Excellent Positive Predictive Value Research Articles

MRI of pelvic endometriosis: evaluation of the mr#Enzian classification and the importance of adenomyosis subtypes.

This study aimed to investigate the utility of the #Enzian classification in magnetic resonance imaging (MRI) for endometriosis assessment, focusing on inter-reader agreement, diagnostic accuracy, and the correlation of adenomyosis with deep endometriosis (DE). This IRB- approved retrospective single-center study included 412 women who underwent MRI evaluation for endometriosis between February 2017 and June 2022. Two experienced radiologists independently analyzed MRI images using the #Enzian classification and assessed the type of adenomyosis, if any. The surgical #Enzian classification served as the gold standard for evaluating preoperative MRI results of 45 patients. Statistical analysis was performed to assess inter-reader agreement and diagnostic accuracy. Inter-reader agreement was substantial to excellent (Cohen's kappa 0.75-0.96) for most compartments except peritoneal involvement (0.39). The preoperative MRI showed mostly substantial to excellent accuracy (0.84-0.98), sensitivity (0.62-1.00), specificity (0.87-1.00), positive (0.58-1.00) and negative predictive values (0.86-1.00) for most compartments, except for peritoneal lesions (0.36, 0.17, 1.00, 1.00, 0.26 respectively). A trend with a higher prevalence of concordant DE in women with MR features of external adenomyosis compared to those with internal adenomyosis was visible (p = 0.067). The mr#Enzian showed mostly high inter-reader agreement and good diagnostic accuracy for various endometriosis compartments. MRI's role is particularly significant in the context of the current paradigm shift towards medical endometriosis treatment. The inclusion of information about the type of adenomyosis in the mr#Enzian classification could enhance diagnostic accuracy and inform treatment planning.

Evaluation of a Commercial Serum Competitive Enzyme-Linked Immunosorbent Assay for Detection of Neospora caninum-Specific Antibodies in Raw Milk of Ruminants

Bovine neosporosis is an infection caused by the protozoan parasite Neospora caninum and has substantial veterinary hazards. Neosporosis cannot be controlled by vaccination or chemotherapy. Thus, accurate diagnosis followed by isolation and culling of infected animals is regarded as the most efficient method of control. In vivo diagnosis often relies on serologic testing of the animals, and milk represents a non-invasive and easy-to-collect sample matrix. However, indirect enzyme-linked immunosorbent assay (ELISA) specifically designed for antibody detection in milk are sometimes not easily available and it is tempting to use ELISA kits that are originally designed for use in serum in milk samples instead. Herein, we evaluated a widely used commercial ELISA (ID Screen® Neospora caninum competition Multispecies ELISA (ID. Vet, Grabels, France)), developed for detection of N. caninum antibodies in serum samples, for its performance on milk samples. Milk samples from dairy ruminants (cows, buffaloes, sheep, and goats; n = 149) were tested in parallel with the serum ELISA and a commercial milk ELISA as a standard test (Neospora caninum Milk Competitive ELISA, ID. Vet, Grabels, France). The detected prevalence values were 28.2% (42/149), 17.4% (26/149), and 17.4% (26/149) using milk ELISA, serum ELISA, and both ELISAs, respectively. Sensitivity, specificity, positive predictive value, and negative predictive value for the serum ELISA used with milk samples were 61.9%, 100%, 100%, and 87%, respectively. The agreement and kappa value between the two ELISAs were 89.3% and 0.70, respectively, suggesting substantial agreement. High values of Pearson correlation coefficient (0.904, p ≥ 0.0001) and area under the receiver operating characteristic (ROC) curve (0.789, p ≥ 0.0001) demonstrated the high diagnostic performance of the serum ELISA in milk samples. Also, a Bland–Altman Plot and histogram describing the frequency of distribution of ELISA optical densities confirmed the high agreement of both serum and milk ELISAs. The current results revealed the high specificity but moderate sensitivity of the serum ELISA used for milk samples compared with the milk ELISA. However, the excellent positive predictive value of the serum ELISA makes it an alternative option in case of the unavailability of milk ELISAs. With this study, we provided additional evidence that a widely used serum ELISA test kit may also be used for the detection of N. caninum antibodies in milk samples.

