ROC Analysis

Urinary Free Glycosaminoglycans Identify Adults at High Risk of Developing Early-stage High-grade Bladder Cancer

Background and objective

Screening for bladder cancer (BCa) could reduce mortality via early detection of early-stage high-grade (Ta/T1 N0 M0 grade 2-3) disease. Noninvasive biomarkers could aid in screening, but current markers lack the specificity required. The urinary free glycosaminoglycan profile (GAGome) is a promising biomarker for early detection of BCa metabolism.

Methods

In a prospective case-control development study, we included patients with BCa or no evidence of disease (NED) and measured the urinary GAGome. We then developed a score to predict the probability of BCa using GAGome features that correlated with BCa versus NED according to Bayesian regression. Next, in a retrospective, population-based, case-control study, we included adults from the Lifelines Cohort Study who were presumed healthy at baseline. All cases with BCa confirmed in the cancer registry by the 2-yr or 6-yr study visit were matched to randomly selected control subjects. We developed a reference logistic regression model using age and sex to predict BCa at 7 yr after baseline. We then added the GAGome score to the model and assessed model improvement using the likelihood ratio test. We dichotomized outputs for the reference model and saturated model (reference + GAGome score) into high-risk versus low-risk categories using a 99% specificity cutoff and estimated the sensitivity for association with BCa at 7 yr.

Key findings and limitations

We prospectively included 51 individuals with BCa and 38 with NED and observed alterations in three GAGome features compatible with BCa. We developed a score that discriminated BCa with an area under the receiver operating characteristic curve of 0.77 (95% confidence interval [CI] 0.67-0.87). We retrospectively selected a cohort of 1088 presumed healthy adults (median age 48 yr, 56% females), of whom 48 had developed BCa by 7 yr after baseline (median time to diagnosis 1.4 yr). The GAGome score was an independent predictor of BCa at 7 yr when added to the reference model (p < 0.001). The sensitivity for BCa at 7 yr for high-risk subjects was 31% (95% CI 20-43%) using the saturated model and 17% (95% CI 4.7-29%) using the reference model at 99% specificity (95% CI 98-99%).

Conclusions and clinical implications

The urinary free GAGome is specifically altered in BCa and can be used for noninvasive identification of adults at high risk of developing BCa, independent of age and sex. This information could be useful for the design of risk-stratified targeted screening programs for BCa.

Patient summary

We tested whether measurement of a class of sugars called glycosaminoglycans (GAGs) in urine could be used for early detection of bladder cancer. Our results show that GAG levels in urine can distinguish people at high risk of developing bladder cancer within 7 years, even if they are healthy at the time of the urine sampling.

Diagnostic value of shear-wave elastography for patellar tendinopathy in female volleyball and basketball athletes: a cross sectional case control study

OBJECTIVES

We aim to investigate the accuracy of shear-wave elastography (SWE) in diagnosing patellar tendinopathy in female volleyball and basketball players. In addition, we compared different parts of the patellar tendon and investigated the effects of different knee angles on elastography measurements.

METHODS

This cross-sectional case-control study evaluated 63 female athletes from professional basketball and volleyball teams (NCT06199583). Patellar tendinopathy diagnoses were made using clinical and ultrasonographic criteria. SWE measurements were taken at 30-degree knee flexion and extension. Rectangular regions of interest boxes were placed in three different parts of the tendon (proximal, middle, distal). The global SWE value was calculated by taking the mean of measurements in the three parts. Receiver operating characteristic (ROC) curves were used to identify significant cutoff points for SWE, and 2 × 2 tables were generated to determine sensitivity and specificity.

RESULTS

Thirteen (20.6%) of the 63 athletes were diagnosed with patellar tendinopathy. The ROC curves have identified different cutoff scores for SWE measurements. The SWE score of 130.75 from the proximal part showed the highest sensitivity of 89% and specificity of 80% (p < 0.001) with a 4.45 likelihood ratio at the 30-degree knee flexion. The likelihood ratio is 1.5 at a 30-degree angle and 1.65 at a 0-degree angle when measuring the entire tendon, whereas other portions indicate a ratio ranging from 1.12 to 1.73.

CONCLUSIONS

Shear-wave elastography is a reliable evaluation method for diagnosing patellar tendinopathy. It has more accuracy when applied to the proximal part and at 30-degree knee flexion compared to measurements taken at knee extension and other parts of the tendon.

Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound

OBJECTIVES

To noninvasively estimate three breast cancer biomarkers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and enhance performance and interpretability via multi-task deep learning.

METHODS

The study included 388 breast cancer patients who received the 3D whole breast ultrasound system (3DWBUS) examinations at Xijing Hospital between October 2020 and September 2021. Two predictive models, a single-task and a multi-task, were developed; the former predicts biomarker expression, while the latter combines tumor segmentation with biomarker prediction to enhance interpretability. Performance evaluation included individual and overall prediction metrics, and Delong's test was used for performance comparison. The models' attention regions were visualized using Grad-CAM + + technology.

RESULTS

All patients were randomly split into a training set (n = 240, 62%), a validation set (n = 60, 15%), and a test set (n = 88, 23%). In the individual evaluation of ER, PR, and HER2 expression prediction, the single-task and multi-task models achieved respective AUCs of 0.809 and 0.735 for ER, 0.688 and 0.767 for PR, and 0.626 and 0.697 for HER2, as observed in the test set. In the overall evaluation, the multi-task model demonstrated superior performance in the test set, achieving a higher macro AUC of 0.733, in contrast to 0.708 for the single-task model. The Grad-CAM + + method revealed that the multi-task model exhibited a stronger focus on diseased tissue areas, improving the interpretability of how the model worked.

CONCLUSION

Both models demonstrated impressive performance, with the multi-task model excelling in accuracy and offering improved interpretability on noninvasive 3DWBUS images using Grad-CAM + + technology.

CRITICAL RELEVANCE STATEMENT

The multi-task deep learning model exhibits effective prediction for breast cancer biomarkers, offering direct biomarker identification and improved clinical interpretability, potentially boosting the efficiency of targeted drug screening.

KEY POINTS

Tumoral biomarkers are paramount for determining breast cancer treatment. The multi-task model can improve prediction performance, and improve interpretability in clinical practice. The 3D whole breast ultrasound system-based deep learning models excelled in predicting breast cancer biomarkers.

Peptides as modulators of FPPS enzyme: A multifaceted evaluation from the design to the mechanism of action

Bone diseases are medical conditions caused by the loss of bone homeostasis consecutive to increased osteoclast activity and diminished osteoblast activity. The mevalonate pathway (MVA) is crucial for maintaining this balance since it drives the post-translational prenylation of small guanosine triphosphatases (GTPases) proteins. Farnesyl pyrophosphate synthase (FPPS) plays a crucial role in the MVA pathway. Consequently, in the treatment of bone-related diseases, FPPS is the target of FDA-approved nitrogen-containing bisphosphonates (N-BPs), which have tropism mainly for bone tissue due to their poor penetration in soft tissues. The development of inhibitors targeting the FPPS enzyme has garnered significant interest in recent decades due to FPPS's role in the biosynthesis of cholesterol and other isoprenoids, which are implicated in cancer, bone diseases, and other conditions. In this study, we describe a multidisciplinary approach to designing novel FPPS inhibitors, combining computational modeling, biochemical assays, and biophysical techniques. A series of peptides and phosphopeptides were designed, synthesized, and evaluated for their ability to inhibit FPPS activity. Molecular docking was employed to predict the binding modes of these compounds to FPPS, while Surface Plasmon Resonance (SPR) and Nuclear Magnetic Resonance (NMR) spectroscopy experiments - based on Saturation Transfer Difference (STD) and an enzymatic NMR assay - were used to measure their binding affinities and kinetics. The biological activity of the most promising compounds was further assessed in cellular assays using murine colorectal cancer (CRC) cells. Additionally, genomics and metabolomics profiling allowed to unravel the possible mechanisms underlying the activity of the peptides, confirming their involvement in the modulation of the MVA pathway. Our findings demonstrate that the designed peptides and phosphopeptides exhibit significant inhibitory activity against FPPS and possess antiproliferative effects on CRC cells, suggesting their potential as therapeutic agents for cancer.

Predictive potential of MRI in differentiating the predominant component in biphasic pleural mesothelioma

PURPOSE

To assess the potential of apparent diffusion coefficient (ADC) values derived from diffusion weighted (DW) MRI preoperatively to predict the predominant histologic component among biphasic pleural mesothelioma (PM) tumors.

METHODS

ADC maps were generated from DW MRI scans. Histology and predominant component of biphasic PM were confirmed following surgical resection. Statistical analyses were done with R (R Foundation for Statistical Computing, Vienna, Austria). Average ADC values corresponding to epithelioid- and sarcomatoid-predominant tumors were compared. ADC thresholding was accomplished by recursive partitioning and confirmed with ROC analysis.

RESULTS

Eighty-four patients with biphasic PM's, 69 (82 %) epithelioid-predominant (BE) and 15(18 %) sarcomatoid-predominant (BS) tumors were evaluated. Thirty-eight (45 %) patients underwent extrapleural pneumonectomy (EPP), 39 (46 %) had extended pleural decortication (ePDC) and 7 (8 %) had pleural decortication (PDC). ADC values ranged between 0.696 x 10-3 to 1.921 x 10-3 mm2/s. BE tumors demonstrated significantly higher ADC values than BS tumors (p = 0.026). ADC values above 0.94 x 10-3 mm2/s were associated with a significant increase of relative risk of being in group BE over group BS (relative risk: 1.47, 95 %CI: 1.05-2.06, p = 0.027) CONCLUSION: Average ADC values of BE tumors were higher than BS tumors and the two groups can be separated by a cut off value of 0.94 X 10-3 mm2/s.

MRI radiomics enhances radiologists' ability for characterizing intestinal fibrosis in patients with Crohn's disease

OBJECTIVES

We aimed to develop MRI-based radiomic models (RMs) to improve the diagnostic accuracy of radiologists in characterizing intestinal fibrosis in patients with Crohn's disease (CD).

METHODS

This retrospective study included patients with refractory CD who underwent MR before surgery from November 2013 to September 2021. Resected bowel segments were histologically classified as none-mild or moderate-severe fibrosis. RMs based on different MR sequence combinations (RM1: T2WI and enhanced-T1WI; RM2: T2WI, enhanced-T1WI, diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC]); RM3: T2WI, enhanced-T1WI, DWI, ADC, and magnetization transfer MRI [MTI]), were developed and validated in an independent test cohort. The RMs' diagnostic performance was compared to that of visual interpretation using identical sequences and a clinical model.

RESULTS

The final population included 123 patients (81 men, 42 women; mean age: 30.26 ± 7.98 years; training cohort, n = 93; test cohort, n = 30). The area under the receiver operating characteristic curve (AUC) of RM1, RM2, and RM3 was 0.86 (p = 0.001), 0.88 (p = 0.001), and 0.93 (p = 0.02), respectively. The decision curve analysis confirmed a progressive improvement in the diagnostic performance of three RMs with the addition of more specific sequences. All RMs performance surpassed the visual interpretation based on the same MR sequences (visual model 1, AUC = 0.65, p = 0.56; visual model 2, AUC = 0.63, p = 0.04; visual model 3, AUC = 0.77, p = 0.002), as well as the clinical model composed of C-reactive protein and erythrocyte sedimentation rate (AUC = 0.60, p = 0.13).

CONCLUSIONS

The RMs, utilizing various combinations of conventional, DWI and MTI sequences, significantly enhance radiologists' ability to accurately characterize intestinal fibrosis in patients with CD.

CRITICAL RELEVANCE STATEMENT

The utilization of MRI-based RMs significantly enhances the diagnostic accuracy of radiologists in characterizing intestinal fibrosis.

KEY POINTS

MRI-based RMs can characterize CD intestinal fibrosis using conventional, diffusion, and MTI sequences. The RMs achieved AUCs of 0.86-0.93 for assessing fibrosis grade. MRI-radiomics outperformed visual interpretation for grading CD intestinal fibrosis.

The Telephone Language Screener (TLS): standardization of a novel telephone-based screening test for language impairment

BACKGROUND

This study aimed at developing and standardizing the Telephone Language Screener (TLS), a novel, disease-nonspecific, telephone-based screening test for language disorders.

METHODS

The TLS was developed in strict pursuance to the current psycholinguistic standards. It comprises nine tasks assessing phonological, lexical-semantic and morpho-syntactic components, as well as an extra Backward Digit Span task. The TLS was administered to 480 healthy participants (HPs), along with the Telephone-based Semantic Verbal Fluency (t-SVF) test and a Telephone-based Composite Language Index (TBCLI), as well as to 37 cerebrovascular/neurodegenerative patients-who also underwent the language subscale of the Telephone Interview for Cognitive Status (TICS-L). An HP subsample was also administered an in-person language battery. Construct validity, factorial structure, internal consistency, test-retest and inter-rater reliability were tested. Norms were derived via Equivalent Scores. The capability of the TLS to discriminate patients from HPs and to identify, among the patient cohort, those with a defective TICS-L, was also examined.

RESULTS

The TLS was underpinned by a mono-component structure and converged with the t-SVF (p < .001), the TBCLI (p < .001) and the in-person language battery (p = .002). It was internally consistent (McDonald's ω = 0.67) and reliable between raters (ICC = 0.99) and at retest (ICC = 0.83). Age and education, but not sex, were predictors of TLS scores. The TLS optimally discriminated patients from HPs (AUC = 0.80) and successfully identified patients with an impaired TICS-L (AUC = 0.92). In patients, the TLS converged with TICS-L scores (p = 0.016).

DISCUSSION

The TLS is a valid, reliable, normed and clinically feasible telephone-based screener for language impairment.

Comparison of blood ΒHBA measurement devices for diagnosis of subclinical pregnancy toxaemia in sheep: A field study

Pregnancy toxaemia is a serious disease that occurs during the last trimester of pregnancy in sheep. Yet, in most cases, the disease may have a subclinical course. This study was aimed at comparing blood βHBA measurement devices for diagnosis of subclinical pregnancy toxaemia in late pregnant sheep. In the study, a total of 50 blood samples were collected from Romanov (n = 30) and cross-bred Hamdani (n = 20) sheep. Blood βHBA levels were measured using TaiDoc TD-4235 and CentriVet βHBA hand-held meter. Randox βHBA (Ranbut) assay was used as a reference laboratory method to compare hand-held meters. βHBA value of 0.8 mmol/L was set as the cut-off value for diagnosis of subclinical pregnancy toxaemia. Statistical analyses were carried out using Minitab 21 and Jamovi software. In the study, the correlation of Randox-TaiDoc TD-4235 and Randox-CentriVet was .822 (p < .001) and .728 (p < .001), respectively. Based on the Ranbut assay, nine ewes out of 50 were diagnosed with subclinical pregnancy toxaemia. Specificity (detection of healthy ewes) and sensitivity (detection of ewes with subclinical pregnancy toxaemia) for TaiDoc TD-4235 and CentriVet hand-held meters were 100%, 77.8%, and 100%, 66.7%, respectively. In the receiver operating characteristic (ROC) analysis, areas under the ROC curve (AUC) were 0.976 and 0.920 for TaiDoc and CentriVet, respectively. Bland-Altman analysis revealed a bias of 0.092 mmol/L for TaiDoc and a bias of 0.132 mmol/L for CentriVet. TaiDoc hand-held meter shows a better correlation with the Randox Ranbut assay and greater sensitivity compared to the CentriVet hand-held meter. In conclusion, both TaiDoc and CentriVet hand-held meters can be securely used in the diagnosis of subclinical pregnancy toxaemia in sheep. For these reasons, subclinical pregnancy toxaemia and these devices will be evaluated within the scope of herd management programme in the sheep industry. It should also be taken into account that these conditions will affect the future fertility of the mother and offspring.

On the Clinimetrics of the Montreal Cognitive Assessment: Cutoff Analysis in Patients with Mild Cognitive Impairment due to Alzheimer's Disease

Background

In the era of disease-modifying therapies, empowering the clinical neuropsychologist's toolkit for timely identification of mild cognitive impairment (MCI) is crucial.

Objective

Here we examine the clinimetric properties of the Montreal Cognitive Assessment (MoCA) for the early diagnosis of MCI due to Alzheimer's disease (MCI-AD).

Methods

Data from 48 patients with MCI-AD and 47 healthy controls were retrospectively analyzed. Raw MoCA scores were corrected according to the conventional Nasreddine's 1-point correction and demographic adjustments derived from three normative studies. Optimal cutoffs were determined while previously established cutoffs were diagnostically reevaluated.

Results

The original Nasreddine's cutoff of 26 and normative cutoffs (non-parametric outer tolerance limit on the 5th percentile of demographically-adjusted score distributions) were overly imbalanced in terms of Sensitivity (Se) and Specificity (Sp). The optimal cutoff for Nasreddine's adjustment showed adequate clinimetric properties (≤23.50, Se = 0.75, Sp = 0.70). However, the optimal cutoff for Santangelo's adjustment (≤22.85, Se = 0.65, Sp = 0.87) proved to be the most effective for both screening and diagnostic purposes according to Larner's metrics. The results of post-probability analyses revealed that an individual testing positive using Santangelo's adjustment combined with a cutoff of 22.85 would have 84% post-test probability of receiving a diagnosis of MCI-AD (LR+ = 5.06).

Conclusions

We found a common (mal)practice of bypassing the applicability of normative cutoffs in diagnosis-oriented clinical practice. In this study, we identified optimal cutoffs for MoCA to be allocated in secondary care settings for supporting MCI-AD diagnosis. Methodological and psychometric issues are discussed.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

Diagnostic value of preoperative measures in selecting post-lingually deafened candidates for cochlear implantation - a different approach

OBJECTIVES

We examined which preoperative diagnostic measure is most suited to serve as a selection criterion to determine adult cochlear implantation (CI) candidacy.

DESIGN

Preoperative diagnostic measures included pure tone audiometry (PTA; 0.5, 1, 2, 4 kHz), speech perception tests (SPT) unaided with headphones and with best-aided hearing aids (in quiet and in noise). Gain in speech perception was used as outcome measure. Performance of preoperative measures was analysed using the area under the curve (AUC) of receiver operating characteristic (ROC) curves.

STUDY SAMPLE

This retrospective longitudinal cohort study included 552 post-lingually deafened adults with CI in a tertiary referral centre in the Netherlands.

RESULTS

Best-aided SPT in quiet was the most accurate in defining which CI candidates improved their speech perception in quiet postoperatively. For an improvement in speech perception in noise, the best-aided SPT in noise was the most accurate in defining which adult would benefit from CI. PTA measures performed lower compared to the SPT measures.

CONCLUSIONS

SPT is better than PTA for selecting CI candidates who will benefit in terms of speech perception. Best-aided SPT in noise was the most accurate for indicating an improvement of speech perception in noise but was only evaluated in high performers with residual hearing. These insights will assist in formulating more effective selection criteria for CI.

Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning

Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.

Identification of exosomal microRNA panel as diagnostic and prognostic biomarker for small cell lung cancer

BACKGROUND

Small cell lung cancer (SCLC) has an exceptionally poor prognosis; as most of the cases are initially diagnosed as extensive disease with hematogenous metastasis. Therefore, the early diagnosis of SCLC is very important and may improve its prognosis.

METHODS

To investigate the feasibility of early diagnosis of SCLC, we examined exosomal microRNAs (miRNAs) present in serum obtained from patients with SCLC. First, exosomes were isolated in serum from patients with SCLC and healthy individuals and were characterized using particle size and protein markers. Additionally, miRNA array was performed to define SCLC-specific exosomal miRNAs. Second, the obtained miRNAs were further validated employing a large cohort. Finally, the ability to diagnose SCLC was estimated by area under the curve (AUC), and intracellular mRNA change patterns were verified through validated miRNAs.

RESULTS

From the miRNA array results, we selected 51-miRNAs based on p-values and top 10 differentially expressed genes, and 25-miRNAs were validated using quantitative reverse transcription-polymerase chain reaction. The 25-miRNAs were further validated employing a large cohort. Among them, 7-miRNAs showed significant differences. Furthermore, 6-miRNAs (miR-3565, miR-3124-5p, miR-200b-3p, miR-6515, miR-3126-3p and miR-9-5p) were up-regulated and 1-miRNA (miR-92b-5p) was down-regulated. The AUC value of each miRNA sets between 0.64 and 0.76, however the combined application of 3-miRNAs (miR-200b-3p, miR-3124-5p and miR-92b-5p) remarkably improved the diagnostic value (AUC = 0.93). Gene ontology analysis revealed that the 3-miRNA panel is linked to various oncogene pathways and nervous system development. When the 3-miRNAs were introduced to cells, the resulting changes in total mRNA expression strongly indicated the presence of lung diseases, including lung cancer. In addition, the 3-miRNA panel was significantly associated with a poorer prognosis, although individual miRNAs have not been validated as prognostic markers.

CONCLUSION

Our study identified SCLC-specific exosomal miRNAs, and the 3-miRNAs panel (miR-200b-3p, miR-3124-5p and miR-92b-5p) may serve as a diagnostic and prognostic marker for SCLC.

Missing values and inconclusive results in diagnostic studies - A scoping review of methods

Most diagnostic studies exclude missing values and inconclusive results from the analysis or apply simple methods resulting in biased accuracy estimates. This may be due to the lack of availability or awareness of appropriate methods. This scoping review aimed to provide an overview of strategies to handle missing values and inconclusive results in the reference standard or index test in diagnostic accuracy studies. Conducting a systematic literature search in MEDLINE, Cochrane Library, and Web of Science, we could identify many articles proposing methods for addressing missing values in the reference standard. There are also several articles describing methods regarding missing values or inconclusive results in the index test. The latter encompass imputation, frequentist and Bayesian likelihood, model-based, and latent class methods. While methods for missing values in the reference standard are regularly applied in practice, this is not true for methods addressing missing values and inconclusive results in the index test. Our comprehensive overview and description of available methods may raise further awareness of these methods and will enhance their application. Future research is needed to compare the performance of these methods under different conditions to give valid and robust recommendations for their usage in various diagnostic accuracy research scenarios.

Statistical modeling and evaluation of the impact of multiplicity classification thresholds on the COVID-19 pool testing accuracy

Prior research on pool testing focus on developing testing methods with the main objective of reducing the total number of tests. However, pool testing can also be used to improve the accuracy of the testing process. The objective of this paper is to improve the accuracy of pool testing using the same number of tests as that of individual testing taking into consideration the probability of testing errors and pool multiplicity classification thresholds. Statistical models are developed to evaluate the impact of pool multiplicity classiffcation thresholds on pool testing accuracy using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The findings indicate that under certain conditions, pool testing multiplicity yields superior testing accuracy compared to individual testing without additional cost. The results reveal that selecting the multiplicity classification threshold is a critical factor in improving the pool testing accuracy and show that the lower the prevalence level the higher the gains in accuracy using multiplicity pool testing. The findings also indicate that performance can be improved using a batch size that is inversely proportional to the prevalence level. Furthermore, the results indicate that multiplicity pool testing not only improves the testing accuracy but also reduces the total cost of the testing process. Based on the findings, the manufacturer's test sensitivity has more significant impact on the accuracy of multiplicity pool testing compared to that of manufacturer's test specificity.

Computational methods of EEG signals analysis for Alzheimer's disease classification

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.

Clinical implementation of a log file-based machine and patient QA system for IMRT and VMAT treatment plans

PURPOSE

To determine the error detection sensitivity of a commercial log file-based system (LINACWatch®, LW) for integration into clinical routine and to compare it with a measurement device (OCTAVIUS 4D, Oct4D) for IMRT and VMAT delivery QA.

MATERIALS AND METHODS

76 VMAT/IMRT plans (H&N, prostate, rectum and breast) preliminarily classified according to their Modulation Complexity Score (MCS) calculated by LW, were considered. Receiver Operating Characteristic (ROC) Curves were used to establish gamma criteria for LW. 12 plans (3 for each site) were intentionally modified in order to introduce delivery errors regarding MLC, jaws, collimator, gantry and MU (for a total set of 168 incorrect plans) and irradiated on Oct4D; the corresponding log files were analysed by LW. Each incorrect plan was compared to the error-free plan using γ-index analysis for MLC, jaws and MU errors investigation and Root-Mean-Square (RMS) values for gantry and collimator errors investigation.

RESULTS

MCS ranges values were: 0.10-0.20 for H&N, 0.21-0.40 for prostate and rectum, 0.41-1.00 for breast. From ROC curves, the Gamma Passing Rate (GPR) thresholds were: 87%, 92%, 99% for H&N, prostate and rectum, and breast, respectively. The 1.5%/1.5 mm/local criteria were adopted for the γ-analysis. LW sensitivity in detecting the introduced errors was higher when compared to Oct4D: 48.5% vs 30.4% respectively.

CONCLUSIONS

LW can be considered useful complement to phantom-based delivery QA of IMRT/VMAT plans. The MCS tool is effective in detecting over or under modulated plans prior to pre-treatment QA. However, rigorous and routinely machine QCs are recommended.

"Nearly off-track lesions" or a short distance from the medial edge of the Hill-Sachs lesion to the medial edge of the glenoid track does not seem to be accurate in predicting recurrence after an arthroscopic Bankart repair in a military population: a case-control study

BACKGROUND

On-track lesions with a short distance from the medial edge of the Hill-Sachs lesion to the medial edge of the glenoid track (nearly off-track) may predispose recurrence after arthroscopic Bankart repair (ABR) in the general population. The aim of this study was to determine if a shorter distance between the medial edge of the Hill-Sachs lesion and the medial edge of the glenoid track could accurately predict recurrence after an ABR in a high-demand military population. It was hypothesized that a shorter distance would not accurately predict recurrence.

MATERIALS AND METHODS

A retrospective monocenter case-control study was performed at the Dutch Central Military Hospital. Patients with an on-track Hill-Sachs lesion who underwent a primary ABR between 2014 and 2019 with a minimal follow-up of 2 years and a preoperative magnetic resonance imaging (MRI) assessment received a questionnaire. The primary outcome was recurrence, defined as a complete dislocation or subluxation. Glenoid bone loss was assessed using a linear-based method on MRI. The distance from the medial edge of the Hill-Sachs lesion to the medial edge of the glenoid track was defined as the distance to dislocation (DTD). A receiver operating characteristic curve was created to determine the predictive value of the DTD for recurrence. Logistic regression was used to determine preoperative risk factors that predispose recurrence. Covariates were selected based on univariable analysis and included gender, body mass index, age at surgery and first dislocation, laterality, smoking habits, overhead shoulder activity during work, preoperative dislocations, sports type and level, bony or labral lesions on MRI, and DTD.

RESULTS

In total, 80 patients with an average follow-up of 4.8 ± 1.9 years completed the questionnaire and were included in the analyses. Seventeen patients (21%) experienced recurrence at the final follow-up. No difference in DTD was observed among patients who experienced recurrence (9 ± 4 mm) compared with patients who did not (9 ± 5 mm; P = .81). The receiver operating characteristic curve demonstrated no predictive power of DTD for recurrence (area under the curve = 0.49). Smoking at the time of surgery (odds ratio: 3.9; confidence interval: 1.2-12.7; P = .02) and overhead shoulder movement during work (odds ratio: 9.3; confidence interval: 1.1-78.0; P = .04) were associated with recurrence according to the logistic regression analysis.

CONCLUSION

A shorter DTD demonstrated no accuracy in predicting recurrence in a military population. Smoking at the time of surgery and overhead shoulder activity during work were associated with recurrence; however, these analyses were underpowered to draw valid conclusions.

The assessment of executive functions to test the integrity of the nigrostriatal network: A pilot study

Background

Parkinson’s disease (PD) is a chronic neurodegenerative disorder characterized by motor and non-motor symptoms. The latter mainly include affective, sleep, and cognitive deficits. Non-demented PD patients often demonstrate impairments in several executive domains following neuropsychological evaluation. The current pilot study aims at assessing the discriminatory power of the Frontal Assessment Battery-15 (FAB15) in differentiating (i) non-demented PD patients and healthy controls and (ii) PD patients with more and less pronounced motor symptoms.

Methods

Thirty-nine non-demented early-stage PD patients in the “on” dopamine state (26 females, mean age = 64.51 years, SD = 6.47, mean disease duration = 5.49 years, SD = 2.28) and 39 healthy participants (24 females, mean age = 62.60 years, SD = 5.51) were included in the study. All participants completed the FAB15. Motor symptoms of PD patients were quantified via the Unified Parkinson’s Disease Rating Scale-Part III (UPDRS-Part III) and Hoehn and Yahr staging scale (H&amp;Y).

Results

The FAB15 score, adjusted according to normative data for sex, age, and education, proved to be sufficiently able to discriminate PD patients from healthy controls (AUC = 0.69 [95% CI 0.60–0.75], SE = 0.06, p = 0.04, optimal cutoff = 11.29). Conversely, the battery lacked sufficient discriminative capability to differentiate PD patients based on the severity of motor symptoms.

Conclusion

The FAB15 may be a valid tool for distinguishing PD patients from healthy controls. However, it might be less sensitive in identifying clinical phenotypes characterized by visuospatial impairments resulting from posteroparietal and/or temporal dysfunctions. In line with previous evidence, the battery demonstrated to be not expendable in the clinical practice for monitoring the severity of PD-related motor symptoms.

Ireland DXA-FRAX may differ significantly and substantially to Web-FRAX

Appropriate use of FRAX reduces the number of people requiring DXA scans, while contemporaneously determining those most at risk. We compared the results of FRAX with and without inclusion of BMD. It suggests clinicians to carefully consider the importance of BMD inclusion in fracture risk estimation or interpretation in individual patients.

PURPOSE

FRAX is a widely accepted tool to estimate the 10-year risk of hip and major osteoporotic fracture in adults. Prior calibration studies suggest this works similarly with or without the inclusion of bone mineral density (BMD). The purpose of the study is to compare within-subject differences between FRAX estimations derived using DXA and Web software with and without the inclusion of BMD.

METHOD

A convenience cohort was used for this cross-sectional study, consisting of 1254 men and women aged between 40 and 90 years who had a DXA scan and complete validated data available for analysis. FRAX 10-year estimations for hip and major osteoporotic fracture were calculated using DXA software (DXA-FRAX) and the Web tool (Web-FRAX), with and without BMD. Agreements between estimates within each individual subject were examined using Bland-Altman plots. We performed exploratory analyses of the characteristics of those with very discordant results.

RESULTS

Overall median DXA-FRAX and Web-FRAX 10-year hip and major osteoporotic fracture risk estimations which include BMD are very similar: 2.9% vs. 2.8% and 11.0% vs. 11% respectively. However, both are significantly lower than those obtained without BMD: 4.9% and 14% respectively, P < 0.001. Within-subject differences between hip fracture estimates with and without BMD were < 3% in 57% of cases, between 3 and 6% in 19% of cases, and > 6% in 24% of cases, while for major osteoporotic fractures such differences are < 10% in 82% of cases, between 10 and 20% in 15% of cases, and > 20% in 3% of cases.

CONCLUSIONS

Although there is excellent agreement between the Web-FRAX and DXA-FRAX tools when BMD is incorporated, sometimes there are very large differences for individuals between results obtained with and without BMD. Clinicians should carefully consider the importance of BMD inclusion in FRAX estimations when assessing individual patients.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

Exploring the ability of strength and dexterity tests to detect hand function impairment in individuals with Parkinson's disease

BACKGROUND

Parkinson's disease (PD) can affect hand function since the beginning of the motor symptoms.

OBJECTIVE

To compare the ability of different hand function tests to: 1) distinguish individuals with PD from healthy controls; 2) differentiate stages of the disease; and 3) indicate changes over time due to disease progression.

METHODS

Twenty-four individuals with PD (Hoehn and Yahr: I-III) and 24 age- and sex-matched controls performed the Jebsen-Taylor Hand Function Test (JTHFT), the Nine-Hole Peg Test (9HPT), and the maximum grip and the maximum pinch strength tests using their right and left hands. Eight individuals with PD (six females and two males) were reassessed after 18 months. The ROC analyses and Mann-Whitney U tests (for disease progression) using the average performance of the hands were done.

RESULTS

Individuals with PD presented worse test performances than controls, except for the writing subtest of the JTHFT and the grip strength test. The JTHFT without the writing subtest (JTHFT_noW) was the most accurate to discriminate PD from controls (AUC = 0.899; sensitivity 75% and specificity 95.8%). The 9HPT and the simulated feeding and moving large, light objects JTHFT subtests were sensitive to distinguish stages, while the 9HPT, the moving small, common objects JTHFT subtest, and the grip strength were sensitive to changes with disease progression.

CONCLUSION

The JTHFT_noW was highly discriminative of the hand function impairments in PD. TwoJTHFT subtests were the most sensitives to distinguish PD stages (i.e. simulated feeding JTHFT subtest) and disease progression (i.e. moving small, common objects JTHFT subtest).

Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC

INTRODUCTION

We aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed tomography (CT) images obtained from patients with NSCLC treated with nivolumab or chemotherapy.

METHODS

The radiomic signature was developed in patients with NSCLC treated with nivolumab in CheckMate-017, -026, and -063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semiautomatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomic signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies.

RESULTS

A baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in the mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy.

CONCLUSIONS

The radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.

Alternative metrics for characterizing longer-term clinical outcomes in difficult-to-treat depression: I. Association with change in quality of life

BACKGROUND

In difficult-to-treat depression (DTD) the outcome metrics historically used to evaluate treatment effectiveness may be suboptimal. Metrics based on remission status and on single end-point (SEP) assessment may be problematic given infrequent symptom remission, temporal instability, and poor durability of benefit in DTD.

METHODS

Self-report and clinician assessment of depression symptom severity were regularly obtained over a 2-year period in a chronic and highly treatment-resistant registry sample (N = 406) receiving treatment as usual, with or without vagus nerve stimulation. Twenty alternative metrics for characterizing symptomatic improvement were evaluated, contrasting SEP metrics with integrative (INT) metrics that aggregated information over time. Metrics were compared in effect size and discriminating power when contrasting groups that did (N = 153) and did not (N = 253) achieve a threshold level of improvement in end-point quality-of-life (QoL) scores, and in their association with continuous QoL scores.

RESULTS

Metrics based on remission status had smaller effect size and poorer discrimination of the binary QoL outcome and weaker associations with the continuous end-point QoL scores than metrics based on partial response or response. The metrics with the strongest performance characteristics were the SEP measure of percentage change in symptom severity and the INT metric quantifying the proportion of the observation period in partial response or better. Both metrics contributed independent variance when predicting end-point QoL scores.

CONCLUSIONS

Revision is needed in the metrics used to quantify symptomatic change in DTD with consideration of INT time-based measures as primary or secondary outcomes. Metrics based on remission status may not be useful.

