Number Of False Positive Cases Research Articles

Contrast enhanced digital mammography as a predictor of breast cancer in patient with pathological nipple discharge

BackgroundPathological nipple discharge (PND) commonly caused by benign diseases, but occasionally it signifies a major medical concern. Ultrasonography, in addition to mammography, is regarded as the standard imaging modality in the diagnosis of PND but their sensitivity in some cases are low, subsequently we used a contrast enhanced mammography (CEDM) as supplementary diagnostic modality in patients with PND. The purpose of our study was to investigate the diagnostic efficacy of CEDM in evaluating PND patients, added values of incorporating the CEDM in the diagnostic workup of patients with PND and to demonstrate its diagnostic significance as a predictor of malignancy in these patients as there have been few studies that have addressed the role of CEDM in the evaluation of PND.ResultsForty seven patients with PND were enrolled in this prospective study and underwent CEDM. The CEDM had high specificity (83.2%) compared to the combined sonomammography (SM) (59.3%), as there was a decrease in the number of false positive cases detected by the CEDM (6 cases) compared to the combined SM (11 cases). Combined (SM) had a moderate degree of agreement (55%, P = 0.01) with the final diagnosis, whereas CEDM had a strong degree of agreement (75%, P < 0.001). Additionally, the combined SM reported 76.6% accuracy with an area under the curve of 0.8, whereas the CEDM had 87.2% accuracy with an area under the curve of 0.89.ConclusionsCEDM had higher specificity, positive predictive value, and accuracy than SM in PND patients, along with its stronger agreement with the final pathology results, subsequently reduce the rate of false positive cases and the rate of recall back, making it a highly accurate malignancy predictor in those patients and can be an invaluable diagnostic imaging tool for identifying associated malignancies.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach.

Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases. This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants. The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets. The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches. The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.

Predicting long-term sickness absence with employee questionnaires and administrative records: a prospective cohort study of hospital employees.

This study aimed to compare the utility of risk estimation derived from questionnaires and administrative records in predicting long-term sickness absence among shift workers. This prospective cohort study comprised 3197 shift-working hospital employees (mean age 44.5 years, 88.0% women) who responded to a brief 8-item questionnaire on work disability risk factors and were linked to 28 variables on their working hour and workplace characteristics obtained from administrative registries at study baseline. The primary outcome was the first sickness absence lasting ≥90 days during a 4-year follow-up. The C-index of 0.73 [95% confidence interval (CI) 0.70-0.77] for a questionnaire-only based prediction model, 0.71 (95% CI 0.67-0.75) for an administrative records-only model, and 0.79 (95% CI 0.76-0.82) for a model combining variables from both data sources indicated good discriminatory ability. For a 5%-estimated risk as a threshold for positive test results, the detection rates were 76%, 74%, and 75% and the false positive rates were 40%, 45% and 34% for the three models. For a 20%-risk threshold, the corresponding detection rates were 14%, 8%, and 27% and the false positive rates were 2%, 2%, and 4%. To detect one true positive case with these models, the number of false positive cases accompanied varied between 7 and 10 using the 5%-estimated risk, and between 2 and 3 using the 20%-estimated risk cut-off. The pattern of results was similar using 30-day sickness absence as the outcome. The best predictive performance was reached with a model including both questionnaire responses and administrative records. Prediction was almost as accurate with models using only variables from one of these data sources. Further research is needed to examine the generalizability of these findings.

The short-term false positives after sacral neuromodulation therapy: Who, how many and why? A prospective descriptive single centre study

