Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

The Chronic Kidney Disease in Children Study (CKiD) equation for children and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation for adults are recommended serum creatinine (SCr)-based calculations for estimating glomerular filtration rate (GFR). However, these equations, as well as their combination, have limitations, notably the problem of implausible changes in GFR during the transition from adolescence to adulthood and overestimation of GFR in young adults. The full age spectrum (FAS) equation addresses these issues but overestimates GFR when SCr levels are low. To develop and validate a modified FAS SCr-based equation combining design features of the FAS and CKD-EPI equations. Cross-sectional analysis with separate pooled data sets for development and validation. Research and clinical studies (n= 13) with measured GFR available. 11251 participants in 7 studies (development and internal validation data sets) and 8378 participants in 6 studies (external validation data set). Clearance of an exogenous marker (reference method), SCr level, age, sex, and height were used to develop a new equation to estimate GFR. The new European Kidney Function Consortium (EKFC) equation is a FAS equation with low bias (-1.2 mL/min/1.73 m2 [95% CI, -2.7 to 0.0 mL/min/1.73 m2] in children and -0.9 mL/min/1.73 m2 [CI, -1.2 to -0.5 mL/min/1.73 m2] in adults) across the FAS (2 to 90 years) and SCr range (40 to 490 µmol/L [0.45 to 5.54 mg/dL]) and with fewer estimation errors exceeding 30% (6.5% [CI, 3.8% to 9.1%] in children and 3.1% [CI, 2.5% to 3.6%] in adults) compared with the CKiD and CKD-EPI equations. No Black patients were included. The new EKFC equation shows improved accuracy and precision compared with commonly used equations for estimating GFR from SCr levels. Swedish Research Council (Vetenskapsrådet).

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study.

Small intracranial aneurysms are being increasingly detected while the rupture risk is not well-understood. We aimed to develop rupture-risk models of small aneurysms by combining clinical, morphologic, and hemodynamic information based on machine learning techniques and to test the models in external validation datasets. From January 2010 to December 2016, five hundred four consecutive patients with only small aneurysms (<5 mm) detected by CTA and invasive cerebral angiography (or surgery) were retrospectively enrolled and randomly split into training (81%) and internal validation (19%) sets to derive and validate the proposed machine learning models (support vector machine, random forest, logistic regression, and multilayer perceptron). Hemodynamic parameters were obtained using computational fluid dynamics simulation. External validation was performed in other hospitals to test the models. The support vector machine performed the best with areas under the curve of 0.88 (95% CI, 0.85-0.92) and 0.91 (95% CI, 0.74-0.98) in the training and internal validation datasets, respectively. Feature ranks suggested hemodynamic parameters, including stable flow pattern, concentrated inflow streams, and a small (<50%) flow-impingement zone, and the oscillatory shear index coefficient of variation, were the best predictors of aneurysm rupture. The support vector machine showed an area under the curve of 0.82 (95% CI, 0.69-0.94) in the external validation dataset, and no significant difference was found for the areas under the curve between internal and external validation datasets (P = .21). This study revealed that machine learning had a good performance in predicting the rupture status of small aneurysms in both internal and external datasets. Aneurysm hemodynamic parameters were regarded as the most important predictors.

Using GFR, Albuminuria, and Their Changes in Clinical Trials and Clinical Care

Purpose: Reduced glomerular filtration rate (GFR) in heart failure patients is associated with poor prognosis. Cardiac resynchronization therapy (CRT) has been associated with cardiac and renal function improvements, but no data are available in chronic kidney disease (CKD) stage 4 patients. Therefore, we evaluated the impact of CRT on 1) renal function and 2) long-term prognosis in CKD stage 4 patients. Methods: A total of 58 consecutive CRT (n=58) patients (mean age 73±10 years, 60% male) in CKD stage 4 with echocardiographic and renal function evaluation at baseline and 6-month follow-up were included. CKD stage 4 was defined by a GFR of 15-29 ml/min/1.73 m2. As a control group, 22 implantable cardioverter-defibrillator (ICD) recipients in CKD stage 4 were selected. CRT recipients with ≥15% reduction in LV end-systolic volume at 6-month follow-up were classified as CRT responders. During long-term follow-up (median 27 months, IQR 12-45 months) all-cause mortality was recorded. Results: At baseline, no significant differences were observed between CRT and ICD recipients except for QRS duration (173±26 vs. 130±30 ms,p<0.001). At 6 months, 17 CRT patients (32%) were classified as responders. The GFR increased significantly in CRT responders at follow-up (from 24±4 to 28±5,p=0.004). In turn, GFR did not improve in CRT non-responders (from 25±4 to 26±8,p=0.362) and in ICD recipients (from 25±4 to 23±10,p=0.385). More importantly, a superior survival at 5-year follow-up was observed among CRT responders as compared to CRT non-responders and ICD recipients (p=0.014;Figure). ![Figure][1] Kaplan-Meier survival curves Conclusion: Response to CRT in CKD stage 4 patients is associated with increased GFR, prevention from further renal function impairment and finally with better long-term prognosis. [1]: pending:yes