Atypical glandular cells in Pap tests: cytology-histology correlation and risk assessment with human papillopmavirus (HPV) testing

Atypical glandular cells (AGC) represent less than 1% of Pap test cases and include a variety of lesions in both the cervix and endometrium. The study aimed to investigate the cytology-histology correlation in AGC patients and to evaluate the clinical utility of hrHPV testing in this diagnostic context. We identified 491 atypical glandular cells (AGC) cases in our quality analysis (QA) database of 336,064 Pap tests interpreted between March 1, 2013 and July 12, 2016. Of these, 251 cases had follow-up biopsies with hrHPV tests in 148 cases. The most common histologic diagnosis associated with AGC was normal/benign or low-grade lesions, comprising 55% of cervical biopsies and 24% of endometrial biopsies. High-grade lesions were identified in 21% of follow-up biopsies. In patients with AGC cytology, a positive hrHPV test significantly increased the likelihood of cervical HSIL or above lesions on biopsy by 26.4 times (OR = 26.4, 95% CI: 5.8-119.4, P < 0.0001). A positive genotyping result for HPV 16 dramatically increased the likelihood of cervical HSIL or above lesions on biopsy (OR = 84, 95% CI: 12.0-590.5, P < 0.0001). The HPV test had a negative predictive value of 97% (CI: 85%-100%). Our study confirms that AGC is a significant diagnosis with an overall risk for high-grade cervical or endometrial lesions as high as 21%. hrHPV testing with genotyping is an effective tool for identifying high-risk individuals within the AGC population, with excellent positive and negative predictive values. This approach is valuable for clinical risk stratification and differential diagnosis in patients with AGC cytology.

Nailfold dermoscopy predicts the absence of a capillaroscopy sclerodermic pattern: The multicentre, prospective VASCUL-R trial.

Nailfold capillaroscopy is recommended to diagnose primary or secondary Raynaud's phenomenon (RP). Capillaroscopy is normal in primary RP, which is the most frequent. Screening for RP capillary anomalies with nailfold dermoscopy has been promising. To determine whether normal nailfold dermoscopy-based on the absence of five criteria that define a sclerodermic pattern-is able to predict normal capillaroscopy with good positive-predictive value (PPV). Prospective, 2-phase (monocentre and multicentre) study on patients at first consultation for RP undergoing nailfold video capillaroscopy (NVC) and nailfold dermoscopy by two different 'blinded' trained observers, respectively, a vascular specialist and a dermatologist, not familiar with capillaroscopy. The five criteria noted were as follows: disorganization, megacapillaries, low capillary density, avascular areas and haemorrhages. Based on 105 patients, the dermoscopy PPV for a normal NVC was 100% (p = 0.015), with 37.9% sensitivity, when no criterion was observed. Excluding haemorrhages, the PPV remained 100% (p < 0.0001), with sensitivity rising to 73.7% and 100% specificity. Normal nailfold dermoscopy with the absence of four easy-to-observe criteria predicts normal NVC with an excellent PPV.

Dynamic airway computed tomography and flexible bronchoscopy for diagnosis of tracheomalacia in children: A comparison study.

Tracheomalacia (TM) is an important cause of respiratory morbidity. Dynamic flexible bronchoscopy is considered the gold standard for diagnosis. Dynamic airway computed tomography (DACT) is a low radiation, noninvasive diagnostic tool utilizing images obtained continuously over several respiratory cycles. We aimed to assess the accuracy of DACT in TM diagnosis. Retrospective analysis of all patients who underwent both DACT and flexible bronchoscopy within 6 months. Airway anterior-posterior (AP) diameter was measured on multiplanar reconstructions CT in both the inspiratory and expiratory phases. Using still images from the bronchoscopy videos, the AP diameter of the trachea was measured at points of maximal and minimal diameter during tidal breathing. Degree of TM on both DACT and flexible bronchoscopy were graded using a scaling system of 50%-74%, 75%-89%, and 90%-100% as described by the European Respiratory Society. Twenty-four patients met inclusion criteria with an average time of 19.5 days between CT and bronchoscopy. The specificity and sensitivity of DACT for the overall diagnosis of TM was 100% and 68%, respectively, with a positive predictive value of 100% and a negative predictive value of 62%. There was a strong positive correlation between DACT and flexible bronchoscopy in the measurement of tracheal AP diameter changes (ρ = 0.773, R2 0.597, p = 0.00001). Mean effective radiation dose for DACT was 0.1 mSv. Ultralow dose DACT has excellent specificity and positive predictive value for both detection of TM and categorizing severity of tracheal collapse but is not sufficiently sensitive to rule it out.

Validation of Temperature Sensors to Monitor Thermoplastic Splint Wear in Hand Surgery Patients.

The aim of this study was to validate the use of temperature sensors to accurately measure thermoplastic volar forearm splint wear in a healthy cohort of volunteers using 5- and 15-minute temperature measurement intervals. A prospective diagnostic study was performed to evaluate the diagnostic accuracy of temperature sensors in monitoring splint wear in 8 healthy volunteers between December 2022 and June 2023. Temperature sensors were molded into thermoplastic volar forearm splints. Volunteers who were familiar with the study aims were asked to keep an exact log of the time spent wearing the splint ("actual wear time"). Sensors recorded temperatures every 5 or 15 minutes, and separate algorithms were developed to determine the sensor-detected wear time compared with the actual wear time as the gold standard. The algorithms were then externally validated with the total population. The 5-minute and 15-minute algorithms demonstrated excellent sensitivity (99.1% vs 96.6%), specificity (99.9% vs 99.9%), positive (99.4% vs 99.5%) and negative (99.9% vs 99.3%) predictive value, and diagnostic accuracy (99.8% vs 99.3%), respectively. The 5-minute algorithm recorded 99.5% of the total splint hours, whereas the 15-minute algorithm recorded 96.1%. There was no significant difference between the actual time per wear session (5.4 ± 2.7 hours) and the time estimated by the 5-minute algorithm (5.4 ± 2.6 hours; P = .40), but there was a significant difference for the 15-minute algorithm (5.2 ± 2.6 hours; P < .001). Temperature sensors can be used to accurately monitor thermoplastic volar forearm splint wear. Diagnostic II.

