Evaluation of Biomarker-Based Glomerular Filtration Rate Estimating Equations in Glomerular Disease.

Introduction: Glomerular filtration rate (GFR) is typically estimated with equations that use biomarkers such as serum creatinine and/or cystatin-C. The impact of these different biomarkers on GFR estimates in glomerular disease patients is unclear. In this study, we compared the different GFR estimating equations in the Cure Glomerulonephropathy (CureGN) cohort of children and adults with glomerular disease. Methods: All available cystatin-C measurements from CureGN study participants were matched to same-day serum creatinine measurements to estimate GFR. To explore the strength of agreement between eGFR values obtained from the "Under 25" (U25) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations, we used intraclass correlation coefficients. Multivariable linear mixed effects models were used to determine which factors were independently associated with differences in eGFR values. Results: A total of 928 cystatin-C measurements were matched to same-day serum creatinine measurements from N = 332 CureGN study participants (58% male, 69% White/Caucasian, 20% Black/African American). Among 628 measurements collected while study participants were under 25 years old, there was moderate agreement (0.731) in serum creatinine versus cystatin-C U25 equations. Models showed that higher eGFR values were associated with larger differences between the two equations (p < 0.001). Among 253 measurements collected while study participants were at least 18 years old, there was excellent agreement (0.891-0.978) among CKD-Epi equations using serum creatinine alone, cystatin-C alone, or the combination of both. Younger age was associated with larger differences between CKD-Epi equations (p = 0.06 to p = 0.016). Conclusion: Excellent agreement between CKD-Epi equations indicates continued use of serum creatinine alone for GFR estimation could be appropriate for adults. In contrast, only moderate agreement between U25 equations indicates a need for more frequent measurement of cystatin-C among children and young adults, especially as eGFR increases.

The Association of Female and Male Preconception Dyslipidemia with Live Birth in Couples Seeking Fertility Treatment.

CONTEXT: Dyslipidemia is common, and resultant endothelial dysfunction may impact reproductive outcomes. No prospective study has examined the effect of preconception lipid parameters in both female and male partners or their interaction on live birth. OBJECTIVE: To determine whether live birth is associated with preconception lipids in both partners by planned fertility treatment. DESIGN: Secondary analysis of the Folic Acid and Zinc Supplementation Trial, conducted between June 2013-December 2017. Couples were followed for nine months after randomization and until delivery. SETTING: Multicenter study. PARTICIPANTS: Couples seeking fertility treatment (n = 2370; females 18-45 years, males ≥18 years). EXPOSURES: Female, male, and couple abnormal versus normal preconception lipid concentrations (total cholesterol [TC], low-density lipoprotein [LDL], high-density lipoprotein [HDL], triglycerides [TG]). MAIN OUTCOME MEASURES: Live birth. RESULTS: Among 2370 couples, most males (84%) and females (76%) had at least one abnormal lipid parameter. Males planning in vitro fertilization (IVF, n = 373) with elevated LDL had lower probability of live birth than those with normal levels (47.4% vs. 59.7%, aRR 0.79, 95% CI 0.65-0.98). In couples planning IVF where both partners had elevated TC or LDL, live birth was lower than those with normal levels (TC: 32.4% vs. 58.0%, aRR 0.53, 95% CI 0.36-0.79; and LDL: 41.9% vs. 63.8%, aRR 0.69, 95% CI 0.55-0.85). Lipid parameters were not associated with live birth for couples planning non-IVF treatments. CONCLUSIONS: Couples planning IVF where both partners had elevated TC or LDL and males planning IVF with elevated LDL had decreased probability of live birth. These findings may support lipid screening in patients seeking fertility treatment for prognostic information for reproductive outcomes.

Automated Text Message-Based Program and Use of Acute Health Care Resources After Hospital Discharge: A Randomized Clinical Trial.

Importance: Postdischarge outreach from the primary care practice is an important component of transitional care support. The most common method of contact is via telephone call, but calls are labor intensive and therefore limited in scope. Objective: To test whether a 30-day automated texting program to support primary care patients after hospital discharge reduces acute care revisits. Design, Setting, and Participants: A 2-arm randomized clinical trial was conducted from March 29, 2022, through January 5, 2023, at 30 primary care practices within a single academic health system in Philadelphia, Pennsylvania. Patients were followed up for 60 days after discharge. Investigators were blinded to assignment, but patients and practice staff were not. Participants included established patients of the study practices who were aged 18 years or older, discharged from an acute care hospitalization, and considered medium to high risk for adverse health events by a health system risk score. All analyses were conducted using an intention-to-treat approach. Intervention: Patients in the intervention group received automated check-in text messages from their primary care practice on a tapering schedule for 30 days following discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox. Patients in the control group received a standard transitional care management telephone call from their practice within 2 business days of discharge. Main Outcomes and Measures: The primary study outcome was any acute care revisit (readmission or emergency department visit) within 30 days of discharge. Results: Of the 4736 participants, 2824 (59.6%) were female; the mean (SD) age was 65.4 (16.5) years. The mean (SD) length of index hospital stay was 5.5 (7.9) days. A total of 2352 patients were randomized to the intervention arm and 2384 were randomized to the control arm. There were 557 (23.4%) acute care revisits in the control group and 561 (23.9%) in the intervention group within 30 days of discharge (risk ratio, 1.02; 95% CI, 0.92-1.13). Among the patients in the intervention arm, 79.5% answered at least 1 message and 41.9% had at least 1 need identified. Conclusions and Relevance: In this randomized clinical trial of a 30-day postdischarge automated texting program, there was no significant reduction in acute care revisits. Trial Registration: ClinicalTrials.gov Identifier: NCT05245773.

Influenza Vaccine Administration and Effectiveness Among Children and Adults With Glomerular Disease.

Introduction: Influenza infections contribute to excess healthcare utilization, morbidity, and mortality in individuals with glomerular disease (GD); however, influenza vaccination may not yield protective immune responses in this high-risk patient population. The objective of the present study was to describe influenza vaccine administration from 2010 to 2019 and explore the effectiveness of influenza vaccination in patients with GD. Methods: We conducted an observational cohort study using healthcare claims for seasonal influenza vaccination (exposure) as well as influenza and influenza-like illness (outcomes) from commercially insured children and adults <65 years of age with primary GD in the Merative MarketScan Research Databases. Propensity score-weighted cox proportional hazards models and ratio-of-hazard ratios (RHR) analyses were used to compare influenza infection risk in years where seasonal influenza vaccines matched or mismatched circulating viral strains. Results: The mean proportion of individuals vaccinated per season was 23% (range 19%-24%). In pooled analyses comparing matched to mismatched seasons, vaccination was minimally protective for both influenza (RHR 0.86, 95% confidence interval [CI]: 0.52-1.41) and influenza-like illness (RHR 0.86, 95% CI 0.59-1.24), though estimates were limited by sample size. Conclusion: Rates of influenza vaccination are suboptimal among patients with GD. Protection from influenza after vaccination may be poor, leading to excess infection-related morbidity in this vulnerable population.

Improving quantification of renal fibrosis using Deep-DUET.

Accurate quantification of renal fibrosis has profound importance in the assessment of chronic kidney disease (CKD). Visual analysis of a biopsy stained with trichrome under the microscope by a pathologist is the gold standard for evaluation of fibrosis. Trichrome helps to highlight collagen and ultimately interstitial fibrosis. However, trichrome stains are not always reproducible, can underestimate collagen content and are not sensitive to subtle fibrotic patterns. Using the Dual-mode emission and transmission (DUET) microscopy approach, it is possible to capture both brightfield and fluorescence images from the same area of a tissue stained with hematoxylin and eosin (H&E) enabling reproducible extraction of collagen with high sensitivity and specificity. Manual extraction of spectrally overlapping collagen signals from tubular epithelial cells and red blood cells is still an intensive task. We employed a UNet++ architecture for pixel-level segmentation and quantification of collagen using 760 whole slide image (WSI) patches from six cases of varying stages of fibrosis. Our trained model (Deep-DUET) used the supervised extracted collagen mask as ground truth and was able to predict the extent of collagen signal with a MSE of 0.05 in a holdout testing set while achieving an average AUC of 0.94 for predicting regions of collagen deposits. Expanding this work to the level of the WSI can greatly improve the ability of pathologists and machine learning (ML) tools to quantify the extent of renal fibrosis reproducibly and reliably.

Automated Reference Kidney Histomorphometry using a Panoptic Segmentation Neural Network Correlates to Patient Demographics and Creatinine.

