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He X, Hu Z, Dev H, Romano DJ, Sharbatdaran A, Raza SI, Wang SJ, Teichman K, Shih G, Chevalier JM, Shimonov D, Blumenfeld JD, Goel A, Sabuncu MR, Prince MR. Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning. Acad Radiol 2024; 31:889-899. [PMID: 37798206 PMCID: PMC10957335 DOI: 10.1016/j.acra.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/28/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023]
Abstract
RATIONALE AND OBJECTIVES Following autosomal dominant polycystic kidney disease (ADPKD) progression by measuring organ volumes requires low measurement variability. The objective of this study is to reduce organ volume measurement variability on MRI of ADPKD patients by utilizing all pulse sequences to obtain multiple measurements which allows outlier analysis to find errors and averaging to reduce variability. MATERIALS AND METHODS In order to make measurements on multiple pulse sequences practical, a 3D multi-modality multi-class segmentation model based on nnU-net was trained/validated using T1, T2, SSFP, DWI and CT from 413 subjects. Reproducibility was assessed with test-re-test methodology on ADPKD subjects (n = 19) scanned twice within a 3-week interval correcting outliers and averaging the measurements across all sequences. Absolute percent differences in organ volumes were compared to paired students t-test. RESULTS Dice similarlity coefficient > 97%, Jaccard Index > 0.94, mean surface distance < 1 mm and mean Hausdorff Distance < 2 cm for all three organs and all five sequences were found on internal (n = 25), external (n = 37) and test-re-test reproducibility assessment (38 scans in 19 subjects). When averaging volumes measured from five MRI sequences, the model automatically segmented kidneys with test-re-test reproducibility (percent absolute difference between exam 1 and exam 2) of 1.3% which was better than all five expert observers. It reliably stratified ADPKD into Mayo Imaging Classification (area under the curve=100%) compared to radiologist. CONCLUSION 3D deep learning measures organ volumes on five MRI sequences leveraging the power of outlier analysis and averaging to achieve 1.3% total kidney test-re-test reproducibility.
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Affiliation(s)
- Xinzi He
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, New York (X.H., R.S.); Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Zhongxiu Hu
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Hreedi Dev
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Dominick J Romano
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Arman Sharbatdaran
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Syed I Raza
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Sophie J Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Kurt Teichman
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - George Shih
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - James M Chevalier
- Department of Medicine, Weill Cornell Medicine, New York, New York (J.M.C., D.S., J.D.B.); The Rogosin Institute, New York, New York (J.M.C., D.S., J.D.B.)
| | - Daniil Shimonov
- Department of Medicine, Weill Cornell Medicine, New York, New York (J.M.C., D.S., J.D.B.); The Rogosin Institute, New York, New York (J.M.C., D.S., J.D.B.)
| | - Jon D Blumenfeld
- Department of Medicine, Weill Cornell Medicine, New York, New York (J.M.C., D.S., J.D.B.); The Rogosin Institute, New York, New York (J.M.C., D.S., J.D.B.)
| | - Akshay Goel
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, New York (X.H., R.S.); Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.)
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medicine, New York, New York (X.H., Z.H., H.D., D.J.R., A.S., S.I.R., S.J.W., K.T., G.S., A.G., R.S., M.R.P.); Columbia University Vagelos College of Physicians and Surgeons, New York, New York (M.R.P.).
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Dev H, Zhu C, Sharbatdaran A, Raza SI, Wang SJ, Romano DJ, Goel A, Teichman K, Moghadam MC, Shih G, Blumenfeld JD, Shimonov D, Chevalier JM, Prince MR. Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease. J Magn Reson Imaging 2023; 58:1153-1160. [PMID: 36645114 PMCID: PMC10947493 DOI: 10.1002/jmri.28593] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates. PURPOSE To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements. STUDY TYPE Retrospective training, prospective testing. SUBJECTS Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility. FIELD STRENGTH/SEQUENCE T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T. ASSESSMENT 2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded. STATISTICAL TESTS Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant. RESULTS In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier. DATA CONCLUSION Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Hreedi Dev
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Chenglin Zhu
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Arman Sharbatdaran
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Syed I. Raza
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Sophie J. Wang
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Dominick J. Romano
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Akshay Goel
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Kurt Teichman
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Mina C. Moghadam
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - George Shih
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Jon D. Blumenfeld
- Department of Medicine, Weill Cornell Medicine, New York City, New York, USA
- The Rogosin Institute, New York City, New York, USA
| | - Daniil Shimonov
- Department of Medicine, Weill Cornell Medicine, New York City, New York, USA
- The Rogosin Institute, New York City, New York, USA
| | - James M. Chevalier
- Department of Medicine, Weill Cornell Medicine, New York City, New York, USA
- The Rogosin Institute, New York City, New York, USA
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
- Columbia College of Physicians and Surgeons, New York City, New York, USA
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3
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Zhu C, Dev H, Sharbatdaran A, He X, Shimonov D, Chevalier JM, Blumenfeld JD, Wang Y, Teichman K, Shih G, Goel A, Prince MR. Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography 2023; 9:1341-1355. [PMID: 37489475 PMCID: PMC10366880 DOI: 10.3390/tomography9040107] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023] Open
Abstract
Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.
