Optimal thresholds by maximizing or minimizing various metrics via ROC-type analysis

Association of Subacute Mild Traumatic Brain Injury Symptoms With Long-Term Persistent Symptoms, Functional Limitations, and Quality of Life

BACKGROUND AND OBJECTIVES

The objective was to evaluate the association of subacute postconcussion symptoms (with the total Rivermead Post-Concussion Questionnaire [RPQ] score) with persistent symptoms, functional limitations, and quality of life at 6 months in patients with mild traumatic brain injury (mTBI).

METHODS

This was a secondary analysis of the Transforming Research and Clinical Knowledge of Traumatic Brain Injury, which was a prospective cohort study of patients with TBI and admission Glasgow Coma Scale score between 13 and 15 at 18 US Level 1 trauma centers through 2014-2018. Participants were included in the study if presenting within 24 hours of external force trauma to the head and met the American Congress of Rehabilitation Medicine's criteria for TBI. Participants completed the RPQ, Glasgow Outcome Scale-Extended (GOSE), and Quality of Life after Brain Injury Overall Scale (QOLIBRI-OS). Primary outcomes were persistent symptoms (≥3 individual RPQ symptoms higher than preinjury level), incomplete recovery (GOSE score <8), and lower quality of life (QOLIBRI-OS score ≤51) at 6 months. Multivariable regression models were developed including RPQ clinical cutoffs at 2 weeks and 3 months and risk factors. Adjusted odds ratios (aORs) and 95% CI are reported for multivariable models. Receiver operating characteristic curves were built to identify discriminative ability of the cutoffs with area under the curve (AUC).

RESULTS

The age of the study cohort (n = 2,000) was 41.1 ± 17.3 years; 33% were female (n = 669), 67% male, 57% White (n = 1,141), and 20% Hispanic (n = 408). RPQ total score ≥14 was associated with higher odds of persistent symptoms (aOR 7.25, 95% CI 5.51-9.54), incomplete recovery (aOR 4.85, 95% CI 3.69-6.39), and lower quality of life (aOR 5.31, 95% CI 3.82-7.40) at 6 months compared with patients below the cutoff. AUC for RPQ total score ≥14 at 2 weeks was 0.76-0.81 across outcomes. RPQ total score ≥12 at 3 months was associated with higher odds of persistent symptoms (aOR 18.22, 95% CI 13.09-25.35), incomplete recovery (aOR 8.44, 95% CI 6.18-11.51), and lower quality of life (aOR 7.45, 95% CI 5.40-10.26) at 6 months compared with patients below the cutoff, with AUCs of 0.80-0.88 across outcomes.

DISCUSSION

Clinical cutoffs for a commonly used TBI symptom questionnaire had acceptable-to-excellent discrimination for 6-month outcomes and can be used by clinicians at 2 weeks after injury to identify patients at risk of chronic impairments and refer for targeted rehabilitation.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that overall TBI symptoms at 2 weeks are predictive of 6-month clinical outcomes.

A nomogram for the prediction of short-term mortality in patients with aneurysmal subarachnoid hemorrhage requiring mechanical ventilation: a post-hoc analysis

Background

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating stroke subtype with high morbidity and mortality. Although several studies have developed a prediction model in aSAH to predict individual outcomes, few have addressed short-term mortality in patients requiring mechanical ventilation. The study aimed to construct a user-friendly nomogram to provide a simple, precise, and personalized prediction of 30-day mortality in patients with aSAH requiring mechanical ventilation.

Methods

We conducted a post-hoc analysis based on a retrospective study in a French university hospital intensive care unit (ICU). All patients with aSAH requiring mechanical ventilation from January 2010 to December 2015 were included. Demographic and clinical variables were collected to develop a nomogram for predicting 30-day mortality. The least absolute shrinkage and selection operator (LASSO) regression method was performed to identify predictors, and multivariate logistic regression was used to establish a nomogram. The discriminative ability, calibration, and clinical practicability of the nomogram to predict short-term mortality were tested using the area under the curve (AUC), calibration plot, and decision curve analysis (DCA).

Results

Admission GCS, SAPS II, rebleeding, early brain injury (EBI), and external ventricular drain (EVD) were significantly associated with 30-day mortality in patients with aSAH requiring mechanical ventilation. Model A incorporated four clinical factors available in the early stages of the aSAH: GCS, SAPS II, rebleeding, and EBI. Then, the prediction model B with the five predictors was developed and presented in a nomogram. The predictive nomogram yielded an AUC of 0.795 [95% CI, 0.731-0.858], and in the internal validation with bootstrapping, the AUC was 0.780. The predictive model was well-calibrated, and decision curve analysis further confirmed the clinical usefulness of the nomogram.