Abstract 14344: Pitfalls of ICD-10 Codes for Identifying Pulmonary Embolism in Electronic Records: Results From the Multicenter PE-EHR+ Study

Background: Many research studies from electronic databases and even the American Heart Association annual statistics for pulmonary embolism (PE) rely on the International Classification of Disease, 10 th modification (ICD-10) codes. However, the validity of ICD-10 codes for PE remains uncertain. Methods: Using a pre-specified protocol, we identified three groups of patients in the Mass General-Brigham Health system hospitals (MGB, 2016-2021) in equal distribution: Those with ICD-10 Principal discharge codes for PE, those with ICD-10 secondary codes for PE, and those without codes for PE (N=578 each). The accuracy of ICD-10 codes for identification of PE was assessed in reference to review of each chart by two independent physicians who used pre-specified criteria for presence of PE. Weighted estimates were obtained by considering the total of number of hospitalizations at MGB in each group. Results: After excluding duplicates, 1712 entered the analysis (age: 60.6 years, 52.3% female). Using the ICD-10 PEs only in the Principal discharge diagnosis, sensitivity and specificity were 60.3% and 94.6%, respectively. Although use of Principal-or-secondary ICD-10 PE codes had a sensitivity of 99.8% in the study sample, the weighted sensitivity in the parent cohort was reduced to 83.2%. The positive predictive value of Principal ICD-10 discharge codes and Principal-or-secondary ICD-10 codes were 91.9% and 79.2%, respectively (Table). Conclusions: Although the Principal discharge diagnosis codes for PE have an excellent positive predictive value, they miss nearly 40% of new PE events in hospitalized patients. Integration of secondary discharge codes only partially improves the sensitivity at the cost of reducing the positive predictive value, highlighting the need for modified strategies, such as use combination with procedure codes, or use of natural language processing -when feasible -for identification of patients with PE.

Index CT-Based Scoring Systems in Operative Blunt Bowel and Mesenteric Injury Identification

IntroductionDetermining the need for surgical management of blunt bowel and mesenteric injury (BBMI) remains a clinical challenge. The Faget score and Bowel Injury Prediction Score (BIPS) have been suggested to address this issue. Their efficacy in determining the need for surgery was examined. MethodsA retrospective review of all adult blunt trauma patients hospitalized at a level 1 trauma center between January 2009 and August 2019 who had small bowel, colon, and/or mesenteric injury was conducted. We further analyzed those who underwent preoperative computed tomography (CT) scanning at our institution. Final index CT reports were retrospectively reviewed to calculate the Faget and BIPS CT scores. All images were also independently reviewed by an attending radiologist to determine the BIPS CT score. ResultsDuring the study period, 14,897 blunt trauma patients were hospitalized, of which 91 had BBMI. Of these, 62 met inclusion criteria. Among patients previously identified as having BBMI in the registry, the retrospectively applied Faget score had a sensitivity of 39.1%, specificity of 81.2%, positive predictive value (PPV) of 85.7%, and negative predictive value (NPV) of 31.7% in identifying patients with operative BBMI. The retrospectively applied BIPS score had a sensitivity of 47.8%, specificity of 87.5%, PPV of 91.7%, and NPV of 36.8% in this cohort. When CT images were reviewed by an attending radiologist using the BIPS criteria, sensitivity was 56.5%, specificity 93.7%, PPV 96.3%, and NPV 42.8%. ConclusionsExisting BBMI scoring systems had limited sensitivity but excellent PPV in predicting the need for operative intervention for BBMI. Attending radiologist review of CT images using the BIPS scoring system demonstrated improved accuracy as opposed to retrospective application of the BIPS score to radiology reports.