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Affiliation(s)
- Chenglin Zhu
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Hreedi Dev
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Arman Sharbatdaran
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Xinzi He
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Daniil Shimonov
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
- The Rogosin Institute, New York, NY 10021, USA
| | - James M. Chevalier
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
- The Rogosin Institute, New York, NY 10021, USA
| | - Jon D. Blumenfeld
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
- The Rogosin Institute, New York, NY 10021, USA
| | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
| | - Kurt Teichman
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - George Shih
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Akshay Goel
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
- Columbia College of Physicians and Surgeons, New York, NY 10032, USA
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4
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Sourial MY, Gone A, Uribarri J, Srivatana V, Sharma S, Shimonov D, Chang M, Mowrey W, Dalsan R, Sedaliu K, Jain S, Ross MJ, Caplin N, Chen W. Outcomes of PD for AKI treatment during COVID-19 in New York City: A multicenter study. Perit Dial Int 2023; 43:13-22. [PMID: 36320182 PMCID: PMC10115518 DOI: 10.1177/08968608221130559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND The high incidence of acute kidney injury (AKI) requiring dialysis associated with COVID-19 led to the use of peritoneal dialysis (PD) for the treatment of AKI. This study aims to compare in-hospital all-cause mortality and kidney recovery between patients with AKI who received acute PD versus extracorporeal dialysis (intermittent haemodialysis and continuous kidney replacement therapy). METHODS In a retrospective observational study of 259 patients with AKI requiring dialysis during the COVID-19 surge during Spring 2020 in New York City, we compared 30-day all-cause mortality and kidney recovery between 93 patients who received acute PD at any time point and 166 patients who only received extracorporeal dialysis. Kaplan-Meier curves, log-rank test and Cox regression were used to compare survival and logistic regression was used to compare kidney recovery. RESULTS The mean age was 61 ± 11 years; 31% were women; 96% had confirmed COVID-19 with median follow-up of 21 days. After adjusting for demographics, comorbidities, oxygenation and laboratory values prior to starting dialysis, the use of PD was associated with a lower mortality rate compared to extracorporeal dialysis with a hazard ratio of 0.48 (95% confidence interval: 0.27-0.82, p = 0.008). At discharge or on day 30 of hospitalisation, there was no association between dialysis modality and kidney recovery (p = 0.48). CONCLUSIONS The use of PD for the treatment of AKI was not associated with worse clinical outcomes when compared to extracorporeal dialysis during the height of the COVID-19 pandemic in New York City. Given the inherent selection biases and residual confounding in our observational study, research with a larger cohort of patients in a more controlled setting is needed to confirm our findings.
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Affiliation(s)
- Maryanne Y Sourial
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
| | - Anirudh Gone
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
| | - Jaime Uribarri
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Vesh Srivatana
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
- The Rogosin Institute, New York, NY, USA
| | - Shuchita Sharma
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Daniil Shimonov
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
- The Rogosin Institute, New York, NY, USA
| | - Michael Chang
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
| | - Wenzhu Mowrey
- Division of Biostatistics, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Rochelle Dalsan
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kaltrina Sedaliu
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
| | - Swati Jain
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
| | - Michael J Ross
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
| | - Nina Caplin
- Division of Nephrology, New York University Langone Health and New York University Grossman School of Medicine, New York, NY, USA
- Department of Medicine, New York City Health and Hospitals/Bellevue, New York, NY, USA
| | - Wei Chen
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Nephrology, Montefiore Medical Center, Bronx, NY, USA
- Division of Nephrology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
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Sharbatdaran A, Romano D, Teichman K, Dev H, Raza SI, Goel A, Moghadam MC, Blumenfeld JD, Chevalier JM, Shimonov D, Shih G, Wang Y, Prince MR. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography 2022; 8:1804-1819. [PMID: 35894017 PMCID: PMC9326744 DOI: 10.3390/tomography8040152] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 12/02/2022] Open
Abstract
Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.
