From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.Comment: Paper accepted to SIPAIM 2022 (in Valparaiso, Chile

Comparison of fast-track diagnostics respiratory pathogens multiplex real-time RT-PCR assay with in-house singleplex assays for comprehensive detection of human respiratory viruses

Fast-track Diagnostics respiratory pathogens (FTDRP) multiplex real-time RT-PCR assay was compared with in-house singleplex real-time RT-PCR assays for detection of 16 common respiratory viruses. The FTDRP assay correctly identified 26 diverse respiratory virus strains, 35 of 41 (85%) external quality assessment samples spiked with cultured virus and 232 of 263 (88%) archived respiratory specimens that tested positive for respiratory viruses by in-house assays. Of 308 prospectively tested respiratory specimens selected from children hospitalized with acute respiratory illness, 270 (87.7%) and 265 (86%) were positive by FTDRP and in-house assays for one or more viruses, respectively, with combined test results showing good concordance (K=0.812, 95% CI = 0.786-0.838). Individual FTDRP assays for adenovirus, respiratory syncytial virus and rhinovirus showed the lowest comparative sensitivities with in-house assays, with most discrepancies occurring with specimens containing low virus loads and failed to detect some rhinovirus strains, even when abundant. The FTDRP enterovirus and human bocavirus assays appeared to be more sensitive than the in-house assays with some specimens. With the exceptions noted above, most FTDRP assays performed comparably with in-house assays for most viruses while offering enhanced throughput and easy integration by laboratories using conventional real-time PCR instrumentation. Published by Elsevier B.V.High Priority Pandemic and Seasonal Influenza Scientific proposal request initiativ

Concurrent Validity of The Expanded Cutting Alignment Scoring Tool (E-CAST)

# Background The Expanded Cutting Alignment Scoring Tool (E-CAST) has been previously shown to be reliable when assessing lower extremity alignment during a 45-degree sidestep cut, however, the validity of this tool remains unknown. The purpose of this study was to assess the concurrent validity of the E-CAST by comparing visually identified movement errors from two-dimensional (2D) video with three-dimensional (3D) biomechanical variables collected using motion capture. # Study Design Cross Sectional # Methods Sixty female athletes (age 14.1 ± 1.5 years) who regularly participated in cutting/pivoting sports performed a sidestep cut with 2D video and 3D motion capture simultaneously recording. One clinician scored the 2D videos for each limb using the E-CAST criteria. Joint angles and moments captured in 3D were computed for the trunk and knee. Receiver operating characteristic (ROC) curve analyses were performed to determine the accuracy of each E-CAST item and to provide cut-off points for risk factor identification. # Results ROC analyses identified a cut-off point for all biomechanical variables with sensitivity and specificity ranging from 70-85% and 55-89%, respectively. Across items, the area under the curve ranged from 0.67 to 0.91. # Conclusion The E-CAST performed with acceptable to outstanding area under the curve values for all variables except static knee valgus. # Level of evidence 3

Systemic lupus erythematosus phenotypes formed from machine learning with a specific focus on cognitive impairment

OBJECTIVE: To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. METHODS: SLE patients ages 18-65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were collected on demographic and clinical variables, disease burden/activity, health-related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and χ2 tests. RESULTS: Of the 238 patients, 90% were female, with a mean age of 41 years (s.d. 12) and a disease duration of 14 years (s.d. 10) at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (P < 0.03) compared with subtype B. Subtype A also had greater levels of subjective cognitive function (P < 0.001), disease burden/damage (P < 0.04), HRQoL (P < 0.001) and psychiatric measures (P < 0.001) compared with subtype B. CONCLUSION: This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multifactorial phenotypes exist. Those with greater disease burden, from SLE-specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately this will aid our understanding of personalized CI trajectories and identification of appropriate treatments

Review: A Publication of LMDA, the Literary Managers and Dramaturgs of the Americas, volume 17, issue 1

Contents include: Editor\u27s Page: A Note from New LMDA President, Brian Quirt; Think Dramaturgically, Act Locally! A Conference Overview; I Was Mugged at My First LMDA Conference; First-Timer Fragments; Conference Photos; Introducing the Lessing (and Joe and Michael); A Message Faxed from Romania; Acceptance Speech, Michael Lupu; Producing The Belle\u27s Stratagem; Dramaturging Justice: The Exonerated Project at the Alley Theatre; Past President Liz Engeleman: Some Appreciations; The Toronto Mini-Conference (reprinted from the LMDA Canada newsletter); Gateway to the Americas, The LMDA Delegation, A Report from Mexico; Imag[in]ing Poverty: Creative Critical Dramaturgy for Suzan-Lori Parks\u27s In the Blood; Hester, La Negrita in Iowa City, Staging Spells and Homelessness in Suzan-Lori Parks\u27s In the Blood; The Future of Theatre is...(a creative contest); Seventh Annual Call for LMDA Residency Proposals. Issue editors: D.J. Hopkins, Madeleine Oldham, Carlenne Lacostahttps://soundideas.pugetsound.edu/lmdareview/1034/thumbnail.jp

Genome-wide association study of pediatric obsessive-compulsive traits: shared genetic risk between traits and disorder

Contains fulltext : 231401.pdf (publisher's version ) (Open Access

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms\u27 concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, \u3e0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, \u3e0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting

No Difference in Two-Dimensional Kinematic Assessment of a 45-Degree Sidestep Cut Compared to Qualitative Assessment

# Background and Purpose The Expanded Cutting Alignment Scoring Tool (E-CAST) is a two-dimensional qualitative scoring system that has demonstrated moderate inter-rater and good intra-rater reliability for the assessment of trunk and lower extremity alignment during a 45-degree sidestep cut. The primary purpose of this study was to examine the reliability of the quantitative version of the E-CAST among physical therapists and to compare the reliability of the quantitative E-CAST to the original qualitative E-CAST. The hypothesis was that the quantitative version of the E-CAST would demonstrate greater inter-rater and intra-rater reliability compared to the qualitative E-CAST. # Study Design Observational cohort, repeated measures reliability study # Methods Twenty-five healthy female athletes (age 13.8±1.4 years) performed three sidestep cuts with two-dimensional video capturing frontal and sagittal views. Two physical therapist raters independently scored a single trial using both views on two separate occasions. Based on the E-CAST criteria, select kinematic measurements were extracted using a motion analysis phone application. Intraclass correlation coefficients and 95% confident intervals were calculated for the total score, and kappa coefficients were calculated per kinematic variable. Correlations were converted to z-scores and compared to the six original criteria for significance (*α*\<0.05). # Results Cumulative intra- and inter-rater reliability were both good (ICC=0.821, 95% CI 0.687-0.898 and ICC=0.752, 95% CI 0.565-0.859). Cumulative intra-rater kappa coefficients ranged from moderate to almost perfect, and cumulative inter-rater kappa coefficients ranged from slight to good. No significant differences were observed between the quantitative and qualitative criteria for either inter- or intra-rater reliability (Z~obs(intra)~= -0.38, *p*=0.352 and Z~obs(inter)~= -0.30, *p*=0.382). # Conclusion The quantitative E-CAST is a reliable tool to assess trunk and lower extremity alignment during a 45-degree sidestep cut. No significant differences were observed in reliability of the quantitative versus qualitative assessment. # Level of evidence 3

Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs