Within-herd transmission of Mycoplasma bovis infections after initial detection in dairy cows.

Mycoplasma bovis outbreaks in cattle, including pathogen spread between age groups, are not well understood. Our objective was to estimate within-herd transmission across adult dairy cows, youngstock, and calves. Results from 3 tests (PCR, ELISA, and culture) per cow and 2 tests (PCR and ELISA) per youngstock and calf were used in an age-stratified susceptible-infected-removed/recovered (SIR) model to estimate within-herd transmission parameters, pathways, and potential effects of farm management practices. A cohort of adult cows, youngstock, and calves on 20 Dutch dairy farms with a clinical outbreak of M. bovis in adult cows were sampled, with collection of blood, conjunctival fluid, and milk from cows, and blood and conjunctival fluid from calves and youngstock, 5 times over a time span of 12 wk. Any individual with at least one positive laboratory test was considered M. bovis-positive. Transmission dynamics were modeled using an age-stratified SIR model featuring 3 age strata. Associations with farm management practices were explored using Fisher's exact tests and Poisson regression. Estimated transmission parameters were highly variable among herds and cattle age groups. Notably, transmission from cows to cows, youngstock, or to calves was associated with R-values ranging from 1.0 to 80 secondarily infected cows per herd, 1.2 to 38 secondarily infected youngstock per herd, and 0.1 to 91 secondarily infected calves per herd, respectively. In case of transmission from youngstock to youngstock, calves or to cows, R-values were 0.7 to 96 secondarily infected youngstock per herd, 1.1 to 76 secondarily infected calves per herd, and 0.1 to 107 secondarily infected cows per herd. For transmission from calves to calves, youngstock or to cows, R-values were 0.5 to 60 secondarily infected calves per herd, 1.1 to 41 secondarily infected youngstock per herd, and 0.1 to 47 secondarily infected cows per herd. Among on-farm transmission pathways, cow-to-youngstock, cow-to-calf, and cow-to-cow were identified as most significant contributors, with calf-to-calf and calf-to-youngstock also having noteworthy roles. Youngstock-to-youngstock was also implicated, albeit to a lesser extent. Whereas the primary focus was a clinical outbreak of M. bovis among adult dairy cows, it was evident that transmission extended to calves and youngstock, contributing to overall spread. Factors influencing transmission and specific transmission pathways were associated with internal biosecurity (separate caretakers for various age groups, number of people involved), external biosecurity (contractors, external employees), as well as indirect transmission routes (number of feed and water stations).

"What Do I Say? How Do I Say it?" Twitter as a Knowledge Dissemination Tool for Mental Health Research.

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

The Relationship Between Rejection of Care Behaviors and Pain and Delirium Severity in Hospital Dementia Care.

Background and Objectives: Rejection of care is common in hospitalized persons living with dementia. However, distinguishing between rejection of care behaviors related to care practices or other causes, such as pain or delirium, is challenging. The purpose of this study is to further understand the relationship between rejection of care and pain and delirium in hospitalized patients with dementia by identifying which rejection of care behaviors are associated with pain and delirium. Research Design and Methods: Care encounters between hospitalized patients with dementia (n = 16) and nursing staff (n = 53) were observed on 88 separate occasions across 35 days. Rejection of care was measured using the 13 behaviors from the Resistiveness to Care Scale. Pain and delirium severity were measured using a variety of scales including the Pain Assessment in Advanced Dementia Scale, Checklist of Nonverbal Pain Indicators, and numeric rating scale for pain severity and the Confusion Assessment Method-Severity short form and Delirium Observation Screening Scale for delirium severity. Linear mixed modeling was used to determine the relationship between rejection of care behaviors and pain and delirium severity for each measure. Results: About 48.9% of the observations included rejection of care, 49.9% included a patient in pain, and 12.5% included a patient with delirium. Cry, push away, scream/yell, and turn away indicated a higher pain severity across pain measures. No rejection of care behaviors were found to indicate delirium severity. Discussion and Implications: Certain rejection of care behaviors may be helpful in identifying pain in hospitalized patients with dementia, suggesting that caregivers should be cognizant of pain when these rejection of care behaviors are present. However, in this sample rejection of care behaviors was not found to be useful for identifying delirium severity in hospital dementia care.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging.

