A novel approach to model cumulative stress: Area under the s-factor curve

OBJECTIVE

Using a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes.

METHOD

We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes.

RESULTS

The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change.

CONCLUSIONS

Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.

Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer

OBJECTIVES

To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients.

METHODS

Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated.

RESULTS

Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets.

CONCLUSION

The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC.

CLINICAL RELEVANCE STATEMENT

Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions.

KEY POINTS

• The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.

Mean rating difference scores are poor measures of discernment: The role of response criteria

Many interventions aim to protect people from misinformation. Here, we review common measures used to assess their efficacy. Some measures only assess the target behavior (e.g., ability to spot misinformation) and therefore cannot determine whether interventions have overly general effects (e.g., erroneously identifying accurate information as misinformation). Better measures assess discernment, the ability to discriminate target from non-target content. Discernment can determine whether interventions are overly general but is often measured by comparing differences in mean ratings between target and non-target content. We show how this measure is confounded by the configuration of response criteria, leading researchers to incorrectly conclude that an intervention improves discernment. We recommend using measures from signal detection theory, such as the area under the receiver operating characteristic curve, to assess discernment.

ROC curve analysis: a useful statistic multi-tool in the research of nephrology

In the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis. Before implementing a biomarker in everyday clinical practice for screening a specific disease context, specific statistic measures are necessary to evaluate the diagnostic accuracy and performance of this biomarker. Receiver Operating Characteristic (ROC) Curve analysis is an important statistical method used to estimate the discriminatory performance of a novel diagnostic test, identify the optimal cut-off value for a test that maximizes sensitivity and specificity, and evaluate the predictive value of a certain biomarker or risk, prediction score. Herein, through practical examples, we aim to present a simple methodological approach to explain in detail the principles and applications of ROC curve analysis in the field of nephrology pertaining diagnosis and prognosis.

Correlation between trough concentration and AUC for elexacaftor, tezacaftor and ivacaftor

Therapeutic drug monitoring (TDM) of elexacaftor, tezacaftor, ivacaftor (ETI) could be a useful tool to increase efficacy and decrease the risk of adverse effects in people with Cystic Fibrosis (pwCF). It is however unclear whether drug exposure should be monitored by assessment of trough (Cmin) levels or determination of the area under the curve (AUC). Hence, in this study the correlation between measured Cmin concentration and AUC was evaluated. Serial plasma samples, including Cmin, were drawn after administration of ETI in order to calculate the AUC and assess the correlation between the two parameters. A linear correlation between Cmin and AUC0-24h was found, with Pearson's r correlation coefficients of 0.963, 0.908 and 0.860 for elexacaftor, tezacaftor and ivacaftor, respectively. Exposure of ETI may be monitored by assessment of Cmin levels.

Evaluation of interleukins (IL-1α, IL-1Ra, IL-12, IL-17A, IL-31, and IL-33) and chemokines (CXCL10 and CXCL16) in the serum of male patients with ankylosing spondylitis

BACKGROUND

A case-control study was performed to explore eight pro-inflammatory and anti-inflammatory cytokines, namely interleukin (IL)-1α, IL-1Ra (IL-1 receptor antagonist), IL-12, IL-17A, IL-31, IL-33, CXCL10 (C-X-C motif chemokine ligand 10), and CXCL16, with the aim to understand their role in ankylosing spondylitis (AS) pathogenesis and evaluate their utility as markers to differentiate between diseased and healthy individuals. Among these cytokines, IL-31 and CXCL16 have not been well studied in AS.

PATIENTS AND METHODS

The study included 94 male patients with AS and 91 age-matched control males. Interleukin and chemokine levels were measured using ELISA kits.

RESULTS

Serum levels of IL-17A, CXCL10, and CXCL16 were significantly elevated in patients compared to controls, while IL-31 levels were significantly decreased in patients. IL-17A, CXCL10, and CXCL16 were associated with an increased risk of AS, while IL-31 was associated with a decreased risk of disease (odds ratio = 1.22, 1.78, 1.14, and 0.89, respectively). As indicated by the area under the curve (AUC), IL-17A, IL-31, CXCL10, and CXCL16 were potential markers to differentiate between AS patients and controls (AUC = 0.877, 0.735, 0.8, and 0.7, respectively). IL-1α, IL-1Ra, IL-12, and IL-33 levels showed no significant variations between patients and controls.

CONCLUSIONS

Among the eight cytokines examined, IL-17A, CXCL10, and CXCL16 were up-regulated in the serum of AS patients, while IL-31 was down-regulated. The levels of IL-1α, IL-1Ra, IL-12, and IL-33 showed no significant differences between patients and controls. Serum levels of all cytokines were not affected by disease duration, HLA-B27 positivity, or disease activity.

Gut microbiota predicts the diagnosis of ulcerative colitis in Saudi children

BACKGROUND

Ulcerative colitis (UC) is an immune-mediated chronic inflammatory condition with a worldwide distribution. Although the etiology of this disease is still unknown, the understanding of the role of the microbiota is becoming increasingly strong.

AIM

To investigate the predictive power of the gut microbiota for the diagnosis of UC in a cohort of newly diagnosed treatment-naïve Saudi children with UC.

METHODS

The study population included 20 children with a confirmed diagnosis of UC and 20 healthy controls. Microbial DNA was extracted and sequenced, and shotgun metagenomic analysis was performed for bacteria and bacteriophages. Biostatistics and bioinformatics demonstrated significant dysbiosis in the form of reduced alpha diversity, beta diversity, and significant difference of abundance of taxa between children with UC and control groups. The receiver operating characteristic curve, a probability curve, was used to determine the difference between the UC and control groups. The area under the curve (AUC) represents the degree of separability between the UC group and the control group. The AUC was calculated for all identified bacterial species and for bacterial species identified by the random forest classification algorithm as important potential biomarkers of UC. A similar method of AUC calculation for all bacteriophages and important species was used.

RESULTS

The median age and range were 14 (0.5-21) and 12.9 (6.8-16.3) years for children with UC and controls, respectively, and 40% and 35% were male for children with UC and controls, respectively. The AUC for all identified bacterial species was 89.5%. However, when using the bacterial species identified as important by random forest classification algorithm analysis, the accuracy increased to 97.6%. Similarly, the AUC for all the identified bacteriophages was 87.4%, but this value increased to 94.5% when the important bacteriophage biomarkers were used.

CONCLUSION

The very high to excellent AUCs of fecal bacterial and viral species suggest the potential use of noninvasive microbiota-based tests for the diagnosis of unusual cases of UC in children. In addition, the identification of important bacteria and bacteriophages whose abundance is reduced in children with UC suggests the potential of preventive and adjuvant microbial therapy for UC.

Estimating rate of change for nonlinear trajectories in the framework of individual measurement occasions: A new perspective on growth curves

Researchers are often interested in examining between-individual differences in within-individual processes. If the process under investigation is tracked for a long time, its trajectory may show a certain degree of nonlinearity, so that the rate of change is not constant. A fundamental goal of modeling such nonlinear processes is to estimate model parameters that reflect meaningful aspects of change, including the parameters related to change and other parameters that shed light on substantive hypotheses. However, if the measurement occasion is unstructured, existing models cannot simultaneously estimate these two types of parameters. This article has three goals. First, we view the change over time as the area under the curve (AUC) of the rate of change versus time ( r - t ) graph. Second, using the instantaneous rate of change midway through a time interval to approximate the average rate of change during that interval, we propose a new specification to describe longitudinal processes. In addition to obtaining the individual change-related parameters and other parameters related to specific research questions, the new specification allows for unequally spaced study waves and individual measurement occasions around each wave. Third, we derive the model-based interval-specific change and change from baseline, two common measures to evaluate change over time. We evaluate the proposed specification through a simulation study and a real-world data analysis. We also provide OpenMx and Mplus 8 code for each model with the novel specification.