Optimal MoCA cutoffs for detecting biologically-defined patients with MCI and early dementia

OBJECTIVE

In this phase II psychometric study on the Montreal cognitive assessment (MoCA), we tested the clinicometric properties of Italian norms for patients with mild cognitive impairment (PwMCI) and early dementia (PwD) and provided optimal cutoffs for diagnostic purposes.

METHODS

Retrospective data collection was performed for consecutive patients with clinically and biologically defined MCI and early dementia. Forty-five patients (24 PwMCI and 21 PwD) and 25 healthy controls were included. Raw MoCA scores were adjusted according to the conventional 1-point correction (Nasreddine) and Italian norms (Conti, Santangelo, Aiello). The diagnostic properties of the original cutoff (< 26) and normative cutoffs, namely, the upper limits (uLs) of equivalent scores (ES) 1, 2, and 3, were evaluated. ROC curve analysis was performed to obtain optimal cutoffs.

RESULTS

The original cutoff demonstrated high sensitivity (0.93 [95% CI 0.84-0.98]) but low specificity (0.44 [0.32-0.56]) in discriminating between patients and controls. Nominal normative cutoffs (ES0 uLs) showed excellent specificity (SP range = 0.96-1.00 [0.88-1.00]) but poor sensitivity (SE range = 0.09-0.24 [0.04-0.36]). The optimal cutoff for Nasreddine's method was 23.50 (SE = 0.82 [0.71-0.90]; SP = 0.72 [0.60-0.82]). Optimal cutoffs were 20.97, 22.85, and 22.29 (SE range = 0.69-0.73 [0.57-0.83], SP range = 0.88-0.92 [0.77-0.97]) for Conti's, Santangelo's, and Aiello's methods, respectively.

CONCLUSION

Using the 1-point correction, combined with a cutoff of 23.50, might be useful in ambulatory settings with a large turnout. Our optimal cutoffs can offset the poor sensitivity of Italian cutoffs.

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists

OBJECTIVES

Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently.

METHODS

We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance.

RESULTS

DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001).

CONCLUSION

DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM.

KEY POINTS

• Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.

Novel gene signature reveals prognostic model in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a type of hematological malignancy and has a poor prognosis. In our study, we aimed to construct a prognostic model of ALL by identifying important genes closely related to ALL prognosis. We obtained transcriptome data (RNA-seq) of ALL samples from the GDC TARGET database and identified differentially expressed genes (DEGs) using the “DESeq” package of R software. We used univariate and multivariate cox regression analyses to screen out the prognostic genes of ALL. In our results, the risk score can be used as an independent prognostic factor to predict the prognosis of ALL patients [hazard ratio (HR) = 2.782, 95% CI = 1.903–4.068, p < 0.001]. Risk score in clinical parameters has high diagnostic sensitivity and specificity for predicting overall survival of ALL patients, and the area under curve (AUC) is 0.864 in the receiver operating characteristic (ROC) analysis results. Our study evaluated a potential prognostic signature with six genes and constructed a risk model significantly related to the prognosis of ALL patients. The results of this study can help clinicians to adjust the treatment plan and distinguish patients with good and poor prognosis for targeted treatment.

Brief cognitive tests as a decision-making tool in primary care. A population and validation study

INTRODUCTION AND OBJECTIVES

Brief cognitive tests (BCT) are used in primary care (PC) for the detection of cognitive impairment (CI). Still, there are little data on their diagnostic utility (DU) in a community setting. This work evaluates the DU at the population level of Fototest, T@M, AD8 questionnaire and MMSE. It provides new cut-off points (CoP) validated in a CI early detection program.

MATERIAL AND METHODS

In the population and validation samples, the evaluation was carried out in two phases, a first of screening and administration of BCT and a second of clinical diagnosis, blinded to the results of the BCT, applying the current NIA-AA criteria. The DU of BCT in the population sample was evaluated with the area under the ROC curve (aROC). Youden index and the CoP with the best specificity that ensured a sensitivity of 80% were used to decide on the most appropriate CoP. The sensitivity, specificity, and predictive values for these CoP were calculated in the validation sample.

RESULTS

260 participants (23.1% with CI) from the population sample and 177 (42.4% with CI) from the validation sample were included. The Fototest has the best UD at the population level (aROC 0.851), which improves with the combination of Fototest and AD8 (aROC 0.875). The proposed CoP are AD8 ≥ 1, Fototest ≤ 35, T@M ≤ 40, and MMSE ≤ 26.

CONCLUSION

BCT are helpful in detecting CI in PC. This work supports the use of more demanding PoC.

Psychometrics, diagnostics and usability of Italian tools assessing behavioural and functional outcomes in neurological, geriatric and psychiatric disorders: a systematic review

Background

Psychometric instruments assessing behavioural and functional outcomes (BFIs) in neurological, geriatric and psychiatric populations are relevant towards diagnostics, prognosis and intervention. However, BFIs often happen not to meet methodological-statistical standards, thus lowering their level of recommendation in clinical practice and research. This work thus aimed at (1) providing an up-to-date compendium on psychometrics, diagnostics and usability of available Italian BFIs and (2) delivering evidence-based information on their level of recommendation.

Methods

This review was pre-registered (PROSPERO ID: CRD42021295430) and performed according to PRISMA guidelines. Several psychometric, diagnostic and usability measures were addressed as outcomes. Quality assessment was performed via an ad hoc checklist, the Behavioural and Functional Instrument Quality Assessment.

Results

Out of an initial N = 830 reports, 108 studies were included (N = 102 BFIs). Target constructs included behavioural/psychiatric symptoms, quality of life and physical functioning. BFIs were either self- or caregiver-/clinician-report. Studies in clinical conditions (including neurological, psychiatric and geriatric ones) were the most represented. Validity was investigated for 85 and reliability for 80 BFIs, respectively. Criterion and factorial validity testing were infrequent, whereas content and ecological validity and parallel forms were almost never addressed. Item response theory analyses were seldom carried out. Diagnostics and norms lacked for about one-third of BFIs. Information on administration time, ease of use and ceiling/floor effects were often unreported.

Discussion

Several available BFIs for the Italian population do not meet adequate statistical-methodological standards, this prompting a greater care from researchers involved in their development.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10072-022-06300-8.

The Italian telephone-based Verbal Fluency Battery (t-VFB): standardization and preliminary clinical usability evidence

Background

This study aimed at standardizing and providing preliminary evidence on the clinical usability of the Italian telephone-based Verbal Fluency Battery (t-VFB), which includes phonemic (t-PVF), semantic (t-SVF) and alternate (t-AVF) verbal fluency tasks.

Methods

Three-hundred and thirty-five Italian healthy participants (HPs; 140 males; age range = 18-96 years; education range = 4-23 years) and 27 individuals with neurodegenerative or cerebrovascular diseases were administered the t-VFB. Switch number and cluster size were computed via latent semantic analyses. HPs underwent the telephone-based Mental State Examination (MMSE) and Backward Digit Span (BDS). Construct validity, factorial structure, internal consistency, test-retest and inter-rater reliability and equivalence with the in-person Verbal Fluency tasks were assessed. Norms were derived via Equivalent Scores. Diagnostic accuracy against clinical populations was assessed.

Results

The majority of t-VFB scores correlated among each other and with the BDS, but not with the MMSE. Switch number correlated with t-PVF, t-SVF, t-AVF scores, whilst cluster size with the t-SVF and t-AVF scores only. The t-VFB was underpinned by a mono-component structure and was internally consistent (Cronbach's α = 0.91). Test-retest (ICC = 0.69-0.95) and inter-rater reliability (ICC = 0.98-1) were optimal. Each t-VFB test was statistically equivalent to its in-person version (equivalence bounds yielding a p < 0.05). Education predicted all t-VFB scores, whereas age t-SVF and t-AVF scores and sex only some t-SVF scores. Diagnostic accuracy against clinical samples was optimal (AUC = 0.81-0.86).

Discussion

The t-VFB is a valid, reliable and normed telephone-based assessment tool for language and executive functioning, equivalent to the in-person version; results show promising evidence of its diagnostic accuracy in neurological populations.

Periprocedural imaging compared to endometrial biopsy: is biopsy required prior to uterine artery embolisation?

AIM

To compare the detection rate of magnetic resonance imaging (MRI) and ultrasound relative to endometrial biopsy for endometrial abnormalities in both pre- and post-menopausal women.

MATERIALS AND METHODS

The present study was an institutional review board-approved, single-institution retrospective analysis of patients who underwent pelvic MRI within 1 year of diagnostic-quality biopsies from 2008-2018 (n=668). There were 303 patients who received uterine artery embolisation (UAE) and 478 patients who received pelvic ultrasound within the study period. Medical records were evaluated for radiological-histopathological correlation, demographics, laboratory studies, and clinical follow-up.