Aim:I. To describe the number of false positive cases (FP), their characteristics and reason of occurrence in sacral neuromodulation therapy (SNM). Methods:A multidisciplinary prospective single-centre study was conducted between March 2018 and December 2021 with a follow-up of 12 months. Patients with therapy-resistant pelvic organ dysfunctions, scheduled for a 2-staged SNM procedure at the Urology (UD) and Colorectal Surgery Department (CRD), were included. All patients completed bowel and bladder diaries at baseline and during the test phase. Patient global impression of change (PGIC) and satisfaction scores concerning urological (US) and bowel symptoms (BS) were surveyed at baseline, at 1, 6 and 12 months after implantation. Patient characteristics and diary outcomes between FP and true positive cases (TP) were compared using non-parametric statistical tests. SPSS 27.0 was used. Clinical trial registration: NCT05313984. Results:The FP ratio at one month follow-up was 16% (11/68), with a FP ratio of 13% (N=6/48) and 25% (N=5/20) for the urology patients and colorectal surgery patients, respectively. There were no significant differences in demographic characteristics between the FP and TP group (p > 0,05), however there is a trend towards FP having worse baseline symptoms than TP. The FP group had a significant lower baseline and test phase 24 h diuresis (p < 0,05), without having a significant different intake than the TP group. Conclusion:At one month after full implantation of a sacral neuromodulator, 16% of the patients showed loss of subjective success. These FP could not be predicted from demographic characteristics, most likely due to the small study population. Although not significant, FP seem to have worse symptoms at baseline than TP, with a significant lower diuresis regardless of fluid intake.

Thermo-SPT: A new skin prick test evaluation framework based on low-cost, portable smartphone thermography.

Although the skin prick test (SPT) is a reliable procedure to confirm IgE-dependent allergic sensitization in patients, the interpretation of the test is still performed manually, resulting in an error-prone procedure for the diagnosis of allergic diseases. To design and implement an innovative SPT evaluation framework using a low-cost, portable smartphone thermography, named Thermo-SPT, to significantly improve the accuracy and reliability of SPT outcomes. Thermographical images were captured every 60 s for a duration of 0 to 15 min using the FLIR One app, and then analysed with the FLIR Tool® . The definition of 'Skin Sensitization Region' area was introduced to analyse the time-lapse thermal changes in skin reactions over several time periods during the SPT. The Allergic Sensitization Index (ASI) and Min-Max Scaler Index (MMS) formulae were also developed to optimize the identification of the peak allergic response time point through the thermal assessment (TA) of allergic rhinitis patients. In these experimental trials, a statistically significant increase in temperature was detected from the fifth minute of TA for all tested aeroallergens (all values ). An increase was observed in the number of false-positive cases, where patients with clinical symptoms not consistent with SPT were evaluated as positive on TA assessment, specifically for patients diagnosed with Phleum pratense and Dermatophagoides pteronyssinus. Our proposed technique, the MMS, has demonstrated improved accuracy in identifying P. pratense and D. pteronyssinus compared with other SPT evaluation metrics, specifically starting from the fifth minute. For patients diagnosed with Cat epithelium, although not statistically significant initially, an increasing trend was determined in the results at the 15 min (ΔT (T15 - T0 ), ; ASIT15 , ). This proposed SPT evaluation framework utilizing a low-cost, smartphone-based thermographical imaging technique can enhance the interpretability of allergic responses during the SPT, potentially reducing the need for extensive manual interpretation experience as standard SPTs.

Aspiration Risk Screening With Tongue Pressure Measurement in Acute Stroke: A Diagnostic Accuracy Study Using STARD Guidelines.

Dysphagia can affect more than 50% of stroke patients in the acute phase. Aspiration pneumonia is a serious complication that can be prevented with dysphagia screening and assessment. Measurement of tongue elevation pressure is suggested to be a useful tool in aspiration risk screening. This study aimed to assess the diagnostic accuracy of maximum anterior tongue elevation strength (Pmax) in acute stroke care. In this prospective study, data were collected in a neurology department (stroke center) where patients formed a consecutive case series. The sample consisted of thirty stroke patients who failed an initial dysphagia screening. Patients underwent anterior tongue elevation strength measurement (index test) during bedside dysphagia assessment by a speech-language pathologist and flexible endoscopic evaluation of swallowing (reference test) by an otorhinolaryngologist on the same day. Outcome variables (index values in kPa, reference values interpreted on the penetration-aspiration scale) were used for estimating measures of diagnostic accuracy in aspiration risk screening. Ten patients aspirated on instrumental evaluation. At the cut-off point of ≤ 34 kPa the analysis showed 90% sensitivity, 35% specificity, 41% positive predictive value, and 88% negative predictive value. The area under the curve (AUC) for Pmax was AUC = 0.700 (95% CI [0.500-0.900]). Although individuals with low anterior tongue elevation strength tend to have a higher risk of aspiration, this variable alone is not capable of screening aspiration in acute stroke. In combination with a thorough noninstrumental bedside examination, it might have the potential to reduce the number of false positive cases. Further studies in this area would be worthwhile.

