5
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Rashidian S, Abell-Hart K, Hajagos J, Moffitt R, Lingam V, Garcia V, Tsai CW, Wang F, Dong X, Sun S, Deng J, Gupta R, Miller J, Saltz J, Saltz M. Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach. JMIR Med Inform 2020; 8:e22649. [PMID: 33331828 PMCID: PMC7775195 DOI: 10.2196/22649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 09/24/2020] [Accepted: 09/27/2020] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the "gold standard" reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. OBJECTIVE This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. METHODS We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. RESULTS When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve-receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. CONCLUSIONS This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.
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Affiliation(s)
- Sina Rashidian
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Kayley Abell-Hart
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Janos Hajagos
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Richard Moffitt
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Veena Lingam
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Victor Garcia
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Chao-Wei Tsai
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Xinyu Dong
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Siao Sun
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Jianyuan Deng
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Joshua Miller
- Department of Medicine, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Mary Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
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6
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Chaudhri I, Moffitt R, Taub E, Annadi RR, Hoai M, Bolotova O, Yoo J, Dhaliwal S, Sahib H, Daccueil F, Hajagos J, Saltz M, Saltz J, Mallipattu SK, Koraishy FM. Association of Proteinuria and Hematuria with Acute Kidney Injury and Mortality in Hospitalized Patients with COVID-19. Kidney Blood Press Res 2020; 45:1018-1032. [PMID: 33171466 DOI: 10.1159/000511946] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/24/2020] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Acute kidney injury (AKI) is strongly associated with poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19), but data on the association of proteinuria and hematuria are limited to non-US populations. In addition, admission and in-hospital measures for kidney abnormalities have not been studied separately. METHODS This retrospective cohort study aimed to analyze these associations in 321 patients sequentially admitted between March 7, 2020 and April 1, 2020 at Stony Brook University Medical Center, New York. We investigated the association of proteinuria, hematuria, and AKI with outcomes of inflammation, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and in-hospital death. We used ANOVA, t test, χ2 test, and Fisher's exact test for bivariate analyses and logistic regression for multivariable analysis. RESULTS Three hundred patients met the inclusion criteria for the study cohort. Multivariable analysis demonstrated that admission proteinuria was significantly associated with risk of in-hospital AKI (OR 4.71, 95% CI 1.28-17.38), while admission hematuria was associated with ICU admission (OR 4.56, 95% CI 1.12-18.64), IMV (OR 8.79, 95% CI 2.08-37.00), and death (OR 18.03, 95% CI 2.84-114.57). During hospitalization, de novo proteinuria was significantly associated with increased risk of death (OR 8.94, 95% CI 1.19-114.4, p = 0.04). In-hospital AKI increased (OR 27.14, 95% CI 4.44-240.17) while recovery from in-hospital AKI decreased the risk of death (OR 0.001, 95% CI 0.001-0.06). CONCLUSION Proteinuria and hematuria both at the time of admission and during hospitalization are associated with adverse clinical outcomes in hospitalized patients with COVID-19.
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Affiliation(s)
- Imran Chaudhri
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Erin Taub
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Raji R Annadi
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Minh Hoai
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Olena Bolotova
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Jeanwoo Yoo
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Simrat Dhaliwal
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Haseena Sahib
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Farah Daccueil
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Janos Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sandeep K Mallipattu
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA.,Renal Section, Northport VA Medical Center, Northport, New York, USA
| | - Farrukh M Koraishy
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA, .,Renal Section, Northport VA Medical Center, Northport, New York, USA,
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7
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Mallipattu SK, Jawa R, Moffitt R, Hajagos J, Fries B, Nachman S, Gan TJ, Saltz M, Saltz J, Kaushansky K, Skopicki H, Abell-Hart K, Chaudhri I, Deng J, Garcia V, Gayen S, Kurc T, Bolotova O, Yoo J, Dhaliwal S, Nataraj N, Sun S, Tsai C, Wang Y, Abbasi S, Abdullah R, Ahmad S, Bai K, Bennett-Guerrero E, Chua A, Gomes C, Griffel M, Kalogeropoulos A, Kiamanesh D, Kim N, Koraishy F, Lingham V, Mansour M, Marcos L, Miller J, Poovathor S, Rubano J, Rutigliano D, Sands M, Santora C, Schwartz J, Shroyer K, Spitzer S, Stopeck A, Talamini M, Tharakan M, Vosswinkel J, Wertheim W, Mallipattu SK, Jawa R, Moffitt R, Hajagos J, Fries B, Nachman S, Gan TJ, Saltz M, Saltz J, Kaushansky K, Skopicki H, Abell-Hart K, Chaudhri I, Deng J, Garcia V, Gayen S, Kurc T, Bolotova O, Yoo J, Dhaliwal S, Nataraj N, Sun S, Tsai C, Wang Y, Abbasi S, Abdullah R, Ahmad S, Bai K, Bennett-Guerrero E, Chua A, Gomes C, Griffel M, Kalogeropoulos A, Kiamanesh D, Kim N, Koraishy F, Lingham V, Mansour M, Marcos L, Miller J, Poovathor S, Rubano J, Rutigliano D, Sands M, Santora C, Schwartz J, Shroyer K, Spitzer S, Stopeck A, Talamini M, Tharakan M, Vosswinkel J, Wertheim W. Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study. Open Forum Infect Dis 2020; 7:ofaa436. [PMID: 33117852 PMCID: PMC7543608 DOI: 10.1093/ofid/ofaa436] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/09/2020] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. METHODS This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients' residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. RESULTS The median age of the study cohort (interquartile range [IQR]) was 62 (49-75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (r s = 0.235; P = .004), with noted "hot spots" in the region. Study patients were predominantly hypertensive (MAP > 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate < 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5-29.5] days) was significantly longer than that of nonsurvivors (15 [10-23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV. CONCLUSIONS This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.
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Affiliation(s)
| | - S K Mallipattu
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Jawa
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Moffitt
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Hajagos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - B Fries
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Nachman
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T J Gan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Kaushansky
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - H Skopicki
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Abell-Hart
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - I Chaudhri
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Deng
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Garcia
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Gayen
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T Kurc
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - O Bolotova
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Yoo
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Dhaliwal
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Nataraj
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Sun
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Tsai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - Y Wang
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Abbasi
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Abdullah
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Ahmad
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Bai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - E Bennett-Guerrero
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Chua
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Gomes
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Griffel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Kalogeropoulos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Kiamanesh
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Kim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - F Koraishy
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Lingham
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Mansour
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - L Marcos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Miller
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Poovathor
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Rubano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Rutigliano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Sands
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Santora
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Schwartz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Shroyer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Spitzer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Stopeck
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Talamini
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Tharakan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Vosswinkel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - W Wertheim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S K Mallipattu
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Jawa
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Moffitt
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Hajagos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - B Fries
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Nachman
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T J Gan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Kaushansky
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - H Skopicki
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Abell-Hart
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - I Chaudhri
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Deng
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Garcia
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Gayen
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T Kurc
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - O Bolotova
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Yoo
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Dhaliwal
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Nataraj
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Sun
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Tsai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - Y Wang
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Abbasi
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Abdullah
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Ahmad
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Bai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - E Bennett-Guerrero
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Chua
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Gomes
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Griffel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Kalogeropoulos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Kiamanesh
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Kim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - F Koraishy
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Lingham
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Mansour
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - L Marcos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Miller
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Poovathor
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Rubano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Rutigliano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Sands
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Santora
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Schwartz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Shroyer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Spitzer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Stopeck
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Talamini
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Tharakan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Vosswinkel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - W Wertheim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
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