RESULTS

In this cohort of 668 patients, there were 37 biopsies positive for malignancy; women with malignancy were older (58 versus 47 years, p<0.0001) and more likely to be post-menopausal (66% versus 12%, p<0.0001). There were 303 patients who underwent UAE and underwent a diagnostic-quality endometrial biopsy during the pre-procedural evaluation, none of whom were post-menopausal and had a mean age of 45 years. In women with abnormal uterine bleeding (AUB) or post-menopausal bleeding (PMB), the sensitivity of MRI for detecting endometrial cancer was 96.2%, with a negative predictive value (NPV) of 99.8%, compared to 68% and 97% for ultrasound, respectively. The receiver operating characteristic (ROC) curve of pre-biopsy MRI in identifying pre-malignant and malignant endometrial pathology demonstrated an AUC of 0.8920 (p<0.0001).

CONCLUSION

In women with AUB or PMB, MRI has a 99.8% NPV in ruling out endometrial cancer. Further consideration should be made towards optimising pre-procedural evaluation for UAE.

Prediction of Fluid Responsiveness by the Effect of the Lung Recruitment Maneuver on the Perfusion Index in Mechanically Ventilated Patients During Surgery

Introduction

Excessive or inadequate fluid administration during perioperative period affects outcomes. Adjustment of volume expansion (VE) by performing fluid responsiveness (FR) test plays an important role in optimizing fluid infusion. Since changes in stroke volume (SV) during lung recruitment maneuver (LRM) can predict FR, and peripheral perfusion index (PI) is related to SV; therefore, we hypothesized that the changes in PI during LRM (ΔPILRM) could predict FR during perioperative period.

Methods

Patients who were scheduled for elective non-laparoscopic surgery under general anesthesia with a mechanical ventilator and who required VE (250 mL of crystalloid solution infusion over 10 min) were included. Before VE, LRM was performed by a continuous positive airway pressure of 30 cm H2O for 30 sec; hemodynamic variables with their changes (PI, obtained by pulse oximetry; and ΔPILRM, calculated by using [(PI before LRM—PI after LRM)/PI before LRM]*100) were obtained before and after LRM. After SV (measured by esophageal doppler) and PI had returned to the baseline values, VE was infused, and the values of these variables were recorded again, before and after VE. Fluid responders (Fluid-Res) were defined by an increase in SV ≥10% after VE. Receiver operating characteristic curves of the baseline values and ΔPILRM were constructed and reported as areas under the curve (AUC) with 95% confidence intervals, to predict FR.

Results

Of 32 mechanically ventilated adult patients included, 13 (41%) were in the Fluid-Res group. Before VE and LRM, there were no differences in the mean arterial pressure (MAP), heart rate, SV, and PI between patients in the Fluid-Res and fluid non-responders (Fluid-NonRes) groups. After LRM, SV, MAP, and, PI decreased in both groups, ΔPILRM was greater in the Fluid-Res group than in Fluid-NonRes group (55.2 ± 17.8% vs. 35.3 ± 17.3%, p &lt; 0.001, respectively). After VE, only SV and cardiac index increased in the Fluid-Res group. ΔPILRM had the highest AUC [0.81 (0.66–0.97)] to predict FR with a cut-off value of 40% (sensitivity 92.3%, specificity 73.7%).

Conclusions

ΔPILRM can be applied to predict FR in mechanical ventilated patients during the perioperative period.

Telephone-based Frontal Assessment Battery (t-FAB): standardization for the Italian population and clinical usability in neurological diseases

Background

Despite the relevance of telephone-based cognitive screening tests in clinical practice and research, no specific test assessing executive functioning is available. The present study aimed at standardizing and providing evidence of clinical usability for the Italian telephone-based Frontal Assessment Battery (t-FAB).

Methods

The t-FAB (ranging 0–12), comprising two subtests, has two versions: one requiring motor responses (t-FAB-M) and the other verbal responses (t-FAB-V). Three hundred and forty-six Italian healthy adults (HPs; 143 males; age range = 18–96 years; education range = 4–23 years) and 40 participants with neurological diseases were recruited. To HPs, the t-FAB was administered along with a set of telephone-based tests: MMSE, verbal fluency (VF), backward digit span (BDS). The in-person version of the FAB was administered to both HPs and clinical groups. Factorial structure, construct validity, inter-rater and test–retest reliability, t-FAB-M vs. t-FAB-V equivalence and diagnostic accuracy were assessed. Norms were derived via Equivalent Scores.

Results

In HPs, t-FAB measures yielded high inter-rater/test–retest reliability (ICC = .78–.94), were internally related (p ≤ .005) and underpinned by a single component, converging with the telephone-based MMSE, VF, BDS (p ≤ .0013). The two t-FAB versions were statistically equivalent in clinical groups (ps of both equivalence bounds < .001). Education predicted all t-FAB scores (p < .001), whereas age only the t-FAB-M score (p ≤ .004). t-FAB scores converge with the in-person FAB in HPs and clinical groups (r_s = .43–.78). Both t-FAB versions were accurate in discriminating HPs from the clinical cohort (AUC = .73-.76).

Discussion

The t-FAB is a normed, valid, reliable and clinically usable telephone-based cognitive screening test to adopt in both clinical and research practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40520-022-02155-3.

Longitudinal Bowel Behavior Assessed by Bowel Ultrasound to Predict Early Response to Anti-TNF Therapy in Patients With Crohn's Disease: A Pilot Study

BACKGROUND

Early changes in bowel behavior during anti-tumor necrosis factor (anti-TNF) induction therapy in Crohn's disease (CD) are relatively unknown. We determined (1) the onset of changes in bowel behavior in CD patients receiving anti-TNF therapy by ultrasound and (2) the feasibility of shear wave elastography (SWE) in predicting early response to anti-TNF therapy.

METHODS

Consecutive ileal or ileocolonic CD patients programmed to initiate anti-TNF therapy were enrolled. Bowel ultrasound was performed at baseline and at weeks 2, 6, and 14. Changes in bowel wall thickness, Doppler signals of the bowel wall (Limberg score), and SWE values were compared using a linear mixed model. Early response to anti-TNF therapy was based on a composite strategy of clinical and colonoscopy assessment at week 14.

RESULTS

Of the 30 patients enrolled in this study, 20 patients achieved a response to anti-TNF therapy at week 14. The bowel wall thickness and SWE value of the response group showed a significant downward trend compared with the nonresponse group (P = .003 and P = .011, respectively). Bowel wall thickness, the Limberg score, and SWE values were significantly reduced as early as week 2 compared with baseline (P < .001, P < .001, and P = .003, respectively) in the response group. Baseline SWE values (21.3 ± 8.7 kPa vs 15.3 ± 4.7 kPa; P = .022) and bowel wall thickness (8.5 ± 2.3 mm vs 6.9 ± 1.5 mm; P = .027) in the nonresponse group were significantly higher than in the response group.

CONCLUSIONS

This pilot study suggested that changes in bowel ultrasound behavior could be assessed as early as week 2 after starting anti-TNF therapy. Bowel ultrasound together with elasticity imaging could predict early response to anti-TNF therapy.

The Tibial Tubercle-Trochlear Groove Distance/Trochlear Dysplasia Index Quotient Is the Most Accurate Indicator for Determining Patellofemoral Instability Risk

PURPOSE

The primary aim of our study was to evaluate diagnostic accuracy of the tibial tubercle-trochlear groove (TT-TG) distance relative to associated quotients produced from trochlear width (TT-TG distance/TW) and trochlear dysplasia index (TT-TG distance/TDI) for detecting patellofemoral instability. Secondary aims included identifying thresholds for risk and comparing differences between cases and controls.

METHODS

Consecutive sampling of electronic medical records produced 48 (21 males, 27 females) patellofemoral instability cases (19 ± 7 years old) and 79 (61 males, 18 females) controls (23 ± 4 years old) who had a history of isolated meniscal lesion, as evaluated by magnetic resonance imaging. Standardized methods were employed with measurements executed in a blinded and randomized manner. A receiver operating characteristic curve assessed accuracy by area under the curve (AUC). The index of union (IU) was employed to identify a threshold for risk. Two-sample t-tests examined group differences. P < .05 denoted statistical significance.

RESULTS

The AUC values were .69 (.60, .79) for TT-TG distance, .81 (.73, .88) for TT-TG distance/TW, and .85 (.78, .91) for TT-TG distance/TDI. Thresholds were 14.7 mm for TT-TG distance, .36 for TT-TG distance/TW, and 1.88 for TT-TG distance/TDI. Cases demonstrated statistically significant (P < .001) greater values for each measure compared with controls: TT-TG distance (15.8 ± 4.2 mm vs 12.9 ± 3.6 mm, [1.4, 4.3]); TT-TG distance/TW (.51 ± .24 vs .31 ± .09, [.13, .27]); TT-TG distance/TDI (3.07 ± 1.55 vs 1.7 ± .7, [.9, 1.84]).