ThinPrep® imaging system-assisted vs manual screening of urinary cytology slides in the detection of the "suspicious for high-grade urothelial carcinoma" category.

The ThinPrep® Imaging System (TIS) is a Food and Drug Administration-approved review system for cervical cytopathology, where it has been shown to increase performance over manually reviewed slides. Application of the TIS to urinary cytology has only been reported in a single study, in 2013. We aimed to compare the agreement of two cytotechnologists' and a pathologist's manual screening (dots) with the fields of view (FOVs) selected by the TIS. We also aimed to track cases in which the TIS could identify missed abnormals and reduce the false-negative fraction. Electronically marked TIS fields (EMTFs) suspicious for high-grade urothelial carcinoma (SHGUC) were controlled by follow-up cystoscopy and histology, where available. A total of 826 consecutive specimens were studied. Of those, 94 (11.4%) were unreadable by the TIS. There were 710 possible comparisons, of which 380 (53.5%) received no dot after manual screening. Of the 330 remaining slides, 149 (45.1%) had at least one dot matching with the TIS FOVs. After TIS reading, EMTFs were noted in 13 of 636 (2.0%) negative cytology cases. Surveillance showed that 3/13 (23.1%, 0.4% of the 710 possible comparisons) of those cases matched with high grade urothelial carcinoma (HGUC), whereas 6/13 (46.1%, 0.8% of the 710 possible comparisons) had negative follow-up at 24 months, and 4/13 (30.8%) were lost for follow-up. The TIS increases the detection rate of SHGUC cells, potentially leading to a slight decrease in the false-negative fraction, but at the expense of a slight but larger increase in the number of false-positive cases. These findings stress the importance of a careful approach to the evaluation of the FOVs.

Analysis of reputation-based mining paradigm under dishonest mining attacks

Since the introduction of Bitcoin, numerous studies on Bitcoin mining attacks have been conducted, and as a result, many countermeasures to these attacks have been proposed. The reputation-based mining paradigm is a comprehensive countermeasure solution to this problem with the goal of regulating the mining process and preventing mining attacks. This is accomplished by incentivizing miners to avoid dishonest mining strategies using reward and punishment mechanisms. This model was validated solely based on game theoretical analyses, and the real-world implications of this model are not known due to the lack of empirical data. To shed light on this issue, we designed a simulated mining platform to examine the effectiveness of the reputation-based mining paradigm through data analysis. We implemented block withholding attacks in our simulation and ran the following three scenarios: Reputation mode, non-reputation mode, and no attack mode. By comparing the results from these three scenarios, interestingly, we found that the reputation-based mining paradigm decreases the number of block withholding attacks, and as a result, the actual revenue of individual miners becomes closer to their theoretical expected revenue. In addition, we observed that the confidence interval test can effectively detect block withholding attacks; however, the test also results in a small number of false positive cases. Since the effectiveness of the reputation-based model relies on attack detection, further research is needed to investigate the effect of this model on other dishonest mining strategies.

False positive repression: Data centric pipeline for object detection in brain MRI

SummaryOne of the problems that often arise during the application of medical research to real life is the high number of false positive cases. This situation causes experts to be warned with false alarms unnecessarily and increases their workload. This study proposes a new data centric approach to reduce bias‐based false positive predictions in brain MRI‐specific medical object detection applications. The proposed method has been tested using two different datasets: Gazi Brains 2020 and BraTS 2020, and three different deep learning‐based object detection models: Mask R‐CNN, YOLOv5, and EfficientDet. According to the results, the proposed pipeline outperformed the classical pipeline, up to 18% on the Gazi Brains 2020 dataset, and up to 24% on the BraTS 2020 dataset for mean specificity value without much change in sensitivity metric. It means that the proposed pipeline reduces false positive rates due to bias in real‐life applications and it can help to reduce the workload of experts.

