Number Of True Positives Research Articles

Diagnostic Value of 3.0 T Magnetic Resonance Imaging in Active Crohn's Disease.

Magnetic resonance enteroclysis (MRE) has been widely applied to diagnose Crohn's disease (CD). Magnetic resonance (MR) at 3.0 T improves signal-to-noise ratio (SNR), shortens image acquisition time, and shows more advantages. This study aimed to retrospectively analyze the diagnostic value of 3.0 T MR imaging for active CD. 48 CD patients hospitalized in our hospital from January 2021 to December 2022 were selected as the study subjects. These 48 CD patients underwent both double-balloon enteroscopy and 3.0 T MRE. All patients' arterial phase signal, venous phase signal, bowel wall, and bowel lumen of MRE were observed to identify whether they suffered from active CD. Based on the results of enteroscopy, the number of true positives, true negatives, false negatives, and false positives diagnosed by MRE were screened; next, the diagnostic accuracy, sensitivity, and specificity of MRE in assessing active CD were calculated. Of the 48 patients, 39 were diagnosed with small bowel CD by MRE, which was not significantly different from the results of enteroscopy (P>0.05). According to MRE diagnostic results, the arterial phase predominantly presented high signal intensity, and the venous phase mainly presented low signal intensity or isointensity. Small bowel CD lesions were primarily characterized by bowel wall thickening, rare pneumatosis enhancement of the bowel wall, bowel lumen pneumatosis or dilatation, and rare strictures. Besides, MRE presented an accuracy of 93.75%, sensitivity of 97.37%, and specificity of 80.00% in diagnosing CD. 3.0 T MR imaging has diagnostic value for active CD and shows certain clinical application value.</P>.

OP135 Machine Learning And Cancer Registry: Evaluation Of The Effectiveness Of Case Coding

IntroductionMachine learning (ML) algorithms are computational procedures that use pattern recognition and inference by learning from previously categorized documents to predict the category to which a new document belongs. The role of machine learning within cancer registries remains unclear given the lack of in-depth testing and guidance from health technology assessment (HTA) agencies. We evaluated the effectiveness of coding new cases through machine learning at the Integrated Cancer Registry.MethodsThe Integrated Cancer Registry covers the eastern area of Sicily in Italy, which has an annual average incidence of about 10,000 cases of malignant neoplasm. Potential new cancer cases were retrieved from pathology services and processed by pathologists who confirmed the neoplastic nature of supposed cases and specified the morphological type and location of the tumors. The current method involves identification by reading the free-text report when International Classification Diseases for Oncology information was not provided. We used the new Microsoft ML.Net Library, a framework developed in response to the challenge of facilitating machine learning pipeline utilization in large software applications. A total of 1,050,952 free-text pathology reports published from 2003 to 2018 were selected separately from all Sicilian pathology services and uploaded to machine learning software that explored eight binary classification algorithms.ResultsWe evaluated each algorithm’s performance by calculating metrics (the number of true positives, true negatives, false positives, and false negatives) from the classification procedure applied to the test dataset. The metrics used were accuracy, F1 score, and area under the receiver operating characteristic curve. With a test set of around 210,000 text diagnoses, each algorithm reached an F1 score of up to 95 percent.ConclusionsMachine learning algorithms capture relevant information about tumors from free-text pathology reports, optimizing the process and reducing waste. With the help of machine learning systems, cancer registries can provide more timely data for research and evaluation of all types of new cancer technologies (drugs, devices, radiology and radiotherapy equipment, diagnostic devices, robotic surgery, and vaccines).

Accuracy of the END-PAC Model in Predicting the Risk of Developing Pancreatic Cancer in Patients with New-Onset Diabetes: A Systematic Review and Meta-Analysis.