CONCLUSION

The TT-TG distance, TT-TG distance/TW, and TT-TG distance/TDI measures were 69%, 81%, and 85%, respectively, accurate for determining patellofemoral instability risk. Thresholds for risk were 14.7 mm for TT-TG distance, .36 for TT-TG distance/TW, and 1.88 for TT-TG distance/TDI. The thresholds reported in this study may help in advancing clinical decision-making.

LEVEL OF EVIDENCE

Level III, diagnostic retrospective comparative observatory trial.

Preload Dependence Fails to Predict Hemodynamic Instability During a Fluid Removal Challenge in Children

OBJECTIVES

Fluid overload increases morbidity and mortality in PICU patients. Active fluid removal improves the prognosis but may worsen organ dysfunction. Preload dependence in adults does predict hemodynamic instability induced by a fluid removal challenge (FRC). We sought to investigate the diagnostic accuracy of dynamic and static markers of preload in predicting hemodynamic instability and reduction of stroke volume during an FRC in children. We followed the Standards for Reporting of Diagnostic Accuracy statement to design conduct and report this study.

DESIGN

Prospective noninterventional cohort study.

SETTINGS

From June 2017 to April 2019 in a pediatric cardiac ICU in a tertiary hospital.

PATIENTS

Patients 8 years old or younger, with symptoms of fluid overload after cardiac surgery, were studied.

INTERVENTIONS

We confirmed preload dependence by echocardiography before and during a calibrated abdominal compression test. We then performed a challenge to remove 10-mL/kg fluid in less than 120 minutes with an infusion of diuretics. Hemodynamic instability was defined as a decrease of 10% of mean arterial pressure.

MEASUREMENT AND MAIN RESULTS

We compared patients showing hemodynamic instability with patients remaining stable, and we built receiver operative characteristic (ROC) curves. Among 58 patients studied, 10 showed hemodynamic instability. The area under the ROC curve was 0.55 for the preload dependence test (95% CI, 0.34-0.75). Using a threshold of 10% increase in stroke volume index (SVi) during calibrated abdominal compression, the specificity was 0.30 (95% CI, 0.00-0.60) and the sensitivity was 0.77 (95% CI, 0.65-0.88). Mean arterial pressure variation and SVi variation were not correlated during fluid removal; r = 0.19; 95% CI -0.07 to 0.43; p = 0.139.

CONCLUSIONS

Preload dependence is not accurate to predict hemodynamic instability during an FRC. Our data do not support a reduction in intravascular volume being mainly responsible for the reduction in arterial pressure during an FRC in children.

Artificial intelligence to identify genetic alterations in conventional histopathology

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold-standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53 and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. This article is protected by copyright. All rights reserved.

Application of the skew exponential power distribution to ROC curves

The bi-Normal ROC model and corresponding metrics are commonly used in medical studies to evaluate the discriminatory ability of a biomarker. However, in practice, many clinical biomarkers tend to have skewed or other non-Normal distributions. And while the bi-Normal ROC model's AUC tends to be unbiased in this setting, providing a reasonable measure of global performance, the corresponding decision thresholds tend to be biased. To correct this bias, we propose using an ROC model based on the skew exponential power (SEP) distribution, whose additional parameters can accommodate skewed, heavy tailed, or other non-Normal distributions. Additionally, the SEP distribution can be used to evaluate whether the bi-Normal model would be appropriate. The performance of these ROC models and the non-parametric approach are evaluated via a simulation study and applied to a real data set involving infections from Klebsiella pneumoniae. The SEP based ROC-model provides some efficiency gains with respect to estimation of the AUC and provides cut-points with improved classification rates. As such, in the presence non-Normal data, we suggest using the proposed SEP ROC model.

Prospective predictive performance comparison between clinical gestalt and validated COVID-19 mortality scores

Most COVID-19 mortality scores were developed at the beginning of the pandemic and clinicians now have more experience and evidence-based interventions. Therefore, we hypothesized that the predictive performance of COVID-19 mortality scores is now lower than originally reported. We aimed to prospectively evaluate the current predictive accuracy of six COVID-19 scores and compared it with the accuracy of clinical gestalt predictions. 200 patients with COVID-19 were enrolled in a tertiary hospital in Mexico City between September and December 2020. The area under the curve (AUC) of the LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV, and NEWS2 scores and the AUC of clinical gestalt predictions of death (as a percentage) were determined. In total, 166 patients (106 men and 60 women aged 56±9 years) with confirmed COVID-19 were included in the analysis. The AUC of all scores was significantly lower than originally reported: LOW-HARM 0.76 (95% CI 0.69 to 0.84) vs 0.96 (95% CI 0.94 to 0.98), qSOFA 0.61 (95% CI 0.53 to 0.69) vs 0.74 (95% CI 0.65 to 0.81), MSL-COVID-19 0.64 (95% CI 0.55 to 0.73) vs 0.72 (95% CI 0.69 to 0.75), NUTRI-CoV 0.60 (95% CI 0.51 to 0.69) vs 0.79 (95% CI 0.76 to 0.82), NEWS2 0.65 (95% CI 0.56 to 0.75) vs 0.84 (95% CI 0.79 to 0.90), and neutrophil to lymphocyte ratio 0.65 (95% CI 0.57 to 0.73) vs 0.74 (95% CI 0.62 to 0.85). Clinical gestalt predictions were non-inferior to mortality scores, with an AUC of 0.68 (95% CI 0.59 to 0.77). Adjusting scores with locally derived likelihood ratios did not improve their performance; however, some scores outperformed clinical gestalt predictions when clinicians' confidence of prediction was <80%. Despite its subjective nature, clinical gestalt has relevant advantages in predicting COVID-19 clinical outcomes. The need and performance of most COVID-19 mortality scores need to be evaluated regularly.

Receiver operating characteristic curve: overview and practical use for clinicians

Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametric/nonparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.

Accuracy of radiographer preliminary clinical evaluation of skeletal trauma radiographs, in clinical practice at a district general hospital

INTRODUCTION

Error in interpretation of trauma radiographs by referrers is a problem which has detrimental effects on the patient and causes unnecessary repeat attendances. Radiographers can reduce errors by offering their opinion at the time of imaging. The Society and College of Radiographers have a longstanding recommendation that Red Dot (RD) schemes should be replaced by Preliminary Clinical Evaluation (PCE). The purpose of the study was to evaluate radiographer interpretation of skeletal trauma radiographs in clinical practice, determine if there was any difference in ability to interpret appendicular and axial studies, and evaluate appropriateness of PCE implementation.

METHODS

A convenience sample of 23 self-selecting radiographers provided RD and PCE on 762 examinations. Each case was compared against the verified report and assigned a true negative/positive or false negative/positive value. Accuracy, sensitivity and specificity were calculated and performance measures between RD versus PCE, and appendicular versus axial were compared using Two-sample Z-Tests. Error analysis was performed and inter-observer consistency determined.

RESULTS

Overall RD and PCE accuracy, sensitivity and specificity for the study were 90%, 72% and 97% (RD), and 92%, 80% and 97% (PCE) respectively. Significant difference was demonstrated for sensitivity with PCE more sensitive than RD (p-value 0.03) and appendicular more sensitive than axial (RD p-value <0.02, PCE p-value <0.0001). Most errors were false negatives. Inter-observer consistency was evaluated by review of 128 cases and no difference between reviewers was established.

CONCLUSION

Radiographers without specific training were able to provide RD and PCE to a high standard. Radiographers interpreted positive findings more accurately using PCE than RD, and positive findings on appendicular cases were interpreted more accurately than those on axial cases.

IMPLICATIONS FOR PRACTICE

This study supports local PCE implementation, contributes to the wider evidence base to justify transition towards PCE and identifies the necessity for local axial image interpretation training.

Evaluation of reader performance during interpretation of breast cancer screening: the Recall and detection Of breast Cancer in Screening (ROCS) trial study design

The magnitude of the tradeoff between recall rate (RR) and cancer detection rate (CDR) in breast-cancer screening is not clear, and it is expected to depend on target population and screening program characteristics. Multi-reader multi-case research studies, which may be used to estimate this tradeoff, rely on enriched datasets with artificially high prevalence rates, which may bias the results. Furthermore, readers participating in research studies are subject to "laboratory" effects, which can alter their performance relative to actual practice. The Recall and detection Of breast Cancer in Screening (ROCS) trial uses a novel data acquisition system that minimizes these limitations while obtaining an estimate of the RR-CDR curve during actual practice in the Dutch National Breast Cancer Screening Program. ROCS involves collection of at least 40,000 probability-of-malignancy ratings from at least 20 radiologists during interpretation of approximately 2,000 digital mammography screening cases each. With the use of custom-built software on a tablet, and a webcam, this data was obtained in the usual reading environment with minimal workflow disruption and without electronic access to the review workstation software. Comparison of the results to short- and medium-term follow-up allows for estimation of the RR-CDR and receiver operating characteristics curves, respectively. The anticipated result of the study is that performance-based evidence from practice will be available to determine the optimal operating point for breast-cancer screening. In addition, this data will be useful as a benchmark when evaluating the impact of potential new screening technologies, such as digital breast tomosynthesis or artificial intelligence. KEY POINTS: • The ROCS trial aims to estimate the recall rate-cancer detection rate curve during actual screening practice in the Dutch National Breast Cancer Screening Program. • The study design is aimed at avoiding the influence of the "laboratory effect" in usual observer performance studies. • The use of a tablet and a webcam allows for the acquisition of probability of malignancy ratings without access to the review workstation software.

Confidence Interval Estimation of the Youden index and corresponding cut-point for a combination of biomarkers under normality

In prognostic/diagnostic medical research, it is often the goal to identify a biomarker that differentiates between patients with and without a condition, or patients that will have good or poor response to a given treatment. The statistical literature is abundant with methods for evaluating single biomarkers for these purposes. However, in practice, a single biomarker rarely captures all aspects of a disease process; therefore, it is often the case that using a combination of biomarkers will improve discriminatory ability. A variety of methods have been developed for combining biomarkers based on the maximization of some global measure or cost-function. These methods usually create a score based on a linear combination of the biomarkers, upon which the standard single biomarker methodologies (such as the Youden's index) are applied. However, these single biomarker methodologies do not account for the multivariable nature of the combined biomarker score. In this article we present generalized inference and bootstrap approaches to estimating confidence intervals for the Youden's index and corresponding cut-point for a combined biomarker. These methods account for inherent dependencies and provide accurate and efficient estimates. A simulation study and real-world example utilize data from a Duchene Muscular Dystrophy study are also presented.

Monocyte distribution width (MDW) as a new tool for the prediction of sepsis in critically ill patients: a preliminary investigation in an intensive care unit

Background

Monocyte Distribution Width (MDW), a simple proxy marker of innate monocyte activation, can be used for the early recognition of sepsis along with Procalcitonin. This study explored the added value of MDW as an early predictor of ensuing sepsis in patients hospitalised in an Intensive Care Unit.

Methods

We performed an observational prospective monocentric study to estimate the analytical performance of MDW in detecting ensuing sepsis in a sample of consecutive patients assisted in an Intensive Care Unit for > 48 h for any reason. Demographic and clinical characteristics, past medical history and other laboratory measurements were included as potential predictors of confirmed sepsis in multivariate logistic regression.

Results

A total of 211 patients were observed, 129 of whom were included in the final sample due to the suspect of ensuing sepsis; of these, 74 (57%) had a confirmed diagnosis of sepsis, which was best predicted with the combination of MDW > 23.0 and PCT > 0.5 ng/mL (Positive Predictive Value, PPV: 92.6, 95% CI: 82.1–97.9). The best MDW cut-off to rule out sepsis was ≤20.0 (Negative Predictive Value, NPV: 86.4, 95% CI: 65.1–97.1). Multivariate analyses using both MDW and PCT found a significant association for MDW > 23 only (OR:17.64, 95% CI: 5.53–67.91).

Conclusion

We found that values of MDW > 23 were associated with a high PPV for sepsis, whereas values of MDW ≤ 20 were associated with a high NPV. Our findings suggest that MDW may help clinicians to monitor ICU patients at risk of sepsis, with minimal additional efforts over standard of care.

Validation and threshold identification of a prescription drug monitoring program clinical opioid risk metric with the WHO alcohol, smoking, and substance involvement screening test

BACKGROUND

Prescription drug monitoring programs (PDMPs) are critical for pharmacists to identify risky opioid medication use. We performed an independent evaluation of the PDMP-based Narcotic Score (NS) metric.

METHODS

This study was a one-time, cross-sectional health assessment within 19 pharmacies from a national chain among adults picking-up opioid medications. The NS metric is a 3-digit composite indicator. The WHO Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) was the gold-standard to which the NS metric was compared. Machine learning determined optimal risk thresholds; Receiver Operating Characteristic curves and Spearman (P) and Kappa (K) coefficients analyzed concurrent validity. Regression analyses evaluated participant characteristics associated with misclassification.

RESULTS

The NS metric showed fair concurrent validity (area under the curve≥0.70; K=0.35; P = 0.37, p < 0.001). The ASSIST and NS metric categorized 37% of participants as low-risk (i.e., not needing screening/intervention) and 32.3% as moderate/high-risk (i.e., needing screening/intervention). Further, 17.2% were categorized as low ASSIST risk but moderate/high NS metric risk, termed false positives. These reported disability (OR=3.12), poor general health (OR=0.66), and/or greater pain severity/interference (OR=1.12/1.09; all p < 0.05; i.e., needing unmanaged-pain screening/intervention). A total of 13.4% were categorized as moderate/high ASSIST risk but low NS metric risk, termed false negatives. These reported greater overdose history (OR=1.24) and/or substance use (OR=1.81-12.66; all p < 0.05).

CONCLUSIONS

The NS metric could serve as a useful initial universal prescription opioid-risk screener given its: 1) low-burden (i.e., no direct assessment); 2) high accuracy (86.5%) of actionable data identifying low-risk patients and those needing opioid use/unmanaged pain screening/intervention; and 3) broad availability.

Utility of Osteoporosis Self-Assessment Tool as a Screening Tool for Osteoporosis in Irish Men and Women: Results of the DXA-HIP Project

Many algorithms have been developed and publicised over the past 2 decades for identifying those most likely to have osteoporosis or low BMD, or at increased risk of fragility fracture. The Osteoporosis Self-assessment Tool index (OSTi) is one of the oldest, simplest, and widely used for identifying men and women with low BMD or osteoporosis. OSTi has been validated in many cohorts worldwide but large studies with robust analyses evaluating this or other algorithms in adult populations residing in the Republic of Ireland are lacking, where waiting times for public DXA facilities are long. In this study we evaluated the validity of OSTi in men and women drawn from a sampling frame of more than 36,000 patients scanned at one of 3 centres in the West of Ireland. 18,670 men and women aged 40 years and older had a baseline scan of the lumbar spine femoral neck and total hip available for analysis. 15,964 (86%) were female, 5,343 (29%) had no major clinical risk factors other than age, while 5,093 (27%) had a prior fracture. Approximately 2/3 had a T-score ≤-1.0 at one or more skeletal sites and 1/3 had a T-score ≤-1.0 at all 3 skeletal sites, while 1 in 5 had a DXA T-score ≤-2.5 at one or more skeletal sites and 5% had a T-score ≤-2.5 at all 3 sites. OSTi generally performed well in our population with area under the curve (AUC) values ranging from 0.581 to 0.881 in men and 0.701 to 0.911 in women. The performance of OSTi appeared robust across multiple sub-group analyses. AUC values were greater for women, proximal femur sites, those without prior fractures and those not taking osteoporosis medication. Optimal OSTi cut-points were '2' for men and '0' for women in our study population. OSTi is a simple and effective tool to aid identification of Irish men and women with low BMD or osteoporosis. Use of OSTi could improve the effectiveness of DXA screening programmes for older adults in Ireland.

Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers

Background

It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports.

Methods

We performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance.

Results

Without order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists.

Conclusions

BERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

Circular RNA OMA1 regulates the progression of breast cancer via modulation of the miR‑1276/SIRT4 axis

Mounting evidence has indicated that circular RNAs (circRNAs) serve essential roles in the tumorigenesis and development of various types of cancer. However, the biological functions and the underlying mechanisms of circRNAs in breast cancer (BC) remain largely elusive. In the present study, the expression pattern of circRNAs in three pairs of BC tissues and adjacent normal tissues was determined using a circRNA microarray. The expression and prognostic value of circOMA1 were evaluated by reverse transcription‑quantitative PCR in 64 pairs of BC tissues and adjacent normal tissues. Survival curves were generated by the Kaplan‑Meier method, and statistical significance was estimated using the log‑rank test. A series of in vitro functional experiments were then performed to investigate the role of circOMA1 in the tumorigenesis of BC. The results revealed that the expression levels of circOMA1 were upregulated in BC tissues, and its expression was markedly associated with tumor size and lymph node metastasis. Receiver operating characteristic analysis demonstrated that the expression of circOMA1 could be used to discriminate between BC tissues and adjacent normal tissues. Functionally, overexpression of circOMA1 promoted the viability, migration and invasion of BC cells, whereas circOMA1 knockdown had the opposite effect. Mechanistic investigations showed that circOMA1 promoted the progression of BC by sponging microRNA (miR)‑1276 and upregulating sirtuin 4 (SIRT4) expression. In conclusion, circOMA1 may act as an oncogenic circRNA in BC via regulation of the miR‑1276/SIRT4 axis.