INTRODUCTION: Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY: A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS: For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS: When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

Bayesian modeling of dynamic behavioral change during an epidemic.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.

A Rare Single Case of COVID-19-Induced Acute Myocarditis and Encephalopathy Presenting Simultaneously.

The ongoing coronavirus disease 2019 (COVID-19) pandemic may result in cardiovascular complications such as myocarditis, while encephalitis is a potentially life-threatening COVID-19-associated central nervous system complication. This case illustrates the possibility of developing severe multisystem symptoms from a COVID-19 infection, despite having received the COVID-19 vaccine within the year. Delay in treatment for myocarditis and encephalopathy can lead to permanent and possibly fatal damage. Our patient, a middle-aged female with a complicated medical history, initially came in without characteristic manifestations of myocarditis such as shortness of breath, chest pain, or arrhythmia, but with an altered mental status. Through further laboratory tests, the patient was diagnosed with myocarditis and encephalopathy, which were resolved within weeks through medical management and physical/occupational therapy. This case presentation describes the first reported case of concomitant COVID-19 myocarditis and encephalitis after receiving a booster dose within the year.

Media reporting on alcohol and other drugs in Australia and the Mindframe guidelines: Baseline data.

INTRODUCTION: The aim of this study was to generate a baseline database of print media reporting on alcohol and other drug (AOD) issues prior to the release of the Mindframe guidelines in March 2019. Specifically, to: (i) describe the content associated with media entries that focus on AOD use in Australian news media; (ii) determine how the media entries compare to several domains associated with recently developed Mindframe guidelines for publicly reporting on AOD; and (iii) identify content factors associated with different scores. METHODS: Media entries between July 2016 and June 2017 were searched for key AOD-related terms using the Australian and New Zealand Newsstream database. Two coding schemes were developed to rate a stratified sample of 50% of the media entries against the Mindframe guidelines. Associations between content and total comparison scores were determined using linear regression models. RESULTS: Detailed coding of the 2007 articles identified as relevant for the current study indicated that a majority (67%) were focused on one of three substances: alcohol, cannabis or methamphetamine. Most of the entries were either law enforcement (22%) or criminal justice related (19%). Entries that focused on methamphetamine scored significantly lower than entries on alcohol when compared to the Mindframe guidelines, similarly entries focused on crime/justice-related topics scored significantly lower than entries focused on positive outcomes. DISCUSSION AND CONCLUSIONS: A disproportionate number of print media entries, particularly those related methamphetamine use, focused on crime or justice-related topics, potentially further contributing to stigma, and emphasising the legal consequences of AOD use.

Incorporating infectious duration-dependent transmission into Bayesian epidemic models.

Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.

An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests.

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.

Applying network analysis to assess the development and sustainability of multi-sector coalitions.

BACKGROUND: Accountable Communities of Health (ACH) models have been popularized through Center for Medicare and Medicaid Innovation (CMMI) grants, including the State Innovation Model (SIM), to encourage the development of community-based coalitions across medical, public health, and social service delivery systems. These models enhance care coordination for patients and are better equipped to address Social Determinants of Health (SDH) needs. METHODS: Network data was collected from participating organizations in seven ACH sites established across Iowa. The application of network analysis quantitatively characterized the relational context of the interorganizational, cross-sector networks which are foundational to achieving the ACH goal of systematic, comprehensive care. Our analysis primarily used logistic network regression modeling (LNRM) to identify network structures and characteristics of organizations that facilitate or impede sustainable connections. RESULTS: Our findings suggest that the ACH was effective at stimulating sustainable connections across sectors and disparate positions of centrality in the network. Factors associated with sustainable connections between organizations included the strength of relationships and the type of collaboration, namely data sharing and resource sharing. Leadership roles designated by the ACH structure were associated with stimulating connections during the grant, but not with sustainment. Network measures of density and transitivity, which peaked during the grant period (compared to pre- and post-grant networks), further implied possible attrition of the ACH intervention effects without incentive to maintain collaborations. CONCLUSIONS: Multi-sector care coordination networks were established, but our findings suggest depreciation of ACH intervention momentum and structure without incentive to maintain collaborations beyond the three-year duration of the grant. Sustainability could be bolstered and ACH goals actualized with reliable long-term funding.