Early post-treatment MRI predicts long-term hepatocellular carcinoma response to radiation segmentectomy

ObjectivesRadiation segmentectomy using yttrium-90 plays an emerging role in the management of early-stage HCC. However, the value of early post-treatment MRI for response assessment is uncertain. We assessed the value of response criteria obtained early after radiation segmentectomy in predicting long-term response in patients with HCC.Materials and methodsPatients with HCC who underwent contrast-enhanced MRI before, early, and 12 months after radiation segmentectomy were included in this retrospective single-center study. Three independent radiologists reviewed images at baseline and 1st follow-up after radiation segmentectomy and assessed lesion-based response according to mRECIST, LI-RADS treatment response algorithm (TRA), and image subtraction. The endpoint was response at 12 months based on consensus readout of two separate radiologists. Diagnostic accuracy for predicting complete response (CR) at 12 months based on the 1st post-treatment MRI was calculated.ResultsEighty patients (M/F 60/20, mean age 67.7 years) with 80 HCCs were assessed (median size baseline, 1.8 cm [IQR, 1.4–2.9 cm]). At 12 months, 74 patients were classified as CR (92.5%), 5 as partial response (6.3%), and 1 as progressive disease (1.2%). Diagnostic accuracy for predicting CR was fair to good for all readers with excellent positive predictive value (PPV): mRECIST (range between 3 readers, accuracy: 0.763–0.825, PPV: 0.966–1), LI-RADS TRA (accuracy: 0.700–0.825, PPV: 0.983–1), and subtraction (accuracy: 0.775–0.825, PPV: 0.967–1), with no difference in accuracy between criteria (p range 0.053 to > 0.9).ConclusionmRECIST, LI-RADS TRA, and subtraction obtained on early post-treatment MRI show similar performance for predicting long-term response in patients with HCC treated with radiation segmentectomy.Clinical relevance statementResponse assessment extracted from early post-treatment MRI after radiation segmentectomy predicts complete response in patients with HCC with high PPV (≥ 0.96).Key Points• Early post-treatment response assessment on MRI predicts response in patients with HCC treated with radiation segmentectomy with fair to good accuracy and excellent positive predictive value.• There was no difference in diagnostic accuracy between mRECIST, LI-RADS, and subtraction for predicting HCC response to radiation segmentectomy.

Validation of an algorithm to identify fractures among patients within the Veterans Health Administration.

To validate an algorithm that identifies fractures using billing codes from the International Classification of Diseases Ninth Revision (ICD-9) and Tenth Revision (ICD-10) for inpatient, outpatient, and emergency department visits in a population of patients. We identified and reviewed a random sample of 543 encounters for adults receiving care within a single Veterans Health Administration healthcare system and had a first fracture episode between 2010 and 2019. To determine if an encounter represented a true incident fracture, we performed chart abstraction and assessed the type of fracture and mechanism. We calculated the positive predictive value (PPV) for the overall algorithm and each component diagnosis code along with 95% confidence intervals. Inverse probabilities of selection sampling weights were used to reflect the underlying study population. The algorithm had an initial PPV of 73.5% (confidence interval [CI] 69.5, 77.1), with low performance when weighted to reflect the full population (PPV 66.3% [CI 58.8, 73.1]). The modified algorithm was restricted to diagnosis codes with PPVs > 50% and outpatient codes were restricted to the first outpatient position, with the exception of one high performing code. The resulting unweighted PPV improved to 90.1% (CI 86.2, 93.0) and weighted PPV of 91.3% (CI 86.8, 94.4). A confirmation sample demonstrated verified performance with PPV of 87.3% (76.0, 93.7). PPVs by location of care (inpatient, emergency department and outpatient) remained greater than 85% in the modified algorithm. The modified algorithm, which included primary billing codes for inpatient, outpatient, and emergency department visits, demonstrated excellent PPV for identification of fractures among a cohort of patients within the Veterans Health Administration system.

Clinical validity of TTMV-HPV DNA liquid biopsies for the diagnosis and surveillance of HPV-associated oropharyngeal carcinoma.

6060 Background: While there are several HPV biomarker assays, a commercially available assay evaluating tumor tissue modified viral DNA (TTMV-HPV DNA) (NavDx, Naveris Inc, Natick, MA) has shown impressive results in clinical trials and small cohort studies. Our aim was to establish the clinical efficacy of plasma TTMV-HPV DNA testing in the diagnosis and surveillance of HPV-associated OPSCC in a contemporary, real-world clinical setting. Methods: In this retrospective analysis, we evaluated the accuracy of the TTMV-HPV DNA assay in detecting HPVOPSCC in the diagnostic (pre-treatment) and surveillance phases of care between April 2020 and October 2022. For the diagnostic cohort, patients with OPSCC and at least one TTMV-HPV DNA measurement prior to initiation of primary therapy were included in the analysis. For the surveillance cohort, all TTMV-HPV DNA tests performed 3 months after completion of primary therapy for pathologically confirmed HPV-associated OPSCC were evaluated, regardless of pre-treatment TTMV-HPV DNA testing. Patients with suspected recurrence, but without pathologic confirmation, were excluded. HPV status was defined utilizing positive p16 staining on immunohistochemistry (93.2% diagnosis; 97.9% surveillance) or HPV polymerase chain reaction/in situ hybridization (79.6% diagnosis; 89.7% surveillance). Results: In the diagnostic cohort, 163 patients were included. The cohort included mostly men (87%) with median age 63, of which 93.3% had HPVOPSCC versus 6.7% had HPV negative OPSCC. The sensitivity in pre-treatment diagnosis was 91.5%; its specificity was 100%. In the surveillance cohort, 591 tests conducted in 290 patients were evaluated. 23 patients had pathologically confirmed recurrences. The assay demonstrated 79.2% sensitivity and 100% specificity in detecting the recurrences. Positive predictive value was 100% and negative predictive value was 98.2%. The median lead time from positive test to pathologic confirmation was 28 days, 6/18 (30%) had lead time &gt;50 days (maximum 507 days). Conclusions: The commercially available NavDx TTMV-HPV DNA assay demonstrated 100% specificity, and therefore excellent positive predictive value, in both diagnosis and surveillance. However, the sensitivity was lower, 91.5% for the diagnosis cohort and 79.2% for the surveillance cohort, signifying that a negative value may require further work-up. Further studies will be required to determine the optimal timing of testing and how results should inform treatment decisions. [Table: see text]