Conclusion

We have developed two models and constructed a nomogram that included five clinical characteristics to predict 30-day mortality in patients with aSAH requiring mechanical ventilation, which may aid clinical decision-making.

Early effects predict trajectories of response to esketamine in treatment-resistant depression

BACKGROUND

The efficacy of esketamine in treatment-resistant depression (TRD) has been confirmed. However, its administration is expensive and restrictive, with limited knowledge on how long the treatment should be continued. Predicting the treatment outcome would benefit patients and alleviate the global treatment cost. We aimed to define distinct trajectories of treatment response and assess their predictability.

METHODS

In this longitudinal study, two independent samples of patients with unipolar or bipolar TRD were treated with esketamine in real-world settings. Depression severity was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS) before each esketamine administration. Latent class analyses were used to define trajectories of response.

RESULTS

In the original sample (N = 50), we identified two classes whose trajectories depicted response and non-response, respectively. The model was validated in the confirmatory sample (N = 55). Class membership was influenced by a few baseline characteristics such as concomitant benzodiazepine medication, number of depressive episodes or polarity. On the other hand, after only two esketamine administrations, the MADRS score predicted the 90-day trajectory of response with an accuracy of 80 %.

LIMITATIONS

This observational study is not placebo-controlled. Therefore, its results and their generalizability need to be confirmed in experimental settings.

CONCLUSIONS

After the first administrations of esketamine, the MADRS score has a good capacity to predict the most plausible trajectory of response. While thresholds and their predictive values need to be confirmed, this finding suggests that clinicians could base on MADRS scores their decision to discontinue treatment because of poor remaining chances of treatment response.

Urine Quantification Following Furosemide for Severe Acute Kidney Injury Prediction in Critically Ill Children

A standardized, quantified assessment of furosemide responsiveness predicts acute kidney injury (AKI) in children after cardiac surgery and AKI progression in critically ill adults. The purpose of this study was to determine if response to furosemide is predictive of severe AKI in critically ill children outside of cardiac surgery. We performed a multicenter retrospective study of critically ill children. Quantification of furosemide response was based on urine flow rate (normalized for weight) measurement 0 to 6 hours after the dose. The primary outcome was presence of creatinine defined severe AKI (Kidney Disease Improving Global Outcomes stage 2 or greater) within 7 days of furosemide administration. Secondary outcomes included mortality, duration of mechanical ventilation and length of stay. A total of 110 patients were analyzed. Severe AKI occurred in 20% ( n  = 22). Both 2- and 6-hour urine flow rate were significantly lower in those with severe AKI compared with no AKI ( p  = 0.002 and p  < 0.001). Cutoffs for 2- and 6-hour urine flow rate for prediction of severe AKI were <4 and <3 mL/kg/hour, respectively. The adjusted odds of developing severe AKI for 2-hour urine flow rate of <4 mL/kg/hour was 4.3 (95% confidence interval [CI]: 1.33-14.15; p  = 0.02). The adjusted odds of developing severe AKI for 6-hour urine flow rate of <3 mL/kg/hour was 6.19 (95% CI: 1.85-20.70; p  = 0.003). Urine flow rate in response to furosemide is predictive of severe AKI in critically ill children. A prospective assessment of urine flow rate in response to furosemide for predicting subsequent severe AKI is warranted.

Incidence and Risk Factors for Necessitating Cerebrospinal Fluid Diversion Following Medulloblastoma Surgery in Children

BACKGROUND

There are insufficient data on pediatric patients with medulloblastoma who require cerebrospinal fluid (CSF) diversion following resection. Therefore, this study aimed to determine the incidence and the characteristics associated with it in this subset of patients.

METHODS

We conducted a single-center, retrospective, observational cohort study of patients aged 18 years or less who underwent medulloblastoma resection at our department between 2010 and 2021. The primary outcome was the incidence of CSF diversion surgery required after resection. Participant demographics, tumor biology, and interventions were analyzed using univariate- and multivariate-adjusted models.