To investigate the performance of the END-PAC model in predicting pancreatic cancer risk in individuals with new-onset diabetes (NOD). The PRISMA statement standards were followed to conduct a systematic review. All studies investigating the performance of the END-PAC model in predicting pancreatic cancer risk in individuals with NOD were included. Two-by-two tables, coupled forest plots and summary receiver operating characteristic plots were constructed using the number of true positives, false negatives, true negatives and false positives. Diagnostic random effects models were used to estimate summary sensitivity and specificity points. A total of 26,752 individuals from four studies were included. The median follow-up was 3 years and the pooled risk of pancreatic cancer was 0.8% (95% CI 0.6-1.0%). END-PAC score ≥ 3, which classifies the patients as high risk, was associated with better predictive performance (sensitivity: 55.8% (43.9-67%); specificity: 82.0% (76.4-86.5%)) in comparison with END-PAC score 1-2 (sensitivity: 22.2% (16.6-29.2%); specificity: 69.9% (67.3-72.4%)) and END-PAC score < 1 (sensitivity: 18.0% (12.8-24.6%); specificity: 50.9% (48.6-53.2%)) which classify the patients as intermediate and low risk, respectively. The evidence quality was judged to be moderate to high. END-PAC is a promising model for predicting pancreatic cancer risk in individuals with NOD. The score ≥3 should be considered as optimum cut-off value. More studies are needed to assess whether it could improve early pancreatic cancer detection rate, pancreatic cancer re-section rate, and pancreatic cancer treatment outcomes.

FTP7.5 Accuracy of the END-PAC model in predicting the risk of developing pancreatic cancer in patients with new-onset diabetes: a systematic review and meta-analysis

Abstract Objectives to evaluate the accuracy of END-PAC model in predicting the risk of pancreatic cancer in patients with new-onset diabetes (NOD). Methods A systematic review in accordance with PRISMA statement standards was conducted to identify all studies evaluating the accuracy of END-PAC model in predicting the risk of pancreatic cancer in patients with NOD. The number of true positives, false negatives, true negatives, and false positives were used to construct two-by-two tables, coupled forest plots and the summary receiver operating characteristic plots. We estimated summary sensitivity and specificity points using diagnostic random effects model. Results We analysed 26752 patients from four studies. The pooled risk of pancreatic cancer was 0.8% (95% CI 0.6%-1.0%) with median follow-up of 3 years. END-PAC score ≥3 [sensitivity: 55.8% (43.9%-67%); specificity: 82.0% (76.4%-86.5%)] which classifies the patients as high risk was associated with better predictive performance than END-PAC score 1–2 [sensitivity: 22.2% (16.6%-29.2%); specificity: 69.9% (67.3%-72.4%)] and END-PAC score &amp;lt;1 [sensitivity: 18.0% (12.8%-24.6%); specificity: 50.9% (48.6%-53.2%)] which classify the patients as intermediate and low risks, respectively. The quality of the available evidence was moderate to high. Conclusions END-PAC model is promising in predicting the risk of pancreatic cancer in patients with new-onset diabetes. END-PAC score ≥3, which classifies the patients as high risk, has the best predictive performance and should be considered as optimum cut-off value. Future comparative studies are required to evaluate whether application of END-PAC model could improve pancreatic cancer early detection rate, pancreatic cancer resection rate, and pancreatic cancer treatment outcomes.

Meta-analysis of the diagnostic value of serum procalcitonin for burn sepsis in adults.

Serum procalcitonin (PCT) reflects the infection status of the organism and the severity of the infection. The purpose of this study was to systematically evaluate the diagnostic value of serum PCT for burn sepsis in adults and to provide a factual basis for future clinical diagnosis and decision-making. On August 16, 2022, six databases were searched in this study and a total of 856 studies were found. The retrieved literature was comprehensively evaluated according to the inclusion and exclusion criteria, and the valid data were extracted and included for analysis. The number of true positives, false positives, true negatives and false negatives were used as indicators to evaluate the diagnostic value of serum PCT for burn sepsis in adults. In total, 15 studies met the inclusion criteria. Meta-analysis showed a combined sensitivity of 0.78 (95% CI: 0.69-0.84), a combined specificity of 0.85 (95% CI: 0.77-0.91), a combined positive likelihood ratio of 5.17 (95% CI: 3.25-8.25), a combined negative likelihood ratio of 0.26 (95% CI: 0.19-0.37), and a combined diagnostic ratio of 19.63 (95% CI: 10.17-37.90). The AUC was 0.88 (95% CI: 0.85-0.90). Serum PCT provides good early diagnostic benefits for burn sepsis in adults. More high-quality studies are required to clarify its specific early diagnostic value.

