The accuracy of the Edinburgh Red Eye Diagnostic Algorithm.
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ABSTRACT:
SUBMITTER: Timlin H
PROVIDER: S-EPMC4429284 | biostudies-other | 2015 May
REPOSITORIES: biostudies-other
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Project description:BackgroundThe screening and diagnosis of intermittent claudication is a challenging process and often relies on the expertise of specialist vascular clinicians. We sought to investigate the diagnostic performance of the Edinburgh Claudication Questionnaire (ECQ) as a screening tool for referrals of suspected intermittent claudication from primary to secondary care.MethodProspectively, 100 referrals from primary care with a stated diagnosis or query regarding intermittent claudication were recruited. All participants who completed the ECQ, underwent an anklebrachial pressure index (ABPI) assessment and treadmill exercise testing. Outcomes of the ECQ were compared to clinical diagnoses of intermittent claudication.ResultsThe ECQ had a sensitivity of 46.8% (95% CI: 27-65%), specificity of 63.2% (95% CI: 43-82%) and accuracy of 53.0% (95% CI: 43-63%). The diagnostic performance was not changed by combining the ECQ with a positive ABPI or post-exercise ABPI outcome for PAD.ConclusionThe ECQ had a poor diagnostic performance in this cohort. Considering the results found here and in other recent studies, the utility of the ECQ as a screening tool and epidemiological survey tool must be questioned. Novel, low-resource diagnostic tools are needed in this population.
| S-EPMC9936437 | biostudies-literature
Project description:Background In 2017, the Liver Imaging Reporting and Data System (LI-RADS) included an algorithm for the assessment of hepatocellular carcinoma (HCC) treated with local-regional therapy. The aim of the algorithm was to enable standardized evaluation of treatment response to guide subsequent therapy. However, the performance of the algorithm has not yet been validated in the literature. Purpose To evaluate the performance of the LI-RADS 2017 Treatment Response algorithm for assessing the histopathologic viability of HCC treated with bland arterial embolization. Materials and Methods This retrospective study included patients who underwent bland arterial embolization for HCC between 2006 and 2016 and subsequent liver transplantation. Three radiologists independently assessed all treated lesions by using the CT/MRI LI-RADS 2017 Treatment Response algorithm. Radiology and posttransplant histopathology reports were then compared. Lesions were categorized on the basis of explant pathologic findings as either completely (100%) or incompletely (<100%) necrotic, and performance characteristics and predictive values for the LI-RADS Treatment Response (LR-TR) Viable and Nonviable categories were calculated for each reader. Interreader association was calculated by using the Fleiss κ. Results A total of 45 adults (mean age, 57.1 years ± 8.2; 13 women) with 63 total lesions were included. For predicting incomplete histopathologic tumor necrosis, the accuracy of the LR-TR Viable category for the three readers was 60%-65%, and the positive predictive value was 86%-96%. For predicting complete histopathologic tumor necrosis, the accuracy of the LR-TR Nonviable category was 67%-71%, and the negative predictive value was 81%-87%. By consensus, 17 (27%) of 63 lesions were categorized as LR-TR Equivocal, and 12 of these lesions were incompletely necrotic. Interreader association for the LR-TR category was moderate (κ = 0.55; 95% confidence interval: 0.47, 0.67). Conclusion The Liver Imaging Reporting and Data System 2017 Treatment Response algorithm had high predictive value and moderate interreader association for the histopathologic viability of hepatocellular carcinoma treated with bland arterial embolization when lesions were assessed as Viable or Nonviable. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Gervais in this issue.
