Background Chronic tubulointerstitial injury on kidney biopsy is usually quantified by the percentage of cortex with interstitial fibrosis/tubular atrophy (IF/TA). Whether other patterns of IF/TA or inflammation in the tubulointerstitium have prognostic importance beyond percentage IF/TA is unclear. Methods We obtained, stained, and digitally scanned full cortical thickness wedge sections of renal parenchyma from patients who underwent a radical nephrectomy for tumor in 2000 to 2015 and morphometrically analyzed the tubulointerstitium of the cortex for percentage IF/TA, IF/TA density (foci per mm2 cortex), percentage subcapsular IF/TA, striped IF/TA, percentage inflammation (both within and outside IF/TA regions), and percentage subcapsular inflammation. Patients were followed with visits every 6-12 months. Progressive chronic kidney disease (CKD) was defined as dialysis, kidney transplantation, or 40% decline from the postnephrectomy estimated glomerular filtration rate (eGFR). Cox models assessed risk of CKD or noncancer mortality with morphometric measures of tubulointerstitial injury after adjustment for percentage IF/TA and clinical characteristics. Results Among 936 patients (mean age, 64 years; postnephrectomy baseline eGFR, 48 ml/min per 1.73m2), 117 progressive CKD events and 183 noncancer deaths occurred over a median 6.4 years. Higher IF/TA density predicted both progressive CKD and noncancer mortality after adjustment for percentage IF/TA and predicted progressive CKD after further adjustment for clinical characteristics. Independent of percentage IF/TA, age, and sex, higher IF/TA density correlated with lower eGFR, smaller nonsclerosed glomeruli, more global glomerulosclerosis, and smaller total cortical volume. Conclusions Higher density of IF/TA foci (a more scattered pattern with more and smaller foci) predicts higher risk of progressive CKD after radical nephrectomy compared with the same percentage of IF/TA but with fewer and larger foci.
BackgroundSemiquantitative visual inspection for glomerulosclerosis, interstitial fibrosis, and arteriosclerosis is often used to assess chronic changes in native kidney biopsies. Morphometric evaluation of these and other chronic changes may improve the prognostic assessment.MethodsWe studied a historical cohort of patients who underwent a native kidney biopsy between 1993 and 2015 and were followed through 2021 for ESKD and for progressive CKD (defined as experiencing 50% eGFR decline, temporary dialysis, or ESKD). Pathologist scores for the percentages of globally sclerosed glomeruli (GSG), interstitial fibrosis and tubular atrophy (IFTA), and arteriosclerosis (luminal stenosis) were available. We scanned biopsy sections into high-resolution images to trace microstructures. Morphometry measures were percentage of GSG; percentage of glomerulosclerosis (percentage of GSG, ischemic-appearing glomeruli, or segmentally sclerosed glomeruli); percentage of IFTA; IFTA foci density; percentage of artery luminal stenosis; arteriolar hyalinosis counts; and measures of nephron size. Models assessed risk of ESKD or progressive CKD with biopsy measures adjusted for age, hypertension, diabetes, body mass index, eGFR, and proteinuria.ResultsOf 353 patients (followed for a median 7.5 years), 75 developed ESKD and 139 experienced progressive CKD events. Visually estimated scores by pathologists versus morphometry measures for percentages of GSG, IFTA, and luminal stenosis did not substantively differ in predicting outcomes. However, adding percentage of glomerulosclerosis, IFTA foci density, and arteriolar hyalinosis improved outcome prediction. A 10-point score using percentage of glomerulosclerosis, percentage of IFTA, IFTA foci density, and any arteriolar hyalinosis outperformed a 10-point score based on percentages of GSG, IFTA, and luminal stenosis >50% in discriminating risk of ESKD or progressive CKD.ConclusionMorphometric characterization of glomerulosclerosis, IFTA, and arteriolar hyalinosis on kidney biopsy improves prediction of long-term kidney outcomes.
Background The rapid emergence of the COVID-19 pandemic globally collapsed health care organizations worldwide. Incomplete knowledge of best practices, progression of disease, and its impact could result in fallible care. Data on symptoms and advancement of the SARS-CoV-2 virus leading to critical care admission have not been captured or communicated well between international organizations experiencing the same impact from the virus. This led to the expedited need for establishing international communication and data collection on the critical care patients admitted with COVID-19. Objective Developing a global registry to collect patient data in the critical care setting was imperative with the goal of analyzing and ameliorating outcomes. Methods A prospective, observational global registry database was put together to record extensive deidentified clinical information for patients hospitalized with COVID-19. Results Project management was crucial for prompt implementation of the registry for synchronization, improving efficiency, increasing innovation, and fostering global collaboration for valuable data collection. The Society of Critical Care Medicine Discovery VIRUS (Viral Infection and Respiratory Illness Universal Study): COVID-19 Registry would compile data for crucial longitudinal outcomes for disease, treatment, and research. The agile project management approach expedited establishing the registry in 15 days and submission of institutional review board agreement for 250 participating sites. There has been enrollment of sites every month with a total of 306 sites from 28 countries and 64,114 patients enrolled (as of June 7, 2021). Conclusions This protocol addresses project management lessons in a time of crises which can be a precept for rapid project management for a large-scale health care data registry. We aim to discuss the approach and methodology for establishing the registry, the challenges faced, and the factors contributing to successful outcomes. Trial Registration ClinicalTrials.gov NCT04323787; https://clinicaltrials.gov/ct2/show/NCT04323787
Summary StatementThe Checklist for Early Recognition and Treatment of Acute Illness and iNjury program is a well-established, interactive, and simulation-based program designed to improve the quality of care delivered in intensive care units. The COVID-19 pandemic created an overwhelming surge of critically ill patients worldwide, and infection control concerns limited healthcare providers' access to in-person and hands-on simulation training when they needed it the most. Virtual simulation offers an alternative to in-person training but is often complex and expensive. We describe our successful development and initial implementation of an inexpensive, simulation-based virtual Checklist for Early Recognition and Treatment of Acute Illness and iNjury program to address the pressing need for effective critical care training in various resource-limited settings both within and outside of the United States. The overall satisfaction rate (“excellent” or “very good” responses) was 94.4% after the virtual simulation workshop. Our initial experience suggests that virtual interactions can be engaging and build strong relationships, like in-person continuing professional education, even using relatively simple technology. This knowledge-to-practice improvement platform can be readily adapted to other disciplines beyond critical care medicine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.