Segmentation of acute pulmonary embolism in computed tomography pulmonary angiography using the deep learning method

Pulmonary embolism is a type of thromboembolism seen in the main pulmonary artery and its branches. This study aimed to diagnose acute pulmonary embolism using the deep learning method in computed tomographic pulmonary angiography (CTPA) and perform the segmentation of pulmonary embolism data. The CTPA images of patients diagnosed with pulmonary embolism who underwent scheduled imaging were retrospectively evaluated. After data collection, the areas that were diagnosed as embolisms in the axial section images were segmented. The dataset was divided into three parts: training, validation, and testing. The results were calculated by selecting 50% as the cut-off value for the intersection over the union. Images were obtained from 1.550 patients. The mean age of the patients was 64.23 ± 15.45 years. A total of 2.339 axial computed tomography images obtained from the 1.550 patients were used. The PyTorch U-Net was used to train 400 epochs, and the best model, epoch 178, was recorded. In the testing group, the number of true positives was determined as 471, the number of false positives as 35, and 27 cases were not detected. The sensitivity of CTPA segmentation was 0.95, the precision value was 0.93, and the F1 score value was 0.94. The area under the curve value obtained in the receiver operating characteristic analysis was calculated as 0.88. In this study, the deep learning method was successfully employed for the segmentation of acute pulmonary embolism in CTPA, yielding positive outcomes.

Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca2+ Imaging Datasets.

Intracellular Ca2+ signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca2+ is routinely performed using fluorescence microscopy with biological indicators. Analysis of deterministic signals is reasonably straightforward as relevant data can be discriminated based on the timing of cellular responses. However, analysis of stochastic, slower oscillatory events, as well as rapid subcellular Ca2+ responses, takes considerable time and effort which often includes visual analysis by trained investigators, especially when studying signals arising from cells embedded in complex tissues. The purpose of the current study was to determine if full-frame time-series and line-scan image analysis workflow of Fluo-4 generated Ca2+ fluorescence data from vascular myocytes could be automated without introducing errors. This evaluation was addressed by re-analyzing a published "gold standard" full-frame time-series dataset through visual analysis of Ca2+ signals from recordings made in pulmonary arterial myocytes of en face arterial preparations. We applied a combination of data driven and statistical approaches with comparisons to our published data to assess the fidelity of the various approaches. Regions of interest with Ca2+ oscillations were detected automatically post hoc using the LCPro plug-in for ImageJ. Oscillatory signals were separated based on event durations between 4 and 40 s. These data were filtered based on cutoffs obtained from multiple methods and compared to the published manually curated "gold standard" dataset. Subcellular focal and rapid Ca2+ "spark" events from line-scan recordings were examined using SparkLab 5.8, which is a custom automated detection and analysis program. After filtering, the number of true positives, false positives, and false negatives were calculated through comparisons to visually derived "gold standard" datasets. Positive predictive value, sensitivity, and false discovery rates were calculated. There were very few significant differences between the automated and manually curated results with respect to quality of the oscillatory and Ca2+ spark events, and there were no systematic biases in the data curation or filtering techniques. The lack of statistical difference in event quality between manual data curation and statistically derived critical cutoff techniques leads us to believe that automated analysis techniques can be reliably used to analyze spatial and temporal aspects to Ca2+ imaging data, which will improve experiment workflow.

Protocol for a systematic review and meta-analysis of the diagnostic test accuracy of host and HPV DNA methylation in cervical cancer screening and management

IntroductionHuman papillomavirus (HPV) is necessary but not sufficient for cervical cancer development. During cervical carcinogenesis, methylation levels increase across host and HPV DNA. DNA methylation has been proposed as a...

ProInfer: An interpretable protein inference tool leveraging on biological networks.

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy.

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Diagnostic value of cell-free DNA in thyroid cancer: A systematic review and meta-analysis.