Estimating effects of longitudinal and cumulative exposure to PFAS mixtures on early adolescent body composition

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of four perfluoroalkyl and polyfluoroalkyl substances (PFAS) at four timepoints from gestation to age 12 years. We evaluated the relations between profiles and z-scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the curve (AUC) for all PFAS. We identified two profiles: a higher (35% of sample) and lower PFAS profile (relative to each other), based on their average PFAS concentrations at all timepoints. The higher PFAS profile had generally lower z-scores for all outcomes, with somewhat larger effects for males, though all CIs crossed the null. For example, the higher PFAS profile was associated with a -0.50 (95% CI: -1.07, 0.08) lower BMI z-score among males but not females (0.04; 95% CI: -0.45, 0.54). We observed similar patterns with AUCs. We found that higher childhood PFAS profile and higher cumulative PFAS mixtures may be associated with altered growth in early adolescence.

MTNR1B gene variations and high pre-pregnancy BMI increase gestational diabetes mellitus risk in Chinese women

AIM

To investigate the association of melatonin receptor 1B (MTNR1B) gene variations and pre-pregnancy body mass index (BMI) with gestational diabetes mellitus (GDM).

MATERIALS AND METHOD

In this study, 1566 Chinese Han pregnant women were enrolled and multiple genetic models were used to evaluate the association between MTNR1B gene polymorphisms and the risk of GDM. The clinical value of pre-pregnancy BMI in predicting GDM was analyzed and evaluated using receiver operating characteristic (ROC) curves. Several methods of analysis were used to examine the impact of gene-gene and gene-BMI interactions on the incidence of GDM influence.

RESULTS

For the MTNR1B gene, rs1387153 (C > T), rs10830962 (C > G), rs4753426 (T > C), and rs10830963 (C > G) are all risk mutations associated with the susceptibility of GDM. The ROC curve analysis indicated that the BMI demonstrated an area under the curve (AUC) of 0.595. Alongside, the sensitivity and specificity stood at 0.676 and 0.474 respectively. The maximum Joden index was found to be 0.150, with a corresponding critical BMI value of 20.5691 kg/m2. Interaction analysis revealed that gene-gene and gene-BMI interactions had no significant effect on GDM occurrence.

CONCLUSION

MTNR1B genetic variations confers the risk to GDM in Chinese women. Furthermore, the high pre-pregnancy BMI (≥20.5691 kg/m2) significantly increases the risk of GDM in Chinese women.

Predictive Performance of an Updated Polygenic Risk Score for Age-Related Macular Degeneration

PURPOSE

A recent genome-wide association study of age-related macular degeneration (AMD) identified new AMD-associated risk variants. These variants now can be incorporated into an updated polygenic risk score (PRS). This study aimed to assess the performance of an updated PRS, PRS2023, in an independent cohort of older individuals with retinal imaging data and to compare performance with an older PRS, PRS2016.

DESIGN

Cross-sectional study.

PARTICIPANTS

A total of 4175 participants of European ancestry, 70 years of age or older, with genotype and retinal imaging data.

METHODS

We used logistic regression models and area under the receiver operating characteristic curve (AUC) to assess the performance of PRS2023 compared with PRS2016. AMD status and severity were graded using color fundus photography.

MAIN OUTCOME MEASURES

Association of PRS2023 and PRS2016 with AMD risk at baseline.

RESULTS

At enrollment among 4175 participants, 2605 participants (62.4%) had no AMD and 853 participants (20.4%), 671 participants (16.1%), and 46 participants (1.1%) had early, intermediate, and late-stage AMD, respectively. More than 27% of the participants with a high PRS2023 (top quartile) had intermediate or late-stage AMD, compared with < 15% for those in the middle 2 quartiles and less than 13% for those in the lowest quartile. Both PRS2023 and PRS2016 were associated significantly with AMD after adjustment for age, sex, smoking status, and lipid levels, with increasing odds ratios (ORs) for worsening AMD grades. PRS2023 outperformed PRS2016 (P = 0.03 for all AMD and P = 0.03 for late AMD, DeLong test comparing AUC). PRS2023 was associated with late-stage AMD with an adjusted OR of 5.05 (95% confidence interval [CI], 3.41-7.47) per standard deviation. The AUC of a model containing conventional or nongenetic risk factors and PRS2023 was 91% (95% CI, 87%-95%) for predicting late-stage AMD, which improved 12% over the model without the PRS (AUC, 79%; P < 0.001 for difference).

CONCLUSIONS

A new PRS, PRS2023, for AMD outperforms a previous PRS and predicts increasing risk for late-stage AMD (with stronger association for more severe imaging-confirmed AMD grades). Our findings have clinical implications for the improved prediction and risk stratification of AMD.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Magnetic resonance imaging of anal cancer: tumor characteristics and early prediction of treatment outcome

PURPOSE

To analyze tumor characteristics derived from pelvic magnetic resonance imaging (MRI) of patients with squamous cell carcinoma of the anus (SCCA) before and during chemoradiotherapy (CRT), and to compare the changes in these characteristics between scans of responders vs. nonresponders to CRT.

METHODS

We included 52 patients with a pelvic 3T MRI scan prior to CRT (baseline scan); 39 of these patients received an additional scan during week 2 of CRT (second scan). Volume, diameter, extramural tumor depth (EMTD), and external anal sphincter infiltration (EASI) of the tumor were assessed. Mean, kurtosis, skewness, standard deviation (SD), and entropy values were extracted from apparent diffusion coefficient (ADC) histograms. The main outcome was locoregional treatment failure. Correlations were evaluated with Wilcoxon's signed rank-sum test and Pearson's correlation coefficient, quantile regression, univariate logistic regression, and area under the ROC curve (AUC) analyses.

RESULTS

In isolated analyses of the baseline and second MRI scans, none of the characteristics were associated with outcome. Comparison between the scans showed significant changes in several characteristics: volume, diameter, EMTD, and ADC skewness decreased in the second scan, although the mean ADC increased. Small decreases in volume and diameter were associated with treatment failure, and these variables had the highest AUC values (0.73 and 0.76, respectively) among the analyzed characteristics.

CONCLUSION

Changes in tumor volume and diameter in an early scan during CRT could represent easily assessable imaging-based biomarkers to eliminate the need for analysis of more complex MRI characteristics.

Classification of light Yorkshire pigs at different production stages using ordinary least squares and machine learning methods

Pig homogeneity and growth are major concerns for the pig industry today. Variability in pigs' size has a strong impact on profitability as uniformity plays a key role in the overall economic value of pigs produced. This research focused on statistical methods to identify pigs at risk of growth retardation at different stages of production. Data from 125 083 Yorkshire pigs at weaning (18-28 d), 59 533 pigs at the end of the nursery period (70-82 d) and 48 862 pigs at slaughter (155-170 d) were analyzed under three different cut-points (lowest 10, 20 and 30%) to characterize light animals. Records were randomly split into 2:1 training:testing sets, and each training data set was analyzed through an ordinary least squares approach and four machine learning algorithms (decision tree, random forest, and two alternative boosting approaches). A wide range of weighting functions were applied to give increased relevance to lighter pigs. Each resulting classification norm was used to classify light pigs in the testing data set. Both sensitivity and specificity were retained to construct the receiver operating characteristic curve, and the statistical performance of each analytical approach was evaluated by the area under the curve (AUC). In all production stages and cut-points, the random forest machine learning algorithm provided the highest AUC, closely followed by boosting procedures. For weaning BW (WW), factors related to birth BW and litter size accounted for more than 75% of the important prediction factors for light pigs. BW at the end of the nursery period and slaughter BW analyses revealed a similar pattern where WW and BW at the end of the nursery period accounted for more than 40 and 50% of statistical importance among the prediction factors, respectively. Machine learning algorithms are useful tools to easily evaluate the risk factors affecting the efficiency and homogeneity in swine. Since the BW at birth and weaning are key factors, sow nutrition and feeding management during gestation and lactation, along with piglet management during lactation, are identified as important influences on pig weight variability.

Development and validation of a prediction model on spontaneous preterm birth in twin pregnancy: a retrospective cohort study

BACKGROUND

This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies.

METHODS

This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67-3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60-3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09-2.56), and obesity (OR, 10.45; 95% CI, 3.91-27.87), nulliparity (OR, 0.58; 95% CI 0.41-0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24-10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44-5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08-8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68-0.74) and 0.68 (95% CI 0.64-0.73), respectively. And then we integrated those risk factors to construct the nomogram.