Deep learning reconstruction algorithm for coronary CT angiography in assessing obstructive coronary artery disease caused by calcified lesions: the clinical application value

Objective: To investigate the image quality of coronary CT angiography (CCTA) subjected to deep learning-based reconstruction algorithm (DLR) method and its diagnostic performance for stenosis caused by coronary calcified lesions. Methods: We enrolled 33 consecutive patients with known or suspected coronary artery disease (CAD) who underwent CCTA and subsequently invasive coronary angiography (ICA) within 1 month in the department of radiology, Peking Union Medical College Hospital between February 2020 and February 2021. Among them, there are 26 males and 7 females, age range from 45 to 86 (61.9±9.0) years. The CCTA images were reconstructed with DLR and hybrid iterative reconstruction (HIR). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated on the aorta root, left main artery, proximal left anterior descending, left circumflex, and right coronary artery of the CCTA images and were used to evaluate the objective image quality (IQ). Subjective IQ score was graded using Likert four-point scale (1 for excellent and 4 for poor). The diagnostic performance of obstructive coronary artery disease caused by calcified lesions on CCTA subjected to DLR and HIR methods were evaluated using ICA as the reference standard. Results: A total of 123 lesions in 33 patients were included in the analysis. Image noise of DLR image was significantly lower than that on HIR image(defined as the standard deviation of the attenuation values in the aortic root: 18.12±3.66 vs 24.19±5.71, P<0.001), CNR and SNR of DLR image in the aortic root were higher (CNR:43.83±23.73 vs 26.38±9.69, P<0.001,SNR:26.66±7.83 vs 21.23±8.65, P<0.001). Subjective scores of DLR was better than HIR image (1.12±0.41 vs 1.46±0.60,P<0.001). The sensitivity, specificity and accuracy of DLR and HIR images for diagnosing obstructive coronary artery disease caused by calcified lesions were 100.0%, 77.4%, 78.9% and 100.0%, 63.5%, 65.9%%, respectively. The number of false positive cases on DLR image decreased by 38% compared with HIR. Conclusions: Artificial intelligence based DLR can significantly reduce the image noise and improve the image quality of CCTA. DLR helps to improve the diagnostic performance of CCTA in assessing obstructive coronary artery disease caused by calcified lesions, which may have good clinical application value.

Interpreting clusters via prototype optimization

In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analysis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely and distinctively as possible, i.e., the explanation is fulfilled by as many as possible individuals of the corresponding cluster, true positive cases, and by as few as possible individuals in the remaining clusters, false positive cases. We assume that a dissimilarity between the individuals is given, and propose distance-based explanations, namely those defined by individuals that are close to its so-called prototype. To find the set of prototypes, we address the biobjective optimization problem that maximizes the total number of true positive cases across all clusters and minimizes the total number of false positive cases, while controlling the true positive rate as well as the false positive rate in each cluster. We develop two mathematical optimization models, inspired by classic Location Analysis problems, that differ in the way individuals are allocated to prototypes. We illustrate the explanations provided by these models and their accuracy in both real-life data as well as simulated data.

Deep Learning-Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA.

The detection of cerebral aneurysms on MRA is a challenging task. Recent studies have used deep learning-based software for automated detection of aneurysms on MRA and have reported high performance. The purpose of this study was to evaluate the incremental value of using deep learning-based software for the detection of aneurysms on MRA by 2 radiologists, a neurosurgeon, and a neurologist. TOF-MRA examinations of intracranial aneurysms were retrospectively extracted. Four physicians interpreted the MRA blindly. After a washout period, they interpreted MRA again using the software. Sensitivity and specificity per patient, sensitivity per lesion, and the number of false-positives per case were measured. Diagnostic performances, including subgroup analysis of lesions, were compared. Logistic regression with a generalized estimating equation was used. A total of 332 patients were evaluated; 135 patients had positive findings with 169 lesions. With software assistance, patient-based sensitivity was statistically improved after the washout period (73.5% versus 86.5%, P < .001). The neurosurgeon and neurologist showed a significant increase in patient-based sensitivity with software assistance (74.8% versus 85.2%, P = .03, and 56.3% versus 84.4%, P < .001, respectively), while the number of false-positive cases did not increase significantly (23 versus 30, P = .20, and 22 versus 24, P = .75, respectively). Software-aided reading showed significant incremental value in the sensitivity of clinicians in the detection of aneurysms on MRA without a significant increase in false-positive findings, especially for the neurosurgeon and neurologist. Software-aided reading showed equivocal value for the radiologist.

Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

Tiroid Nodüllerinin Değerlendirilmesinde Bethesda Sistemi ve İnce İğne Aspirasyon Biopsisinin Yeri

Aim: In this study, the role of fine needle aspiration biopsy (FNAB) evaluated with the Bethesda System in the diagnosis of thyroid nodules was investigated. Materials and Methods: Total 2284 FNAB materials of 1846 patients were performed in the Pathology Department of Kutahya Health Sciences University, Evliya Celebi Traning and Research Hospital between 2016 and 2019 and reported according to the Bethesda category. Among these cases, diagnostic sensitivity, specificity, accuracy, positive and negative predictive values of FNAB were calculated by making cytohistopathologica comparison in 328 cases who underwent total thyroidectomy. Results: In the study, 1538 of the 1846 patients included were female (83.31%) and 308 (16.68%) were male. The average age was 49.92 years (Age range 19-87). The final cytological diagnoses of 2284 FNABs were as follows: 287 (12.57%) nondiagnostic or unsatisfactory, 1517 (66.42%) benign cytology, 316 (13.83%) atypia of undetermined significance/follicular lesion of undetermined significance, 63 (2.75%) follicular neoplasm or suspicious for a follicular neoplasm, 59 (2.59%) suspicious for malignancy and 42 (1.84%) malignant cytology. Histologically, 245 (74.70%) of the 328 cases were benign, while 83 (25.30%) were malignant. The number of true negative cases was 216 (65.85%), the number of true positive cases was 62 (19.90%), the number of false positive cases was 29 (8.84%) and the number of false negative cases was 21 (6.40%). In this case, diagnostic sensitivity, specificity, accuracy rates, positive and negative predictive values were 74.7%, 88.16%, 84.76%, 68.13% and 91.14%, respectively. Conclusion: In our study, it was observed that the sensitivity, specificity, accuracy rates, positive and negative predictive values of thyroid FNABs were compatible with the literature. We think that as the experience of the clinician and cytopathologist increases, false negative and positive results will decrease and at the same time, the confidence in thyroid FNABs will increase even more with the participation of some recent molecular methods (BRAF and RET/PTC, AX8/PPARG and RAS) applied to FNABs.

Evaluating the sensitivity of SARS-CoV-2 infection rates on college campuses to wastewater surveillance.

As college campuses reopened in fall 2020, we saw a large-scale experiment unfold on the efficacy of various strategies to contain the SARS-CoV-2 virus. Traditional individual surveillance testing via nasal swabs and/or saliva is among the measures that colleges are pursuing to reduce the spread of the virus on campus. Additionally, some colleges are testing wastewater on their campuses for signs of infection, which can provide an early warning signal for campuses to locate COVID-positive individuals. However, a representation of wastewater surveillance has not yet been incorporated into epidemiological models for college campuses, nor has the efficacy of wastewater screening been evaluated relative to traditional individual surveillance testing, within the structure of these models. Here, we implement a new model component for wastewater surveillance within an established epidemiological model for college campuses. We use a hypothetical residential university to evaluate the efficacy of wastewater surveillance for maintaining low infection rates. We find that wastewater sampling with a 1-day lag to initiate individual screening tests, plus completing the subsequent tests within a 4-day period can keep overall infections within 5% of the infection rates seen with traditional individual surveillance testing. Our results also indicate that wastewater surveillance can effectively reduce the number of false positive cases by identifying subpopulations for surveillance testing where infectious individuals are more likely to be found. Through a Monte Carlo risk analysis, we find that surveillance testing that relies solely on wastewater sampling can be fragile against scenarios with high viral reproductive numbers and high rates of infection of campus community members by outside sources. These results point to the practical importance of additional surveillance measures to limit the spread of the virus on campus and the necessity of a proactive response to the initial signs of outbreak.