Elderspeak communication and pain severity as modifiable factors to rejection of care in hospital dementia care.

BACKGROUND: Rejection of care (RoC) occurs when persons living with dementia (PLWD) withstand or oppose the efforts of their caregiver. Improvements in hospital dementia care are needed, and one way to address this need is by identifying factors that lead to RoC, particularly those that are modifiable. Elderspeak communication is an established antecedent to RoC among PLWD in nursing homes. The purpose of this study was to extend these results to acute care settings by determining the impact of elderspeak communication by nursing staff on RoC by hospitalized PLWD. METHODS: Care encounters between nursing staff and PLWD were audio-recorded, transcribed verbatim, and coded for semantic, pragmatic, and prosodic features of elderspeak. RoC behaviors was scored in real-time using the Resistiveness to Care Scale. A Bayesian repeated-measures hurdle model was used to evaluate the association between elderspeak and both the presence and severity of RoC. RESULTS: Eighty-eight care encounters between 16 PLWD and 53 nursing staff were audio-recorded for elderspeak and scored for RoC. Nearly all (96.6%) of the encounters included some form of elderspeak. Almost half of the care encounters (48.9%) included RoC behaviors. A 10% decrease in elderspeak was associated with a 77% decrease in odds of RoC (OR = 0.23, 95% CI = 0.03, 0.68) and a 16% decrease ( e ß =  0.84, CI = 0.73, 0.96) in the severity of RoC. A one-unit decrease in pain severity was associated with 73% reduced odds of RoC (OR = 0.27, CI = 0.12, 0.45) and a 28% decrease ( e ß =  0.72, CI = 0.64, 0.80) in the severity of RoC. CONCLUSIONS: Both elderspeak by nursing staff and RoC by PLWD are present and pervasive in acute care. Pain and elderspeak are two modifiable factors of RoC in hospitalized PLWD. Person-centered interventions are needed that address communication practices and pain management for hospitalized PLWD.

The Use of Self-Reported Functional Limitation to Examine Pregnancy and Reproductive Health Experiences in a National Sample of Women.

Context: A lack of consensus in the literature examining reproductive health experiences of women with disability prevails, in part, due to various operational definitions of disability. Methods: Results from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) were utilized to assess reproductive health, disability, and demographic variables among women aged 20-44. Disability was assessed using the six functional limitation subgroups. Analyses included modified Poisson regression and negative binomial regression. Results: One hundred eighty-two (14%) women reported having any functional limitation. Women with at least one functional limitation (WWFL) were significantly more likely than women without a functional limitation (WWOFL) to have had a hysterectomy and had more cesarean deliveries. WWFL did not differ significantly from WWOFL in key pregnancy outcomes (ever been pregnant, number of pregnancies, or number of unsuccessful pregnancies). A high degree of overlap between mobility and self-care (66.1%), cognitive and independent living (61%), and mobility and independent living (37.4%) limitations was found. Conclusions: This work summarizes key reproductive health variables among US women of reproductive age and contextualizes disability experiences through subgroup and overlap analysis. Subgroup analysis results demonstrate the need for detailed operational definitions of disability to accurately capture experiences of women with different limitations, and overlap analysis indicates the interconnectedness of limitations among this group. Findings call for future exploration of reproductive health-related similarities and differences between WWD and women without disability, and employment of detailed operational definitions of disability.

Characteristics of elderspeak communication in hospital dementia care: Findings from The Nurse Talk observational study.