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Affiliation(s)
- Arman Sharbatdaran
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Dominick Romano
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Kurt Teichman
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Hreedi Dev
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Syed I. Raza
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Akshay Goel
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Mina C. Moghadam
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Jon D. Blumenfeld
- The Rogosin Institute and Department of Medicine Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (J.D.B.); (J.M.C.); (D.S.)
| | - James M. Chevalier
- The Rogosin Institute and Department of Medicine Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (J.D.B.); (J.M.C.); (D.S.)
| | - Daniil Shimonov
- The Rogosin Institute and Department of Medicine Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (J.D.B.); (J.M.C.); (D.S.)
| | - George Shih
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
| | - Yi Wang
- Departments of Radiology at Weill Cornell Medicine and Biomedical Engineering, Cornell University, New York, NY 10065, USA;
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (A.S.); (D.R.); (K.T.); (H.D.); (S.I.R.); (A.G.); (M.C.M.); (G.S.)
- Columbia College of Physicians and Surgeons, Cornell University, New York, NY 10027, USA
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6
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Chen W, Caplin N, El Shamy O, Sharma S, Sourial MY, Ross MJ, Sourial MH, Prudhvi K, Golestaneh L, Srivatana V, Dalsan R, Shimonov D, Sanchez-Russo L, Atallah S, Uribarri J. Use of peritoneal dialysis for acute kidney injury during the COVID-19 pandemic in New York City: a multicenter observational study. Kidney Int 2021; 100:2-5. [PMID: 33930411 PMCID: PMC8079266 DOI: 10.1016/j.kint.2021.04.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/29/2021] [Accepted: 04/01/2021] [Indexed: 12/21/2022]
Abstract
To demonstrate feasibility of acute peritoneal dialysis (PD) for acute kidney injury during the coronavirus disease 2019 (COVID-19) pandemic, we performed a multicenter, retrospective, observational study of 94 patients who received acute PD in New York City in the spring of 2020. Patient comorbidities, severity of disease, laboratory values, kidney replacement therapy, and patient outcomes were recorded. The mean age was 61 ± 11 years; 34% were women; 94% had confirmed COVID-19; 32% required mechanical ventilation on admission. Compared to the levels prior to initiation of kidney replacement therapy, the mean serum potassium level decreased from 5.1 ± 0.9 to 4.5 ± 0.7 mEq/L on PD day 3 and 4.2 ± 0.6 mEq/L on day 7 (P < 0.001 for both); mean serum bicarbonate increased from 20 ± 4 to 21 ± 4 mEq/L on PD day 3 (P = 0.002) and 24 ± 4 mEq/L on day 7 (P < 0.001). After a median follow-up of 30 days, 46% of patients died and 22% had renal recovery. Male sex and mechanical ventilation on admission were significant predictors of mortality. The rapid implementation of an acute PD program was feasible despite resource constraints and can be lifesaving during crises such as the COVID-19 pandemic.
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Affiliation(s)
- Wei Chen
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, New York, USA; Division of Nephrology, Montefiore Medical Center, Bronx, New York, USA; Division of Nephrology, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA.
