Kidney international
-
Kidney international · Feb 2020
Multiparametric magnetic resonance imaging shows promising results to assess renal transplant dysfunction with fibrosis.
Here we assessed the diagnostic value of a quantitative multiparametric magnetic resonance imaging (mpMRI) protocol for evaluation of renal allograft dysfunction with fibrosis. Twenty-seven renal transplant patients, including 15 with stable functional allografts (eGFR mean 71.5 ml/min/1.73m2), and 12 with chronic dysfunction/established fibrosis (eGFR mean 30.1 ml/min/1.73m2), were enrolled in this prospective single-center study. Sixteen of the patients had renal biopsy (mean 150 days) before the MRI. ⋯ The combination of ΔT1 and cortical ADC had excellent cross-validated diagnostic performance for detection of chronic dysfunction with fibrosis. Cortical ADC and T1 had good performance for predicting eGFR decline at 18 months (4 or more ml/min/1.73m2/year). Thus, the combination of cortical ADC and T1 measurements shows promising results for the non-invasive assessment of renal allograft histology and outcomes.
-
Kidney international · Feb 2020
Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.
Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language processing (NLP) can be utilized to identify symptoms from narrative clinical documentation. Here we utilized NLP to identify seven patient symptoms from notes of maintenance hemodialysis patients of the BioMe Biobank and validated our findings using a separate cohort and the MIMIC-III database. ⋯ NLP had higher specificity in inpatient notes but higher sensitivity in outpatient notes and performed similarly across pain severity subgroups. Thus, NLP had higher sensitivity compared to ICD codes for identification of seven common hemodialysis-related symptoms, with comparable specificity between the two methods. Hence, NLP may be useful for the high-throughput identification of patient-centered outcomes when using electronic health records.