Problems with Bazett QTc correction in paediatric screening of prolonged QTc interval

BackgroundBazett formula is frequently used in paediatric screening for the long QT syndrome (LQTS) and proposals exist that using standing rather than supine electrocardiograms (ECG) improves the sensitivity of LQTS diagnosis. Nevertheless, compared to adults, children have higher heart rates (especially during postural provocations) and Bazett correction is also known to lead to artificially prolonged QTc values at increased heart rates. This study assessed the incidence of erroneously increased QTc values in normal children without QT abnormalities.MethodsContinuous 12-lead ECGs were recorded in 332 healthy children (166 girls) aged 10.7 ± 2.6 years while they performed postural manoeuvring consisting of episodes (in the following order) of supine, sitting, standing, supine, standing, sitting, and supine positions, each lasting 10 min. Detailed analyses of QT/RR profiles confirmed the absence of prolonged individually corrected QTc interval in each child. Heart rate and QT intervals were measured in 10-s ECG segments and in each segment, QTc intervals were obtained using Bazett, Fridericia, and Framingham formulas. In each child, the heart rates and QTc values obtained during supine, sitting and standing positions were averaged. QTc durations by the three formulas were classified to < 440 ms, 440–460 ms, 460–480 ms, and > 480 ms.ResultsAt supine position, averaged heart rate was 77.5 ± 10.5 beat per minute (bpm) and Bazett, Fridericia and Framingham QTc intervals were 425.3 ± 15.8, 407.8 ± 13.9, and 408.2 ± 13.1 ms, respectively. At sitting and standing, averaged heart rate increased to 90.9 ± 10.1 and 100.9 ± 10.5 bpm, respectively. While Fridericia and Framingham formulas showed only minimal QTc changes, Bazett correction led to QTc increases to 435 ± 15.1 and 444.9 ± 15.9 ms at sitting and standing, respectively. At sitting, Bazett correction identified 51, 4, and 0 children as having the QTc intervals 440–460, 460–480, and > 480 ms, respectively. At sitting, these numbers increased to 118, 11, and 1, while on standing these numbers were 151, 45, and 5, respectively. Irrespective of the postural position, Fridericia and Framingham formulas identified only a small number (< 7) of children with QT interval between 440 and 460 ms and no children with longer QTc.ConclusionDuring screening for LQTS in children, the use of Bazett formula leads to a high number of false positive cases especially if the heart rates are increased (e.g. by postural manoeuvring). The use of Fridericia formula can be recommended to replace the Bazett correction not only for adult but also for paediatric ECGs.

Evaluation of NTRK immunohistochemistry as a screening method for NTRK gene fusion detection in non-small cell lung cancer

PurposeThe small molecule inhibitors larotrectinib and entrectinib have recently been approved as cancer agnostic drugs in patients with tumours harbouring a rearrangement of the neurotrophic tropomyosin receptor kinase (NTRK). These oncogenic fusions are estimated to occur in 0.1–3 % of non-small cell lung cancers (NSCLC). Although molecular techniques are most reliable for fusion detection, immunohistochemical analysis is considered valuable for screening. Therefore, we evaluated the newly introduced diagnostic immunohistochemical assay (clone EPR17341) on a representative NSCLC cohort. MethodsCancer tissue from 688 clinically and molecularly extensively annotated NSCLC patients were comprised on tissue microarrays and stained with the pan-TRK antibody clone EPR17341. Positive cases were further analysed with the TruSight Tumor 170 RNA assay (Illumina). Selected cases were also tested with a NanoString NTRK fusion assay. For 199 cases, NTRK RNA expression data were available from previous RNA sequencing analysis. ResultsAltogether, staining patterns for 617 NSCLC cases were evaluable. Of these, four cases (0.6 %) demonstrated a strong diffuse cytoplasmic and membranous staining, and seven cases a moderate staining (1.1 %). NanoString or TST170-analysis could not confirm an NTRK fusion in any of the IHC positive cases, or any of the cases with high mRNA levels. In the four cases with strong staining intensity in the tissue microarray, whole section staining revealed marked heterogeneity of NTRK protein expression. ConclusionThe presence of NTRK fusion genes in non-small cell lung cancer is exceedingly rare. The use of the immunohistochemical NTRK assay will result in a small number of false positive cases. This should be considered when the assay is applied as a screening tool in clinical diagnostics.

