An open-source framework for end-to-end analysis of electronic health record data.

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Ulnar-Sided Wrist Pain: A Diagnostic Evaluation Guide From 30-Plus Years of Experience.

INTRODUCTION: While multiple ulnar-sided wrist pain (USWP) diagnostic evaluation guides have been presented, none have included original clinical data or statistical analysis. The purpose of this study is to provide a diagnostic evaluation guide derived from original clinical data and analysis to help clinicians arrive at a differential diagnosis for USWP. METHODS: Using a computer search of patients presenting with sprains, instability, and laxity of the wrist, 385 patient charts were identified. Patient demographics, mechanism of injury, subjective complaints, physical findings, and diagnostic test findings were reviewed. Statistical analysis was performed to determine sensitivity and specificity of diagnostic methods on their ability to identify lunotriquetral ligament tears, triangular fibrocartilage complex (TFCC) tears, and ulnar impaction syndrome. Diagnostic arthroscopy was used as the reference standard. RESULTS: Ninety-three patients, comprising 101 cases of USWP, were included in the study. The onset of injury was traumatic in 83 out of 101 cases with motor vehicle accidents (N=46) being the most common, followed by overuse (N=18), and a fall onto an outstretched hand (N=16). The ulnocarpal tenderness test exhibited sensitivity/specificity of 72%/33%; lunotriquetral ligament laxity test of 42%/62%; bone scan of 80%/33%; radiocarpal arthrogram of 90%/98% for TFCC tears and 50%/91% for lunotriquetral ligament tears; midcarpal arthrogram of 82%/86% for lunotriquetral ligament tears. The mean ulnar variance on standard posteroanterior view radiograph was 0.95 mm, increasing to 2.67 mm on gripping posteroanterior view. CONCLUSION: Physicians should suspect a lunotriquetral ligament and/or TFCC tear with the acute onset of USWP following a loaded dorsiflexed mechanism of injury. Ulnocarpal tenderness tests and pre-operative ulnar variance measures are effective for increasing suspicion of USW pathology. Bone scans are helpful in diagnosing ulnar impaction syndrome in conjunction with radiographic findings. A combination of midcarpal arthrogram for lunotriquetral ligament tears and radiocarpal arthrogram for TFCC tears should be employed.

Integrating single-cell RNA-seq datasets with substantial batch effects.

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

Predictors of Readmission and Reoperation Following Shoulder Arthroplasty in Patients Under 45 Years of Age.

BACKGROUND: The use of shoulder arthroplasty has increased among all age groups, albeit most prominently in older patients. While previous studies have investigated predictors of short-term readmission and reoperation in the general population, there is a paucity of literature available on these in patients under 45 years of age. This study aimed to identify the predictors of 30-day readmission and reoperation following shoulder arthroplasty in patients under 45 years of age. METHODS: A retrospective query in the American College of Surgeons National Surgical Quality Improvement Program database from 2011 to 2019 was used to identify patients who underwent primary reverse and anatomic total shoulder arthroplasty and hemiarthroplasty. Multivariate logistic regression was used to identify predictors of 30-day readmission and reoperation. RESULTS: A total of 530 patients were included. Multivariate regression revealed that Black race and Hispanic ethnicity were independent predictors of readmission. Functional dependence, hypertension requiring medication, and prolonged length of stay predicted reoperation. Finally, low hematocrit and prolonged length of stay predicted morbidity. DISCUSSION: Identifying and accounting for these risk factors for poor outcomes may help improve perioperative risk stratification. As a result, these findings have the potential to reduce healthcare costs associated with readmission and reoperation following shoulder arthroplasty in young patients. Our results also highlight the underlying disparities in healthcare outcomes among racial and ethnic groups that must be considered.

Population-level integration of single-cell datasets enables multi-scale analysis across samples.

The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

Delineating mouse ß-cell identity during lifetime and in diabetes with a single cell atlas.

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin ß-cell ablation model. The ß-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that ß-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD ß-cells. We also report pathways that are shared between ß-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of ß-cell responses to different stressors, providing a roadmap for the understanding of ß-cell plasticity, compensation and demise.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Clinical Outcomes of Lunocapitate Arthrodesis: A Retrograde Single-Screw Approach.