BACKGROUND: Elderspeak communication is typically viewed as patronizing and infantilizing by older adults and can lead to resistive behaviors in persons living with dementia. Little is known about the presence of elderspeak communication in hospitals in the United States. Understanding this phenomenon in the hospital setting is needed in order to improve hospital dementia care. OBJECTIVES: The purpose of the Nurse Talk study was to (1) describe attributes of elderspeak use in hospital dementia care and to (2) determine what characteristics are associated with nursing staff use of elderspeak communication with hospitalized patients with dementia. DESIGN: A cross-sectional observational study design was used to collect and analyze audio-recordings of nursing staff during care for hospitalized patients with dementia. SETTING: Three hospital units in one Midwestern university hospital in the United States. PARTICIPANTS: A convenience sample of 53 staff nurses and nursing assistants that provided direct care to 16 patients with mild or more severe dementia recruited from October 2019 through mid-March 2020. METHODS: Eighty-eight care encounters were audio-recorded and coded for elderspeak communication using the Iowa Coding of Elderspeak scheme to determine the frequency and characteristics of elderspeak communication. A linear mixed effects model was used to determine what characteristics were associated with elderspeak and the frequency of elderspeak use by nursing staff to hospitalized patients with dementia. RESULTS: Over a quarter (28.7%) of all nursing staff speech directed towards patients with dementia constituted elderspeak and nearly all (96.6%) care encounters included some elderspeak. Particularly common attributes of elderspeak were minimizing words and mitigating expressions, childish terms and phrases, and collective pronoun substitution. A statistically significant interaction was identified between staff role and age (95% CI: -0.02, -0.00, p = .008) in predicting the frequency of elderspeak use, indicating that elderspeak was used more often by older staff nurses, whereas the age of nursing assistants remained constant across elderspeak use. Statically significant effects for delirium and length of stay were also demonstrated. Elderspeak use was 12.5% higher with patients with delirium (95% CI: 0.02, 0.23, p = .025) and increased 1.5% for each additional day the patient with dementia was hospitalized (95% CI: 0.00, 0.03, p = .035). CONCLUSIONS: Elderspeak is present and pervasive in the acute care setting. Interventions targeted towards older staff nurses and nursing staff from hospital units that care for patients with delirium and longer lengths of stay are needed. TWEETABLE ABSTRACT: This study identified that nursing staff are frequently using elderspeak (infantilizing speech) with hospitalized patients with dementia. @claireshaw_phd @IowaNursing.

Modeling Population and Subject-Specific Growth in a Latent Trait Measured by Multiple Instruments over Time using a Hierarchical Bayesian Framework.

Psychometric growth curve modeling techniques are used to describe a person's latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model's utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.

Evaluating a state child care assistance program using administrative data.

Government supported child care assistance (CCA) programs support low-income families by subsidizing costs of child care for working parents. In the United States, federal policy outlines several goals designed to guide the development and implementation of state CCA programs, however the intended outcomes are difficult to quantify. Without the collection and analysis of data related to the families and providers involved in the program, states are not able to effectively evaluate their success. This leaves program administrators and community advocates without valuable information needed for decision-making. Administrative data collected by the state CCA programs as part of their implementation offers a low-cost and effective approach to assessing program performance and execution, but there is currently no guidance for distilling the large quantities of information existing in these complex records. In this article, we present a novel evaluation framework for using administrative data to evaluate CCA program success guided by both policy and literature. We illustrate the benefit of our approach using the state of Iowa's CCA data system and offer recommendations for decision-makers seeking to use a data-driven approach to program evaluation.

Prevalence and significance of incidental findings on computed tomography pulmonary angiograms: A retrospective cohort study.

OBJECTIVE: The purpose of this study was to analyze the prevalence and significance of incidental findings on computed tomography pulmonary angiography (CTPA) studies and to assess the diagnostic yield of CTPA in identifying an alternate diagnosis to pulmonary embolism (PE) on PE negative exams. METHODS: All patients who had a CTPA exam for PE evaluation between Jan 2016 and Dec 2018 with a negative PE result were included in the study. A total of 2083 patients were identified. We retrospectively queried the electronic medical record and the radiology report and recorded the following: Age, Sex, BMI, Patient location and Incidental findings. The incidental findings were classified into type 1 (Alternate diagnosis other than PE which could explain the patient's symptoms), type 2 (non-emergent findings which needed further work up) and type 3 findings (non-emergent findings which did not need further work up). Logistic regression analysis was performed to determine what factors affected the probability of finding a type 1 incidental (alternate diagnosis) or a type 2 incidental. RESULTS: 74.5% of the patients in our study had at least one incidental finding. Type 1 incidental findings (alternate diagnosis to PE) were found in 864 patients (41.5%). The most common type 1 finding was pneumonia followed by fluid overload. Male sex, increased age and lower BMI were significantly associated with increased odds of a type 1 incidental(p < 0.05). Similarly, all the patient locations had significantly different odds of finding a type-1 incidental, with ICU having the highest odds, followed by inpatient, ED and outpatient locations (p < 0.05). 563 patients (27%) had at least one type 2 incidental findings and the most common type 2 findings were progressive lung malignancy/ metastatic disease and new pulmonary nodule. Increased age was significantly associated with the probability of a type 2 finding (p < 0.05). CONCLUSIONS: CTPA may suggest an alternative diagnosis to pulmonary embolism in approximately 40% of the patients with a negative study. The probability of finding an alternate diagnosis (type 1 incidental) is higher in elderly patients and in patients referred from ICU and inpatient units.