Newborn Screening for Congenital Adrenal Hyperplasia: Review of Factors Affecting Screening Accuracy.

Newborn screening for 21-hydroxylase deficiency (21OHD), the most common form of congenital adrenal hyperplasia, has been performed routinely in the United States and other countries for over 20 years. Screening provides the opportunity for early detection and treatment of patients with 21OHD, preventing salt-wasting crisis during the first weeks of life. However, current first-tier screening methodologies lack specificity, leading to a large number of false positive cases, and adequate sensitivity to detect all cases of classic 21OHD that would benefit from treatment. This review summarizes the pathology of 21OHD and also the key stages of fetal hypothalamic-pituitary-adrenal axis development and adrenal steroidogenesis that contribute to limitations in screening accuracy. Factors leading to both false positive and false negative results are highlighted, along with specimen collection best practices used by laboratories in the United States and worldwide. This comprehensive review provides context and insight into the limitations of newborn screening for 21OHD for laboratorians, primary care physicians, and endocrinologists.

Validation of the Patient Health Questionnaire-9 (PHQ-9) for Detecting Depression Among Pregnant Women in Lima, Peru.

Depression during pregnancy is linked to adverse perinatal and offspring outcomes. The Patient Health Questionnaire-9 (PHQ-9) has been validated for identifying depression in pregnant women in limited cultural contexts. Construct validity and reliability have been assessed in Lima, Peru, but criterion validity has not. This study aimed to comprehensively evaluate the PHQ-9 among pregnant Peruvian women in the Pregnancy Outcomes, Maternal and Infant Study (PrOMIS). Using Composite International Diagnostic Interview (CIDI) criteria for past-12-month major depressive disorder as the reference standard, sensitivity, specificity, and predictive value of the PHQ-9 for detecting depression were assessed at various cutpoints of the PHQ-9. Confirmatory factor analysis (CFA) was used to evaluate one- and two-factor structures for the PHQ-9. Cronbach's alpha was computed for the entire PHQ-9 scale and for subscales supported by CFA. A cutpoint of ≥8 maximized combined sensitivity (61%) and specificity (62%). At this cutpoint, positive predictive value was low (15%) and negative predictive values was high (93%). Reliability for the full scale was high (α=0.80). Both one- and two-factor solutions were appropriate for this population, but a two-factor solution containing an affective/mood factor (α=0.67) and a somatic factor (α=0.75) was optimal (CFI=0.93, RMSEA=0.075). Among pregnant women in Lima, screening with the PHQ-9 can identify those in need of mental health care, but may identify a large number of false positive cases.

Role of Hematological Indices as a Screening Tool of Beta Thalassemia Trait in Eastern Uttar Pradesh: An Institutional Study.

A large majority of microcytic hypochromic anemia have defects in cellular hemoglobin synthesis due to either iron deficiency or thalassemia trait; both differing in management and prognosis. HPLC and serum iron profile as confirmatory tests are unavailable at health care centers. Blanket therapy of iron supplements is therefore given in all such cases which may cause iron overload in thalassemia cases. Easy to use and cost effective screening methods are desirable. The present study was undertaken to evaluate the diagnostic accuracy of twelve indices to effectively screen cases of thalassemia trait and differentiate them from iron deficiency anemia. Routine samples from the hematology lab with Hb < 13 gm/dl, MCV < 80fl and MCH < 27pg were screened. Taking HPLC and serum ferritin as gold standard, out of total 1353 cases, 98 cases of thalassemia trait (HbA2 > 3.5 on HPLC) and 1102 cases of iron deficiency anemia (serum ferritin < 12g/ml) were evaluated using discrimination indices. Diagnostic accuracy for each index was calculated. While few indices showed a sensitivity of 100%, their specificity was low which meant more number of false positive cases. Based on Youden's Index, which measures the diagnostic tests ability to balance sensitivity and specificity, the best three indices in the decreasing order of their efficacy in our study were Ricerca Index (RI), Green and King Index (GKI) and Mentzer Index (MI). MI is considered a reliable index by many clinicians since a long time, however RI and GKI were found to have a better diagnostic accuracy based on our study.