Information on COVID-19, Kidney Disease, and Telemedicine.

Daniil Shimonov, M.D.

Specialties:

  • Nephrology
  • Polycystic Kidney Disease

Board Certifications:

  • Internal Medicine
  • Nephrology

Clinical and Academic Appointments:

  • Nephrologist, The Rogosin Institute
  • Instructor in Medicine, Weill Cornell Medicine
  • Assistant Attending Physician, NewYork-Presbyterian Hospital

Clinical Expertise:

  • Chronic Kidney Disease
  • Peritoneal dialysis
  • Home hemodialysis
  • Critical Care Nephrology
  • Polycystic Kidney Disease

Education and Training:

  • Medical School: SUNY Upstate Medical University (Syracuse, NY)
  • Residency: NYU-Winthrop University Hospital
  • Fellowship in Nephrology: NewYork-Presbyterian/Weill Cornell Medicine

Locations:

Rogosin Manhattan East
505 East 70th Street
New York, NY 10021
646-317-0715

Research

  • Chronic Kidney Disease
  • Peritoneal Dialysis
  • Home Hemodialysis
  • COVID-19 and kidney disease

Publications

  • Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting
    Zhongxiu Hu, Arman Sharbatdaran, Xinzi He, Chenglin Zhu, Jon D Blumenfeld, Hanna Rennert, Zhengmao Zhang, Andrew Ramnauth, Daniil Shimonov, James M Chevalier, Martin R Prince...

    Sci Rep. 2024 Jun 14;14(1):13794. doi: 10.1038/s41598-024-62776-8.

    ABSTRACT

    Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was 2.1 % ± 2 % compared to 1.1 % ± 1 % ( p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or 1.4 % ± 1 % ( p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( p = 0.05 ) and PKD2 mutation ( p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

    PMID:38877066 | PMC:PMC11178802 | DOI:10.1038/s41598-024-62776-8

  • A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression
    Chenglin Zhu, Xinzi He, Jon D Blumenfeld, Zhongxiu Hu, Hreedi Dev, Usama Sattar, Vahid Bazojoo, Arman Sharbatdaran, Mohit Aspal, Dominick Romano, Kurt Teichman, Hui Yi Ng He, Yin Wang, Andrea Soto Figueroa, Erin Weiss, Anna G Prince, James M Chevalier, Daniil Shimonov, Mina C Moghadam, Mert Sabuncu, Martin R Prince...

    Biomedicines. 2024 May 20;12(5):1133. doi: 10.3390/biomedicines12051133.

    ABSTRACT

    Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment's efficacy. Deep learning for segmenting the kidneys has improved these measurements' speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

    PMID:38791095 | PMC:PMC11118119 | DOI:10.3390/biomedicines12051133

  • Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning
    Xinzi He, Zhongxiu Hu, Hreedi Dev, Dominick J Romano, Arman Sharbatdaran, Syed I Raza, Sophie J Wang, Kurt Teichman, George Shih, James M Chevalier, Daniil Shimonov, Jon D Blumenfeld, Akshay Goel, Mert R Sabuncu, Martin R Prince...

    Acad Radiol. 2024 Mar;31(3):889-899. doi: 10.1016/j.acra.2023.09.009. Epub 2023 Oct 3.

    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.

    PMID:37798206 | PMC:PMC10957335 | DOI:10.1016/j.acra.2023.09.009

  • Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease
    Chenglin Zhu, Hreedi Dev, Arman Sharbatdaran, Xinzi He, Daniil Shimonov, James M Chevalier, Jon D Blumenfeld, Yi Wang, Kurt Teichman, George Shih, Akshay Goel, Martin R Prince...

    Tomography. 2023 Jul 12;9(4):1341-1355. doi: 10.3390/tomography9040107.

    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.

    PMID:37489475 | PMC:PMC10366880 | DOI:10.3390/tomography9040107

  • Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease
    Hreedi Dev, Chenglin Zhu, Arman Sharbatdaran, Syed I Raza, Sophie J Wang, Dominick J Romano, Akshay Goel, Kurt Teichman, Mina C Moghadam, George Shih, Jon D Blumenfeld, Daniil Shimonov, James M Chevalier, Martin R Prince...

    J Magn Reson Imaging. 2023 Oct;58(4):1153-1160. doi: 10.1002/jmri.28593. Epub 2023 Jan 16.

    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.

    PMID:36645114 | PMC:PMC10947493 | DOI:10.1002/jmri.28593

  • Outcomes of PD for AKI treatment during COVID-19 in New York City: A multicenter study
    Maryanne Y Sourial, Anirudh Gone, Jaime Uribarri, Vesh Srivatana, Shuchita Sharma, Daniil Shimonov, Michael Chang, Wenzhu Mowrey, Rochelle Dalsan, Kaltrina Sedaliu, Swati Jain, Michael J Ross, Nina Caplin, Wei Chen, New York City Peritoneal Dialysis Consortium...

