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Ouyang D, Theurer J, Stein NR, Hughes JW, Elias P, He B, Yuan N, Duffy G, Sandhu RK, Ebinger J, Botting P, Jujjavarapu M, Claggett B, Tooley JE, Poterucha T, Chen JH, Nurok M, Perez M, Perotte A, Zou JY, Cook NR, Chugh SS, Cheng S, Albert CM. Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study. Lancet Digit Health 2024; 6:e70-e78. [PMID: 38065778 DOI: 10.1016/s2589-7500(23)00220-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/01/2023] [Accepted: 10/18/2023] [Indexed: 12/22/2023]
Abstract
BACKGROUND Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING National Heart, Lung, and Blood Institute.
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Affiliation(s)
- David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - John Theurer
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nathan R Stein
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - J Weston Hughes
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Pierre Elias
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Bryan He
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Neal Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Grant Duffy
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Roopinder K Sandhu
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Patrick Botting
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Melvin Jujjavarapu
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Brian Claggett
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James E Tooley
- Division of Cardiology, Stanford University, Palo Alto, CA, USA
| | - Tim Poterucha
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jonathan H Chen
- Division of Bioinformatics Research, Stanford University, Palo Alto, CA, USA
| | - Michael Nurok
- Division of Anesthesia, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Marco Perez
- Division of Cardiology, Stanford University, Palo Alto, CA, USA
| | - Adler Perotte
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - James Y Zou
- Department of Computer Science, Stanford University, Palo Alto, CA, USA; Department of Medicine, and Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Nancy R Cook
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sumeet S Chugh
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Christine M Albert
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Tooley JE, Marcus GM, Scheinman M. An unruly case of atrioventricular block. Heart Rhythm 2023; 20:1337-1338. [PMID: 37648362 DOI: 10.1016/j.hrthm.2022.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 09/01/2023]
Affiliation(s)
- James E Tooley
- Division of Cardiology, Section on Electrophysiology, University of California San Francisco, San Francisco, California
| | - Gregory M Marcus
- Division of Cardiology, Section on Electrophysiology, University of California San Francisco, San Francisco, California
| | - Melvin Scheinman
- Division of Cardiology, Section on Electrophysiology, University of California San Francisco, San Francisco, California.
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Wang SXY, Horomanski A, Tooley JE, Reejhsinghani R, White AA. A change of heart. J Hosp Med 2022; 18:444-448. [PMID: 36479928 DOI: 10.1002/jhm.13016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/31/2022] [Accepted: 11/04/2022] [Indexed: 12/13/2022]
Affiliation(s)
- Samantha X Y Wang
- Department of Medicine, Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Audra Horomanski
- Department of Medicine, Division of Immunology & Rheumatology, Stanford University School of Medicine, Stanford, California, USA
| | - James E Tooley
- Department of Medicine, Division of Cardiology, UCSF School of Medicine, San Francisco, California, USA
| | - Risheen Reejhsinghani
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Andrew A White
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
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Affiliation(s)
- James E Tooley
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.,Department of Medicine (Cardiovascular Medicine), Stanford University Medical Center, Palo Alto, CA, USA
| | - Mintu P Turakhia
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.,Department of Medicine (Cardiovascular Medicine), Stanford University Medical Center, Palo Alto, CA, USA.,VA Palo Alto Health Care System, 3801 Miranda Ave - 111C, Palo Alto CA 94304, USA
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Hughes JW, Yuan N, He B, Ouyang J, Ebinger J, Botting P, Lee J, Theurer J, Tooley JE, Nieman K, Lungren MP, Liang DH, Schnittger I, Chen JH, Ashley EA, Cheng S, Ouyang D, Zou JY. Deep learning evaluation of biomarkers from echocardiogram videos. EBioMedicine 2021; 73:103613. [PMID: 34656880 PMCID: PMC8524103 DOI: 10.1016/j.ebiom.2021.103613] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 07/11/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Laboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocardiogram videos can provide additional value in understanding disease states and can evaluate common biomarkers results. METHODS We developed EchoNet-Labs, a video-based deep learning algorithm to detect evidence of anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), as well as values of ten additional lab tests directly from echocardiograms. We included patients (n = 39,460) aged 18 years or older with one or more apical-4-chamber echocardiogram videos (n = 70,066) from Stanford Healthcare for training and internal testing of EchoNet-Lab's performance in estimating the most proximal biomarker result. Without fine-tuning, the performance of EchoNet-Labs was further evaluated on an additional external test dataset (n = 1,301) from Cedars-Sinai Medical Center. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal and external test datasets. FINDINGS On the held-out test set of Stanford patients not previously seen during model training, EchoNet-Labs achieved an AUC of 0.80 (0.79-0.81) in detecting anemia (low hemoglobin), 0.86 (0.85-0.88) in detecting elevated BNP, 0.75 (0.73-0.78) in detecting elevated troponin I, and 0.74 (0.72-0.76) in detecting elevated BUN. On the external test dataset from Cedars-Sinai, EchoNet-Labs achieved an AUC of 0.80 (0.77-0.82) in detecting anemia, of 0.82 (0.79-0.84) in detecting elevated BNP, of 0.75 (0.72-0.78) in detecting elevated troponin I, and of 0.69 (0.66-0.71) in detecting elevated BUN. We further demonstrate the utility of the model in detecting abnormalities in 10 additional lab tests. We investigate the features necessary for EchoNet-Labs to make successful detection and identify potential mechanisms for each biomarker using well-known and novel explainability techniques. INTERPRETATION These results show that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods. FUNDING J.W.H. and B.H. are supported by the NSF Graduate Research Fellowship. D.O. is supported by NIH K99 HL157421-01. J.Y.Z. is supported by NSF CAREER 1942926, NIH R21 MD012867-01, NIH P30AG059307 and by a Chan-Zuckerberg Biohub Fellowship.