Language Growth in Children with Mild to Severe Hearing Loss who Received Early Intervention by 3 Months or 6 Months of Age.

Purpose: To evaluate the impact of hearing screening, diagnosis, and early intervention (EI) by 3 months or 6 months of age on language growth trajectories for children with hearing loss (HL) relative to children with normal hearing (NH). Method: We recruited 133 children with mild to severe HL through universal newborn hearing screening records and referrals from audiologists in the United States and 116 children with NH who served as a comparison group. Examiners administered a battery of developmentally appropriate language measures between 12 months and 8 years of age. We constructed latent growth curve models of global language, grammar, and vocabulary using Bayesian statistics. Results: Children with HL demonstrated no significant differences in initial language skills compared to children with NH. Children in the 1-3-6 group also showed no difference in language growth compared to children with NH. The slope for the 1-2-3 group was significantly steeper than children with NH for global language and grammar. Conclusions: This study documents the positive impact of EI on language outcomes in children with congenital HL. It is among the first to provide evidence to support the potential effects of very early intervention by 3 months of age.

The utility of renal sonographic measurements in differentiating children with high grade congenital hydronephrosis.

INTRODUCTION: Current grading systems for hydronephrosis include a subjective determination of parenchymal 'thickness' and suffer from poor reliability. Use of more objective ultrasonographic measurements including medullary pyramidal thickness (PT) may be useful in augmenting current grading systems by decreasing subjectivity and enhancing prognostic ability. OBJECTIVE: To evaluate the utility of PT measurements in patients with SFU grades 3 and 4 hydronephrosis, we assessed the: 1) the inter-rater reliability of PT measurements, 2) the correlation between relative renal function on nuclear renal scan and PT, and 3) the pyeloplasty predictive ability of PT alone and in combination with SFU grade and/or other sonographic measurements in multivariate statistical models. STUDY DESIGN: We retrospectively reviewed 110 children with SFU grade III and IV hydronephrosis. Most patients presented with a history of prenatally detected hydronephrosis at a median age (IQR) of 1.7 months (0.6-5.2). Sixty-two kidneys were followed without operative intervention while 63 underwent pyeloplasty. Indications for surgery included an obstructive drainage pattern with a T1/2 > 20 min on diuretic renal scan in addition to decreased relative renal function less than 40%, increasing hydronephrosis on serial ultrasounds, and/or a decline in relative renal function >10% on serial renal scans. The median age at the time of pyeloplasty was 5.7 months (2.8-13.7). The median time from initial presentation to final follow-up for all patients was 28 months (18.3-44.6). The PT, APD, and renal length were measured on sonographic images. The inter-rater reliability for SFU grading was only fair whereas it was excellent for PT measurements. Receiver operating characteristic (ROC) curves were generated for inclusive multivariate models for prediction of pyeloplasty with and without SFU grade. DISCUSSION AND CONCLUSIONS: PT is a reliable and useful measurement to characterize the hydronephrotic kidney parenchyma and a PT > 3 mm occurs significantly more frequently in patients with a DRF ≥ 45% (p = 0.0056). PT alone was predictive of subsequent pyeloplasty (AUC = 0.781). A novel pyeloplasty predictive score (PPS) using only objective measurements including PT, APD, and renal length was more accurate than a PPS that incorporated SFU grade (AUC of 0.885 and 0.866, respectively). Utilization of PT ≤ 3 mm as a criterion for 'thinned parenchyma' in the SFU, UTD, and other hydronephrosis grading systems should be considered if confirmed by additional studies.

Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?

Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311-0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.