| | - Nina Caplin
- Division of Nephrology, New York University Langone Health and New York University Grossman School of Medicine, New York, New York, USA; Department of Medicine, New York City Health and Hospitals/Bellevue, New York, New York, USA
| | - Osama El Shamy
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shuchita Sharma
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Michael J Ross
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, New York, USA; Division of Nephrology, Montefiore Medical Center, Bronx, New York, USA
| | - Mina H Sourial
- Division of Nephrology, Montefiore Medical Center, Bronx, New York, USA
| | - Kalyan Prudhvi
- Division of Nephrology, Montefiore Medical Center, Bronx, New York, USA
| | - Ladan Golestaneh
- Division of Nephrology, Montefiore Medical Center, Bronx, New York, USA
| | - Vesh Srivatana
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA; The Rogosin Institute, New York, New York, USA
| | - Rochelle Dalsan
- Division of Nephrology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Daniil Shimonov
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA; The Rogosin Institute, New York, New York, USA
| | - Luis Sanchez-Russo
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sara Atallah
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jaime Uribarri
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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7
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Lee JR, Silberzweig J, Akchurin O, Choi ME, Srivatana V, Lin J, Liu F, Malha L, Lubetzky M, Dadhania DM, Shankaranarayanan D, Shimonov D, Neupane S, Salinas T, Bhasin A, Varma E, Leuprecht L, Gerardine S, Lamba P, Goyal P, Caliendo E, Tiase V, Sharma R, Park JC, Steel PA, Suthanthiran M, Zhang Y. Characteristics of Acute Kidney Injury in Hospitalized COVID-19 Patients in an Urban Academic Medical Center. Clin J Am Soc Nephrol 2021; 16:284-286. [PMID: 32948642 PMCID: PMC7863636 DOI: 10.2215/cjn.07440520] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- John R. Lee
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- Department of Transplantation Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Jeffrey Silberzweig
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- The Rogosin Institute, New York, New York
| | - Oleh Akchurin
- The Rogosin Institute, New York, New York
- Division of Pediatric Nephrology, Department of Pediatrics, Weill Cornell Medicine, New York, New York
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Mary E. Choi
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Vesh Srivatana
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- The Rogosin Institute, New York, New York
| | - Jonathan Lin
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- The Rogosin Institute, New York, New York
| | - Frank Liu
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- The Rogosin Institute, New York, New York
| | - Line Malha
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
| | - Michelle Lubetzky
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- Department of Transplantation Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Darshana M. Dadhania
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- Department of Transplantation Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | | | - Daniil Shimonov
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Sanjay Neupane
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Thalia Salinas
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Aarti Bhasin
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Elly Varma
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Lorenz Leuprecht
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Supriya Gerardine
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Perola Lamba
- NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Parag Goyal
- Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | | | - Victoria Tiase
- The Value Institute, NewYork-Presbyterian Hospital, New York, New York
| | - Rahul Sharma
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York
| | - Joel C. Park
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York
| | - Peter A.D. Steel
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York
| | - Manikkam Suthanthiran
- Division of Nephrology and Hypertension, Weill Cornell Medicine, NewYork, New York
- Department of Transplantation Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York, New York
| | - Yiye Zhang
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
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8
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Shankaranarayanan D, Muthukumar T, Barbar T, Bhasin A, Gerardine S, Lamba P, Leuprecht L, Neupane SP, Salinas T, Shimonov D, Varma E, Liu F. Anticoagulation Strategies and Filter Life in COVID-19 Patients Receiving Continuous Renal Replacement Therapy: A Single-Center Experience. Clin J Am Soc Nephrol 2021; 16:124-126. [PMID: 32943397 PMCID: PMC7792651 DOI: 10.2215/cjn.08430520] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | | | - Tarek Barbar
- Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Aarti Bhasin
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Supriya Gerardine
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Perola Lamba
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Lorenz Leuprecht
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Sanjay P. Neupane
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Thalia Salinas
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Daniil Shimonov
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Elly Varma
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
| | - Frank Liu
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York,The Rogosin Institute, New York, New York
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9
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Affiliation(s)
- Daniil Shimonov
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
- The Rogosin Institute, New York, New York
| | - Vesh Srivatana
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York
- The Rogosin Institute, New York, New York
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10
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Shankaranarayanan D, Neupane SP, Varma E, Shimonov D, Gerardine S, Bhasin A, Lamba P, Leuprecht L, Salinas T, Afaneh C, Bellorin-Marin OE, Srivatana V. Peritoneal Dialysis for Acute Kidney Injury During the COVID-19 Pandemic in New York City. Kidney Int Rep 2020; 5:1532-1534. [PMID: 32838084 PMCID: PMC7377796 DOI: 10.1016/j.ekir.2020.07.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/02/2020] [Accepted: 07/15/2020] [Indexed: 12/28/2022] Open
Affiliation(s)
| | - Sanjay P. Neupane
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Elly Varma
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Daniil Shimonov
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Supriya Gerardine
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Aarti Bhasin
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Perola Lamba
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Lorenz Leuprecht
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Thalia Salinas
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
| | - Cheguevara Afaneh
- Department of Surgery, Weill Cornell Medicine, New York, New York, USA
| | | | - Vesh Srivatana
- Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York, USA
- The Rogosin Institute, New York, New York, USA
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