Reference histomorphometric data of healthy human kidneys are lacking due to laborious quantitation requirements. We leveraged deep learning to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine in a multinational set of reference kidney tissue sections. A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in digitized images of 79 periodic acid-Schiff (PAS)-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were measured from the segmented classes. Regression analysis was used to determine the relationship of histomorphometric parameters with age, sex, and serum creatinine. The model achieved high segmentation performance for all test compartments. We found that the size and density of nephrons, arteries/arterioles, and the baseline level of interstitium vary significantly among healthy humans, with potentially large differences between subjects from different geographic locations. Nephron size in any region of the kidney was significantly dependent on patient creatinine. Slight differences in renal vasculature and interstitium were observed between sexes. Finally, glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased as a function of age. We show that precise measurements of kidney histomorphometric parameters can be automated. Even in reference kidney tissue sections with minimal pathologic changes, several histomorphometric parameters demonstrated significant correlation to patient demographics and serum creatinine. These robust tools support the feasibility of deep learning to increase efficiency and rigor in histomorphometric analysis and pave the way for future large-scale studies.

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine.

Background: Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections. Methods: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the relationship of histomorphometric parameters with age, sex, and SCr. Results: Our deep-learning model achieved high segmentation performance for all test compartments. The size and density of nephrons and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Nephron size was significantly dependent on SCr. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of age. Conclusions: Using deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics and SCr. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis.

Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology.

Background: The heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)-based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice. Methods: We studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient. Results: A total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 and AUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results. Conclusions: Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation.

Ridge Regression for Functional Form Identification of Continuous Predictors of Clinical Outcomes in Glomerular Disease.

Introduction: Penalized regression models can be used to identify and rank risk factors for poor quality of life or other outcomes. They often assume linear covariate associations, but the true associations may be nonlinear. There is no standard, automated method for determining optimal functional forms (shapes of relationships) between predictors and the outcome in high-dimensional data settings. Methods: We propose a novel algorithm, ridge regression for functional form identification of continuous predictors (RIPR) that models each continuous covariate with linear, quadratic, quartile, and cubic spline basis components in a ridge regression model to capture potential nonlinear relationships between continuous predictors and outcomes. We used a simulation study to test the performance of RIPR compared to standard and spline ridge regression models. Then, we applied RIPR to identify top predictors of Patient-Reported Outcomes Measurement Information System (PROMIS) adult global mental and physical health scores using demographic and clinical characteristics among N = 107 glomerular disease patients enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). Results: RIPR resulted in better predictive accuracy than the standard and spline ridge regression methods in 56-80% of simulation repetitions under a variety of data characteristics. When applied to PROMIS scores in NEPTUNE, RIPR resulted in the lowest error for predicting physical scores, and the second-lowest error for mental scores. Further, RIPR identified hemoglobin quartiles as an important predictor of physical health that was missed by the other models. Conclusion: The RIPR algorithm can capture nonlinear functional forms of predictors that are missed by standard ridge regression models. The top predictors of PROMIS scores vary greatly across methods. RIPR should be considered alongside other machine learning models in the prediction of patient-reported outcomes and other continuous outcomes.

Pregnancy History and Kidney Disease Progression Among Women Enrolled in Cure Glomerulonephropathy.

Introduction: Preeclampsia increases the risk for future chronic kidney disease (CKD). Among those diagnosed with CKD, it is unclear whether a prior history of preeclampsia, or other complications in pregnancy, negatively impact kidney disease progression. In this longitudinal analysis, we assessed kidney disease progression among women with glomerular disease with and without a history of a complicated pregnancy. Methods: Adult women enrolled in the Cure Glomerulonephropathy study (CureGN) were classified based on a history of a complicated pregnancy (defined by presence of worsening kidney function, proteinuria, or blood pressure; or a diagnosis of preeclampsia, eclampsia, or hemolysis, elevated liver enzymes, and low platelets [HELLP] syndrome), pregnancy without these complications, or no pregnancy history at CureGN enrollment. Linear mixed models were used to assess estimated glomerular filtration rate (eGFR) trajectories and urine protein-to-creatinine ratios (UPCRs) from enrollment. Results: Over a median follow-up period of 36 months, the adjusted decline in eGFR was greater in women with a history of a complicated pregnancy compared to those with uncomplicated or no pregnancies (-1.96 [-2.67, -1.26] vs. -0.80 [-1.19, -0.42] and -0.64 [-1.17, -0.11] ml/min per 1.73 m2 per year, P = 0.007). Proteinuria did not differ significantly over time. Among those with a complicated pregnancy history, eGFR slope did not differ by timing of first complicated pregnancy relative to glomerular disease diagnosis. Conclusions: A history of complicated pregnancy was associated with greater eGFR decline in the years following glomerulonephropathy (GN) diagnosis. A detailed obstetric history may inform counseling regarding disease progression in women with glomerular disease. Continued research is necessary to better understand pathophysiologic mechanisms by which complicated pregnancies contribute to glomerular disease progression.

Tobacco exposure in adults and children with proteinuric glomerulopathies: a NEPTUNE cohort study.

BACKGROUND: Tobacco exposure has been recognized as a risk factor for cardiovascular disease (CVD) and progression of kidney disease. Patients with proteinuric glomerulopathies are at increased risk for cardiovascular morbidity and mortality. Multiple studies have linked tobacco exposure to CVD and chronic kidney disease, but the relationships between smoking and proteinuric glomerulopathies in adults and children have not been previously explored. METHODS: Data from the Nephrotic Syndrome Study Network (NEPTUNE), a multi-center prospective observational study of participants with proteinuric glomerulopathies, was analyzed. 371 adults and 192 children enrolled in NEPTUNE were included in the analysis. Self-reported tobacco exposure was classified as non-smoker, active smoker, former smoker, or exclusive passive smoker. Baseline serum cotinine levels were measured in a sub-cohort of 178 participants. RESULTS: The prevalence of active smokers, former smokers and exclusive passive smoking among adults at baseline was 14.6%, 29.1% and 4.9%, respectively. Passive smoke exposure was 16.7% among children. Active smoking (reference non-smoking) was significantly associated with greater total cholesterol among adults (ß 17.91 95% CI 0.06, 35.76, p = 0.049) while passive smoking (reference non-smoking) was significantly associated with greater proteinuria over time among children (ß 1.23 95% CI 0.13, 2.33, p = 0.03). Higher cotinine levels were associated with higher baseline eGFR (r = 0.17, p = 0.03). CONCLUSION: Tobacco exposure is associated with greater risk for CVD and worse kidney disease outcomes in adults and children with proteinuric glomerulopathies. Preventive strategies to reduce tobacco exposure may help protect against future cardiovascular and kidney morbidity and mortality in patients with proteinuric glomerulopathies.

Vitamin D supplementation in children and young adults with persistent proteinuria secondary to glomerular disease.

BACKGROUND: Vitamin D deficiency is common in glomerular disease. Supplementation may be ineffective due to ongoing urinary losses of vitamin D binding protein. We sought to determine if daily cholecalciferol supplementation would increase vitamin D concentrations in children with glomerular disease and persistent proteinuria, without adverse effects. METHODS: Eighteen participants at least 5 years of age with primary glomerular disease and urine protein:creatinine ratio ≥ 0.5 were enrolled from four pediatric nephrology practices to receive cholecalciferol supplementation: 4,000 IU or 2,000 IU per day for serum 25 hydroxyvitamin vitamin D (25OHD) concentrations < 20 ng/mL and 20 ng/mL to < 30 ng/mL, respectively. Measures of vitamin D and mineral metabolism were obtained at baseline and weeks 6 and 12. Multivariable generalized estimating equation (GEE) regression estimated mean percent changes in serum 25OHD concentration. RESULTS: Median baseline 25OHD was 12.8 ng/mL (IQR 9.3, 18.9) and increased to 27.8 ng/mL (20.5, 36.0) at week 6 (p < 0.001) without further significant increase at week 12. A total of 31% of participants had a level ≥ 30 ng/mL at week 12. Supplementation was stopped in two participants at week 6 for mildly elevated calcium and phosphorus, respectively, with subsequent declines in 25OHD of > 20 ng/mL. In the adjusted GEE model, 25OHD was 102% (95% CI: 64, 141) and 96% (95% CI: 51, 140) higher versus baseline at weeks 6 and 12, respectively (p < 0.001). CONCLUSION: Cholecalciferol supplementation in vitamin D deficient children with glomerular disease and persistent proteinuria safely increases 25OHD concentration. Ideal dosing to fully replete 25OHD concentrations in this population remains unknown. CLINICAL TRIAL: NCT01835639. A higher resolution version of the Graphical abstract is available as Supplementary information.

A novel agreement statistic using data on uncertainty in ratings.

Many existing methods for estimating agreement correct for chance agreement by adjusting the observed proportion agreement by the probability of chance agreement based on different assumptions. These assumptions may not always be appropriate, as demonstrated by pathologists' ratings of kidney biopsy descriptors. We propose a novel agreement statistic that accounts for the empirical probability of chance agreement, estimated by collecting additional data on rater uncertainty for each rating. A standard error estimator for the proposed statistic is derived. Simulation studies show that in most cases, our proposed statistic is unbiased in estimating the probability of agreement after removing chance agreement.

Immunosuppression Exposure and Risk of Infection-Related Acute Care Events in Patients With Glomerular Disease: An Observational Cohort Study.

Rationale & Objective: Infections cause morbidity and mortality in patients with glomerular disease. The relative contributions from immunosuppression exposure and glomerular disease activity to infection risk are not well characterized. To address this unmet need, we characterized the relationship between time-varying combinations of immunosuppressant exposure and infection-related acute care events while controlling for disease activity, among individuals with glomerular disease. Study Design: Prospective, multicenter, observational cohort study. Setting & Participants: Adults and children with biopsy-proven minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, or immunoglobulin A nephropathy/vasculitis were enrolled at 71 clinical sites in North America and Europe. A total of 2,388 Cure Glomerulonephropathy Network participants (36% aged <18 years) had at least 1 follow-up visit and were included in the analysis. Exposures: Immunosuppression exposure modeled on a weekly basis. Outcome: Infections leading to an emergency department visit or hospitalization. Analytical Approach: Marginal structural models were used to estimate the effect of time-varying immunosuppression exposure on hazard of first infection-related acute care event while accounting for baseline sociodemographic and clinical factors, and time-varying disease activity. Results: A total of 2,388 participants were followed for a median of 3.2 years (interquartile range, 1.6-4.6), and 15% experienced at least 1 infection-related emergency department visit or hospitalization. Compared to no immunosuppression exposure, steroid exposure, steroid with any other immunosuppressant, and nonsteroid immunosuppressant exposure were associated with a 2.65-fold (95% CI, 1.83-3.86), 2.68-fold (95% CI, 1.95-3.68), and 1.7-fold (95% CI, 1.29-2.24) higher risk of first infection, respectively. Limitations: Absence of medication dosing data, lack of a control group, and potential bias in ascertainment of outcome events secondary to the coronavirus 2 pandemic. Conclusions: Corticosteroids with or without concomitant additional immunosuppression significantly increased risk of infection leading to acute care utilization in adults and children with glomerular disease.

Evaluation of an Automated Text Message-Based Program to Reduce Use of Acute Health Care Resources After Hospital Discharge.

Importance: Posthospital contact with a primary care team is an established pillar of safe transitions. The prevailing model of telephone outreach is usually limited in scope and operationally burdensome. Objective: To determine whether a 30-day automated texting program to support primary care patients after hospital discharge is associated with reductions in the use of acute care resources. Design, Setting, and Participants: This cohort study used a difference-in-differences approach at 2 academic primary care practices in Philadelphia from January 27 through August 27, 2021. Established patients of the study practices who were 18 years or older, were discharged from an acute care hospitalization, and received the usual transitional care management telephone call were eligible for the study. At the intervention practice, 604 discharges were eligible and 430 (374 patients, of whom 46 had >1 discharge) were enrolled in the intervention. At the control practice, 953 patients met eligibility criteria. The study period, including before and after the intervention, ran from August 27, 2020, through August 27, 2021. Exposure: Patients received automated check-in text messages from their primary care practice on a tapering schedule during the 30 days after discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox. Main Outcomes and Measures: The primary study outcome was any emergency department (ED) visit or readmission within 30 days of discharge. Secondary outcomes included any ED visit or any readmission within 30 days, analyzed separately, and 30- and 60-day mortality. Analyses were based on intention to treat. Results: A total of 1885 patients (mean [SD] age, 63.2 [17.3] years; 1101 women [58.4%]) representing 2617 discharges (447 before and 604 after the intervention at the intervention practice; 613 before and 953 after the intervention at the control practice) were included in the analysis. The adjusted odds ratio (aOR) for any use of acute care resources after implementation of the intervention was 0.59 (95% CI, 0.38-0.92). The aOR for an ED visit was 0.77 (95% CI, 0.45-1.30) and for a readmission was 0.45 (95% CI, 0.23-0.86). The aORs for death within 30 and 60 days of discharge at the intervention practice were 0.92 (95% CI, 0.23-3.61) and 0.63 (95% CI, 0.21-1.85), respectively. Conclusions and Relevance: The findings of this cohort study suggest that an automated texting program to support primary care patients after hospital discharge was associated with significant reductions in use of acute care resources. This patient-centered approach may serve as a model for improving postdischarge care.

A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.

Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions: Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.

PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool.

Introduction: Podocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set. Methods: Whole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid-Schiff counterstain were acquired. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool. Results: PodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome. Conclusion: PodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.