An increasing number of studies have shown the potential diagnostic value of cell-free DNA (cfDNA) as a new biomarker in the management of thyroid cancer (TC); however, the accuracy of research results is inconsistent. This meta-analysis is the first to synthesize published results and evaluate the application value of circulating cfDNA in the diagnosis of TC. A search strategy was developed according to PICO (P: Patient; I: Intervention; C: Comparison; O: Outcome) principles. We searched 5 databases until October 2022. Original studies that examined cfDNA for the diagnosis of TC and used pathology as the gold standard were included in this meta-analysis. A random-effects model was used to pool the data extracted from individual studies, including the number of patients and the numbers of true positives, false positives, true negatives, and false negatives. A total of 622 patients with TC, 547 patients with benign thyroid nodules, and 98 healthy individuals were included in 20 studies reported in 14 articles. The types of cfDNA included in the research include specific mutations of cfDNA, methylation of cfDNA, the content of cfDNA, and cfDNA index. After rigorous statistical analysis, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.76 (95% confidence interval [CI] 0.62-0.85), 0.87 (95% CI 0.78-0.93), 5.08 (95% CI 3.3-10.3), 0.28 (95% CI 0.17-0.46), 21 (95% CI 9-49), and 0.89 (95% CI 0.86-0.91), respectively. The meta-regression results showed that the number of cfDNAs, cfDNA methylation status, and sample size were the sources of heterogeneity in the specificity of the study. A subgroup analysis showed that the quantitative analysis group (cfDNA level) had a higher diagnostic accuracy than that of the qualitative analysis group (cfDNA methylation, mutation, or integrity index), with a sensitivity of 0.84, specificity of 0.89, and area under the curve of 0.91. The results of this meta-analysis suggest that cfDNA has value as an adjunct for the diagnosis of TC. Quantitative detection of cfDNA can achieve relatively high diagnostic accuracy. However, due to heterogeneity, the test results based on cfDNA for TC should be interpreted with caution.

Optimizing Screening for Colorectal Cancer: An Algorithm Combining Fecal Immunochemical Test, Blood-Based Cancer-Associated Proteins and Demographics to Reduce Colonoscopy Burden

BackgroundFecal Immunochemical Test (FIT) is widely used in population-based screening for colorectal cancer (CRC). This had led to major challenges regarding colonoscopy capacity. Methods to maintain high sensitivity without compromising the colonoscopy capacity are needed. This study investigates an algorithm that combines FIT result, blood-based biomarkers associated with CRC, and individual demographics, to triage subjects sent for colonoscopy among a FIT positive (FIT+) screening population and thereby reduce the colonoscopy burden. Materials and methodsFrom the Danish National Colorectal Cancer Screening Program, 4048 FIT+ (≥100 ng/mL Hemoglobin) subjects were included and analyzed for a panel of 9 cancer-associated biomarkers using the ARCHITECT i2000. Two algorithms were developed: 1) a predefined algorithm based on clinically available biomarkers: FIT, age, CEA, hsCRP and Ferritin; and 2) an exploratory algorithm adding additional biomarkers: TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, B2M and sex to the predefined algorithm. The diagnostic performances for discriminating subjects with or without CRC in the 2 models were benchmarked against the FIT alone using logistic regression modeling. ResultsThe discrimination of CRC showed an area under the curve (AUC) of 73.7 (70.5-76.9) for the predefined model, 75.3 (72.1-78.4) for the exploratory model, and 68.9 (65.5-72.2) for FIT alone. Both models performed significantly better (P < .001) than the FIT model. The models were benchmarked vs. FIT at cutoffs of 100, 200, 300, 400, and 500 ng/mL Hemoglobin using corresponding numbers of true positives and false positives. All performance metrics were improved at all cutoffs. ConclusionA screening algorithm including a combination of FIT result, blood-based biomarkers and demographics outperforms FIT in discriminating subjects with or without CRC in a screening population with FIT results above 100 ng/mL Hemoglobin.