| S-EPMC6614909 | biostudies-literature
Project description:BackgroundIdentification of lobar spontaneous intracerebral haemorrhage associated with cerebral amyloid angiopathy (CAA) is important because it is associated with a higher risk of recurrent intracerebral haemorrhage than arteriolosclerosis-associated intracerebral haemorrhage. We aimed to develop a prediction model for the identification of CAA-associated lobar intracerebral haemorrhage using CT features and genotype.MethodsWe identified adults with first-ever intracerebral haemorrhage diagnosed by CT, who died and underwent research autopsy as part of the Lothian IntraCerebral Haemorrhage, Pathology, Imaging and Neurological Outcome (LINCHPIN) study, a prospective, population-based, inception cohort. We determined APOE genotype and radiologists rated CT imaging appearances. Radiologists were not aware of clinical, genetic, and histopathological features. A neuropathologist rated brain tissue for small vessel diseases, including CAA, and was masked to clinical, radiographic, and genetic features. We used CT and APOE genotype data in a logistic regression model, which we internally validated using bootstrapping, to predict the risk of CAA-associated lobar intracerebral haemorrhage, derive diagnostic criteria, and estimate diagnostic accuracy.FindingsAmong 110 adults (median age 83 years [IQR 76-87], 49 [45%] men) included in the LINCHPIN study between June 1, 2010 and Feb 10, 2016, intracerebral haemorrhage was lobar in 62 (56%) participants, deep in 41 (37%), and infratentorial in seven (6%). Of the 62 participants with lobar intracerebral haemorrhage, 36 (58%) were associated with moderate or severe CAA compared with 26 (42%) that were associated with absent or mild CAA, and were independently associated with subarachnoid haemorrhage (32 [89%] of 36 vs 11 [42%] of 26; p=0·014), intracerebral haemorrhage with finger-like projections (14 [39%] of 36 vs 0; p=0·043), and APOE ɛ4 possession (18 [50%] of 36 vs 2 [8%] of 26; p=0·0020). A prediction model for CAA-associated lobar intracerebral haemorrhage using these three variables had excellent discrimination (c statistic 0·92, 95% CI 0·86-0·98), confirmed by internal validation. For the rule-out criteria, neither subarachnoid haemorrhage nor APOE ɛ4 possession had 100% sensitivity (95% CI 88-100). For the rule-in criteria, subarachnoid haemorrhage and either APOE ɛ4 possession or finger-like projections had 96% specificity (95% CI 78-100).InterpretationThe CT and APOE genotype prediction model for CAA-associated lobar intracerebral haemorrhage shows excellent discrimination in this cohort, but requires external validation. The Edinburgh rule-in and rule-out diagnostic criteria might inform prognostic and therapeutic decisions that depend on identification of CAA-associated lobar intracerebral haemorrhage.FundingUK Medical Research Council, The Stroke Association, and The Wellcome Trust.
| S-EPMC5818029 | biostudies-literature
Project description:IntroductionVestibular assessment in children with sensorineural hearing loss (SNHL) is critical for early vestibular rehabilitation therapy to promote (motor) development or guide decision making towards cochlear implantation (timing of surgery and laterality). It can be challenging from a clinical viewpoint to decide which vestibular tests should be performed for a pediatric patient. The aim of this study was to evaluate the diagnostic accuracy of several clinically available vestibular tests in children with SNHL, and to provide recommendations for the implementation of vestibular testing of children in clinical practice, to screen for vestibular hypofunction (VH).MethodsA two-center retrospective chart review was conducted. Eighty-six patients between the age of 0 and 18 years were included in this study with SNHL. Vestibular tests included video headimpulse test (VHIT), caloric test (performed at the age of four or higher), rotatory chair and cervical vestibular evoked myogenic potential (cVEMP). A combination of the clinical assessment and (combinations of) vestibular test outcomes determined the diagnosis. The diagnostic quality of tests and combination of tests was assessed by diagnostic accuracy, sensitivity and specificity.ResultsVH was diagnosed in 44% of the patients. The VHIT and caloric test showed the highest diagnostic accuracy compared to the rotatory chair and cVEMP. All combinations of VHIT, caloric test and cVEMP showed improvement of the diagnostic accuracy compared to the respective tests when performed singularly. All combinations of tests showed a relatively similar diagnostic accuracy, with the VHIT combined with the caloric test scoring the highest. Adding a third test did not substantially improve the diagnostic accuracy.DiscussionVestibular testing is feasible and VH is highly prevalent in children with SNHL. A proposed diagnostic algorithm recommends starting with VHIT, followed by cVEMP for children under the age of four, and caloric testing for older children if VH is not confirmed with the first test. Performing a third test is redundant as the diagnostic accuracy does not improve substantially. However, challenges remain, including the lack of a gold standard and the subjective nature of the diagnosis, highlighting the need for standardized testing and increased understanding of VH in this population.
| S-EPMC10867167 | biostudies-literature
Project description:INTRODUCTION. Liquid biopsies are a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. MATERIALS AND METHODS. TEPs from 297 subjects (53 EC patients, 40 patients with benign gynecologic conditions and 204 healthy women) were RNA-sequenced. DNA sequencing was performed in 519 primary tumor tissue samples and in16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS. Platelet-dedicated classifier yielded AUC of 93.1% in test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was relatively low, with AUC of 60.7%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 91.4% and ctDNA blood samples with AUC of 87.5%. CONCLUSIONS Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work, involving more cases, is warranted.