Early identification of seizure freedom with medical treatment in patients with mesial temporal lobe epilepsy and hippocampal sclerosis.

Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is usually associated with a poor response to antiseizure medications. We focused on MTLE-HS patients who were seizure free on medication to: (1) determine the clinical factors associated with seizure freedom and (2) develop a machine-learning classifier to better earlier identify those patients. We performed a retrospective, multicentric study comparing 64 medically treated seizure-free MTLE-HS patients with 200 surgically treated drug-resistant MTLE-HS patients. First, we collected medical history and seizure semiology data. Then, we developed a machine-learning classifier based on clinical data. Medically treated seizure-free MTLE-HS patients were seizure-free for at least 2years, and for a median time of 7years at last follow-up. Compared to drug-resistant MTLE-HS patients, they exhibited: an older age at epilepsy onset (22.5 vs 8.0years, p < 0.001), a lesser rate of: febrile seizures (39.0% vs 57.5%, p = 0.035), focal aware seizures (previously referred to as aura)(56.7% vs 90.0%, p < 0.001), autonomic focal aware seizures in presence of focal aware seizure (17.6% vs 59.4%, p < 0.001), dystonic posturing of the limbs (9.8% vs 47.0%, p < 0.001), gestural (27.4% vs 94.0%, p < 0.001), oro-alimentary (32.3% vs 75.5%, p < 0.001) or verbal automatisms (12.9% vs 36.0%, p = 0.001). The classifier had a positive predictive value of 0.889, a sensitivity of 0.727, a specificity of 0.962, a negative predictive value of 0.893. Medically treated seizure-free MTLE-HS patients exhibit a distinct clinical profile. A classifier built with readily available clinical data can identify them accurately with excellent positive predictive value. This may help to individualize the management of MTLE-HS patients according to their expected pharmacosensitivity.

Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus

BackgroundChina has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment. ObjectiveThe establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future. MethodsA total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model. ResultsThe XGBoost model had the highest AUC (0.951, 95% CI 0.925–0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern. ConclusionThis study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.

Prediction of malignancy in mediastinal lymph nodes during endobronchial ultrasound: A comparative validation study.

In this study, we aimed to compare the diagnostic performances of three existing prediction tools in visually identifying a malignant lymph node. Between April 2016 and January 2021, a total of 827 lymph nodes of 259 patients (211 males, 48 females; mean age: 61.1±7.2 years; range, 41 to 79 years) who underwent endobronchial ultrasound procedure for diagnosis and/or staging of lung cancer and diagnosis of mediastinal lymphadenopathy of unknown origin were retrospectively analyzed. This external validation study was designed to compare the diagnostic yields of the prediction tools developed by Shafiek et al., Alici et al., and Canada Lymph Node Score (CLNS). Endobronchial ultrasoundguided transbronchial needle aspiration results and predictions were compared to gold-standard tool. Overall, endobronchial ultrasound-guided transbronchial needle aspiration had a sensitivity, specificity, positive and negative predictive value, and accuracy of 95.6%, 100%, 100%, 97.6%, and 98.4%, respectively. Diagnostic performances of proposed tools were quite remarkable. Among them, Alici algorithm had a higher sensitivity and negative predictive value, which were matched by excellent specificity and positive predictive value offered by CLNS ≥3 and Shafiek tool. The area under the curve value of CLNS ≥3 was higher than Shafiek tool and CLNS ≥2. Conventional prediction tools relying on simple real-time sonographic features were found to be consistent by the means of diagnostic performance in this external validation dataset. Despite being inferior to cytology, their superior performance was proven with defined individual strengths and weaknesses.