Endoscopic Assessment of Radiological Stage IVA Cervical Cancer: A Bivariate Meta-Analysis Supporting an Evidence-Based Staging Algorithm

<h3>Purpose/Objective(s)</h3> To optimize the use of confirmatory endoscopic exams (cystoscopy/proctoscopy) in the staging of locally advanced cervical cancer (LACC), the present study evaluates the predictive value of radiological exams (CT and MRI) to detect bladder/rectum invasion. <h3>Materials/Methods</h3> A systematic search of databases (PubMed and EMBASE) was performed (CRD42021270329). The inclusion criteria were: a) cervix cancer diagnosis; b) staging CT and/or MRI (index test); c) staging cystoscopy and/or proctoscopy (standard test); and d) numbers of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) provided. A random-effects bivariate meta-analysis of positive predictive value (PPV) and negative predictive value (NPV) was performed with moderator analyses by imaging modality (CT and MRI) and prevalence. <h3>Results</h3> Nineteen studies met the inclusion criteria, totaling 3480 and 1641 patients for bladder and rectum analyses, respectively. For bladder invasion (prevalence ranged from 0.9% to 34.5%), the overall PPV was 45% (95% confidence interval, 33%-57%, based on 19 studies). Per subgroup, the PPV was 31% for MRI/prevalence ≤6%, 33% for CT/prevalence ≤6%, and 69% for CT/prevalence >6%. For rectal invasion (prevalence ranged from 0.4% to 20.0%), the overall PPV was 30% (95% confidence interval, 17%-47%, based on 8 studies). Per subgroup, the PPV was 36% for MRI/prevalence ≤1%, 17% for MRI/prevalence >1%, and 38% for CT/prevalence >1%. The overall NPV for bladder invasion and rectal invasion were 98% (95% confidence interval, 97%-99%) and 100% (95% confidence interval, 99%-100%), respectively. Considering prevalence and radiological modality, the point estimate of NPV varied from 95% to 100% for bladder invasion and from 99% to 100% for rectum invasion. <h3>Conclusion</h3> Due to low PPV (<50%) of radiological staging, endoscopic exams may be necessary to correctly assess radiological stage IVA LACC. However, they are not necessary after negative radiological exam (NPV ≥95%).

Diagnostic accuracy of magnetic resonance imaging for nerve injury in obstetric brachial plexus injury: protocol for systematic review and meta-analysis

BackgroundEarly and accurate clinical diagnosis of the extent of obstetric brachial plexus injury (OBPI) is challenging. The current gold standard for delineating the nerve injury is surgical exploration, and synchronous reconstruction is performed if indicated. Magnetic resonance imaging (MRI) is a non-invasive method of assessing the anatomy and severity of nerve injury in OBPI but the diagnostic accuracy is unclear.The primary objective of this review is to determine the diagnostic accuracy of MRI in comparison to surgical brachial plexus exploration for detecting root avulsion in children under 5 with OBPI. The secondary objectives are to determine its’ diagnostic accuracy for detecting nerve abnormality and detecting pseudomeningocele(s) in this group.MethodsThis review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).We will include studies reporting the accuracy of MRI (index test) compared to surgical exploration (reference standard) in detecting any of the three target conditions (root avulsion, any nerve abnormality and pseudomeningocele) in children under five with OBPI. Case reports and studies where the number of true positives, false positives, true negatives and false negatives cannot be derived will be excluded. We plan to search PubMed, Embase and CENTRAL for relevant studies from database inception to 15 June 2022. We will also search grey literature (medRxiv, bioRxiv and Google Scholar) and perform forward and backward citation chasing. Screening and full-text assessment of eligibility will be conducted by two independent reviewers, who will then both extract the relevant data. The QUADAS-2 tool will be used to assess methodological quality and risk of bias of included studies by two reviewers independently. The following test characteristics for the target conditions will be extracted: true positives, false positives, true negatives and false negatives. Estimates of sensitivity and specificity with 95% confidence intervals will be shown in forest plots for each study. If appropriate, summary sensitivities and specificities for target conditions will be obtained via meta-analyses using a bivariate model.DiscussionThis study will aim to clarify the diagnostic accuracy of MRI for detecting nerve injury in OBPI and define its clinical role.Systematic review registrationPROSPERO CRD42021267629.

Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis.