Introduction: Chronic kidney disease (CKD) is common in patients with peripheral arterial disease (PAD), but patients with severe CKD have been excluded from many trials and no objective performance goals exist for patients with PAD and CKD. We sought to analyze the association between severity of CKD and cardiovascular and limb-related outcomes among patients with PAD. Methods: We reviewed records of all patients at our institution who underwent lower extremity angiography between 2006 and 2013. We analyzed outcomes including mortality, major adverse cardiovascular event (MACE) rate, and major adverse limb event (MALE) rate according to clinical stage of CKD, determined by calculating each patient’s glomerular filtration rate using the Cockcroft-Gault equation. We used Cox proportional hazard modeling to account for covariates, along with Bonferroni correction for multiple comparisons. Results: Of 773 patients, 45% had CKD stage 3-5. The patients had a median age of 67, were 58% male, 51% diabetic, and 57% presented with critical limb ischemia (CLI). During a median follow-up time of 3.2 years, patients with higher stages of CKD had an increased rate of death (Figure 1, p&lt;0.001). CKD stages 4 and 5 were significant predictors of mortality in a multivariate model (HR 3.2 and 2.4 vs. CKD 1, P&lt;0.001 and P&lt;0.01, respectively). An analysis of MACE by CKD stage demonstrated similar results (CKD 4 HR 2.2, p&lt;0.01; CKD 5 HR 2.0, p&lt;0.01). CKD stage also predicted MALE in a univariate analysis (p&lt;0.01), driven by increased limb events among patients with CKD stage 5 (p&lt;0.01). However, CKD stage did not demonstrate a significantly increased hazard of MALE in a multivariate Cox model. Conclusions: Patients with PAD who also have CKD have increased rates of adverse outcomes. This relationship seems to be more robust for major cardiovascular events and overall mortality than for major limb events. Future studies should investigate how management of PAD should differ for patients with CKD.

Application of GFR-estimating equations in Chinese patients with chronic kidney disease

INTRODUCTION:Conventional white light imaging (WLI) endoscopy is the most common screening technique used for detecting early esophageal squamous cell carcinoma (ESCC). Nevertheless, it is difficult to detect and delineate margins of early ESCC using WLI endoscopy. This study aimed to develop an artificial intelligence (AI) model to detect and delineate margins of early ESCC under WLI endoscopy.METHODS:A total of 13,083 WLI images from 1,239 patients were used to train and test the AI model. To evaluate the detection performance of the model, 1,479 images and 563 images were used as internal and external validation data sets, respectively. For assessing the delineation performance of the model, 1,114 images and 211 images were used as internal and external validation data sets, respectively. In addition, 216 images were used to compare the delineation performance between the model and endoscopists.RESULTS:The model showed an accuracy of 85.7% and 84.5% in detecting lesions in internal and external validation, respectively. For delineating margins, the model achieved an accuracy of 93.4% and 95.7% in the internal and external validation, respectively, under an overlap ratio of 0.60. The accuracy of the model, senior endoscopists, and expert endoscopists in delineating margins were 98.1%, 78.6%, and 95.3%, respectively. The proposed model achieved similar delineating performance compared with that of expert endoscopists but superior to senior endoscopists.DISCUSSION:We successfully developed an AI model, which can be used to accurately detect early ESCC and delineate the margins of the lesions under WLI endoscopy.

Prognostic Value of Chronic Kidney Disease After Transcatheter Aortic Valve Implantation

Lateral lymph node metastasis (LLNM) is very common in medullary thyroid carcinoma (MTC), but there is still controversy about how to manage cervical lateral lymph nodes, especially for clinically negative MTC. The aim of this study is to develop and validate a nomogram for predicting LLNM risk in MTC. A total of 234 patients from two hospitals were retrospectively enrolled in this study and divided into LLNM positive group and LLNM negative group based on the pathology. The correlation between LLNM and preoperative clinical and ultrasound variables were evaluated by univariable and multivariable logistic regression analysis. A nomogram was generated to predict the risk of the LLNM of MTC patients, validated by external dataset, and evaluated in terms of discrimination, calibration, and clinical usefulness. The training, internal, and external validation datasets included 152, 51, and 31 MTC patients, respectively. According to the multivariable logistic regression analysis, gender (male), relationship to thyroid capsule and serum calcitonin were independently associated with LLNM in the training dataset. The predictive nomogram model developed with the aforementioned variables showed favorable performance in estimating risk of LLNM, with the area under the ROC curve (AUC) of 0.826 in the training dataset, 0.816 in the internal validation dataset, and 0.846 in the external validation dataset. We developed and validated a model named MTC nomogram, utilizing available preoperative variables to predict the probability of LLNM in patients with MTC. This nomogram will be of great value for guiding the clinical diagnosis and treatment process of MTC patients.

Effect of Testicular Germ Cell Tumor Therapy on Renal Function

IntroductionAsthma is one of the commonest chronic conditions in the world. Subtypes of asthma have been defined, typically from clinical datasets on small, well-characterised subpopulations of asthma patients. We sought to define asthma subtypes from large longitudinal primary care electronic health records (EHRs) using cluster analysis. MethodsIn this retrospective cohort study, we extracted asthma subpopulations from the Optimum Patient Care Research Database (OPCRD) to robustly train and test algorithms, and externally validated findings in the Secure Anonymised Information Linkage (SAIL) Databank. In both databases, we identified adults with an asthma diagnosis code recorded in the three years prior to an index date. Train and test datasets were selected from OPCRD using an index date of Jan 1, 2016. Two internal validation datasets were selected from OPCRD using index dates of Jan 1, 2017 and 2018. Three external validation datasets were selected from SAIL using index dates of Jan 1, 2016, 2017 and 2018. Each dataset comprised 50,000 randomly selected non-overlapping patients. Subtypes were defined by applying multiple correspondence analysis and k-means cluster analysis to the train dataset, and were validated in the internal and external validation datasets. ResultsWe defined six asthma subtypes with clear clinical interpretability: low inhaled corticosteroid (ICS) use and low healthcare utilisation (30% of patients); low-to-medium ICS use (36%); low-to-medium ICS use and comorbidities (12%); varied ICS use and comorbid chronic obstructive pulmonary disease (4%); high (10%) and very high ICS use (7%). The subtypes were replicated with high accuracy in internal (91–92%) and external (84–86%) datasets. ConclusionAsthma subtypes derived and validated in large independent EHR databases were primarily defined by level of ICS use, level of healthcare use, and presence of comorbidities. This has important clinical implications towards defining asthma subtypes, facilitating patient stratification, and developing more personalised monitoring and treatment strategies.

The aim of this study was to develop and validate a deep learning-based algorithm (DLA) for automatic detection and grading of motion-related artifacts on arterial phase liver magnetic resonance imaging (MRI). Multistep DLA for detection and grading of motion-related artifacts, based on the modified ResNet-101 and U-net, were trained using 336 arterial phase images of gadoxetic acid-enhanced liver MRI examinations obtained in 2017 (training dataset; mean age, 68.6 years [range, 18-95]; 254 men). Motion-related artifacts were evaluated in 4 different MRI slices using a 3-tier grading system. In the validation dataset, 313 images from the same institution obtained in 2018 (internal validation dataset; mean age, 67.2 years [range, 21-87]; 228 men) and 329 from 3 different institutions (external validation dataset; mean age, 64.0 years [range, 23-90]; 214 men) were included, and the per-slice and per-examination performances for the detection of motion-related artifacts were evaluated. The per-slice sensitivity and specificity of the DLA for detecting grade 3 motion-related artifacts were 91.5% (97/106) and 96.8% (1134/1172) in the internal validation dataset and 93.3% (265/284) and 91.6% (948/1035) in the external validation dataset. The per-examination sensitivity and specificity were 92.0% (23/25) and 99.7% (287/288) in the internal validation dataset and 90.0% (72/80) and 96.0% (239/249) in the external validation dataset, respectively. The processing time of the DLA for automatic grading of motion-related artifacts was from 4.11 to 4.22 seconds per MRI examination. The DLA enabled automatic and instant detection and grading of motion-related artifacts on arterial phase gadoxetic acid-enhanced liver MRI.

The diagnosis of thyroid-associated ophthalmopathy (TAO) usually requires a comprehensive examination, including clinical symptoms, radiological examinations, and blood tests. Therefore, cost-effective and noninvasive methods for the detection of TAO are needed. This study aimed to establish a deep learning-based system to detect TAO based on retinal photographs. The multicenter observational study included retinal photographs taken from TAO patients and normal participants in 2 hospitals in China. Forty-five-degree retinal photographs, centered on the midpoint between the optic disc and the macula, were captured by trained ophthalmologists. The authors first trained a convolutional neural network model to identify TAO using data collected from one hospital. After internal validation, the model was further evaluated in another hospital as an external validation data set. The study included 1182 retinal photographs of 708 participants for model development, and 365 retinal photographs (189 participants) were obtained as the external validation data set. In the internal validation, the area under the receiver operator curve was 0.900 (95% CI: 0.889-0.910) and the accuracy was 0.860 (95% CI: 0.849-0.869). In the external data set, the model reached an area under the curve of 0.747 (95% CI: 0.728-0.763) and achieved an accuracy of 0.709 (95% CI: 0.690-0.724). Deep learning-based systems may be promising for identifying TAO in normal subjects using retinal fundus photographs. It may serve as a cost-effective and noninvasive method to detect TAO in the future.

Should Estimated Glomerular Filtration Rate Be Adjusted for Race?