RESULTS

Of the 183 patients admitted to our department, 131 (71.6%) participated in this study. The incidence of permanent CSF diversion was 26.0% (95% confidence interval [CI]: 18.7 to 34.3). Factors independently associated with requirement of permanent CSF diversion were medulloblastoma volume >46.4 cm³ (odds ratio [OR]: 2.919, 95% CI: 1.191 to 7.156) and CSF channel invasion (OR: 2.849, 95% CI: 1.142 to 7.102). The duration of manifestation may be a covariate of tumor volume with increased risk of requirement for permanent CSF diversion (OR: 1.006, 95% CI: 1.000 to 1.013), and tumor volume may be a predictor in patients who underwent subtotal resection (OR: 4.900, 95% CI: 0.992 to 24.208, P = 0.05). Finally, patients who required permanent CSF diversion were divided according to medulloblastoma molecular subgroups, and no significant differences were found.

CONCLUSION

We report major predictive factors for permanent CSF diversion surgery in patients with medulloblastoma. Our study suggests that the presence of postresection hydrocephalus is not high enough to warrant permanent, prophylactic CSF diversion in all patients.

Long-term changes in milk component immunoglobulins reflect milk oral immunotherapy outcomes in Finnish children

BACKGROUND

Milk oral immunotherapy (OIT) may increase the amount of milk protein that can be ingested without triggering an allergic reaction. It is important to understand why some patients benefit from the treatment while others do not.

OBJECTIVE

The aim was to define the differences in the milk allergen component-specific (casein, α-lactalbumin, ß-lactoglobulin) immunoglobulin (sIg [sIgE, sIgG4, and sIgA]) levels relative to the long-term outcomes of milk OIT.

METHODS

In this long-term, open-label follow-up study, 286 children started milk OIT between 2005 and 2015. Follow-up data were collected at two points: the post-buildup phase and long term (range 1-11 years, median 6 years). Comparisons of sIg levels were made among three outcome groups of self-reported long-term milk consumption (high-milk dose, low-milk dose, and avoidance).

RESULTS

A total of 168 (59%) of the 286 patients on OIT participated. Most patients (57%) were in the high-dose group; here, 80% of these patients had a baseline casein sIgE value less than 28 kUA/L, they had the lowest casein sIgE levels at all time (p < .001), their casein sIgG4/IgE levels increased, and long-term casein sIgA was highest compared with the low-dose and avoidance groups (p = .02). Low-milk dose group had the highest casein sIgG4/IgE levels in long term (p = .002).

CONCLUSION

The baseline Ig profiles and responses to milk OIT differed depending on long-term milk consumption. Lower casein sIgE levels were associated with better outcome. Milk casein sIgA differed in the long term among high-milk consumers.

Detecting disabilities in everyday life: evidence from a geriatric assessment

BACKGROUND

The activities of daily living (ADL) score is a widely used index to establish the degree of independence from any help in everyday life situations. Measuring ADL accurately is time-consuming and costly. This paper presents a framework to approximate ADL via variables usually collected in comprehensive geriatric assessments. We show that the selected variables serve as good indicators in explaining the physical disabilities of older patients.

METHODS

Our sample included information from a geriatric assessment of 326 patients aged between 64 and 99 years in a hospital in Tyrol, Austria. In addition to ADL, 23 variables reflecting the physical and mental status of these patients were recorded during the assessment. We performed least absolute shrinkage and selection operator (LASSO) to determine which of these variables had the highest impact on explaining ADL. Then, we used receiver operating characteristic (ROC) analysis and logistic regression techniques to validate our model performance. Finally, we calculated cut-off points for each of the selected variables to show the values at which ADL fall below a certain threshold.

RESULTS

Mobility, urinary incontinence, nutritional status and cognitive function were most closely related to ADL and, therefore, to geriatric patients' functional limitations. Jointly, the selected variables were able to detect neediness with high accuracy (area under the ROC curve (AUC) = 0.89 and 0.91, respectively). If a patient had a limitation in one of these variables, the probability of everyday life disability increased with a statistically significant factor between 2.4 (nutritional status, 95%-CI 1.5-3.9) and 15.1 (urinary incontinence, 95%-CI 3.6-63.4).

CONCLUSIONS

Our study highlights the most important impairments of everyday life to facilitate more efficient use of clinical resources, which in turn allows for more targeted treatment of geriatric patients. At the patient level, our approach enables early detection of functional limitations and timely indications of a possible need for assistance in everyday life.