Validity &amp; Reliability of a Korean-version of Eating Assessment Tool (K-EAT-10): Predicting the Risk of Aspiration in Stroke Patients

Objectives: This study aims to investigate the reliability and validity of a Korean version of the Eating Assessment Tool (EAT-10) as a clinical evaluation tool for dysphagia with stroke patients and determine its clinical usefulness as a screening tool. Methods: A total of 50 patients with stroke (15 without dysphagia, and 35 with dysphagia, identified through a videofluoroscopic swallowing study (VFSS) participated in this study. The internal consistency, test-retest reliability, and concurrent validity of the adapted Korean version of EAT-10(K-EAT-10) were evaluated. In addition, the K- EAT-10 scores were compared between two groups according to demographic characteristics, post-stroke conditions, and swallowing function. Moreover, K-EAT-10 scores were compared with the VFSS findings to estimate the sensitivity, specificity, and positive and negative predictions of aspiration. Results: The internal consistency of the K-EAT-10 questionnaire showed a Cronbach α coefficient of .091, and the correlation coefficient of the test- retest reliability was .988. In addition, when correlation analysis was performed between the total score of K-EAT-10 and the K-DHI, the correlation coefficient was .904. In the receiver operating characteristic (ROC) curve analysis of K-EAT-10, a cut-off point for the K-EAT-10 questionnaire was set at 10 points with a sensitivity of 86.7% and specificity of 77.1%, and the area under the curve was shown to be 0.86. In addition, the positive predictive value (PPV) for the K-EAT-10 was 91%, and the negative predictive value (NPV) was 60%. Conclusion: Korean version of EAT-10 revealed excellent internal consistency, test-retest reliability, concurrent validity and positive predictive value for aspiration. The results suggest that the Korean adaptation of EAT-10 can be useful as a clinical screening test for dysphagia, especially predicting aspiration risk in patients with stroke.

Identifying monoclonal gammopathy of undetermined significance from electronic health records.

Monoclonal gammopathy of undetermined significance (MGUS) precedes multiple myeloma (MM). Use of electronic health records may facilitate large-scale epidemiologic research to elucidate risk factors for the progression of MGUS to MM or other lymphoid malignancies. We evaluated the accuracy of an electronic health records-based approach for identifying clinically diagnosed MGUS cases for inclusion in studies of patient outcomes/ progression risk. Data were retrieved from Kaiser Permanente Southern California's comprehensive electronic health records, which contain documentation of all outpatient and inpatient visits, laboratory tests, diagnosis codes and a cancer registry. We ascertained potential MGUS cases diagnosed between 2008 and 2014 using the presence of an MGUS ICD-9 diagnosis code (273.1). We initially excluded those diagnosed with MM within 6months after MGUS diagnosis, then subsequently those with any lymphoid malignancy diagnosis from 2007 to 2014. We reviewed medical charts for 100 randomly selected potential cases for evidence of a physician diagnosis of MGUS, which served as our gold standard for case confirmation. To assess sensitivity, we also investigated the presence of the ICD-9 code in the records of 40 randomly selected and chart review-confirmed MGUS cases among patients with a laboratory report of elevated circulating monoclonal (M-) protein (a key test for MGUS diagnosis) and no subsequent lymphoid malignancy (as described above). The positive predictive value (PPV) for the ICD-9 code was 98%. All MGUS cases confirmed by chart review also had confirmatory laboratory test results. Of the confirmed cases first identified via M-protein test results, 88% also had the ICD-9 diagnosis code. The diagnosis code-based approach has excellent PPV and likely high sensitivity for detecting clinically diagnosed MGUS. The generalizability of this approach outside an integrated healthcare system warrants further evaluation.

Utilization of Natural Language Processing in Venous Thromboembolism Identification