CONCLUSIONS

The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners.

Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation

OBJECTIVE

This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients.

METHODS

A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively.

RESULTS

A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732-0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613-0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728-0.881) in the testing set.

CONCLUSION

This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization.

CLINICAL RELEVANCE STATEMENT

This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment.

KEY POINTS

• Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening

OBJECTIVES

To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density.

METHODS

Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D).

RESULTS

Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively).

CONCLUSIONS

Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts.

CLINICAL RELEVANCE STATEMENT

Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts.

KEY POINTS

• Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Juvenile dermatomyositis: association between nail fold capillary end row loop- area under the curve- and disease damage indicators

BACKGROUND

Juvenile Dermatomyositis (JDM) is a rare autoimmune disease characterized by skin and muscle inflammation. The loss of nail fold capillary end row loops (ERL) is evidence of small vessel involvement in JDM. This study aimed to examine the specific association of ERL over the disease course with evidence of JDM disease damage.

METHODS

We analyzed data from 68 initially treatment-naïve JDM children who had been observed for at least five years with multiple ERL density assessments. The JDM disease course were categorized into monocyclic short, monocyclic long, polycyclic, and chronic. The ERL capillary count was cumulatively evaluated using the area under the curve (AUC) method.

RESULTS

The mean ERL density for the treatment-naive JDM was significantly lower than that of their healthy age-matched controls (4.8 ± 1.6 /mm vs. 7.9 ± 0.9 /mm; p < 0.0001). The ERL AUC was significantly lower in children with a chronic disease course compared to those with a monocyclic short (p = 0.001) or monocyclic long disease course (p = 0.013). JDM patients with lipodystrophy had lower ERL AUC than those without lipodystrophy (p = 0.04). There was no association between ERL AUC and calcifications or fractures.

CONCLUSION

Persistently decreased ERL capillary density, reflected by low ERL AUC, is associated with a chronic disease course and lipodystrophy in JDM. Despite medical therapy, the mean ERL count remained below normal even after five years, particularly in polycyclic and chronic cases. It is not clear that restoring normal capillary density is currently feasible in children with JDM.

Biomarkers for distinguishing tuberculous pleural effusion from non-tuberculosis effusion: a retrospective study

BACKGROUND

Pleural effusion (PE) is a common clinical feature that presents a diagnostic challenge for clinicians. In this retrospective study, we aimed to assess the biomarkers, ratios, and multiple indicators in serum and Pleural effusion for the differential diagnosis of tuberculous pleural effusion (TPE) from non-tuberculosis effusion (non-TPE).

METHODS

The participants, who were divided into two groups: TPE and non-TPE (MPE and PPE), from Ningbo First Hospital, were incorporated in this study. The clinical and laboratory features were collected and analyzed using logistic regression analysis. Twelve biomarkers and their ratios in serum and PE were investigated for TPE versus non-TPE. Additionally, the value of multiple indicators for joint diagnosis was estimated.

RESULTS

Biomarkers and ratios showed good diagnostic performance. The five variables including Serum ADA, IGRA, Effusion ADA, Effusion ADA/Serum ADA and Effusion LDH/Effusion ADA were identified as valuable parameters for differential diagnosis of TPE from non-TPE. The combined diagnosis of the five indexes yielded the highest diagnostic accuracy for TPE with an AUC (0.919), sensitivity (90.30%), and specificity (94.50%).

CONCLUSIONS

The biomarkers and ratios demonstrated strong diagnostic performance, and the utilization of multiple indicators for joint diagnosis can improve the diagnostic efficacy of tuberculous pleurisy.

Impact of antimicrobial stewardship program-driven educational intervention for vancomycin loading dose on mortality

BACKGROUND

Although the loading dose (LD) of vancomycin (VCM) contributes to its efficacy, it may not be conducted adequately. Herein, the objective was to evaluate the effect of LD on patient prognosis using therapeutic drug monitoring by pharmacists and elucidate the impact of an antimicrobial stewardship program (ASP)-driven educational intervention on the LD implementation rate and patient prognosis.

MATERIALS AND METHODS

First, a retrospective cohort study was conducted involving 121 adult patients administered with VCM and compared with 28-day mortality in LD and non-LD groups. To avoid confounding, the propensity score method was employed. Second, post-training with ASP-driven lectures, a questionnaire survey was conducted for healthcare workers, including physicians, nurses, and pharmacists. The rates of VCM LD implementation and 28-day mortality were compared during a period of one year and 9 months between the pre-ASP (n = 38) and post-ASP (n = 33) groups.

RESULTS

After propensity score matching, the 28-day mortality in the LD group was significantly improved, suggesting that the early increase in blood levels of VCM due to an LD is an important factor influencing patient prognosis. After the lecture, a questionnaire survey revealed that the understanding rates of "well" and "slightly well" for educational lectures exceeded 80% of all healthcare workers. The rate of LD implementation significantly increased to 63.6% (21/33) in the post-ASP group compared with 31.6% (12/38) in the pre-ASP group (p = 0.007), and the 28-day mortality declined from 23.7% (9/38) to 6.1% (2/33) (p = 0.041).

CONCLUSION

This method of ASP-driven educational intervention would facilitate LD implementation, improving patient prognosis.

Evaluation of Vancomycin Area Under the Concentration-Time Curve Predictive Performance Using Bayesian Modeling Software With and Without Peak Concentration: An Academic Hospital Experience for Adult Patients Without Renal Impairment

Background

The revised U.S. consensus guidelines on vancomycin therapeutic drug monitoring (TDM) recommend obtaining trough and peak samples to estimate the area under the concentration-time curve (AUC) using the Bayesian approach; however, the benefit of such two-point measurements has not been demonstrated in a clinical setting. We evaluated Bayesian predictive performance with and without peak concentration data using clinical TDM data.

Methods

We retrospectively analyzed 54 adult patients without renal impairment who had two serial peak and trough concentration measurements in a ≤1-week interval. The concentration and AUC values were estimated and predicted using Bayesian software (MwPharm++; Mediware, Prague, Czech Republic). The median prediction error (MDPE) for bias and median absolute prediction error (MDAPE) for imprecision were calculated based on the estimated AUC and measured trough concentration.

Results

AUC predictions using the trough concentration had an MDPE of -1.6% and an MDAPE of 12.4%, whereas those using both peak and trough concentrations had an MDPE of -6.2% and an MDAPE of 16.9%. Trough concentration predictions using the trough concentration had an MDPE of -8.7% and an MDAPE of 18.0%, whereas those using peak and trough concentrations had an MDPE of -13.2% and an MDAPE of 21.0%.

Conclusions

The usefulness of the peak concentration for predicting the AUC on the next occasion by Bayesian modeling was not demonstrated; therefore, the practical value of peak sampling for AUC-guided dosing can be questioned. As this study was conducted in a specific setting and generalization is limited, results should be interpreted cautiously.

Daily cortisol variations are predicted proximally by self-efficacy beliefs at work and indirectly by perceived self-regulatory abilities in managing negative emotions

In the present ecological study, we analyzed the relations of a set of self-efficacy beliefs at work to parameters of diurnal cortisol variation. Specifically, using data collected during two consecutive working days from 166 workers, we tested a mediation model positing social and work-related self-efficacy beliefs as mediators of the relations between self-regulatory emotional self-efficacy beliefs in managing negative emotions and cortisol indicators. Results from the multilevel mediation analyses supported the proposed model for work-related self-efficacy, which resulted as a significant mediator of the relation between self-regulatory emotional self-efficacy beliefs in managing negative emotions and the overall cortisol daily production indexed by computing the area under the curve with respect to the ground. Findings suggest the importance of self-efficacy beliefs for workers' physiological adjustment. Theoretical and practical contributions of the findings are discussed.