Aims/IntroductionRecently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus. However, its predictive ability remains inconclusive. This meta‐analysis evaluated the current ability of ML algorithms for predicting incident type 2 diabetes mellitus.Materials and MethodsWe systematically searched longitudinal studies published from 1 January 1950 to 17 May 2020 using MEDLINE and EMBASE. Included studies had to compare ML’s classification with the actual incidence of type 2 diabetes mellitus, and present data on the number of true positives, false positives, true negatives and false negatives. The dataset for these four values was pooled with a hierarchical summary receiver operating characteristic and a bivariate random effects model.ResultsThere were 12 eligible studies. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.81 (95% confidence interval [CI] 0.67–0.90), 0.82 [95% CI 0.74–0.88], 4.55 [95% CI 3.07–6.75] and 0.23 [95% CI 0.13–0.42], respectively. The area under the summarized receiver operating characteristic curve was 0.88 (95% CI 0.85–0.91).ConclusionsCurrent ML algorithms have sufficient ability to help clinicians determine whether individuals will develop type 2 diabetes mellitus in the future. However, persons should be cautious before changing their attitude toward future diabetes risk after learning the result of the diabetes prediction test using ML algorithms.

P595 Validation of a predictive algorithm for thiopurine induced hepatotoxicity in IBD patients

Abstract Background The thiopurines, azathioprine (AZA) and mercaptopurine (MP), are effective and remain standard treatment options in steroid sparing and maintaining remission in patients with inflammatory bowel disease (IBD). Approximately 25% of patients discontinue within three months after treatment initiation due to adverse events, of which about half due to hepatotoxicity. We hypothesise that identification of patients with an increased risk of adverse events and timely treatment optimisation (e.g. dose adjustment, adjuvant allopurinol) can prevent treatment failure due to adverse events. The primary objective of this prospective observational multicentre study was to optimise and validate a proposed hepatotoxicity predictive algorithm in IBD patients starting AZA or MP therapy. Methods We adapted an algorithm from a previous study to predict the risk of developing hepatotoxicity in thiopurine drug treatment using multivariable logistic regression and a receiver operating characteristic curve. The determinants age, BMI and 6-MMPR-concentration one week after start of treatment (T=1) were inserted as continuous variables in this algorithm. Sex was inserted as a dichotomous variable. Inclusion criteria were adult thiopurine-naive IBD patients initiating AZA or MP treatment. Subjects were treated according to guidelines and followed for 12 weeks. The primary study outcome was hepatotoxicity within 12 weeks, defined as ALAT &amp;gt; 2x ULN or an R value ((ALAT / ULN ALAT)/(AP / ULN ALP)) ≥ 5. The hepatotoxicity- and no-hepatotoxicity groups were compared using the Mann-Whitney U-test. Results In total, 255 patients were included from Dec 2015 to June 2019, of whom 26 were excluded because of no actual start with AZA or MP (n=18), loss to follow up (n=7) or no IBD diagnosis (n=1). Out of 229 patients 21 (9%) developed hepatotoxicity. Five out of 21 patients (24%) in the hepatotoxicity-group and 123 out of 208 patients (59%) in the no-hepatotoxicity-group had to be excluded for analysis (see Figure 1). Ninety-three % of patients received MP with a median dose of 0.7 mg/kg (95%CI 0.3–1.4 mg/kg). Median dose of AZA (7%) was 2.0 mg/kg (95%CI 1.1 - 2.6 mg/kg). There was a difference between the hepatotoxicity and no-hepatotoxicity group in BMI (28 versus 24, p=0.022) and 6-MMPR/6-TGN ratio at T=1 (16 versus 9, p=0.027). In Table 1, the number of true positives, true negatives, false positives and false negatives are presented. The diagnostic values are shown in Table 2. A specificity of 77% (95%CI 66–85%) and a sensitivity of 50% (95%CI 26–75%) was obtained. Conclusion In conclusion, the adapted algorithm does not accurately predict hepatotoxicity in this cohort of patients on relatively low-dose thiopurine treatment.

Application Value of Combined Detection of DCE-MRI and Serum Tumor Markers HE4, Ki67, and HK10 in the Diagnosis of Ovarian Cancer.