Introduction: Capturing venous thromboembolism (VTE) outcomes using only International Classification of Disease (ICD) codes can lead to misclassification of events and to inaccurate conclusions. Manual chart review, the gold standard, is labor intensive and not always feasible. The current study aims to validate an improved computable VTE phenotype algorithm combining ICD with natural language processing (NLP) in patients undergoing hematopoietic cell transplantation (HCT). Methods: All patients undergoing first allogeneic HCT at Fred Hutchinson Cancer Center (FHCC) from 2006-2019 were included in the current study. To capture as many as possible VTE events, we used a sensitive screening method within one year before and after the transplant date that encompassed 1) all patients with ICD-9 and ICD-10 codes for acute, chronic, or historical pulmonary embolism (PE), deep venous thrombosis (DVT), and phlebitis and thrombophlebitis, and 2) all patients with at least 1 radiology report with a pertinent VTE-related keyword from venous doppler ultrasound, contrast computed tomography, or ventilation perfusion scans. All patients from this screened subset were then reviewed by chart abstractors (JA, KM) to establish the gold standard of incident VTE events, which was defined as the new onset of radiologically confirmed PE, lower extremity DVT, or upper extremity/catheter related DVT within 1-year post-transplant. We then tested the performance of the acute VTE ICD-9/10 codes (selective codes from 415, 451, 453; I26, I80, I82) from inpatient or outpatient encounters from stem cell infusion until 1-year post-transplant. We also tested the utility of a NLP algorithm, a method that has been previously validated in a separate cancer cohort using unstructured radiology impressions (PMID: 35647478). Finally, we compared the performance of combining these two algorithms against the gold standard to report positive predictive value (PPV) and sensitivity (Sn). Results: Among 2,879 patients who underwent allogeneic HCT over 15 years, 740 (26%) met study inclusion criteria. Based on the gold standard of detailed medical record review, 275 (10%) were found to have a radiologically confirmed VTE event within 1-year post-transplant (Figure 1). A further 389 (14%) historical VTE events were confirmed before transplant. The acute VTE ICD-9/10 codes identified 339 patients and the NLP algorithm predicted 245 patients to have VTE (including 155 overlap). The ICD codes alone for acute VTE had an estimated Sn of 73% and PPV of 59%. The NLP radiology algorithm alone for VTE had an estimated Sn of 73% and PPV of 82%. Approximately 3 in 10 VTE events were missed in each algorithm. However, the combination of ICD or NLP identified 245/275 of all VTE events (Sn 89%). The PPVs for ICD+/NLP+, ICD-/NLP+, and ICD+/NLP- were 89%, 64%, and 27%, respectively (Table 1). In summary, those with concordant ICD/NLP prediction had excellent PPVs, and approximately 8% of patients with discordant ICD/NLP (n=234/2,879) would require additional chart review to achieve a final PPV >90% and Sn >90%. Conclusion: In the current study, we found that the sensitivity for either the acute ICD codes or the NLP algorithm alone was sub-optimal (missing 3 in 10), and a combined screen should be considered (missing 1 in 10). The use of ICD-9/10 codes alone for new VTE had poor accuracy in our cohort (PPV of 59%), suggesting that additional features are needed, such as concurrent anticoagulation medications. In contrast, the NLP algorithm was validated with high PPV 82% (89% when combined with positive acute ICD screen) in the current cohort and may not require additional confirmation, though caution should be taken for its usage in other studies without dedicated follow-up where radiology reports are captured and stored in one unified healthcare system. One limitation of the study is the lack of review of patients initially screened negative by either ICD codes or radiology keyword searches. While the initial screen was designed to be highly sensitive, we may have missed small number of true VTE events and the reported Sn in this study represents the best-case scenario. In conclusion, while computable phenotype algorithms represent a promising future for the identification of VTE, a hybrid approach involving manual chart review (for only cases where the ICD and NLP screens disagree) may provide the highest yield and help minimize the labor intensive manual review. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Arcch: Improving the Performance of Clonal Hematopoiesis Variant Calling and Interpretation Using a Consensus Based Approach

Introduction: The acquisition of somatic mutations in hematopoietic stem and progenitor stem cells with resultant clonal expansion is known as clonal hematopoiesis (CH). CH is linked to a higher risk of hematologic malignancies and other adverse health outcomes. The prevalence of CH is dependent on sequencing technique. With ultra-high-depth sequencing, CH can be detected in most adults. However, CH variant calling at very low variant allele fractions (VAF) is challenging due to difficulty in distinguishing low frequency CH mutations from sequencing artifacts. Here we develop and validate a novel CH variant calling approach that combines ensemble based variant calling with advanced artifact filtering and outperforms commonly used somatic variant callers. Methods: We sequenced a tumor-normal dilution series from six AML patients and 27 technical controls with a targeted panel utilizing unique molecular indexes that included nine common CH genes (DNMT3A, TET2, ASXL1, TP53, PPM1D, JAK2, SF3B1, SRSF2) with a mean unique coverage of 18,268x. In total we identified 32 mutations in these six samples validated by an independent sequencing panel performed for the purpose of clinical testing. The variant allele fraction (VAF) in the resulting tumor-dilution samples ranged from 0.1%-50% VAF. Results: For higher VAF mutations (>5%), Mutect2, Lofreq, and Vardict showed reasonable sensitivity (range: 0.70-1.00) and positive predictive values (PPV) (range: 0.80-1.00). However, at lower VAF, PPV was poor; for mutations between 0.1-0.4% VAF, the PPV ranged between 0.01-0.22. We applied additional filters based on sequencing quality, depth, regional complexity, and an empiric estimate of sequencing error at the position of a given called variant using technical controls. Taking the consensus of three callers and including our additional filters, we not only retained sensitivity and PPV for higher VAF variants above 1% (0.92 sensitivity, 0.95 PPV) but improved the PPV with retained sensitivity for variants between 1-0.1% VAF (0.91 sensitivity, 0.83 PPV). Applying our consensus approach to blood samples drawn from 31 healthy individuals sequenced using the same targeted panel with variants validated using an orthogonal approach, we saw excellent sensitivity and PPV across a wide range of VAFs (Sensitivity 0.99 [>1% VAF], 0.92 [0.2-1% VAF], and 0.82 [0.1-0.2% VAF]; PPV 1.00 [>1% VAF], 0.98 [0.2-1% VAF], 0.97 [0.2-0.1% VAF]). We compared our consensus calling and advanced filtering approach to a machine learning model. A decision-tree-based ensemble machine learning algorithm, XGBoost, was trained on the tumor-dilution samples with validation using 31 blood samples with orthogonal sequencing. The variant caller output (allele counts, flags, etc.), additional false positive filtering metrics, and statistical results from the technical controls were used as features. Sensitivity and PPV was high for mutations above 1% VAF (0.97 sensitivity and 0.82 PPV) with the performance declining for lower VAF mutations (0.72 sensitivity and 0.65 PPV). Thus, a machine learning approach did not outperform consensus calling with advanced artifact filtering. Finally, we developed a custom annotation pipeline for CH variant pathogenicity classification incorporating previously reported CH mutations in large cohorts, cancer somatic mutation frequency databases and curated cancer driver knowledge databases. Conclusion: We developed ArCCH, an advanced artifact filtering and consensus CH calling pipeline that includes custom annotation features to facilitate CH pathogenicity determination. Our results show that our consensus approach with advanced error correction filters substantially improves the performance of CH variant calling beyond commonly used single variant callers. This end-to-end flexible workflow package is publicly available on Terra and will facilitate future CH studies.

