Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification.

PURPOSE: Less invasive decision support tools are desperately needed to identify occult high-risk disease in men with prostate cancer (PCa) on active surveillance (AS). For a variety of reasons, many men on AS with low- or intermediate-risk disease forgo the necessary repeat surveillance biopsies needed to identify potentially higher-risk PCa. Here, we describe the development of a blood-based immunocyte transcriptomic signature to identify men harboring occult aggressive PCa. We then validate it on a biopsy-positive population with the goal of identifying men who should not be on AS and confirm those men with indolent disease who can safely remain on AS. This model uses subtraction-normalized immunocyte transcriptomic profiles to risk-stratify men with PCa who could be candidates for AS. MATERIALS AND METHODS: Men were eligible for enrollment in the study if they were determined by their physician to have a risk profile that warranted prostate biopsy. Both training (n = 1017) and validation cohort (n = 1198) populations had blood samples drawn coincident to their prostate biopsy. Purified CD2+ and CD14+ immune cells were obtained from peripheral blood mononuclear cells, and RNA was extracted and sequenced. To avoid overfitting and unnecessary complexity, a regularized regression model was built on the training cohort to predict PCa aggressiveness based on the National Comprehensive Cancer Network PCa guidelines. This model was then validated on an independent cohort of biopsy-positive men only, using National Comprehensive Cancer Network unfavorable intermediate risk and worse as an aggressiveness outcome, identifying patients who were not appropriate for AS. RESULTS: The best final model for the AS setting was obtained by combining an immunocyte transcriptomic profile based on 2 cell types with PSA density and age, reaching an AUC of 0.73 (95% CI: 0.69-0.77). The model significantly outperforms (P < .001) PSA density as a biomarker, which has an AUC of 0.69 (95% CI: 0.65-0.73). This model yields an individualized patient risk score with 90% negative predictive value and 50% positive predictive value. CONCLUSIONS: While further validation in an intended-use cohort is needed, the immunocyte transcriptomic model offers a promising tool for risk stratification of individual patients who are being considered for AS.

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis.

RATIONALE & OBJECTIVE: The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD. STUDY DESIGN: Predictive modeling study. SETTING & PARTICIPANTS: 42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers. NEW PREDICTORS & ESTABLISHED PREDICTORS: Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR. OUTCOME: For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival). ANALYTICAL APPROACH: We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points. RESULTS: All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%. LIMITATIONS: The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids. CONCLUSIONS: A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality. PLAIN-LANGUAGE SUMMARY: Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

Evaluating Factors Associated With Telehealth Appropriateness in Outpatient Rheumatoid Arthritis Encounters Using the Encounter Appropriateness Score for You (EASY).

OBJECTIVE: Telehealth has been proposed as a safe and effective alternative to in-person care for rheumatoid arthritis (RA). The purpose of this study was to evaluate factors associated with telehealth appropriateness in outpatient RA encounters. METHODS: A prospective cohort study (January 1, 2021, to August 31, 2021) was conducted using electronic health record data from outpatient RA encounters in a single academic rheumatology practice. Rheumatology providers rated the telehealth appropriateness of their own encounters using the Encounter Appropriateness Score for You (EASY) immediately following each encounter. Robust Poisson regression with generalized estimating equations modeling was used to evaluate the association of telehealth appropriateness with patient demographics, RA clinical characteristics, comorbid noninflammatory causes of joint pain, previous and current encounter characteristics, and provider characteristics. RESULTS: During the study period, 1823 outpatient encounters with 1177 unique patients with RA received an EASY score from 25 rheumatology providers. In the final multivariate model, factors associated with increased telehealth appropriateness included higher average provider preference for telehealth in prior encounters (relative risk [RR] 1.26, 95% CI 1.21-1.31), telehealth as the current encounter modality (RR 2.27, 95% CI 1.95-2.64), and increased patient age (RR 1.05, 95% CI 1.01-1.09). Factors associated with decreased telehealth appropriateness included moderate (RR 0.81, 95% CI 0.68-0.96) and high (RR 0.57, 95% CI 0.46-0.70) RA disease activity and if the previous encounters were conducted by telehealth (RR 0.83, 95% CI 0.73-0.95). CONCLUSION: In this study, telehealth appropriateness was most associated with provider preference, the current and previous encounter modality, and RA disease activity. Other factors like patient demographics, RA medications, and comorbid noninflammatory causes of joint pain were not associated with telehealth appropriateness.

Evaluating Provider Perceptions of Telehealth Utility in Outpatient Rheumatology Telehealth Encounters.