2021-12-01 | GSE184904 | GEO
Project description:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.
2023-01-30 | GSE215240 | GEO
Project description:BackgroundClinical practice of Chinese medicine requires little information for differentiation of Zang-fu patterns. This study is to test the impact of information amount on the diagnostic accuracy of pattern differentiation algorithm (PDA) using stochastic simulation of cases.MethodsA dataset with 69 Zang-fu single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). A variable quantity of available information (N(%)) was randomly sampled to generate 100 true positive and 100 true negative manifestation profiles per pattern to which PDA was applied. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. The algorithm performed pattern differentiation by ranking a list of diagnostic hypotheses by the amount of explained information F(%). Accuracy, sensitivity, specificity and negative and positive predictive values were calculated.ResultsUse the Four Examinations resulted in the best accuracy with the smallest cutoff value (N(%)= 28.5%), followed by Ip+AO+Iq (33.5%), Ip+AO (51.5%) and Ip (52.0%). All tested combinations provided concave-shaped curves for accuracy, indicating an optimal value subject to N(%-cutoff). Use of N(%-cutoff) as a secondary criterion resulted in 94.7% (94.3; 95.1) accuracy, 89.8% (89.1; 90.6) sensitivity, and 99.5% (99.3; 99.7) specificity with the Four Examinations.ConclusionPattern differentiation based on both explained and optimum available information (F(%) and N(%-cutoff)) is more accurate than using explained and available information without cutoff (F(%) and N(%)). Both F(%) and N(%-cutoff) should be used as PDA's objective criteria to perform Zang-fu single pattern differentiation.
| S-EPMC2806360 | biostudies-literature
Project description:ObjectivesTo perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm.Materials and methodsOncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale.ResultsFifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%.ConclusionsDLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm.Clinical relevance statementDeep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm.Key points• Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.
| S-EPMC10957592 | biostudies-literature
Project description:BackgroundThe discovery of the cell-free foetal DNA (cffDNA) circulating in the maternal plasma enabled prediction of foetal RHD thus eliminating the risks associated with invasive procedures. Non-invasive foetal RHD genotyping has now become the standard approach in developed countries for management of alloimmunised women and is also used for targeted antenatal prophylaxis in non-alloimmunised women.Materials and methodscffDNA was extracted from the plasma of 217 RhD negative pregnant women at a gestational age of 10-34 weeks. The foetal RHD genotype was determined by real-time polymerase chain reaction (real-time PCR) amplification of exons 4, 5 and 10 in duplicates. After an initial 54 samples, foetal typing was carried out with RHD exons 5 and 10 for the remaining samples. CCR5, SRY and RASSF1A genes were used as controls. Results were compared with cord blood serological typing at birth.ResultsOut of the 217 women, 193 were non-immunised and 24 were alloimmunised. A conclusive diagnosis was obtained in 203 samples. Diagnosis was inconclusive in 14 samples; of these, foetal RHD genotype could be resolved in six samples after maternal and paternal RHD genotyping. A 100% diagnostic accuracy, sensitivity and specificity were demonstrated in 209 women who had had a conclusive result. When the inconclusive samples were included, diagnostic accuracy and sensitivity were more than 95% and specificity was 78.95%.DiscussionAnti-D is still the leading cause of haemolytic disease of the foetus and the newborn in India. There is, therefore, a need to establish and develop an algorithm for antenatal RhD negative women in India. The positive results of non-invasive foetal RHD genotyping, from the start of the 10th week of gestation using two RHD exons giving 100% diagnostic accuracy, show promise for routine diagnostic use to the benefit of the antenatal RhD negative Indian population.
| S-EPMC9068359 | biostudies-literature
Project description:PurposePrompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy.MethodsPatients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared.ResultsSynovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751-0.923) than synovial WBC count (0.740, 95% confidence interval 0.684-0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application ( www.septicknee.com ).ConclusionThe diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model.Level of evidenceDiagnostic study Level III (Case-control study).
| S-EPMC8458173 | biostudies-literature