the receiver operating characteristic curve has been extensively addressed in the literature. The purpose of this article is threefold: (1) to provide an extensive review of the existing methods for biomarker combination; (2) to propose a new combination method, namely, the nonparametric stepwise approach; (3) to use leave-one-pair-out cross-validation method, instead of re-substitution method, which is overoptimistic and hence might lead to wrong conclusion, to empirically evaluate and compare the performance of different linear combination methods in yielding the largest area under receiver operating characteristic curve. A data set of Duchenne muscular dystrophy was analysed to illustrate the applications of the discussed combination methods.