    Perit Dial Int. 2023 Jan;43(1):13-22. doi: 10.1177/08968608221130559. Epub 2022 Nov 1.

    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.

    PMID:36320182 | PMC:PMC10115518 | DOI:10.1177/08968608221130559

  • Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease
    Arman Sharbatdaran, Dominick Romano, Kurt Teichman, Hreedi Dev, Syed I Raza, Akshay Goel, Mina C Moghadam, Jon D Blumenfeld, James M Chevalier, Daniil Shimonov, George Shih, Yi Wang, Martin R Prince...

    Tomography. 2022 Jul 13;8(4):1804-1819. doi: 10.3390/tomography8040152.

    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.

    PMID:35894017 | PMC:PMC9326744 | DOI:10.3390/tomography8040152

  • Use of peritoneal dialysis for acute kidney injury during the COVID-19 pandemic in New York City: a multicenter observational study
    Wei Chen, Nina Caplin, Osama El Shamy, Shuchita Sharma, Maryanne Y Sourial, Michael J Ross, Mina H Sourial, Kalyan Prudhvi, Ladan Golestaneh, Vesh Srivatana, Rochelle Dalsan, Daniil Shimonov, Luis Sanchez-Russo, Sara Atallah, Jaime Uribarri, NYC-PD Consortium...

    Kidney Int. 2021 Jul;100(1):2-5. doi: 10.1016/j.kint.2021.04.017. Epub 2021 Apr 28.

    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.

    PMID:33930411 | PMC:PMC8079266 | DOI:10.1016/j.kint.2021.04.017

  • Characteristics of Acute Kidney Injury in Hospitalized COVID-19 Patients in an Urban Academic Medical Center
    John R Lee, Jeffrey Silberzweig, Oleh Akchurin, Mary E Choi, Vesh Srivatana, Jonathan Lin, Frank Liu, Line Malha, Michelle Lubetzky, Darshana M Dadhania, Divya Shankaranarayanan, Daniil Shimonov, Sanjay Neupane, Thalia Salinas, Aarti Bhasin, Elly Varma, Lorenz Leuprecht, Supriya Gerardine, Perola Lamba, Parag Goyal, Eric Caliendo, Victoria Tiase, Rahul Sharma, Joel C Park, Peter A D Steel, Manikkam Suthanthiran, Yiye Zhang...

    Clin J Am Soc Nephrol. 2021 Feb 8;16(2):284-286. doi: 10.2215/CJN.07440520. Epub 2020 Sep 18.

    NO ABSTRACT

    PMID:32948642 | PMC:PMC7863636 | DOI:10.2215/CJN.07440520

  • Anticoagulation Strategies and Filter Life in COVID-19 Patients Receiving Continuous Renal Replacement Therapy: A Single-Center Experience
    Divya Shankaranarayanan, Thangamani Muthukumar, Tarek Barbar, Aarti Bhasin, Supriya Gerardine, Perola Lamba, Lorenz Leuprecht, Sanjay P Neupane, Thalia Salinas, Daniil Shimonov, Elly Varma, Frank Liu...

    Clin J Am Soc Nephrol. 2020 Dec 31;16(1):124-126. doi: 10.2215/CJN.08430520. Epub 2020 Sep 17.

    NO ABSTRACT

    PMID:32943397 | PMC:PMC7792651 | DOI:10.2215/CJN.08430520

  • Peritoneal Dialysis for Acute Kidney Injury During the COVID-19 Pandemic in New York City
    Divya Shankaranarayanan, Sanjay P Neupane, Elly Varma, Daniil Shimonov, Supriya Gerardine, Aarti Bhasin, Perola Lamba, Lorenz Leuprecht, Thalia Salinas, Cheguevara Afaneh, Omar E Bellorin-Marin, Vesh Srivatana...

    Kidney Int Rep. 2020 Sep;5(9):1532-1534. doi: 10.1016/j.ekir.2020.07.017. Epub 2020 Jul 23.

    NO ABSTRACT

    PMID:32838084 | PMC:PMC7377796 | DOI:10.1016/j.ekir.2020.07.017

  • Peritoneal Dialysis for Acute Kidney Injury during the COVID-19 Pandemic
    Daniil Shimonov, Vesh Srivatana

    Clin J Am Soc Nephrol. 2020 Dec 7;15(12):1829-1831. doi: 10.2215/CJN.09240620. Epub 2020 Aug 14.

    NO ABSTRACT

    PMID:32801119 | PMC:PMC7769024 | DOI:10.2215/CJN.09240620

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