BACKGROUND: A single-site retrospective study was designed to evaluate the clinical outcomes of single-screw lunocapitate arthrodesis (LCA) using a retrograde approach for the treatment of scapholunate advanced collapse (SLAC) wrist. METHODS: We retrospectively identified 31 patients (33 cases) between September 2010 and December 2019 with SLAC wrist changes who were treated with single-screw LCA. Objective outcomes included time to fusion, union rate, range of motion, and grip and pinch strength recovery. Subjective outcomes included Disabilities of the Arm, Shoulder, and Hand (DASH) scores. RESULTS: We report on 33 cases (7 female), mean age 58.4 years (range: 41-85), with SLAC wrist who underwent LCA. Our cohort reported a 94% union rate and a 90-day mean time to fusion. Final active wrist range of motion was 38° dorsiflexion, 35° volarflexion, 17° radial deviation, 17° ulnar deviation, 82° pronation, and 83° supination (mean: 450.8 days). Final grip and pinch strengths recovered was 75% gross grip, 84% lateral pinch, and 75% precision pinch (mean: 379.0 days) compared with the contralateral side. The mean postoperative DASH score was 27 (mean: 1203.9 days). Two nonunions were observed. Two hardware complications occurred: one symptomatic screw and one screw fatigue fracture. CONCLUSIONS: We found retrograde single-screw LCA to be an effective salvage procedure for SLAC wrist. LCA is a less-taxing procedure, requires shorter operating time, and produces range of motion and grip and pinch strength recovery comparable to those of 4-corner arthrodesis. Furthermore, the viability of single-screw fixation may reduce hardware-related operative costs without compromising union rates.

Venous Thromboembolism Rates Have Not Decreased in Elective Lumbar Fusion Surgery from 2011 to 2020.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: This study aimed to (1) evaluate for any temporal trends in the rates of VTE, deep venous thrombosis (DVT), pulmonary embolism (PE), and mortality from 2011 to 2020 and (2) identify the predictors of VTE following lumbar fusion surgery. METHODS: Annual incidences of 30-day VTE, DVT, PE, and mortality were calculated for each of the operation year groups from 2011 to 2020. Multivariable Poisson regression was utilized to test the association between operation year and primary outcomes, as well as to identify significant predictors of VTE. RESULTS: A total of 121,205 patients were included. There were no statistically significant differences in VTE, DVT, PE, or mortality rates among the operation year groups. Multivariable regression analysis revealed that compared to 2011, operation year 2019 was associated with significantly lower rates of DVT. Age, BMI, prolonged operation time, prolonged length of stay, non-home discharge, anterior fusion, smoking status, functional dependence, and chronic steroid use were identified as independent predictors of VTE following lumbar fusion. Female sex, Hispanic ethnicity, and outpatient surgery setting were identified as protective factors from VTE in this cohort. CONCLUSIONS: Rates of VTE after lumbar fusion have remained mostly unchanged between 2011 and 2020. Older age, higher BMI, longer operation time, prolonged length of stay, non-home discharge, anterior fusion, smoking, functional dependence, and steroid use were independent predictors of VTE after lumbar fusion, while female sex, Hispanic ethnicity, and outpatient surgery were the protective factors.

Best practices for single-cell analysis across modalities.

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Single-Portal Antegrade Endoscopic Trigger Finger Release: Cadaveric and Clinical Outcomes.

BACKGROUND: This study aimed to examine the relationship between anatomical surface landmarks in fresh frozen cadavers as related to in vivo endoscopic trigger finger release (ETFR) and present clinical outcomes after a single-portal antegrade ETFR technique. METHODS: Endoscopic trigger finger release was performed on 40 cadaveric digits. Each digit was dissected and the following measurements were recorded: distance from palmar digital crease and A1 pulley, length of the A1 pulley, percentage of A1 pulley released, and injury to vulnerable anatomy. A retrospective chart review was performed on 48 patients (62 digits) treated with ETFR. Outcome measures included grip and pinch strength, range of motion, Disability of Arm, Shoulder, and Hand (DASH) questionnaires, and Visual Analog Scale (VAS) pain scores. RESULTS: Release of the A1 pulley was achieved in 33 of the 40 cadaveric digits (83%) with an A2 pulley laceration rate of 25%. No flexor tendon or neurovascular injuries occurred. Gross grasp, lateral pinch, 3-jaw chuck, and precision pinch strength had 85%, 90%, 82%, and 90% recovery, respectively. At the final follow-up, average metacarpophalangeal joint, proximal interphalangeal joint, and distal interphalangeal joint range of motion were within the normal limits. Mean VAS scores decreased from 5.7 preoperatively to 1.0 postoperatively and mean DASH score at the final follow-up was 4.8. CONCLUSIONS: With the use of anatomical surface landmarks, ETFR may be performed in an efficient and reproducible manner. Patients treated with ETFR had low complication rates, good functional recovery, and improved pain at short-term follow-up. Further study of long-term outcomes and cost-effectiveness of ETFR is warranted.