OBJECTIVE: This study aims to explore the factors associated with rheumatology providers' perceptions of telehealth utility in real-world telehealth encounters. METHODS: From September 14, 2020 to January 31, 2021, 6 providers at an academic medical center rated their telehealth visits according to perceived utility in making treatment decisions using the following Telehealth Utility Score (TUS) (1 = very low utility to 5 = very high utility). Modified Poisson regression models were used to assess the association between TUS scores and encounter diagnoses, disease activity measures, and immunomodulatory therapy changes during the encounter. RESULTS: A total of 481 telehealth encounters were examined, of which 191 (39.7%) were rated as "low telehealth utility" (TUS 1-3) and 290 (60.3%) were rated as "high telehealth utility" (TUS 4-5). Encounters with a diagnosis of inflammatory arthritis were significantly less likely to be rated as high telehealth utility (adjusted relative risk [aRR], 0.8061; p = 0.004), especially in those with a concurrent noninflammatory musculoskeletal diagnosis (aRR, 0.54; p = 0.006). Other factors significantly associated with low telehealth utility included higher disease activity according to current and prior RAPID3 scores (aRR, 0.87 and aRR, 0.89, respectively; p < 0.001) and provider global scores (aRR, 0.83; p < 0.001), as well as an increase in immunomodulatory therapy (aRR, 0.70; p = 0.015). CONCLUSIONS: Provider perceptions of telehealth utility in real-world encounters are significantly associated with patient diagnoses, current and prior disease activity, and the need for changes in immunomodulatory therapy. These findings inform efforts to optimize the appropriate utilization of telehealth in rheumatology.

Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.

The use of machine learning models to predict clinical outcomes from (longitudinal) electronic health record (EHR) data is becoming increasingly popular due to advances in deep architectures, representation learning, and the growing availability of large EHR datasets. Existing models generally assume access to the same data sources during both training and inference stages. However, this assumption is often challenged by the fact that real-world clinical datasets originate from various data sources (with distinct sets of covariates), which though can be available for training (in a research or retrospective setting), are more realistically only partially available (a subset of such sets) for inference when deployed. So motivated, we introduce Contrastive Learning for clinical Outcome Prediction with Partial data Sources (CLOPPS), that trains encoders to capture information across different data sources and then leverages them to build classifiers restricting access to a single data source. This approach can be used with existing cross-sectional or longitudinal outcome classification models. We present experiments on two real-world datasets demonstrating that CLOPPS consistently outperforms strong baselines in several practical scenarios.

Adaptive Discretization for Event PredicTion (ADEPT).

Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals. By avoiding placing strong parametric assumptions on the event density, this approach tends to improve prediction performance, particularly when data are plentiful. However, in clinical settings with limited available data, it is often preferable to judiciously partition the event time space into a limited number of intervals well suited to the prediction task at hand. In this work, we develop Adaptive Discretization for Event PredicTion (ADEPT) to learn from data a set of cut points defining such a partition. We show that in two simulated datasets, we are able to recover intervals that match the underlying generative model. We then demonstrate improved prediction performance on three real-world observational datasets, including a large, newly harmonized stroke risk prediction dataset. Finally, we argue that our approach facilitates clinical decision-making by suggesting time intervals that are most appropriate for each task, in the sense that they facilitate more accurate risk prediction.

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Purpose: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group. Methods: We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values. Results: In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Conclusions: Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. Translational Relevance: Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.

Image Quality Assessment Using Convolutional Neural Network in Clinical Skin Images.

The image quality received for clinical evaluation is often suboptimal. The goal is to develop an image quality analysis tool to assess patient- and primary care physician-derived images using deep learning model. Dataset included patient- and primary care physician-derived images from August 21, 2018 to June 30, 2022 with 4 unique quality labels. VGG16 model was fine tuned with input data, and optimal threshold was determined by Youden's index. Ordinal labels were transformed to binary labels using a majority vote because model distinguishes between 2 categories (good vs bad). At a threshold of 0.587, area under the curve for the test set was 0.885 (95% confidence interval = 0.838-0.933); sensitivity, specificity, positive predictive value, and negative predictive value were 0.829, 0.784, 0.906, and 0.645, respectively. Independent validation of 300 additional images (from patients and primary care physicians) demonstrated area under the curve of 0.864 (95% confidence interval = 0.818-0.909) and area under the curve of 0.902 (95% confidence interval = 0.85-0.95), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for the 300 images were 0.827, 0.800, 0.959, and 0.450, respectively. We demonstrate a practical approach improving the image quality for clinical workflow. Although users may have to capture additional images, this is offset by the improved workload and efficiency for clinical teams.

Epigenetic and transcriptional responses in circulating leukocytes are associated with future decompensation during SARS-CoV-2 infection.

To elucidate host response elements that define impending decompensation during SARS-CoV-2 infection, we enrolled subjects hospitalized with COVID-19 who were matched for disease severity and comorbidities at the time of admission. We performed combined single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on peripheral blood mononuclear cells (PBMCs) at admission and compared subjects who improved from their moderate disease with those who later clinically decompensated and required invasive mechanical ventilation or died. Chromatin accessibility and transcriptomic immune profiles were markedly altered between the two groups, with strong signals in CD4+ T cells, inflammatory T cells, dendritic cells, and NK cells. Multiomic signature scores at admission were tightly associated with future clinical deterioration (auROC 1.0). Epigenetic and transcriptional changes in PBMCs reveal early, broad immune dysregulation before typical clinical signs of decompensation are apparent and thus may act as biomarkers to predict future severity in COVID-19.