Sample size calculation for phylogenetic case linkage

Sample size calculations are an essential component of the design and evaluation of scientific studies. However, there is a lack of clear guidance for determining the sample size needed for phylogenetic studies, which are becoming an essential part of studying pathogen transmission. We introduce a statistical framework for determining the number of true infector-infectee transmission pairs identified by a phylogenetic study, given the size and population coverage of that study. We then show how characteristics of the criteria used to determine linkage and aspects of the study design can influence our ability to correctly identify transmission links, in sometimes counterintuitive ways. We test the overall approach using outbreak simulations and provide guidance for calculating the sensitivity and specificity of the linkage criteria, the key inputs to our approach. The framework is freely available as the R package phylosamp, and is broadly applicable to designing and evaluating a wide array of pathogen phylogenetic studies.

Sequencing the genetic material of viral and bacterial pathogens has become an important part of tracking and combating human infectious diseases. Specifically, comparing the pathogen DNA or RNA sequences collected from infected individuals can allow researchers and public health experts to determine who infected whom, or detect when a pathogen entered a specific country or geographic area. However, it is often impossible to collect samples from every single infected person, and these missing sequences can pose problems for this type of analysis, especially if there is some bias behind which samples were selected for sequencing. We have developed a mathematical framework that allows users to determine the probability their conclusions about pathogen transmission are correct given the number and proportion of samples from a pathogen outbreak they have sequenced. This framework is freely available, easy to use, and broadly generalizable to any pathogen, and we hope that it can be used to inform the design and sampling strategies behind future sequencing-based studies.

Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning

We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The dataset included 561 images containing two or more catheters, 167 images with only one, and 49 with none. Performance was measured with average precision (AP), calculated from the area under the precision-recall curve. On our test data, the algorithm achieved an overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989 (0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984) for UVCs. Performance was similar for the set of 58 test images consisting of two or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997 (0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990) for UVCs. Our network thus achieves strong performance in the simultaneous detection of these four catheter types. Radiologists may use such an algorithm as a time-saving mechanism to automate reporting of catheters on radiographs.

Weighted network measures reveal differences between dementia types: An EEG study

The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non‐invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age‐matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior–anterior patterns within the β‐band and greater network segregation within the θ‐band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β‐ and θ‐band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α‐band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.

Choosing between the BP and BN sequential strategies

Cost and burden of diagnostic testing may be reduced if fewer tests can be applied. Sequential testing involves selecting a sequence of tests, but only administering subsequent tests dependent on results of previous tests. This research provides guidance to choosing between single tests or the believe the positive (BP) and believe the negative (BN) sequential testing strategies, using accuracy (as measured by the Youden Index) as the primary determinant. Approximately 75% of the parameter combinations examined resulted in either BP or BN being recommended based on a higher accuracy at the optimal point. In about half of the scenarios BP was preferred, and the other half, BN, with the choice often a function of the value of the ratio of standard deviations of those without and with disease (b). Large values of b for the first test of the sequence tended to be associated with preference for BN as opposed to BP, while small values of b appear to favor BP. When there was no preference between sequences and/or single tests based on the Youden Index, cost of the sequence was considered. In this case, disease prevalence plays a large role in the selection of strategies, with lower values favoring BN and sometimes higher values favoring BP. The cost threshold for the sequential strategy to be preferred over a single, more accurate test, was often quite high. It appears that while sequential strategies most often increase diagnostic accuracy over a single test, sequential strategies are not always preferred.

Over Expressed TKTL1, CIP-2A, and B-MYB Proteins in Uterine Cervix Epithelium Scrapings as Potential Risk Predictive Biomarkers in HR-HPV-Infected LSIL/ASCUS Patients