Complications Following Endoscopic and Open Trigger Finger Release: A Retrospective Comparative Study.

BACKGROUND: Open trigger finger release (OTFR) and endoscopic trigger finger release (ETFR) are effective methods in treating stenosing tenosynovitis. However, a paucity of literature exists comparing the techniques. This study describes and compares postoperative complications following OTFR and ETFR at a single institution. METHODS: Patients undergoing trigger finger release between 2018 and 2020 within a single institution were identified. Electronic medical records were reviewed for patient demographics, surgical history, surgical characteristics, and clinical outcomes. Major and minor postoperative complications were assessed. Secondary outcome measures included tourniquet time and procedure time. Statistical analysis evaluated associations between postoperative complications, surgical technique, patient demographics, and surgical characteristics. RESULTS: In total, 57 patients (80 digits) were included in the study: 42 digits treated with OTFR and 38 digits treated with ETFR. Mean follow-up time was 57.6 ± 69.0 days (range, 7-307 days) for ETFR and 34.2 ± 26.3 days (range, 6-120 days) for OTFR. Overall, major, and minor complication rates for the cohort were 8.8%, 1.8% and 7.0%, respectively. There were no major complications following ETFR and 1 following OTFR (4%), the isolated case being postoperative Chronic regional pain syndrome. Minor complication rates were similar following OTFR (8%) and ETFR (6%). Persistent digit stiffness and swelling were found to be the most prevalent minor complications (n = 2, respectively), followed by wound dehiscence (n = 1). Female patients were significantly more likely to experience postoperative complications. CONCLUSIONS: Major complications following trigger finger release are unlikely; however, minor complications are prominent. Patients treated with OTFR and ETFR showed similar postoperative complication rates. Continued investigations into the benefits of ETFR are warranted.

splatPop: simulating population scale single-cell RNA sequencing data.

Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression.

Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape.

Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. Since 2016, the scRNA-tools database has cataloged software tools for analyzing scRNA-seq data. With the number of tools in the database passing 1000, we provide an update on the state of the project and the field. This data shows the evolution of the field and a change of focus from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find that open science practices reward developers with increased recognition and help accelerate the field.

Sfaira accelerates data and model reuse in single cell genomics.

Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for cell type classification tailored to datasets annotated at different levels of coarseness. We demonstrate the utility of sfaira by training models across anatomic data partitions on 8 million cells.

Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems.

An Australian water utility has developed a Legionella High Level Risk Assessment (LHLRA) which provides a semi-qualitative assessment of the risk of Legionella proliferation and human exposure in engineered water systems using a combination of empirical observation and expert knowledge. Expanding on this LHLRA, we propose two iterative Bayesian network (BN) models to reduce uncertainty and allow for a probabilistic representation of the mechanistic interaction of the variables, built using data from 25 groundwater treatment plants. The risk of Legionella exposure in groundwater aeration units was quantified as a function of five critical areas including hydraulic conditions, nutrient availability and growth, water quality, system design (and maintenance), and location and access. First, the mechanistic relationship of the variables was conceptually mapped into a fishbone diagram, parameterised deterministically using an expert elicited weighted scoring system and translated into BN. The "sensitivity to findings" analysis of the BN indicated that system design was the most influential variable while elemental accumulation thresholds were the least influential variable for Legionella exposure. The diagnostic inference was used in high and low-risk scenarios to demonstrate the capabilities of the BNs to examine probable causes for diverse conditions. Subsequently, the causal relationship of Legionella growth and human exposure were improved through a conceptual bowtie representation. Finally, an improved model developed the predictors of Legionella growth and the risk of human exposure through the interaction of operational, water quality monitoring, operational parameters, and asset conditions. The use of BNs modelling based on risk estimation and improved functional decision outputs offer a complementary and more transparent alternative approach to quantitative analysis of uncertainties than the current LHLRA.

Opportunities and challenges in long-read sequencing data analysis.

Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk.

Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.