ITGB4 Serves as an Identification and Prognosis Marker Associated with Immune Infiltration in Small Cell Lung Carcinoma

Integrin beta 4 (ITGB4) is a vital factor for numerous cancers. However, no reports regarding ITGB4 in small cell lung carcinoma (SCLC) have been found in the existing literature. This study systematically investigated the expression and clinical value of ITGB4 in SCLC using multi-center and large-sample (n = 963) data. The ITGB4 expression levels between SCLC and control tissues were compared using standardized mean difference and Wilcoxon rank-sum test. The clinical significance of the gene in SCLC was observed using Cox regression and Kaplan-Meier curves. ITGB4 is overexpressed in multiple cancers and represents significant value in distinguishing among cancer samples (AUC = 0.91) and predicting the prognoses (p < 0.05) of patients with different cancers. In contrast, decreased ITGB4 mRNA expression was determined in SCLC (SMD < 0), and this finding was further confirmed at protein levels using in-house specimens (p < 0.05). This decrease in expression may be attributed to the regulatory role of estrogen receptor 1. ITGB4 may participate in the progression of SCLC by affecting several signaling pathways (e.g., tumor necrosis factor signaling pathway) and a series of immune cells (e.g., dendritic cells) (p < 0.05). The gene may serve as a potential marker for predicting the disease status (AUC = 0.97) and prognoses (p < 0.05) of patients with SCLC. Collectively, ITGB4 was identified as an identification and prognosis marker associated with immune infiltration in SCLC.

The value of dual-energy computed tomography (DECT) in the diagnosis of urinary calculi: a systematic review and meta-analysis of retrospective studies

Objective

Dual-energy computed tomography (DECT) imaging technology opens a new idea and method for analyzing stone composition, which can obtain several quantitative parameters reflecting tissue-related information and energy images different from traditional images. However, the application of DECT in diagnosing urinary calculi remains unknown. This study aims to evaluate the value of DECT in diagnosing urinary calculi by meta-analysis.

Methods

PubMed, EMBASE, Web of Science, and the Cochrane Library were searched to articles published from the establishment of the databases to April 18, 2023. We reviewed the articles on the diagnosis of urinary calculi detected by DECT, established standards, screened the articles, and extracted data. Two researchers carried out data extraction and the Cohen's unweighted kappa was estimated for inter-investigator reliability. The quality of the literature was evaluated by the diagnostic test accuracy quality evaluation tool (QUADAS-2). The heterogeneity and threshold effects were analyzed by Meta-Disc 1.4 software, and the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic ratio were calculated. The combined receiver-operating characteristic (ROC) curve was drawn, and the value of DECT in the diagnosis of urinary calculi was evaluated by the area under the curve (AUC). The meta-analysis was registered at PROSPERO (CRD42023418204).

Results

One thousand and twenty-seven stones were detected in 1,223 samples from 10 diagnostic tests. The analyzed kappa alternated between 0.78-0.85 for the document's retrieval and detection procedure. The sensitivity of DECT in the diagnosis of urinary calculi was 0.94 (95% CI [0.92-0.96]). The positive likelihood ratio (PLR) of DECT in the diagnosis of urinary stones was 0.91 (95% CI [0.88-0.94]), and the negative likelihood ratio (NLR) was 0.08 (95% CI [0.05-0.11]). The specificity of DECT for detecting urinary calculi was 0.91 (95% CI [0.88-0.94]). The area under the curve of the summary receiver operator characteristic (SROC) was 0.9875. The sensitivity of dual-energy CT in the diagnosis of urinary calculi diameter <3 mm was 0.94 (95% CI [0.91-0.96]). The PLR of DECT in the diagnosis of urinary stones diameter <3 mm was 10.79 (95% CI [5.25 to 22.17]), and the NLR was 0.08 (95% CI [0.05-0.13]). The specificity of DECT for detecting urinary calculi <3 mm was 0.91 (95% CI [0.87-0.94]). The SROC was 0.9772.

Conclusion

The DECT has noble application value in detecting urinary calculi.

Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value

This review article provides a concise guide to interpreting receiver operating characteristic (ROC) curves and area under the curve (AUC) values in diagnostic accuracy studies. ROC analysis is a powerful tool for assessing the diagnostic performance of index tests, which are tests that are used to diagnose a disease or condition. The AUC value is a summary metric of the ROC curve that reflects the test's ability to distinguish between diseased and nondiseased individuals. AUC values range from 0.5 to 1.0, with a value of 0.5 indicating that the test is no better than chance at distinguishing between diseased and nondiseased individuals. A value of 1.0 indicates perfect discrimination. AUC values above 0.80 are generally consideredclinically useful, while values below 0.80 are considered of limited clinical utility. When interpreting AUC values, it is important to consider the 95% confidence interval. The confidence interval reflects the uncertainty around the AUC value. A narrow confidence interval indicates that the AUC value is likely accurate, while a wide confidence interval indicates that the AUC value is less reliable. ROC analysis can also be used to identify the optimal cutoff value for an index test. The optimal cutoff value is the value that maximizes the test's sensitivity and specificity. The Youden index can be used to identify the optimal cutoff value. This review article provides a concise guide to interpreting ROC curves and AUC values in diagnostic accuracy studies. By understanding these metrics, clinicians can make informed decisions about the use of index tests in clinical practice.

Predicting complicated appendicitis is possible without the use of sectional imaging-presenting the NoCtApp score

PURPOSE

Appendicitis is among the most common acute conditions treated by general surgery. While uncomplicated appendicitis (UA) can be treated delayed or even non-operatively, complicated appendicitis (CA) is a serious condition with possible long-term morbidity that should be managed with urgent appendectomy. Distinguishing both conditions is usually done with computed tomography. The goal of this study was to develop a model to reliably predict CA with widespread available clinical and laboratory parameters and without the use of sectional imaging.

METHODS

Data from 1132 consecutive patients treated for appendicitis between 2014 and 2021 at a tertiary care hospital were used for analyses. Based on year of treatment, the data was divided into training (n = 696) and validation (n = 436) samples. Using the development sample, candidate predictors for CA-patient age, gender, body mass index (BMI), American Society of Anesthesiologist (ASA) score, duration of symptoms, white blood count (WBC), total bilirubin and C-reactive protein (CRP) on admission and free fluid on ultrasound-were first investigated using univariate logistic regression models and then included in a multivariate model. The final development model was tested on the validation sample.

RESULTS

In the univariate analysis age, BMI, ASA score, symptom duration, WBC, bilirubin, CRP, and free fluid each were statistically significant predictors of CA (each p < 0.001) while gender was not (p = 0.199). In the multivariate analysis BMI and bilirubin were not predictive and therefore not included in the final development model which was built from 696 patients. The final development model was significant (x2 = 304.075, p < 0.001) with a sensitivity of 61.7% and a specificity of 92.1%. The positive predictive value (PPV) was 80.4% with a negative predictive value (NPV) of 82.0%. The receiver operator characteristic of the final model had an area under the curve of 0.861 (95% confidence interval 0.830-0.891, p < 0.001. We simplified this model to create the NoCtApp score. Patients with a point value of ≤ 2 had a NPV 95.8% for correctly ruling out CA.

CONCLUSIONS

Correctly identifying CA is helpful for optimizing patient treatment when they are diagnosed with appendicitis. Our logistic regression model can aid in correctly distinguishing UA and CA even without utilizing computed tomography.

Hepatocyte proliferation activity in untreated rats, measured by immunohistochemical detection of Ki-67: The effect of age on the repeated-dose liver micronucleus assay

The repeated-dose liver micronucleus (RDLMN) assay is a widely accepted method for detecting genotoxic substances. We investigated the effect of animal age on this assay. Proliferation activity in the liver tissue of untreated rats at age = 3.5, 6, 8, 10, or 12 weeks was measured via immunohistochemical expression of Ki-67 protein. The percentage of Ki-67-positive hepatocytes decreased markedly with age, reaching very low levels after 10 weeks, indicating decline with age of proliferative capacities in the liver. We calculated the area under the curve (AUC) of the approximate curve generated from the percentage of Ki-67-positive cells, to estimate the hepatocyte proliferation activity over the dosing period in the two regimens of the 4-week RDLMN assay: dosing initiated at age = 6 or 8 weeks. Hepatocyte proliferation activity of the former regimen was approximately double that of the latter. We also calculated the AUC for the juvenile-rat method, in which rats are treated for two days at age = 3.5 weeks. The AUC calculated for that method was approximately half of that for the 4-week repeated-dosing regimen initiated at 6 weeks of age. These findings suggest that the 4-week RDLMN assay with dosing initiated at age = 6 weeks could be approximately twice as sensitive as the other two methods.