High oncogenic risk human papillomaviruses (HR-HPVs) promote cervical carcinoma development, the fourth most common feminine cancer. A slow oncodevelopmental phase—defined histopathologically as Cervical Intraepithelial Neoplasia (CIN) grades 1–3, or cytologically as Low- or High-grade Squamous Intraepithelial Lesions (LSIL or HSIL)—precedes the malignancy. Cervical carcinoma screenings through HR-HPV genotyping and Pap smears are regularly performed in Western countries. Faulty cytology screening or genotyping or patients' non-compliance with follow-ups can let slip an oncoprogression diagnosis. Novel biomarker tests flanking HR-HPV genotyping and cytology could objectively predict the risk of disease progression thus helping triage LSIL/ASCUS patients. Here, anonymized leftovers of fresh cervical epithelium scrapings from twice (LSIL/ASCUS and HR-HPV DNA)-positive and twice (Pap smear- and HR-HPV DNA)-negative (control) patients in a proteome-preserving solution served to assess the biomarker worth of three cervical carcinoma-related proteins, i.e., B-MYB (or MYBL2), Cancerous Inhibitor of PP2A (CIP-2a), and transketolase-like1 (TKTL1). Leftovers anonymity was strictly kept and storage at −80°C, protein extraction, immunoblotting, and band densitometry were blindly performed. Only after tests completion, the anonymous yet code-corresponding HR-HPV-genotyping and cytology data allowed to assign each sample to the twice-positive or twice-negative group. Descriptive statistics showed that the three proteins levels significantly increased in the twice-positive vs. twice-negative scrapings. Diagnostic ROC curve analysis identified each protein's Optimal Decision Threshold (OTD) showing that TKTL1 and CIP-2a are stronger risk predictive biomarkers (Sensitivity, 0.91–0.93; Specificity, 0.77–0.83) than B-MYB. Logistic Regression coupled with Likelihood-Ratio Tests confirmed that a highly significant relation links increasing TKTL1/CIP-2a/B-MYB protein levels in twice-positive cervical scrapings to the risk of HR-HPV-driven oncoprogression. Finally, a 3 year clinical follow-up showed that 13 patients (50% of total) of the twice-positive group with biomarker values over OTDs compliantly underwent scheduled colposcopy and biopsy. Of these, 11 (i.e., 84.7%) received a positive histological diagnosis, i.e., CIN1 (n = 5; 38.5%) or CIN2/CIN2+ (n = 6; 46,2%). Therefore, TKTL1/CIP-2a/B-MYB protein levels could objectively predict oncoprogression risk in twice (HR-HPV- and Pap smear)-positive women. Further studies will assess the translatability of these findings into clinical settings.

Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool

OBJECTIVE

To develop and validate a risk assessment tool called the Prescription Medication Non-Adherence Prediction Tool (Rx-NAPT) to predict medication nonadherence in patients with fibromyalgia.

METHODS

This was a retrospective cohort study using claims data from South Carolina Medicaid. Patients with fibromyalgia who were ≥18 years old and who had filled at least 1 prescription medication for pregabalin, duloxetine, or milnacipran from January 1, 2005, through June 30, 2011 were included. Medication possession ratios (MPRs) were calculated to classify patients as adherent (MPR ≥ 80%) or nonadherent (MPR < 80%). Multivariable logistic models using 100 bootstrap replications (with replacement) were used to identify factors associated with medication nonadherence, including age, gender, race, days' supply, medication type, and fibromyalgia-related comorbidity score. Weighted β coefficients of the predictors were used to create the Rx-NAPT. Youden's J statistic was used to classify nonadherent patients into different levels of risk.

RESULTS

The study sample comprised 6,626 patients with fibromyalgia, where 4,804 (72.50%) were non-adherent and 1,822 (27.50%) were adherent to their prescribed medication(s). Logistic regression models showed that 7 predictors (gender, age, race, fibromyalgia-related comorbidity score, medication type, health maintenance organization coverage, emergency room visit) were statistically significant in ≥50% of the bootstrapped samples. The final model demonstrated reasonable discrimination (area under the curve [AUC] = 0.6224) and calibration (Hosmer-Lemeshow goodness-of-fit; P > 0.05) statistics and was validated internally (AUC = 0.6372).

CONCLUSION

Poor adherence with medication remains an important barrier to providing optimal care. Our risk prediction model provides an easy tool to help clinicians better identify patients with fibromyalgia who may not take their medications as prescribed.

Hyperspectral imaging analysis of a photonic crystal bead array for multiplex bioassays

For multiplex bioassays, one effective strategy is to employ microfluidic chips based on an array of photonic crystal beads (PCBs) that are encoded by their characteristic reflection spectrum (CRS). In this paper, we report a hyperspectral imaging system and algorithms for the high throughput decoding of a PCB array and subsequent detection. The results showed that the decoding accuracy of up to ∼500 PCBs is 98.56% with an excellent ability to extract low-intensity fluorescence intensities. The results also demonstrated hyperspectral imaging techniques which can simultaneously obtain both spatial and spectral information as powerful tools in the analysis of multiplex bioassays or microfluidic chips.

Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point.

Correlation of Transient Elastography With Severity of Cystic Fibrosis-related Liver Disease

OBJECTIVES

The aim of the study was to evaluate whether liver stiffness measurement (LSM), determined by transient elastography, correlates with presence and severity of liver disease in children and young adults with cystic fibrosis (CF).

METHODS

Subjects underwent LSM at routine CF visits. Presence and severity of cystic fibrosis liver disease (CFLD) was determined by clinical parameters. Subjects were classified as no CFLD, CFLD without portal hypertension (PHTN), and CFLD with PHTN. LSM was compared with aspartate aminotransferase/platelet ratio index (APRI) as a correlate to severity of CFLD.

RESULTS

A total of 249 subjects (53% boys; mean age 14 ± 7 years; 7 [3%] <2 years and 74 [30%] 18-25 years) underwent LSM. Subjects were classified as 158 (64%) with no CFLD, 73 (29%) CFLD without PHTN, and 18 (7%) CFLD with PHTN. The median (interquartile range) LSM was different among the 3 groups: 4.4 (3.8-5.4), 5.1 (4.4-6.3), and 14.1 (8.8-24.8) kPa, respectively, with all pairwise comparisons different from one another (P < 0.0001). Similarly, median (interquartile range) APRI was different in groups 1 and 2 compared with CFLD with PHTN: 0.22 (0.17-0.27), 0.24 (0.17-0.33), and 0.53 (0.24-0.84), respectively (P < 0.01). Analysis of receiver operating characteristics for discriminating CFLD with PHTN from the other groups resulted in cut-points at 6.2 kPa (LSM) and 0.35 (APRI). LSM was superior to APRI in discriminating CFLD with PHTN from other groups, with areas under the curve 0.91 (LSM) versus 0.78 (APRI) (P = 0.05).

CONCLUSIONS

Liver stiffness, as determined by transient elastography, correlates with the presence and severity of CFLD. Although APRI provided some information regarding severity of liver disease, LSM performed better than APRI in this population.

Incidence and risk factors for postoperative vomiting following atrial septal defect repair in children

BACKGROUND

The incidence and risk factors for postoperative vomiting (POV) after pediatric cardiac surgery has not been studied.

AIMS

This study sought to assess the incidence and risk factors for POV in children undergoing surgical repair of an atrial septal defect (ASD).

METHODS

We retrospectively collected perioperative data from 160 patients who underwent surgical repair of an ASD and met early extubation criteria. Demographic and clinical data that could potentially influence the incidence of POV were collected. Univariate analysis was performed using Student t-test or Wilcoxon rank test to identify factors associated with POV. Continuous variables were dichotomized based on the cutoff values derived from the receiver operating characteristic (ROC) curve analysis and the Youden-J index. We used multivariate logistic regression analysis using backward selection to determine the independent predictors using a univariate cutoff of P < 0.10 for inclusion and P > 0.05 for removal to determine factors independently associated with POV. The accuracy of our multivariate model was assessed by the area under the ROC curve (AUC).

RESULTS

Overall the incidence of POV was 37.5% in all the children who underwent surgical ASD repair. POV did not significantly differ between patients who received and did not receive antiemetics intraoperatively. Age of ≥4 years and cardiopulmonary bypass (CPB) time ≥51 min were identified as independent risk predictors for POV and the AUC of logistic regression model was 0.650 (95% confidence interval; 0.565-0.735).

CONCLUSIONS

The incidence of POV in children undergoing surgical ASD repair was 37.5%. Age ≥4 years and CPB time ≥51 min were identified as independent predictors.

Improved nonparametric estimation of the optimal diagnostic cut-off point associated with the Youden index under different sampling schemes

A diagnostic cut-off point of a biomarker measurement is needed for classifying a random subject to be either diseased or healthy. However, the cut-off point is usually unknown and needs to be estimated by some optimization criteria. One important criterion is the Youden index, which has been widely adopted in practice. The Youden index, which is defined as the maximum of (sensitivity + specificity -1), directly measures the largest total diagnostic accuracy a biomarker can achieve. Therefore, it is desirable to estimate the optimal cut-off point associated with the Youden index. Sometimes, taking the actual measurements of a biomarker is very difficult and expensive, while ranking them without the actual measurement can be relatively easy. In such cases, ranked set sampling can give more precise estimation than simple random sampling, as ranked set samples are more likely to span the full range of the population. In this study, kernel density estimation is utilized to numerically solve for an estimate of the optimal cut-off point. The asymptotic distributions of the kernel estimators based on two sampling schemes are derived analytically and we prove that the estimators based on ranked set sampling are relatively more efficient than that of simple random sampling and both estimators are asymptotically unbiased. Furthermore, the asymptotic confidence intervals are derived. Intensive simulations are carried out to compare the proposed method using ranked set sampling with simple random sampling, with the proposed method outperforming simple random sampling in all cases. A real data set is analyzed for illustrating the proposed method.

Automated neuromelanin imaging as a diagnostic biomarker for Parkinson's disease

BACKGROUND

We aimed to analyze the diagnostic accuracy of an automated segmentation and quantification method of the SNc and locus coeruleus (LC) volumes based on neuromelanin (NM)-sensitive MRI (NM-MRI) in patients with idiopathic (iPD) and monogenic (iPD) Parkinson's disease (PD).

METHODS

Thirty-six patients (23 idiopathic and 13 monogenic PARKIN or LRRK2 mutations) and 37 age-matched healthy controls underwent 3T-NM-MRI. SNc and LC volumetry were performed using fully automated multi-image atlas segmentation. The diagnostic performance to differentiate PD from controls was measured using the area under the curve (AUC) and likelihood ratios based on receiver operating characteristic (ROC) analyses.

RESULTS

We found a significant reduction of SNc and LC volumes in patients, when compared to controls. ROC analysis showed better diagnostic accuracy when using SNc volume than LC volume. Significant differences between ipsilateral and contralateral SNc volumes, in relation to the more clinically affected side, were found in patients with iPD (P = 0.007). Contralateral atrophy in the SNc showed the highest power to discriminate PD subjects from controls (AUC, 0.93-0.94; sensitivity, 91%-92%; specificity, 89%; positive likelihood ratio: 8.4-8.5; negative likelihood ratio: 0.09-0.1 at a single cut-off point). Interval likelihood ratios for contralateral SNc volume improved the diagnostic accuracy of volumetric measurements.

CONCLUSION

SNc and LC volumetry based on NM-MRI resulting from the automated segmentation and quantification technique can yield high diagnostic accuracy for differentiating PD from health and might be an unbiased disease biomarker. © 2015 International Parkinson and Movement Disorder Society.

Role of the Apparent Diffusion Coefficient in the Prediction of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer

BACKGROUND

We evaluated the diagnostic performance of the baseline diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) in the prediction of a complete pathologic response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer stratified according to the tumor phenotype.

PATIENTS AND METHODS

We retrospectively studied 225 patients with stage II, III, and IV breast cancer who had undergone contrast-enhanced magnetic resonance imaging (MRI) and DWI before and after NAC, followed by breast surgery.

RESULTS

The tumor phenotypes were luminal (n = 143; 63.6%), triple-negative (TN) (n = 37; 16.4%), human epidermal growth factor receptor 2 (HER2)-enriched (n = 17; 7.6%), and hybrid (hormone receptor-positive/HER2(+); n = 28; 12.4%). After NAC, a pCR was observed in 39 patients (17.3%). No statistically significant difference was observed in the mean ADC value between a pCR and no pCR in the general population (1.132 ± 0.191 × 10(-3) mm(2)/s vs. 1.092 ± 0.189 × 10(-3) mm(2)/s, respectively; P = .23). The optimal ADC cutoff value in the general population was 0.975 × 10(-3) mm(2)/s (receiver operating characteristic [ROC] area under the curve [AUC], 0.587 for the prediction of a pCR). After splitting the population into subgroups according to tumor phenotype, we observed a significant or nearly significant difference in the mean ADC value among the responders versus the nonresponders in the TN (P = .06) and HER2(+) subgroups (P = .05). No meaningful difference was seen in the luminal and hybrid subgroups (P = .59 and P = .53, respectively). In contrast, in the TN and HER2(+) subgroups (cutoff value, 0.995 × 10(-3) mm(2)/s and 0.971 × 10(-3) mm(2)/s, respectively), we observed adequate ROC AUCs (0.766 and 0.813, respectively).

CONCLUSION

The pretreatment ADC value is not capable of predicting the pCR in the overall population of patients with locally advanced breast cancer. Nonetheless, an ameliorated diagnostic performance was observed in specific phenotype subgroups (ie, TN and HER2(+) tumors).

Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From Serous Oligocystic Adenomas in Spectral CT Images

OBJECTIVE

This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed computer-aided diagnosis (CAD) scheme to differentiate pancreatic serous oligocystic adenomas (SOAs) from mucinous cystic neoplasms of pancreas cystic lesions.