Simultaneous Entrainment Response Assessment at Multiple Sites.

The entrainment response, defined as the difference between the postpacing interval and the tachycardia cycle length (TCL) recorded from a mapping catheter, allows to track down the components of the tachycardia loop. The aim of this study was to evaluate if the postpacing interval measured simultaneously from multiple sites that are remote from the pacing site (PPIR) could be clinically useful in mapping re-entrant circuits. Ninety-two episodes of entrainment response in 29 patients with different macro-re-entrant tachycardias were evaluated using a standardized entrainment protocol. The spatial distribution of different values of PPIR-TCL in a simulation and a computational model of an entrained re-entrant tachycardia was also analyzed. The PPIR exceeded TCL by more than 20 milliseconds only if both pacing and recording sites were outside the tachycardia circuit. The PPIR-TCL at in-circuit sites was always≤20 milliseconds. Sites with negative PPIR-TCL values were found either outside or inside the tachycardia circuit. Assessment of entrainment response from catheters remote from the pacing site may enhance spatial mapping of the tachycardia circuit. The PPIR-TCL above 20 milliseconds has an excellent positive predictive value in identifying sites outside the tachycardia circuit.

IMproving PULmonary hypertension Screening by Echocardiography: IMPULSE

BackgroundThe world symposium on pulmonary hypertension (PH) has proposed that PH be defined as a mean pulmonary artery pressure (mPAP) > 20 mmHg as assessed by right heart catheterisation (RHC). Transthoracic echocardiography (TTE) is an established screening tool used for suspected PH. International guidelines recommend a multi-parameter assessment of the TTE PH probability although effectiveness has not been established using real world data.Study aimsTo determine accuracy of the European Society of Cardiology (ESC) and British Society of Echocardiography (BSE) TTE probability algorithm in detecting PH in patients attending a UK PH centre. To identify echocardiographic markers and revised algorithms to improve the detection of PH in those with low/intermediate BSE/ESC TTE PH probability.MethodsTTE followed by RHC (within 4 months after) was undertaken in patients for suspected but previously unconfirmed PH. BSE/ESC PH TTE probabilities were calculated alongside additional markers of right ventricular (RV) longitudinal and radial function, and RV diastolic function. A refined IMPULSE algorithm was devised and evaluated in patients with low and/or intermediate ESC/BSE TTE PH probability.ResultsOf 310 patients assessed, 236 (76%) had RHC-confirmed PH (average mPAP 42.8 ± 11.7). Sensitivity and specificity for detecting PH using the BSE/ESC recommendations was 89% and 68%, respectively. 36% of those with low BSE/ESC TTE probability had RHC-confirmed PH and BSE/ESC PH probability parameters did not differ amongst those with and without PH in the low probability group. Conversely, RV free wall longitudinal strain (RVFWLS) was lower in patients with vs. without PH in low BSE/ESC probability group (− 20.6 ± 4.1% vs − 23.8 ± 3.9%) (P < 0.02). Incorporating RVFWLS and TTE features of RV radial and diastolic function (RVFAC and IVRT) within the IMPULSE algorithm reduced false negatives in patients with low BSE/ESC PH probability by 29%. The IMPULSE algorithm had excellent specificity and positive predictive value in those with low (93%/80%, respectively) or intermediate (82%/86%, respectively) PH probability.ConclusionExisting TTE PH probability guidelines lack sensitivity to detect patients with milder haemodynamic forms of PH. Combining additional TTE makers assessing RV radial, longitudinal and diastolic function enhance identification of milder forms of PH, particularly in those who have a low BSE/ESC TTE PH probability.