Empty stomach together with menstrual bleeding as predictors of committed suicides among women of reproductive age: What a primary physician must know

Background

Women of reproductive age group (WoRAG) are among the most vulnerable groups to suicide in India. The present study intended to develop a mathematical model to differentiate suicides from homicides among WoRAG.

Methods

It was a cross-sectional study based on a record review of autopsy at Patna, India, from 2016 to 2021. The cause of deaths was ascertained by autopsies and other records independently by two investigators to reduce the interobserver bias. Independent variables were tested with confirmed suicides to calculate statistically significant association. These variables were further used for developing prediction models for the suicides by multivariate logistic regression analysis.

Results

Out of total of 520 autopsies of WoRAG performed by investigators, the cause of death has been confirmed for 62. Of them, 30 were confirmed as suicides. In univariate analysis, suicides were associated with the menstrual bleed (OR 35 CI 6.9,179), gastric emptying (OR 3.9 CI 1.2,12.8), hanging, poisoning, and drowning as mode of death (OR 435 CI 37.4,5061.9). By logistic regression, three prediction models were built to predict suicide; Model I: gastric emptying, Model II: menstrual bleed, and Model III: including both. The area under the curve (AUC) for Models I, II, and III was 0.67 (95%CI 0.34,0.99), 0.92 (95%CI 0.75,1.00), and 0.94 (95%CI 0.82,1.00), respectively. The AUC of Model III differs significantly from that of Model I (P value 0.03) but not with Model II (P value 0.11).

Conclusion

Menstrual bleed, gastric emptying, and mode of death may be used as a supplement tool in ascertaining the cause of death among WoRAG in medical and legal proceedings.

Predicting busulfan exposure in patients undergoing hematopoietic stem cell transplantation using machine learning techniques

PURPOSE

This study aimed to establish an optimal model to predict the busulfan (BU) area under the curve at steady state (AUCss) by using machine learning (ML).

PATIENTS AND METHODS

Seventy-nine adult patients (age ≥18 years) who received BU intravenously and underwent therapeutic drug monitoring from 2013 to 2021 at Fujian Medical University Union Hospital were enrolled in this retrospective study. The whole dataset was divided into a training group and test group at the ratio of 8:2. BU AUCss were considered as the target variable. Nine different ML algorithms and one population pharmacokinetic (pop PK) model were developed and validated, and their predictive performance was compared.

RESULTS

All ML models were superior to the pop PK model (R2 = 0.751, MSE = 0.722, 14 and RMSE = 0.830) in model fitting and had better predictive accuracy. The ML model of BU AUCss established through support vector regression (SVR) and gradient boosted regression trees (GBRT) had the best predictive ability (R2 = 0.953 and 0.953, MSE = 0.323 and 0.326, and RMSE = 0.423 and 0.425).

CONCLUSION

All the ML models can potentially be used to estimate BU AUCss with the aim of facilitating rational use of BU on the individualized level, especially models built by SVR and GBRT algorithms.

Relationship between image information content and observer performance in digital intraoral radiography

OBJECTIVES

This study conducted a receiver operating characteristic (ROC) analysis by applying improved cluster signal-to-noise (CSN) analysis to digital intraoral radiographs and develop an observer-free method of analyzing image quality related to the observer performance in the detection task.

METHODS

Two aluminum step phantoms with a thickness interval of 1.0 mm were used for this study. One phantom had holes of increasing depth (from 0.05 to 0.35 mm) and the other had no holes. Phantom images were obtained under various exposure dose and image capture modes using a dental X-ray unit, a photostimulable phosphor imaging plate, and scanner system. These phantom images were analyzed using the FindFoci plugin in ImageJ software. Subsequently, true positive rates (TPRs) and false positive rates (FPRs) were calculated by analyzing phantom images with and without holes. We constructed ROC curves by plotting the TPRs against the FPRs and calculated the area under the ROC curve (AUC). Using the same phantom images with holes, eight observers assessed the number of detectable holes. Correlations between observer detection performance and AUC values were evaluated.

RESULTS

AUC values increased as the exposure dose increased and showed different tendencies depending on the image capture mode. The AUC values showed a high correlation with observer detection performance (r = 0.76).

CONCLUSIONS

AUC values obtained from CSN analysis reflect image quality and replace the observer detection performance test of image quality.

Copula modeling for the estimation of measures of marker classification and predictiveness performance with survival outcomes

The discriminative and predictive power of a continuous-valued marker for survival outcomes can be summarized using the receiver operating characteristic and predictiveness curves, respectively. In this paper, fully parametric and semi-parametric copula-based constructions of the joint model of the marker and the survival time are developed for characterizing, plotting, and analyzing both curves along with other underlying performance measures. The formulations require a copula function, a parametric specification for the margin of the marker, and either a parametric distribution or a non-parametric estimator for the margin of the time to event, to respectively characterize the fully parametric and semi-parametric joint models. Estimation is carried out using maximum likelihood and a two-stage procedure for the parametric and semi-parametric models, respectively. Resampling-based methods are used for computing standard errors and confidence bounds for the various parameters, curves, and associated measures. Graphical inspection of residuals from each conditional distribution is employed as a guide for choosing a copula from a set of candidates. The performance of the estimators of various classification and predictiveness measures is assessed in simulation studies, assuming different copula and censoring scenarios. The methods are illustrated with the analysis of two markers using the familiar primary biliary cirrhosis data set.

Use of the neutrophil-to-lymphocyte ratio and an oxygen requirement to predict disease severity in patients with COVID-19

We investigated the association between complete blood count, including neutrophil-to-lymphocyte ratio (NLR) in combination with patient characteristics, and coronavirus disease (COVID-19) outcomes to identify the best prognostic indicator. We analyzed data of patients with confirmed COVID-19 from the nationwide database of the Japan COVID-19 Task Force between February 2020 and November 2021. A composite outcome was defined as the most severe condition, including noninvasive positive-pressure ventilation, high-flow nasal cannula, invasive mechanical ventilation, extracorporeal membrane oxygenation, or death. Of 2425 patients in the analysis, 472 (19.5%) experienced a composite outcome. NLR was the best predictor of composite outcomes, with an area under the curve (AUC) of 0.81, and a sensitivity and specificity of 72.3% and 75.7%, respectively, using a cut-off value of 5.04. The combination of NLR and an oxygen requirement on admission had the highest AUC (0.88). This simple combination may help identify patients at risk of progression to severe disease.

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve (ROC AUC) has become the common standard metric to evaluate binary classifications in most scientific fields. The ROC curve has true positive rate (also called sensitivity or recall) on the y axis and false positive rate on the x axis, and the ROC AUC can range from 0 (worst result) to 1 (perfect result). The ROC AUC, however, has several flaws and drawbacks. This score is generated including predictions that obtained insufficient sensitivity and specificity, and moreover it does not say anything about positive predictive value (also known as precision) nor negative predictive value (NPV) obtained by the classifier, therefore potentially generating inflated overoptimistic results. Since it is common to include ROC AUC alone without precision and negative predictive value, a researcher might erroneously conclude that their classification was successful. Furthermore, a given point in the ROC space does not identify a single confusion matrix nor a group of matrices sharing the same MCC value. Indeed, a given (sensitivity, specificity) pair can cover a broad MCC range, which casts doubts on the reliability of ROC AUC as a performance measure. In contrast, the Matthews correlation coefficient (MCC) generates a high score in its [Formula: see text] interval only if the classifier scored a high value for all the four basic rates of the confusion matrix: sensitivity, specificity, precision, and negative predictive value. A high MCC (for example, MCC [Formula: see text] 0.9), moreover, always corresponds to a high ROC AUC, and not vice versa. In this short study, we explain why the Matthews correlation coefficient should replace the ROC AUC as standard statistic in all the scientific studies involving a binary classification, in all scientific fields.

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

Allergen bronchoprovocation: correlation between FEV1 maximal percent fall and area under the FEV1 curve and impact of allergen on recovery

BACKGROUND

House dust mite (HDM) induces greater responses than other allergens during allergen bronchoprovocation (ABP) testing. The two standardized methods for reporting results of ABP tests are the maximal percent fall in forced expiratory volume in one second (FEV_{1, max}; %) and the area under the FEV₁ vs time curve (AUC; %FEV₁ x min). The relationship between these methods has not been previously investigated.

AIMS

We aimed to measure the correlation between FEV_{1, max} and AUC during the early asthmatic response (EAR) and the late asthmatic response (LAR), and to determine if the EAR recovery period for HDM would be longer than other allergens (cat, grass, horse, and ragweed).

METHODS

We retrospectively calculated the AUC and correlation between FEV_{1, max} and AUC during the EAR_(0-2 h) and LAR_(3-7 h) for each allergen. We compared EAR_(0-3 h) and LAR_(3-7 h) FEV_{1, max}, AUC and absolute difference in FEV_{1, max} to the most recovered FEV₁ (FEV_{1, min}). We performed pairwise comparisons of correlation and slope values using Fischer's r to z transformation and t-tests, respectively. AUC and absolute differences in FEV_{1, max} and FEV_{1, min} were compared using a one-way ANOVA test, followed by a post-hoc Scheffe test.

RESULTS

Correlation between the FEV_{1, max} and AUC during the EAR_(0-2 h) (n = 221) was 0.807, and was 0.798 during the LAR_(3-7 h) (n = 157 of 221), (difference p = 0.408). The EAR_(0-3 h) AUC and FEV_{1, max} did differ between allergens (both p < 0.0001) but the LAR_(3-7 h) AUC and FEV_{1, max} did not (p = 0.548 and 0.824, respectively). HDM did not have a larger AUC or FEV_{1, max}, than all other allergens during the EAR_(0-3 h) or the LAR_(3-7 h). The absolute difference between the FEV_{1, max} and FEV_{1, min} during the EAR_(0-3 h) did not differ between allergens (p = 0.180).

CONCLUSION

The FEV_{1, max} and AUC for both the EAR_(0-2 h) and LAR_(3-7 h) had excellent correlation, with no significant difference. Thus, significant bronchoconstriction will likely result in a longer recovery period. There was no evidence of delayed EAR_(0-3 h) recovery following HDM challenges, so HDM did not induce a larger response compared to all the other allergens examined.

REGISTRATION

Not registered. This is not a clinical trial. (This study is a retrospective analysis of data collected during several registered trials.).

Quantifying the daily intake of water from morning and spot urine samples; retrospective analysis of a clinical trial in volunteers

BACKGROUND

The hydration status can be indicated by biomarkers in the urine. However, the sensitivity and specificity of single measurements of biomarkers in morning urine and spot urine samples to quantify previous and current daily water ingestion is unclear.

METHODS

The water content of food and liquid consumed by 20 volunteers (mean age 42 years) was calculated daily for two weeks. The volunteers increased their consumption of water by approximately 30% during the second week. They measured their excreted urine volume and sampled the morning urine and 24-h collections of urine for analysis of osmolality and creatinine during the first four days of both weeks (N = 157). The same biomarkers of hydration were measured in spot samples taken at every voiding on the other days (N = 762). Receiver operating characteristic (ROC) curves were used to study the ability of pre-specified ranges of biomarkers to quantify the water intake.

RESULTS

The biomarkers in the morning urine obtained during normal fluid intake quantified the water consumption with an average area under the ROC curve (AUC) of 0.72 for osmolality and 0.66 for creatinine. Spot urine yielded an AUC of 0.74 for osmolality and 0.70 for creatinine. The AUCs obtained for days of increased fluid intake were approximately 10% lower. Large intakes (3-4 L daily) were identified with a sensitivity of 50-80% and low intakes (< 1.5-2 L) with a sensitivity of 20-50%, while false positives occurred in approximately 10%.

CONCLUSION

Biomarkers in morning urine and spot urine samples distinguished between large and small daily water intakes. Osmolality was slightly superior to creatinine. The indications were less useful during days of increased fluid intake.

Multiple spectrophotometric determinations of Chlorthalidone and Cilnidipine using propylene carbonate - As a step towards greenness

Analyzing a drug based on its overlapping spectra requires the use of sophisticated equipment and more hazardous solvents, which is detrimental to ecological sustainability. There is a critical need to create a simple, unique, and cost-effective approach for detecting a mixture of compounds in a safer environment. The aim was to develop an eco-friendly, stability-indicating assay method to determine Chlorthalidone (CLD) and Cilnidipine (CIL) in bulk and tablet dosage form using four different Ultra-Violet (UV) spectrophotometric methods. The ratio difference method showed absorbance peaks at 256.01 nm, 220.87 nm, first ratio difference spectra at 267.21 nm, 288.03 nm, and second ratio difference spectra at 309.2 nm, 280.03 nm. The area under curve techniques showed an absorbance range of around 224-232 nm for CIL and 218-227 nm for CLD. Further, the spectral changes have been assessed at various conditions like acid, base, oxidation, and UV radiation, and it has been found that CLD tends to lose its spectral property by more than 45%. The method developed for two drugs has obeyed Beers law with the selected concentration range of 7-13 μg/mL for CLD and 8.75-16.25 μg/mL for CIL. The developed method was finally evaluated using four tools, and the results showed green pictographically representation in the GAPI and score near to 100 for AES and closer to 1 for AGREE indicated that the method was found to be most eco-friendly. The findings were easy to replicate, adoptive with major regulatory requirements, environmentally friendly, fast, and inexpensive to perform. This approach does not require any specific expensive equipment, and it might be inexpensive to use in the future to assess laboratory and commercial mixtures.

Estimating the optimal linear combination of predictors using spherically constrained optimization

BACKGROUND

In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve. For ordinal responses, the optimal predictor combination can similarly be obtained by maximization of the hypervolume under the manifold (HUM). Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases.

RESULTS

We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem, which we refer to as Spherically Constrained Optimization Routine (SCOR). Through extensive simulation studies, we demonstrate that the proposed method achieves better performance than existing methods including the step-down algorithm. Finally, we illustrate the proposed method to predict the severity of swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck.

CONCLUSIONS

Our proposed method addresses an important challenge in combining multiple biomarkers to predict an ordinal outcome. This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various stages of progression or a toxicity with multiple grades of severity. We provide the implementation of our proposed SCOR method as an R package, available online at https://CRAN.R-project.org/package=SCOR .

Clinical validation of the two-point method for predicting vancomycin AUC based on peak and trough plasma concentrations

BACKGROUND

Vancomycin area under the curve/minimum inhibitory concentration (AUC/MIC) has been proposed as a therapeutic drug monitoring (TDM) target to dose vancomycin. It is time-consuming to estimate AUCs using traditional methods. A two-point trough-peak method is more straightforward for calculating the vancomycin AUC. However, the technique and the AUC/MIC target have not been validated in Chinese patients.

AIM

To compare the clinical outcomes of vancomycin therapy in Chinese older adults (aged > 60 years) between the trough-only and the two-point peak-trough AUC TDM approaches.

METHOD

The patients were divided into study and control groups according to TDM approaches. A trough-based TDM was used in the control group (target trough level 15-20 mg/L). Stanford University has provided a method to predict vancomycin AUC using peak-valley concentration alone (two-point method). A two-point trough-peak TDM approach was employed in the study group (target AUC/MIC ≥ 400). The effect of vancomycin was evaluated in terms of clinical findings, laboratory values, and bacteriologic responses. The effects of treatment and kidney functions were compared between the two groups.

RESULTS

A total of 389 patients met the study inclusion criteria, and 189 were excluded based on the exclusion criteria. Of the 200 patients, 80 received the two-point TDM approach (the study group), and 120 were monitored using the trough-based approach (the control group). The average age was 69.8 ± 7.1 years. Staphylococcus aureus (34%) was the most common Gram-positive bacteria. No vancomycin-related nephrotoxicity was observed in either group. The percentages of patients with an excellent response to vancomycin therapy were significantly higher in the study group than in the control group, 90% (72/80) versus 73.3% (88/120), P = 0.0039.

CONCLUSION

The two-point peak-trough method is practical for obtaining vancomycin AUC. The AUC/MIC ≥ 400 target demonstrates treatment effectiveness and safety in older Chinese patients.

Development of pharmacogenomic algorithm to optimize nateglinide dose for the treatment of type 2 diabetes mellitus

BACKGROUND

Nateglinide is a meglitinide used for the treatment of type 2 diabetes mellitus. Individual studies demonstrated the association of CYP2C9, SLCO1B1, and MTNR1B variants with the safety and efficacy of nateglinide. The current study aimed to develop a pharmacogenomic algorithm to optimize nateglinide therapy.

METHODS

Multiple linear regression (MLR) and classification and regression tree (CART) were used to develop a pharmacogenomic algorithm for nateglinide dosing based on the published nateglinide pharmacokinetic data on the area under the curve data (AUC) and C_max (n = 143). CYP2C9 metabolizer phenotype, SLCO1B1, MTNR1B genotypes, and CYP2C9 inhibitor usage were used as the input variables. The results and associations were further confirmed by meta-analysis and in silico studies.

RESULTS

The MLR models of AUC and C_max explain 87.4% and 59% variability in nateglinide pharmacokinetics. The Bland and Altman analysis of the nateglinide dose predicted by these two MLR models showed a bias of ± 26.28 mg/meal. The CART algorithm was proposed based on these findings. This model is further justified by the meta-analysis showing increased AUCs in CYP2C9 intermediate metabolizers and SLCOB1 TC and CC genotypes compared to the wild genotypes. The increased AUC in SLCO1B1 mutants is due to decreased binding affinity of nateglinide to the mutant affecting the influx of nateglinide into hepatocytes. MTNR1B rs10830963 G-allele-mediated poor response to nateglinide is attributed to increased transcriptional factor binding causing decreased insulin secretion.

CONCLUSION

CYP2C9, SLCO1B1, and MTNR1B genotyping help in optimizing nateglinide therapy based on this algorithm and ensuring safety and efficacy.

Predictive validity of ESSENCE Q screening tool for early detection of neurodevelopmental disorder in children

INTRODUCTION

Neurodevelopmental disorders (NDD) are a group of conditions that typically manifest early during the child's development with lifelong consequences. Early identification using efficient screening tools can positively modify the natural history of the disorder. ESSENCE Q is a simple questionnaire to detect ESSENCE (Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations to reflect the co-existence of NDDs in children less than five years). There are limited studies on the validity of ESSENCE Q in detecting NDDs in young children in India.

METHODS

We did a cross-sectional study in a tertiary care hospital to evaluate the validity of ESSENCE Q in detecting neurodevelopmental disorders. We translated the ESSENCE Q and subsequently used it to screen for NDD in 100 children aged 12-60 months. Clinical consensus diagnosis by two experienced experts was taken as the gold standard.

RESULTS

23% were diagnosed as having NDD as per the clinical consensus diagnosis. Around half the children (46%) were "at-risk for NDD" based on the ESSENCE Q scale. We found an optimal cut-off for ESSENCE Q of more than or equal to 4 with a sensitivity of 96%, a specificity of 82%, and a Youden index of 0.77.

CONCLUSION

ESSENCE Q has good predictive validity to be used as a quick and easy screening tool to detect NDDs in young children under the age of 5 years.

Development and validation of a prediction model for tuberculous pleural effusion: a large cohort study and external validation

Background

Distinguishing tuberculous pleural effusion (TPE) from non-tuberculosis (TB) benign pleural effusion (BPE) remains to be a challenge in clinical practice. The aim of the present study was to develop and validate a novel nomogram for diagnosing TPE.

Methods

In this retrospective analysis, a total of 909 consecutive patients with TPE and non-TB BPE from Ningbo First Hospital were divided into the training set and the internal validation set at a ratio of 7:3, respectively. The clinical and laboratory features were collected and analyzed by logistic regression analysis. A diagnostic model incorporating selected variables was developed and was externally validated in a cohort of 110 patients from another hospital.

Results

Six variables including age, effusion lymphocyte, effusion adenosine deaminase (ADA), effusion lactatedehy drogenase (LDH), effusion LDH/effusion ADA, and serum white blood cell (WBC) were identified as valuable parameters used for developing a nomogram. The nomogram showed a good diagnostic performance in the training set. A novel scoring system was then established based on the nomogram to distinguish TPE from non-TB BPE. The scoring system showed good diagnostic performance in the training set [area under the curve (AUC) (95% confidence interval (CI)), 0.937 (0.917–0.957); sensitivity, 89.0%, and specificity, 89.5%], the internal validation set [AUC (95%CI), 0.934 (0.902–0.966); sensitivity, 88.7%, and specificity, 90.3%], and the external validation set [(AUC (95%CI), 0.941 (0.891–0.991); sensitivity, 93.6%, and specificity, 87.5%)], respectively.

Conclusions

The study developed and validated a novel scoring system based on a nomogram originated from six clinical parameters. The novel scoring system showed a good diagnostic performance in distinguishing TPE from non-TB BPE and can be conveniently used in clinical settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02051-4.

Cumulative Clinical Benefits of Biologics in the Treatment of Patients with Moderate-to-Severe Psoriasis over 1 Year: a Network Meta-Analysis

Introduction

Both early clinical improvement and long-term maintenance of clinical efficacy of treatments matter to patients with psoriasis. We compared cumulative clinical benefits of treatment with biologics over 1 year based on the area under the curve (AUC) for Psoriasis Area and Severity Index (PASI) 100 and PASI 90 responses in patients with moderate-to-severe psoriasis using a network meta-analysis (NMA).

Methods

Published phase 3 randomized, placebo- or active-controlled clinical trial data for biologics approved for the treatment of moderate-to-severe psoriasis were obtained from a systematic literature review up to 30 September 2020. Eighteen clinical trials that included data from baseline to 48 or 52 weeks where AUC could be calculated were included. Data were compared using a fixed-effect model with a separate random-effect baseline model to account for effects of the placebo arm. Cumulative clinical benefit was estimated using the AUC for PASI 100 and PASI 90 responses (complete and almost-complete skin clearance, respectively). Normalized AUC was compared using Bayesian NMA. Cumulative days of response were calculated using normalized AUC and study duration.

Results

Interleukin (IL)-17 and IL-23 inhibitors demonstrated greater cumulative clinical benefits for both PASI 100 and PASI 90 versus IL-12/23 and tumor necrosis factor inhibitors. Over 52 weeks, cumulative days with PASI 100 were greatest with ixekizumab [158.7 (95% credible interval, 147.4, 170.0) days] followed by risankizumab [154.0 (144.9, 163.4) days]; PASI 90 days were greatest with risankizumab [249.3 (239.5, 259.2) days] followed by ixekizumab [238.8 (227.1, 250.8) days]. Both ixekizumab and risankizumab showed greater cumulative days with PASI 100 or PASI 90 responses versus secukinumab [117.9 (110.7, 125.2) and 215.5 (208.2, 223.1) days, respectively] and greater cumulative days with PASI 100 versus guselkumab [130.7 (120.5, 140.9) days].

Conclusion

For complete and almost-complete skin clearance, ixekizumab and risankizumab provided the greatest cumulative clinical benefits over 1 year.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13555-022-00690-5.

Evaluating the performance of drug-repurposing technologies

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Efficacy of 0.19 mg Fluocinolone Acetonide Implant in Non-infectious Posterior Uveitis Evaluated as Area Under the Curve

Introduction

This study reports the outcomes of fluocinolone acetonide intravitreal implant (FAc, Iluvien^®, SIFI, Italy) in patients affected by macular edema secondary to chronic non-infectious uveitis of the posterior segment (NIU-PS).

Methods

This was a retrospective study of patients with NIU-PS and macular thickening undergoing FAc implant at San Raffaele Hospital (Milan, Italy). Clinical data, including best-corrected visual acuity (BCVA), intraocular pressure (IOP), and central macular thickness (CMT), were collected at the time of FAc administration (baseline) and at 1, 6, and 12 months. The area under the curve (AUC) of the BCVA (AUC_BCVA) and CMT (AUC_CMT) was correlated with baseline factors; β estimates and 95% confidence interval (CI) are provided.

Results

Ten eyes of seven patients (60 ± 12 years; 4 male, 57%) were included. The BCVA significantly improved from month 6 (p = 0.03). The CMT improved from month 1 and was persistently lower than baseline until month 12 (p < 0.001). The AUC_BCVA correlated with baseline BCVA (β = 2.5 logMAR; 95% CI 1.59–3.41; p < 0.001), while the mean AUC_CMT positively correlated with the baseline CMT (β = 2.1 μm; 95% CI 0.41–3.80; p = 0.02). No adverse events were recorded over 1 year.

Conclusions

Better visual acuity at the time of FAc administration was associated with better visual function after FAc. Less severe macular edema correlated with better anatomic response. The FAc implant was a safe option for resolving macular edema secondary to NIU-PS.

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker

BACKGROUND

Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance.

METHODS

We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions.

RESULTS

In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling.

CONCLUSIONS

RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

Cyclosporine A C1.5 monitoring reflects the area under the curve in children with nephrotic syndrome: a single-center experience

BACKGROUND

The currently used single-monitoring method for drug-blood-level evaluation in cyclosporine A (CsA) treatment for nephrotic syndrome (NS) was established through hourly measurements based on adult organ transplantation. However, the pharmacokinetics may differ due to different concomitant medications, age, and conditions. This study was conducted to determine the measurement timing that best reflects the CsA area under the curve (AUC) in pediatric NS.

METHODS

This retrospective study included children aged 2-14 years who were started on CsA treatment for idiopathic NS during 2013-2020. AUC_0-4 was calculated from 7 points, before and 0.5, 1, 1.5, 2, 3, and 4 h after administration. Mean values at each timing were compared with age-dependent different drug forms. Correlation between AUC_0-4 and measurement timing was analyzed.

RESULTS

There were 13 patients (11 boys) whose median age during testing was 7.3 years, and the total number of measurements was 94. The highest timing of CsA concentrations was found in C₁ 59.6%. The content liquid used at younger ages had a faster absorption time to peak value and lower blood concentration than those of capsules. Among the significant correlations observed, AUC_0-4 and C_1.5 showed the strongest significant correlation coefficient (r = 0.93, P < 0.001).

CONCLUSION

In pediatric NS, CsA metabolism may be faster than that in previous organ transplantation. Compared with C₂, C_1.5 monitoring may result in better disease control as it can best reflect the AUC_0-4 and peak values associated with side effects, which are indicators of therapeutic efficacy.

Pharmacokinetically guided, once-daily intravenous busulfan in combination with fludarabine for elderly AML/MDS patients as a conditioning regimen for allogeneic stem cell transplantation

The efficacy of pharmacokinetically (PK) guided, once-daily administration of busulfan (BU) was evaluated in elderly patients with acute myeloid leukemia/myelodysplastic syndrome (AML/MDS). Twenty-one patients (median age 61) received 30 mg/m² fludarabine for 6 days and BU for 4 days, starting from 3.2 mg/m² and subsequently adjusted to the target area under the curve (AUC) of 6000 µmol-min/L. The median AUC of day 1 (AUC1), AUC4, and their average were 4871.3, 6021.0, and 5368.1 µmol-min/L, respectively. Veno-occlusive disease/sinusoidal obstructive syndrome (VOD/SOS) occurred in five patients (24%) but all recovered well. Four patients (20%) had non-infectious pulmonary complications (NIPCs). Patients with high AUC1 had frequent gastrointestinal adverse events, but similar incidence of VOD/SOS and NIPCs. Two-year overall survival (OS), non-relapse mortality (NRM), and relapse rates were 44.4%, 28.6%, and 29.1%, respectively. Patients with high AUC1 had significantly high NRM (57.1% vs. 14.3%, P = 0.04) and inferior OS (14.3% vs. 60.1%, P = 0.002), while patients with high AUC4 had a significantly low relapse rate (8.3% vs. 55.6%, P = 0.02). In conclusion, once-daily BU and a PK-guided dose intensification were beneficial for reducing relapse in elderly patients with AML/MDS. However, caution should be exercised as rapid BU dose elevation may contribute to NRM.

Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor

OBJECTIVES

To investigate whether quantitative DCE-MRI (qDCE-MRI) could help distinguish breast phyllodes tumor (PT) grades.

MATERIALS AND METHODS

This retrospective study included 67 breast PTs (26 benign lesions, 25 borderline lesions, and 16 malignant lesions) from April 2016 to July 2020. MRI was performed with a 1.5-T MR system. Perfusion parameters (K^trans, k_ep, v_e, iAUC60) derived from qDCE-MRI, tumor size, and the mean ADC value were correlated with histologic grades using Spearman's rank correlation coefficient. K^trans, k_ep, v_e, and iAUC60 of three histologic grades were also calculated and compared.

RESULTS

The Spearman correlation coefficient with histologic grade of the tumor size was 0.578 (p < 0.001); the ADC value was not correlated with histologic grades of breast PT (p = 0.059). The K^trans, k_ep, v_e, and iAUC60 of benign breast PTs were significantly lower than those of borderline breast PTs (p < 0.001) and lower than those of malignant breast PTs (p < 0.001). In comparison, the K^trans, v_e, and iAUC60 of borderline breast PTs were significantly lower than those of malignant breast PTs (p < 0.001, p < 0.001, p = 0.007, respectively). For ROC analysis, AUCs of K^trans, v_e, and iAUC60 were higher than tumor size and ADC value for differentiating three PT grades.

CONCLUSION

Quantitative and semi-quantitative perfusion parameters (K^trans, v_e, and iAUC60, especially K^trans) derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs. Therefore, qDCE-MRI may be helpful for preoperative differentiating breast PT grades.

KEY POINTS

• Quantitative dynamic contrast-enhanced MRI can be used as a complementary noninvasive method to improve the differential diagnosis of breast PT. • K^trans, v_e, and iAUC60 derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs.

Validation of the Korean Version of the Psychosis Screener to Identify Patients With Psychosis

OBJECTIVE

This study aimed to validate the Korean version of a short screening tool for psychosis as the first stage in finding undiagnosed psychosis in the community.

METHODS

The sample contained 126 consecutive psychiatric outpatients in National Medical Center, Seoul, Korea, between July 20 and July 22, 2020. The Psychosis Screener (PS) comprises 7 items covering psychotic symptoms. The presence of each psychotic symptom was determined by a trained mental health professional and coded "yes" or "no." Two psychiatrists reviewed the medical records independently and extracted the ICD-10-based diagnoses. Any differences between the two clinicians were resolved by consensus, and the agreed diagnosis was used as a gold standard in the study.

RESULTS

Among 126 psychiatric outpatients who were enrolled in a consecutive manner during the study period, the proportion of psychosis was 15.1%. The PS showed 78.9% sensitivity and 72.0% specificity when the optimal cut-off was 2, indicating that a score of 2 or more on the screener identified a likely case of psychosis. The area under the curve for the PS was 0.78 (95% CI: 0.67-0.87).

CONCLUSION

The Korean version of the PS has an ability to discriminate between those who meet the diagnostic criteria for psychosis and those who do not in a high-prevalence group.

Between-group comparison of area under the curve in clinical trials with censored follow-up: Application to HIV therapeutic vaccines

In clinical trials, longitudinal data are commonly analyzed and compared between groups using a single summary statistic such as area under the outcome versus time curve (AUC). However, incomplete data, arising from censoring due to a limit of detection or missing data, can bias these analyses. In this article, we present a statistical test based on splines-based mixed-model accounting for both the censoring and missingness mechanisms in the AUC estimation. Inferential properties of the proposed method were evaluated and compared to ad hoc approaches and to a non-parametric method through a simulation study based on two-armed trial where trajectories and the proportion of missing data were varied. Simulation results highlight that our approach has significant advantages over the other methods. A real working example from two HIV therapeutic vaccine trials is presented to illustrate the applicability of our approach.