MATERIALS AND METHODS

This study was conducted from January 2010 to October 2013. Twenty-three patients (5 men and 18 women; mean age, 43.96 years old) with SOA and 19 patients (3 men and 16 women; mean age, 41.74 years old) with MCN were included in this retrospective study. Two types of features were collected by dual-energy spectral CT imaging as follows: conventional and additional quantitative spectral CT features. Classification results of the CAD scheme were compared using the conventional features and full feature data set. Important features were selected using support vector machine classification method combined with feature-selection technique. The optimal cutoff values of selected features were determined through receiver-operating characteristic curve analyses.

RESULTS

Combining conventional features with additional spectral CT features improved the overall accuracy from 88.37% to 93.02%. The selected features of the proposed CAD scheme were tumor size, contour, location, and low-energy CT values (43 keV). Iodine-water basis material pair densities in both arterial phase (AP) and portal venous phase (PP) were important factors for differential diagnosis of SOA and MCN. The optimal cutoff values of long axis, short axis, 40 keV monochromatic CT value in AP, iodine (water) density in AP, 43 keV monochromatic CT value in PP, and iodine (water) density in PP were 3.4 mm, 3.1 mm, 35.7 Hu, 0.32533 mg/mL, 39.4 Hu, and 0.348 mg/mL, respectively.

CONCLUSION

The combination of conventional features and additional information provided by dual-energy spectral CT shows a high accuracy in the CAD scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of SOAs and MCNs with machine learning algorithms.

Real-world characterization and differentiation of the Global Initiative for Chronic Obstructive Lung Disease strategy classification

BACKGROUND

This study aimed to characterize and differentiate the Global Initiative for Chronic Obstructive Lung Disease (GOLD) strategy 2011 cut points through the modified Medical Research Council dyspnea scale (mMRC) and chronic obstructive pulmonary disease (COPD) assessment test (CAT).

METHODS

Analysis of COPD patient data from the 2012 Adelphi Respiratory Disease Specific Program was conducted in Europe and US. Matched data from physicians and patients included CAT and mMRC scores. Receiver operating characteristic curves and kappa analysis determined a cut point for CAT and mMRC alignment and thus defined patient movement ("movers") within GOLD groups A-D, depending on the tool used. Logistic regression analysis, with a number of physician- and patient-reported covariates, characterized those movers.

RESULTS

Comparing GOLD-defined high-symptom patients using mMRC and CAT cut points (≥2 and ≥10, respectively), there were 890 (53.65%) movers; 887 of them (99.66%) moved from less symptomatic GOLD groups A and C (using mMRC) to more symptomatic groups B and D (using CAT). For receiver operating characteristic (area under the curve: 0.82, P<0.001) and kappa (maximized: 0.45) recommended CAT cut points of ≥24 and ≥26, movers reduced to 429 and 403 patients, respectively. Logistic regression analysis showed variables significantly associated with movers were related to impact on normal life, age, cough, and sleep (all P<0.05). Within movers, direction of movement was significantly associated with the same variables (all P<0.05).

CONCLUSION

Use of current mMRC or CAT cut points leads to inconsistencies for COPD assessment classification. It is recommended that cut points are aligned and both tools administered simultaneously for optimal patient care and to allow for closer management of movers. Our research may suggest an opportunity to investigate a combined score approach to patient management based on the worst result of mMRC and CAT. The reduced number of remaining movers may then identify patients who have greater impact of disease and may require a more personalized treatment plan.

Pursuing optimal thresholds to recommend breast biopsy by quantifying the value of tomosynthesis

A 2% threshold has been traditionally used to recommend breast biopsy in mammography. We aim to characterize how the biopsy threshold varies to achieve the maximum expected utility (MEU) of tomosynthesis for breast cancer diagnosis. A cohort of 312 patients, imaged with standard full field digital mammography (FFDM) and digital breast tomosynthesis (DBT), was selected for a reader study. Fifteen readers interpreted each patient's images and estimated the probability of malignancy using two modes: FFDM versus FFDM + DBT. We generated receiver operator characteristic (ROC) curves with the probabilities for all readers combined. We found that FFDM+DBT provided improved accuracy and MEU compared with FFDM alone. When DBT was included in the diagnosis along with FFDM, the optimal biopsy threshold increased to 2.7% as compared with the 2% threshold for FFDM alone. While understanding the optimal threshold from a decision analytic standpoint will not help physicians improve their performance without additional guidance (e.g. decision support to reinforce this threshold), the discovery of this level does demonstrate the potential clinical improvements attainable with DBT. Specifically, DBT has the potential to lead to substantial improvements in breast cancer diagnosis since it could reduce the number of patients recommended for biopsy while preserving the maximal expected utility.