Objective To investigate the application value of the combined detection of DCE-MRI and serum tumor markers (HE4, Ki67, and HK10) in the diagnosis of ovarian cancer. Methods The clinical data of 40 patients with advanced ovarian cancer (AOC) confirmed by surgery and pathology in our hospital from February 2019 to February 2020 were retrospectively analyzed. All patients received DCE-MRI, the detection of serum tumor markers HE4, Ki67, and HK10, and the combined detection of DCE-MRI and the serum tumor markers (HE4, Ki67, and HK10). The application value of the three detection methods was analyzed. Results The number of true positives in the single detection (DCE-MRI detection and the detection of serum HE4, Ki67, and HK10) was notably lower than that in the combined detection. The sensitivity, specificity, and accuracy of the single detection were notably lower compared with the combined detection. The area under the curve in the ROC of the combined detection was notably larger than that of the single detection. The results of the combined detection were better than those of the single detection (P < 0.05), with the highest sensitivity of the combined detection. Conclusion The combined detection of DCE-MRI and the serum tumor markers (HE4, Ki67, and HK10) can effectively improve the diagnostic accuracy of AOC patients, with high sensitivity and specificity, which has an important diagnostic value in clinic.

Aplicação de um sistema de pontuação de alerta precoce em uma enfermaria pediátrica de um hospital terciário

OBJECTIVE: To evaluate the performance the Brighton Pediatric Early Warning Score for an early identification of clinical deterioration in children admitted to the pediatric ward of a tertiary hospital. METHODS: A prospective cohort observational study, involving 325 children with acute illnesses admitted to a pediatric ward, aged 29 days to 13 years, between August 2018 and March 2020. To assess the Brighton Pediatric Early Warning Score diagnostic accuracy, it was used the Cardiff and Vale Pediatric Early Warning System as reference, and the software used was the R Core Team 2020. The effectiveness of the Brighton Pediatric Early Warning Score was assessed using the indicators of sensitivity, specificity, positive predictive value, negative predictive value, area under the ROC curve, number of true positives and negatives and false positives and negatives. RESULTS: The Brighton Pediatric Early Warning Score showed a sensitivity of 19.2%, a specificity of 99.1%, a positive predictive value of 90.5%, a negative predictive value of 73.7%, and an area under the ROC curve of 0.839. In addition to that, for scores larger than 3, there were 19 true positives, 224 true negatives, 2 false positives and 80 false negatives. CONCLUSION: The Brighton Pediatric Early Warning Score performed well in the scenario analysis chosen by this research, showing how easy it is to use it for an early recognition of clinical deterioration in children admitted to pediatric wards.

Diagnostic Accuracy of Ultrasonographic Respiratory Variation in the Inferior Vena Cava, Subclavian Vein, Internal Jugular Vein, and Femoral Vein Diameter to Predict Fluid Responsiveness: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis aimed to investigate the ultrasonographic variation of the diameter of the inferior vena cava (IVC), internal jugular vein (IJV), subclavian vein (SCV), and femoral vein (FV) to predict fluid responsiveness in critically ill patients. Relevant articles were obtained by searching PubMed, EMBASE, and Cochrane databases (articles up to 21 October 2021). The number of true positives, false positives, false negatives, and true negatives for the index test to predict fluid responsiveness was collected. We used a hierarchical summary receiver operating characteristics model and bivariate model for meta-analysis. Finally, 30 studies comprising 1719 patients were included in this review. The ultrasonographic variation of the IVC showed a pooled sensitivity and specificity of 0.75 and 0.83, respectively. The area under the receiver operating characteristics curve was 0.86. In the subgroup analysis, there was no difference between patients on mechanical ventilation and those breathing spontaneously. In terms of the IJV, SCV, and FV, meta-analysis was not conducted due to the limited number of studies. The ultrasonographic measurement of the variation in diameter of the IVC has a favorable diagnostic accuracy for predicting fluid responsiveness in critically ill patients. However, there was insufficient evidence in terms of the IJV, SCV, and FV.

Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values P ≥ 0.05 . The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different P ≥ 0.05 . In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference P ≥ 0.05 . The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant P &lt; 0.05 . In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors.