Risk Of False Positives Research Articles

Peritonsillar Abscess Outcomes with and Without Computed Tomography: A Retrospective Cohort Study.

Peritonsillar abscess (PTA) is a common deep space head and neck infection, which can be diagnosed with or without computed tomography (CT). CT poses a risk for false positives, leading to unnecessary abscess drainage attempts without benefit, whereas needle or open aspiration without imaging could potentially lead to missed abscess in need of treatment. This study considered the utility and impact of obtaining CT scans in patients with suspected PTA by comparing outcomes between CT and non-CT usage. Retrospective cohort analysis using TriNetX datasets compared the outcomes of two cohorts: PTA without CT and PTA with CT. Measured outcomes included incision and drainage; quinsy adenotonsillectomy; recurrent PTA; airway emergency/obstruction; repeat emergency department (ED) visits; and need for antibiotics, opiates, or steroids. Odds ratios (OR) were calculated using a cohort analysis. The CT usage group had increased odds of receiving antibiotics (OR 3.043, [2.043-4.531]), opiates (OR 1.614, [1.138-1.289]), and steroids (OR 1.373, [1.108-1.702]), as well as a higher likelihood of returning to the ED (OR 5.900, [3.534-9.849]) and developing a recurrent PTA (OR 1.943, [1.410-2.677]). No significant differences were observed in the incidence of incision and drainage, quinsy adenotonsillectomy, or airway emergency/obstruction. Our study indicated that CT scans for PTA diagnosis were associated with increased prescription of antibiotics, opioids, steroids, return ED visits, and recurrent PTA. Future prospective trials are needed to determine if the use of CT scans indicates higher patient acuity that explains the potential negative outcomes. 2 Laryngoscope, 134:4911-4917, 2024.

Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.

Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsqsoft). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes. The accuracy of imputing sequence variants from real high-density genotypes was higher than from low-density genotypes. In our software benchmark, all programs performed well with only minor differences in accuracy. While there was a close relationship between empirical imputation accuracy and the imputation Rsqsoft, this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsqsoft threshold for removing poorly imputed variants must be customised according to the software and this should be accounted for when merging data from multiple studies, such as in meta-GWAS studies. We also found that imposing an Rsqsoft filter has a positive impact on genomic regions with poor imputation accuracy due to large segmental duplications that are susceptible to error-prone alignment. Overall, our results showed that on average the imputation accuracy for INDEL was approximately 6% lower than SNP for all software programs. Importantly, the imputation accuracy for the non-PAR (non-Pseudo-Autosomal Region) of the X chromosome was comparable to autosomal imputation accuracy, while for the PAR it was substantially lower, particularly when starting from low-density genotypes. This study provides an empirically derived approach to apply customised software-specific Rsqsoft thresholds for downstream analyses of imputed variants, such as needed for a meta-GWAS. The very poor empirical imputation accuracy for variants on the PAR when starting from low density genotypes demonstrates that this region should be imputed starting from a higher density of real genotypes.

Detection of circulating tumor cells in non-metastatic prostate cancer through integration of a microfluidic CTC enrichment system and multiparametric flow cytometry.

Prostate cancer (PCa) is the second most common cancer among men and the fifth leading cause of cancer death. Circulating tumor cell (CTC) enumeration and characterisation in PCa has been shown to provide valuable information on prognosis of disease, therapy management and detection of resistance. Here, Cellsway's microfluidic platform for high-throughput enrichment of intact CTC populations was used to isolate CTCs from the blood of 20 localised PCa patients and 10 healthy donor samples to evaluate the clinical performance of the technology. To enumerate and characterise CTCs, a multi-parameter flow cytometry analysis was performed on the enriched CTC suspension using CTC-specific biomarkers. CTCs were detected in 17 of 20 patient samples, which corresponds to 85% CTC positivity. The median CTC count per 7.5 ml blood was 2 (1-9). In 80% of patients (n = 16), the number of CTCs ranged from 1 to 5, and in 5% of patients (n = 1) the number of CTCs was above 5. No CTCs were observed in the blood samples of 10 healthy volunteers, demonstrating the high specificity and low risk of false positives of the technology.

Communicating Risk in Imaging: A Scoping Review of Risk Presentation in Patient Decision Aids

Best practices exist for communicating medical information to patients, however there is less emphasis on methods to communicate risks, especially in medical imaging. We conducted a scoping review of patient decision aids in medical imaging and characterized the presentation methods of imaging risks. Embase, Medline, CINAHL, and PsychINFO were searched to identify studies involving patient decision aids utilized in diagnostic imaging that communicated the risks. Study characteristics included the number and types of risks included, as well as the presentation type and how the probability of risks were communicated. The final study included 46 articles encompassing 27 distinct patient decision aids. Mammography was the most common imaging scenario (22/46), lung cancer screening (18/46), traumatic brain injury (5/46), and urolithiasis (1/46). All patient decision aids included risks associated with imaging, however the number of risk types varied from 2 to 9 (mean: 4, SD: 2). We identified 12 risks across the 27 decision aids, however no single study included all risks. Overall, most risks (65%) were communicated with text, and the presentation mode varied by type of risk. False positive risks were most commonly communicated using a visual format while radiation risk was most commonly communicated using text format. There was no consistent manner of communicating risk to patients and visual methods such as icon arrays were not consistently used. The variability of both included risks and the risk presentation modes in the PDAs may affect decision-making, especially in patients and caregivers with lower health literacy and numeracy.

Dual-mode detection of α-fetoprotein using the photothermal effect and peroxidase-like activity of Au@Cu/Cu2O-rGO

α-Fetoprotein (AFP) is widely recognized as an important marker for monitoring hepatocellular carcinoma (HCC), and its monitoring using two different transduction mechanisms is an effective way to avoid the risk of false positives or false negatives. In this paper, Au@Cu/Cu2O-rGO was used as a photothermal converter as well as an actuator to promote the decomposition of hydrogen peroxide (H2O2), which was further designed as a probe for dual-mode detection to quantitatively assess AFP. The composite nanomaterials possessed photothermal conversion efficiencies (η) of up to 54.9 % and catalytically generated signals up to 1.6 times greater, relative to a single material. Based on the generated temperature and current signals, AFP has been sensitively detected in the range of 0.01–100 ng/mL, with limits of detection (LOD) of 5.62 pg/mL and 1.23 pg/mL, respectively. The dual-mode assay combines portability with high accuracy for the detection of human health systems.

Model-based algorithms to ascertain smoking in administrative health data: a registry-based validation study

Objectives We developed a machine-learning model-based algorithm (MBA) for smoking in Administrative Health Data (AHD). The validity of this MBA was compared to a rule-based algorithm (RBA). Approaches The study included adults (≥18 years) from a clinical registry containing self-reported current smoking from 2017 to 2020 in Manitoba, Canada. Clinical data were linked to up to five years of hospitalization, physician billing claims, and prescription medication records. The RBA was based on diagnosis codes for tobacco use and nicotine dependence medication. MBAs, constructed using random forest (RF) models, included these indicators in addition to comorbid condition diagnoses and sociodemographic factors. Sensitivity, specificity, positive and negative predictive values (PPV, NPV), and 95% confidence intervals (CIs) were estimated. Results The cohort comprised 24,718 individuals (88.6% female); prevalence of current smokers was 10.0%. The RBA had sensitivity of 27.3% (95% CI: 24.2-30.7), specificity of 96.6% (95% CI: 96.1-97.0), and PPV of 47.2% (95% CI: 42.9-51.5). The MBA had sensitivity of 68.6% (95% CI: 65.1-71.9), specificity of 76.3% (95% CI: 75.2-77.3), and PPV of 24.3% (95% CI: 23.2-25.6). NPV was high irrespective of algorithms. Stratified analyses revealed similar estimates for males and females, and the number of years of AHD did not affect the MBA results. ConclusionsAn RF-based MBA for smoking ascertainment in linked AHD sources improved sensitivity compared to the RBA. However, the RBA excelled in specificity and PPV. ImplicationBalancing accurate smoker identification with the risk of false positives is crucial when choosing an algorithm to ascertain current smokers using AHD.

Analysis of the effectiveness of cross-reactive carbohydrate antigen determinant antibody adsorbents in identifying allergen-specific IgE antibodies

This study aimed to investigate the influence of anti-cross-reactive carbohydrate determinant IgE antibodies (anti-CCD IgE) on the detection of allergen-specific IgE (sIgE) antibodies, as well as the application value of anti-CCD IgE adsorbents in detecting allergen sIgE. In this cross-sectional study, a total of 2 636 test samples from patients who received treatment in West China Hospital of Sichuan University and tested allergen sIgE using the western blot method from October 2020 to May 2021 were analyzed. In these samples, 709 samples tested postive of allergen sIgE. 46 stochastic venous serum samples that tested positive in both sIgE and anti-CCD IgE and 1 serum sample that tested positive in sIgE but negative in anti-CCD IgE were collected. These samples were processed by anti-CCD IgE adsorbents, followed by allergen sIgE detection. The difference between the two detection results before and after adsorption was analyzed. The allergen test results showed that the positive rate of anti-CCD IgE in samples was 2.6% (69/2 636) during the period of sample collection. After treatment with anti-CCD IgE adsorbents, the top three allergen-sIgE of the positive rate changed from tree combination 2 (willow/poplar/elm), common ragweed and peanut to dust mite combination, cockroach and crab. The positive anti-CCD IgE results of 46 samples all turned negative and the total positive sIgE antibody dropped by 62.8%; the positive rate of sIgE antibodies with the class result ≥2 significantly decreased after treatment with anti-CCD IgE adsorbents, especially the positive rate of common ragweed dropped by 96.2%. The results of positive samples showed that multiple sIgE antibodies declined by different ranges, involving up to 11 antibodies with a maximum decline of 4 classes. Strongly positive sIgE antibodies (the class result ≥4) also had a high conversion rate of negative (25.0%-100%). The positive sIgE antibodies in about 60% of the samples decreased by more than 2, and the sIgE antibodies in 17.4% of the samples turned completely negative. There was no change in the allergen sIgE detection results of the sample with negative anti-CCD IgE after treatment. In conclusion, sIgE antibodies including targeting common ragweed, humulus, tree combination 2 (willow/poplar/elm), etc. are susceptible to false positives caused by anti-CCD IgE. Treatment of samples with anti-CCD IgE adsorbents can significantly reduce the risk of false positives caused by anti-CCD IgE. It is necessary to pretreat samples that were anti-CCD IgE positive with anti-CCD IgE adsorbents, which can make laboratory results more accurate and provide a reference for diagnosis and prevention of allergic diseases.

Combined Fluorescence-Guided Surgery with 5-Aminolevulinic Acid and Fluorescein in Glioblastoma: Technical Description and Report of 100 Cases.

Fluorescence-guided resection (FGR) of glioblastomas has been previously explored with the use of 5-amivelulinic acid (5-ALA) and sodium fluoresceine (SF), allowing us to maximize the extent of resection (EoR). In this study, we highlight the most relevant concerns regarding this technique and present the methods and results from the experience of our center. A case series of 100 patients operated on in AOU Città della Salute e della Scienza in Turin with a histological diagnosis of glioblastoma (grade IV, according to WHO 2021) was retrospectively analyzed. Both 5-ALA and SF were administered and intraoperatively assessed with an optical microscope. 5-ALA is the only approved drug for FGR in glioblastoma, reporting an increased EoR. Nevertheless, SF can be positively used in addition to 5-ALA to reduce the risk of false positives without increasing the rate of adverse effects. In our experience, SF was used to guide the initial phase of resection while 5-ALA was used to visualize tumor spots within the surgical cavity. In 96% of cases, gross total resection was achieved, with supra-maximal resection in 11% of cases. Combined FGR using 5-ALA and SF seems to be a promising method of increasing the extent of resection and to improving the prognosis in glioblastoma patients.

Hearing Screening for Congenital CytoMegaloVirus-Exploring Parents' Experiences of Completing Targeted Congenital Cytomegalovirus Screening at the Time of Their Infants' Newborn Hearing Screening.

Background/Objectives: Congenital cytomegalovirus (cCMV) is the leading infectious cause of sensorineural hearing loss and neurodevelopmental disabilities, with prompt detection (<21 days of life) required to enable accurate diagnosis and anti-viral treatment where clinically appropriate. International guidelines recommend cCMV screening for infants who do not pass their Universal Newborn Hearing Screening (UNHS). This study aimed to explore parental experiences of targeted cCMV screening through the UNHS in Victoria, Australia between 2019 and 2020 (HearS-cCMV study). Methods: A qualitative study comprising 18 semi-structured interviews with parents who took saliva swabs from their infants who did not pass their UNHS. A maximum variation sampling strategy was used with data analysed using thematic analysis. Results: Four themes described 18 parents' experiences of cCMV screening: (1) parents' lack of CMV awareness prior to cCMV screening; (2) overall positive experience; (3) varied understanding of CMV post screening; and (4) parents were glad to screen their infant for cCMV. Enablers of targeted cCMV screening included the swab being simple and non-invasive, being easier to complete in the hospital than at home, and the screening being well delivered by the staff. Barriers included a potential increase in anxiety, especially with false positives, and the timing of cCMV screening coinciding with their infant not passing UNHS being difficult for some parents. Conclusions: Parent experiences of targeted cCMV screening were positive. Increasing public knowledge of cCMV and training staff members to complete the CMV swab would reduce the risk of false positives and associated parental anxiety. This would facilitate successful routine targeted cCMV screening.

Distinguishing genuine from simulated dissociative identity disorder using the Morel Emotional Numbing Test.

Few studies have identified validity tests that are appropriate for use in individuals with dissociative identity disorder (DID). This study investigated whether the Morel Emotional Numbing Test (MENT) could distinguish between genuine and simulated DID. Thirty-five participants with DID diagnosed via a semistructured interview were compared to 88 participants taking an abnormal psychology course who were instructed to simulate DID. Group comparison analyses were conducted to examine differences in the MENT errors. Univariate logistic regression and receiver operating characteristic curve analyses examined how well the MENT errors discriminated between groups and identified a cutoff optimizing sensitivity while maintaining high specificity. Simulators had significantly more errors across all three MENT sets compared to genuine DID participants. The total number of MENT errors across sets most strongly and significantly predicted genuine versus simulated DID groups. Receiver operating characteristic curve analyses indicated that the MENT could discriminate between genuine and simulated DID with outstanding classification accuracy (area under the curve = .95). A cutoff of ≥ 10 total errors on the MENT yielded an optimal balance of sensitivity (.86) and specificity (.94). However, the previously defined cutoff of ≥ 8 total errors also demonstrated high sensitivity (.87) and specificity (.89). Findings provide preliminary evidence for the MENT as a validity test for DID populations. Although the commonly used cutoff of ≥ 8 errors on the MENT demonstrated excellent psychometric properties, we recommend using a cutoff of ≥ 10 errors for individuals with DID to minimize the risk of false positives while maintaining strong sensitivity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Unveiling the adverse events of Nusinersen in spinal muscular atrophy management based on FAERS database

This study aims to collect and analyze adverse event (AE) reports related to Nusinersen from the FAERS database. The study employed a combination of signal quantification techniques, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS), to enhance the accuracy of signal detection and reduce the risk of false positives or negatives. Between the first quarter of 2017 and the third quarter of 2023, the FAERS database collected a total of 11,485,105 drug AE reports, of which 5772 were related to Nusinersen. Through signal mining analysis, 218 preferred term (PT) signals involving 27 system organ classes (SOCs) were identified. The study discovered AEs related to metabolism and nutrition disorders, psychiatric disorders, and cardiac disorders SOCs, which were not mentioned in the product information. Additionally, complications directly related to the intrathecal administration of Nusinersen, such as increased CSF pressure, positive CSF red blood cell count, and AEs related to the method of drug use, such as neuromuscular scoliosis and cerebrospinal fluid reservoir placement, were highlighted. Notably, AEs related to renal function abnormalities, such as abnormal Urine protein/creatinine ratio and protein urine presence, showed higher frequency and signal strength. The findings of this study emphasize the importance of comprehensive safety monitoring in the clinical application of Nusinersen. These results are significant for guiding future clinical practices, improving disease management strategies, and developing safer treatment protocols.

High-Throughput Covalent Modifier Screening with Acoustic Ejection Mass Spectrometry.

Interests in covalent drugs have grown in modern drug discovery as they could tackle challenging targets traditionally considered "undruggable". The identification of covalent binders to target proteins typically involves directly measuring protein covalent modifications using high-resolution mass spectrometry. With a continually expanding library of compounds, conventional mass spectrometry platforms such as LC-MS and SPE-MS have become limiting factors for high-throughput screening. Here, we introduce a prototype high-resolution acoustic ejection mass spectrometry (AEMS) system for the rapid screening of a covalent modifier library comprising ∼10,000 compounds against a 50 kDa-sized target protein─Werner syndrome helicase. The screening samples were arranged in a 1536-well format. The sample buffer containing high-concentration salts was directly analyzed without any cleanup steps, minimizing sample preparation efforts and ensuring protein stability. The entire AEMS analysis process could be completed within a mere 17 h. An automated data analysis tool facilitated batch processing of the sample data and quantitation of the formation of various covalent protein-ligand adducts. The screening results displayed a high degree of fidelity, with a Z' factor of 0.8 and a hit rate of 2.3%. The identified hits underwent orthogonal testing in a biochemical activity assay, revealing that 75% were functional antagonists of the target protein. Notably, a comparative analysis with LC-MS showcased the AEMS platform's low risk of false positives or false negatives. This innovative platform has enabled robust high-throughput covalent modifier screening, featuring a 10-fold increase in library size and a 10- to 100-fold increase in throughput when compared with similar reports in the existing literature.

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients’ stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Automatic Estimation of Daily Volcanic Sulfur Dioxide Gas Flux From TROPOMI Satellite Observations: Application to Etna and Piton de la Fournaise

AbstractUnderstanding the dynamics of sulfur dioxide (SO2) degassing is of primary importance for tracking temporal variations in volcanic activity. Here we introduce the novel “disk method,” which aims at estimating the daily volcanic SO2 mass flux from satellite images (such as those provided by Sentinel‐5P/TROPOspheric Monitoring Instrument [TROPOMI]). The method calculates a “proto‐flux” using a regression, as a function of distance, of SO2 mass integrated in a series of nested circular domains centered on a volcano. After regression, a single multiplication by plume speed suffices to deduce the SO2 mass flux, without requiring a subsequent regression. This way, a range of plume speed and plume altitude scenarios can be easily explored. Noise level in the image is simultaneously evaluated by the regression, which allows for estimating posterior uncertainties on SO2 flux and improving the level of detection for weak sources in noisy environments. A statistical test is also introduced to automatically detect occurrences of volcanic degassing, lowering the risk of false positives. Application to multi‐year time‐series at Etna (2021) and Piton de la Fournaise (2021–2023) demonstrates (a) a reliable quantification of SO2 emissions across a broad range of degassing styles (from passive degassing to effusive or paroxysmal events), and (b) a reasonable day‐to‐day correlation between SO2 flux and seismic energy. The method is distributed as an open‐source software, and is implemented in an interactive web application within the “Volcano Space Observatory Portal,” facilitating near‐real‐time exploitation of the TROPOMI archive for both volcano monitoring and assessment of volcanic atmospheric hazards.

THE REAL TIME HIGH PRECISION CRIMINAL FACE RECOGNITION FOR ATM SECURITY IN DEEP LEARNING

The proposed ATM security system, utilizing composable deep face recognition, emphasizes user protection during transactions with a specific focus on eye retina accuracy. In the event of a potential threat identified through real-time face detection and tracking, an automatic alert is initiated. Prioritizing precision, the system employs advanced face detection algorithms for accurate location and tracking, even in dynamic scenarios. A down sampling technique ensures efficient processing of facial data, crucial for meticulous identification in crowded ATM locations. The integration of a face tracking ID unit enhances accuracy, providing continuous monitoring and persistence in identification, particularly focusing on the distinctive patterns in the eye retina. The scoring method, rooted in face tracking and embedding distance, significantly elevates user identification reliability, minimizing the risk of false positives, especially in criminal identification based on unique eye retina features. In response to potential threats, the system promptly generates automatic alerts and dispatches secure emails to relevant entities, including the affected card user, the respective bank, and cybersecurity authorities. These emails include comprehensive information, such as a high-precision image of the detected criminal's face, detailed facial recognition data, and a thorough summary of their known criminal history. This accuracy-driven and retina focused approach enhances safety, emphasizing continuous refinement and adherence to privacy and ethical considerations during implementation. KEYWORDS: Crime prevention, down-sampling, face recognition.

Screening of substance use in pregnancy: A Danish cross-sectional study.

There is a paucity of objectively verified data on substance use among Danish pregnant women. We estimated the prevalence of substance use including alcohol and nicotine among the general population of Danish pregnant women. In this anonymous, national, cross-sectional, descriptive study, pregnant women were invited when attending an ultrasound scan between November 2019 and December 2020 at nine Danish hospitals. Women submitted a urine sample and filled out a questionnaire. Urine samples were screened on-site with a qualitative urine dipstick for 15 substances including alcohol, nicotine, opioids, amphetamines, cannabis, and benzodiazepines. All screen-positive urine samples underwent secondary quantitative analyses with gold standard, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Results were compared to questionnaire information to analyze the validity of self-reporting and to examine possible cross-reactions. A total of 1903 of 2154 invited pregnant women participated (88.3%). The prevalence of dipstick-positive urine samples was 25.0%. 44.0% of these were confirmed positive, resulting in a total confirmed prevalence of 10.8%. The prevalence of nicotine use was 10.1%-and for all other substances, <0.5%. Nicotine use was more prevalent among younger pregnant women, while other substance use appeared evenly distributed over age groups. Self-reporting of use of nicotine products was high (71.1%), but low for cannabis and alcohol intake (0% and 33.3%, respectively). Prescription medication explained almost all cases of oxycodone, methylphenidate, and benzodiazepine use. Substance use among pregnant women consisted mainly of nicotine. Dipstick screening involved risks of false negatives and false positives. Except for alcohol intake and cannabis use, dipstick analyses did not seem to provide further information than self-reporting. LC-MS/MS analyses remain gold standard, and future role of dipstick screenings should be discussed.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Pre-problem families: predictive analytics and the future as the present

Predictive analytics is seen as a way of identifying the risk of future problems in families. Integral to such automated predictive analysis is a shift in time frames that redraws the relationship between families and the state, to potentially intervene on an anticipatory basis of ‘what hasn’t happened but might’. In the process, human subjects are reformulated as disembodied objects of data-driven futures. The article explains this process and fills a significant gap in knowledge about parents’ views of this development. We draw on group and individual discussions with parents across Great Britain to consider their understanding of predictive analytics and how comfortable they are with it. Parents’ concerns focused on inaccuracies in the data used for prediction, the unfair risk of false positives and false negatives, the deterministic implications of the past predicting the future, and the disturbing potential of being positioned in what was a pre-problem space. We conclude with policy implications.

Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.

Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and ethnicity, age, and breast density) on the performance of an AI algorithm interpreting negative screening digital breast tomosynthesis (DBT) examinations. Materials and Methods This retrospective cohort study identified negative screening DBT examinations from an academic institution from January 1, 2016, to December 31, 2019. All examinations had 2 years of follow-up without a diagnosis of atypia or breast malignancy and were therefore considered true negatives. A subset of unique patients was randomly selected to provide a broad distribution of race and ethnicity. DBT studies in this final cohort were interpreted by a U.S. Food and Drug Administration-approved AI algorithm, which generated case scores (malignancy certainty) and risk scores (1-year subsequent malignancy risk) for each mammogram. Positive examinations were classified based on vendor-provided thresholds for both scores. Multivariable logistic regression was used to understand relationships between the scores and patient characteristics. Results A total of 4855 patients (median age, 54 years [IQR, 46-63 years]) were included: 27% (1316 of 4855) White, 26% (1261 of 4855) Black, 28% (1351 of 4855) Asian, and 19% (927 of 4855) Hispanic patients. False-positive case scores were significantly more likely in Black patients (odds ratio [OR] = 1.5 [95% CI: 1.2, 1.8]) and less likely in Asian patients (OR = 0.7 [95% CI: 0.5, 0.9]) compared with White patients, and more likely in older patients (71-80 years; OR = 1.9 [95% CI: 1.5, 2.5]) and less likely in younger patients (41-50 years; OR = 0.6 [95% CI: 0.5, 0.7]) compared with patients aged 51-60 years. False-positive risk scores were more likely in Black patients (OR = 1.5 [95% CI: 1.0, 2.0]), patients aged 61-70 years (OR = 3.5 [95% CI: 2.4, 5.1]), and patients with extremely dense breasts (OR = 2.8 [95% CI: 1.3, 5.8]) compared with White patients, patients aged 51-60 years, and patients with fatty density breasts, respectively. Conclusion Patient characteristics influenced the case and risk scores of a Food and Drug Administration-approved AI algorithm analyzing negative screening DBT examinations. © RSNA, 2024.

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature.

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.