Predictive signature of murine and human host response to typical and atypical pneumonia.

BACKGROUND: Pneumonia due to typical bacterial, atypical bacterial and viral pathogens can be difficult to clinically differentiate. Host response-based diagnostics are emerging as a complementary diagnostic strategy to pathogen detection. METHODS: We used murine models of typical bacterial, atypical bacterial and viral pneumonia to develop diagnostic signatures and understand the host's response to these types of infections. Mice were intranasally inoculated with Streptococcus pneumoniae, Mycoplasma pneumoniae, influenza or saline as a control. Peripheral blood gene expression analysis was performed at multiple time points. Differentially expressed genes were used to perform gene set enrichment analysis and generate diagnostic signatures. These murine-derived signatures were externally validated in silico using human gene expression data. The response to S. pneumoniae was the most rapid and robust. RESULTS: Mice infected with M. pneumoniae had a delayed response more similar to influenza-infected animals. Diagnostic signatures for the three types of infection had 0.94-1.00 area under the receiver operator curve (auROC). Validation in five human gene expression datasets revealed auROC of 0.82-0.96. DISCUSSION: This study identified discrete host responses to typical bacterial, atypical bacterial and viral aetiologies of pneumonia in mice. These signatures validated well in humans, highlighting the conserved nature of the host response to these pathogen classes.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance.

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Actualización del manejo de microbacterias no tuberculosas / Update on the management of non-tuberculous mycobacteria

Desde el año 2007 se han generado guías de diagnóstico y tratamiento de micobacterias no tuberculosas (MNTB), la última de las cuales fue desarrollada en el año 2020 por ATS/ERS/ESCMID/IDSA, en ella se actualizan los criterios diagnósticos, los criterios para determinar el inicio de tratamiento y recomendaciones de esquema de antibióticos para las especies más frecuentes. En paralelo se han ido desarrollando terapias alternativas como la fagoterapia. El objetivo de la presente revisión es dar a conocer los cambios que traen estas últimas guías y actualizar algunas de las últimas novedades con respecto al manejo de las micobacterias no tuberculosas.

Since 2007, guidelines for diagnosis and treatment of non-tuberculous Mycobacteria have been generated, the latest of which was developed by ATS/ERS/ESCMID/IDSA, in which the diagnostic criteria, and the criteria for determining the initiation of treatment and antibiotic scheme recommendations for the most frequent species are updated. At the same time, alternative therapies such as phage therapy have been developed. The objective of this review is to show the changes that these latest guidelines bring and update some of the latest developments regarding the management of non-tuberculous Mycobacteria.

Micobacterias no tuberculosas en pacientes con fibrosis quística / Non-tuberculous mycobacteria in cystic fibrosis patients

In recent years there has been a global increase in nontuberculous mycobacteria isolates, especially in patients with cystic fibrosis. As its clinical and radiological characteristics overlap with other infectious agents, diagnostic guidelines were generated based on evidence from patients who do not present cystic fibrosis. A long-term treatment is necessary, involving multiple antibiotics, and the response rate is low. There are variations in the criteria adopted by different centers with regard to lung transplantation in this group of patients.

En los últimos años se ha producido un aumento a nivel mundial del aislamiento de micobacterias no tuberculosas, especialmente en pacientes con fibrosis quística. Como sus características clínicas y radiológicas se superponen con las de otros agentes infecciosos se generaron orientaciones diagnósticas basadas en evidencia de pacientes que no presentan fibrosis quística. El tratamiento es prolongado, involucra múltiples antibióticos y la tasa de respuesta es baja. Existen variaciones en los criterios adoptados por los distintos centros con respecto al trasplante pulmonar en este grupo de pacientes.

Dois casos de cisto broncogênico do mediastino em lactentes sintomáticos / Two cases of mediastinal bronchogenic cyst in symptomatic infants

Os cistos broncogênicos são malformações relativamente infreqüentes, que causam 10% das massas e 60% dos cistos mediastinais. No entanto, uma intervenção cirúrgica, geralmente, é necessária para a confirmação do diagnóstico, para a exclusão de malignidade e prevenção de complicações, tais como hemorragia e infecção. Descrevemos dois casos de cisto broncogênico do mediastino, em lactentes com menos de 1 ano de vida. Os diagnósticos foram suspeitados devido à evolução com sintomas digestivos, associados à compressão esofagiana, demonstrada na radiografia de tórax e na seriografia esôfago-gástrica com bário. A tomografia computadorizada de tórax reforçou a hipótese diagnóstica e ambos os pacientes foram submetidos à cirurgia, sendo o diagnóstico confirmado com a histopatologia das lesões. Os dois casos obtiveram total sucesso terapêutico, tornando-se assintomáticos após a cirurgia. Este trabalho reforça a importância de se incluir a possibilidade do diagnóstico de cisto broncogênico mediastinal, em lactentes com sintomas digestivos de refluxo gastro-esofagiano que não melhoram com o tratamento clínico adequado.