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Affiliation(s)
- J Weston Hughes
- Department of Computer Science, Stanford University, Palo Alto, CA 94025
| | - Neal Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048
| | - Bryan He
- Department of Computer Science, Stanford University, Palo Alto, CA 94025
| | - Jiahong Ouyang
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, 94025
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048
| | - Patrick Botting
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048
| | - Jasper Lee
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048
| | - John Theurer
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048
| | - James E Tooley
- Department of Medicine, Stanford University, Palo Alto, CA, 94025
| | - Koen Nieman
- Department of Medicine, Stanford University, Palo Alto, CA, 94025; Department of Radiology, Stanford University, Palo Alto, CA, 94025
| | | | - David H Liang
- Department of Medicine, Stanford University, Palo Alto, CA, 94025
| | | | - Jonathan H Chen
- Department of Medicine, Stanford University, Palo Alto, CA, 94025
| | - Euan A Ashley
- Department of Medicine, Stanford University, Palo Alto, CA, 94025
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048.
| | - James Y Zou
- Department of Computer Science, Stanford University, Palo Alto, CA 94025; Department of Electrical Engineering, Stanford University, Palo Alto, CA, 94025; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, 94025.
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Abstract
Atrial fibrillation is a common arrhythmia associated with significant morbidity, mortality and decreased quality of life. Mobile health devices marketed directly to consumers capable of detecting atrial fibrillation through methods including photoplethysmography, single-lead ECG as well as contactless methods are becoming ubiquitous. Large-scale screening for atrial fibrillation is feasible and has been shown to detect more cases than usual care-however, controversy still exists surrounding screening even in older higher risk populations. Given widespread use of mobile health devices, consumer-driven screening is happening on a large scale in both low-risk and high-risk populations. Given that young people make up a large portion of early adopters of mobile health devices, there is the potential that many more patients with early onset atrial fibrillation will come to clinical attention requiring possible referral to genetic arrythmia clinic. Physicians need to be familiar with these technologies, and understand their risks, and limitations. In the current review, we discuss current mobile health devices used to detect atrial fibrillation, recent and upcoming trials using them for diagnosis of atrial fibrillation, practical recommendations for patients with atrial fibrillation diagnosed by a mobile health device and special consideration in young patients.
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Affiliation(s)
- James E Tooley
- Cardiovascular Medicine, Stanford University, Stanford, California, USA
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Tooley JE, Sceats LA, Bohl DD, Read B, Kin C. Frequency and timing of short-term complications following abdominoperineal resection. J Surg Res 2018; 231:69-76. [PMID: 30278971 DOI: 10.1016/j.jss.2018.05.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [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: 01/27/2018] [Revised: 03/28/2018] [Accepted: 05/04/2018] [Indexed: 12/22/2022]
Abstract
BACKGROUND Abdominoperineal resection (APR) is primarily used for rectal cancer and is associated with a high rate of complications. Though the majority of APRs are performed as open procedures, laparoscopic APRs have become more popular. The differences in short-term complications between open and laparoscopic APR are poorly characterized. METHODS We conducted a retrospective cohort study using the American College of Surgeons National Surgical Quality Improvement Program database to determine the frequency and timing of onset of 30-d postoperative complications after APR and identify differences between open and laparoscopic APR. RESULTS A total of 7681 patients undergoing laparoscopic or open APR between 2011 and 2015 were identified. The total complication rate for APR was high (45.4%). APRs were commonly complicated by blood transfusion (20.1%), surgical site infection (19.3%), and readmission (12.3%). Laparoscopic APR was associated with a 14% lower total complication rate compared to open APR (36.0% versus 50.1%, P < 0.001). This was primarily driven by a decreased rate of transfusion (10.7% versus 24.9%, P < 0.001) and surgical site infection (15.5% versus 21.2%, P < 0.001). Laparoscopic APR had shorter length of stay and decreased reoperation rate but similar rates of readmission and death. Cardiopulmonary complications occurred earlier in the postoperative period after APR, whereas infectious complications occurred later. CONCLUSIONS Short-term complications following APR are common and occur more frequently in patients who undergo open APR. This, along with factors such as risk of positive pathologic margins, surgeon skill set, and patient characteristics, should contribute to the decision-making process when planning rectal cancer surgery.
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Affiliation(s)
- James E Tooley
- Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Lindsay A Sceats
- Department of Surgery, Stanford University School of Medicine, Stanford, California
| | - Daniel D Bohl
- Department of Orthopedic Surgery, Rush University School of Medicine, Chicago, Illinois
| | - Blake Read
- Department of Colon and Rectal Surgery, Mount Sinai School of Medicine, New York, New York
| | - Cindy Kin
- Department of Surgery, Stanford University School of Medicine, Stanford, California.
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Tooley JE, Bohl DD, Kulkarni S, Rodriguez-Davalos MI, Mangi A, Mulligan DC, Yoo PS. Perioperative outcomes of coronary artery bypass graft in renal transplant recipients in the United States: results from the Nationwide Inpatient Sample. Clin Transplant 2016; 30:1258-1263. [PMID: 27440000 DOI: 10.1111/ctr.12816] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND Cardiovascular disease is the leading cause of morbidity and mortality in patients with chronic kidney disease (CKD). In fact, death from cardiovascular disease is the number one cause of graft loss in kidney transplant (KTx) patients. Compared to patients on dialysis, CKD patients with KTx have increased quality and length of life. It is not known, however, whether outcomes of coronary artery bypass graft (CABG) surgery differ between CKD patients with KTx or on dialysis. METHODS This was a retrospective cohort study comparing CKD patients with KTx or on dialysis undergoing CABG surgery included in the Nationwide Inpatient Sample from 2002 to 2011. Logistic and linear regression models were used to estimate the adjusted associations of KTx on all-cause in-hospital mortality, length of stay, cost of hospitalization, and rate of complications in CABG surgery. RESULTS CKD patients with KTx had decreased all-cause in-hospital mortality (2.68% vs 5.86%, odds ratio (OR)=0.56, 95% confidence interval (CI)=0.32 to 0.99, P=.046), length of stay (β=-2.96, 95% CI=-3.67 to -2.46, P<.001), and total hospital charges (difference=-$38 884, 95% CI=-$48 173 to -29 596, P<.001). They also had decreased rate of a number of perioperative complications. CONCLUSIONS CKD patient with KTx have better perioperative outcomes in CABG surgery compared to patients on dialysis.
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Affiliation(s)
- James E Tooley
- Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
| | - Daniel D Bohl
- Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
| | - Sanjay Kulkarni
- Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
| | | | - Abeel Mangi
- Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
| | - David C Mulligan
- Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
| | - Peter S Yoo
- Department of Surgery, Yale University School of Medicine, New Haven, CT, USA.
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Tooley JE, Vudattu N, Choi J, Cotsapas C, Devine L, Raddassi K, Ehlers MR, McNamara JG, Harris KM, Kanaparthi S, Phippard D, Herold KC. Changes in T-cell subsets identify responders to FcR-nonbinding anti-CD3 mAb (teplizumab) in patients with type 1 diabetes. Eur J Immunol 2015; 46:230-41. [PMID: 26518356 DOI: 10.1002/eji.201545708] [Citation(s) in RCA: 45] [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: 04/01/2015] [Revised: 08/30/2015] [Accepted: 10/16/2015] [Indexed: 12/15/2022]
Abstract
The mechanisms whereby immune therapies affect progression of type 1 diabetes (T1D) are not well understood. Teplizumab, an FcR nonbinding anti-CD3 mAb, has shown efficacy in multiple randomized clinical trials. We previously reported an increase in the frequency of circulating CD8(+) central memory (CD8CM) T cells in clinical responders, but the generalizability of this finding and the molecular effects of teplizumab on these T cells have not been evaluated. We analyzed data from two randomized clinical studies of teplizumab in patients with new- and recent-onset T1D. At the conclusion of therapy, clinical responders showed a significant reduction in circulating CD4(+) effector memory T cells. Afterward, there was an increase in the frequency and absolute number of CD8CM T cells. In vitro, teplizumab expanded CD8CM T cells by proliferation and conversion of non-CM T cells. Nanostring analysis of gene expression of CD8CM T cells from responders and nonresponders versus placebo-treated control subjects identified decreases in expression of genes associated with immune activation and increases in expression of genes associated with T-cell differentiation and regulation. We conclude that CD8CM T cells with decreased activation and regulatory gene expression are associated with clinical responses to teplizumab in patients with T1D.
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Affiliation(s)
- James E Tooley
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Nalini Vudattu
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Jinmyung Choi
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Chris Cotsapas
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Lesley Devine
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Khadir Raddassi
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - James G McNamara
- National Institutes of Allergy and Infectious Diseases, NIH, Bethesda, MD, USA
| | | | | | | | - Kevan C Herold
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
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Abstract
PURPOSE OF REVIEW Biomarkers of type 1 diabetes (T1D) are important for assessing risk of developing disease, monitoring disease progression, and determining responses to clinical treatments. Here we review recent advances in the development of biomarkers of T1D with a focus on their utility in clinical trials. RECENT FINDINGS Measurements of autoantibodies and metabolic outcomes have been the foundation of monitoring T1D for the past 20 years. Recent advancements have led to improvements in T-cell-specific assays that have been used in large-scale clinical trials to measure antigen-specific T cell responses. Additionally, new tools are being developed for the measurement of β cell mass and death that will allow for more direct measurement of disease activity. Lastly, recent studies have used both immunologic and nonimmunologic biomarkers to identify responders to treatments in clinical trials. SUMMARY Use of biomarkers in the study of T1D has largely not changed over the past 20 years; however, recent advancements in the field are establishing new techniques that allow for more precise monitoring of disease progression. These new tools will ultimately lead to an improvement in understanding of disease and will be utilized in clinical trials.
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Affiliation(s)
- James E Tooley
- Department of Immunobiology and Internal Medicine, Yale University, New Haven, Connecticut, USA
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Tooley JE, Waldron-Lynch F, Herold KC. New and future immunomodulatory therapy in type 1 diabetes. Trends Mol Med 2012; 18:173-81. [PMID: 22342807 DOI: 10.1016/j.molmed.2012.01.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2011] [Revised: 01/01/2012] [Accepted: 01/02/2012] [Indexed: 02/07/2023]
Abstract
Type 1 diabetes is a common autoimmune disease that affects millions of people worldwide and has an incidence that is increasing at a striking rate, especially in young children. It results from the targeted self-destruction of the insulin-secreting β cells of the pancreas and requires lifelong insulin treatment. The effects of chronic hyperglycemia - the result of insulin deficiency - include secondary endorgan complications. Over the past two decades our increased understanding of the pathogenesis of this disease has led to the development of new immunomodulatory treatments. None have yet received regulatory approval, but this report highlights recent progress in this area.
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Affiliation(s)
- James E Tooley
- Department of Immunobiology, Yale University, New Haven, CT 06511, USA
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Tooley JE, Khangulov V, Lees JPB, Schlessman JL, Bewley MC, Heroux A, Bosch J, Hill RB. The 1.75 Å resolution structure of fission protein Fis1 from Saccharomyces cerevisiae reveals elusive interactions of the autoinhibitory domain. Acta Crystallogr Sect F Struct Biol Cryst Commun 2011; 67:1310-5. [PMID: 22102223 DOI: 10.1107/s1744309111029368] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2011] [Accepted: 07/20/2011] [Indexed: 11/10/2022]
Abstract
Fis1 mediates mitochondrial and peroxisomal fission. It is tail-anchored to these organelles by a transmembrane domain, exposing a soluble cytoplasmic domain. Previous studies suggested that Fis1 is autoinhibited by its N-terminal region. Here, a 1.75 Å resolution crystal structure of the Fis1 cytoplasmic domain from Saccharomyces cerevisiae is reported which adopts a tetratricopeptide-repeat fold. It is observed that this fold creates a concave surface important for fission, but is sterically occluded by its N-terminal region. Thus, this structure provides a physical basis for autoinhibition and allows a detailed examination of the interactions that stabilize the inhibited state of this molecule.
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Affiliation(s)
- James E Tooley
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
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Uno JK, Mah AT, Tooley JE, Xu H, Kiela PR, Ghishan FK. Altered Expression of Renal Sodium‐Phosphate Cotransporter in Mice with Chemically Induced Colitis. FASEB J 2007. [DOI: 10.1096/fasebj.21.6.a1320-c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jennifer K Uno
- MedicineUniversity of North Carolina at Chapel Hill605 Jones Ferry Rd #RR01CarrboroNC27510
| | - Amanda T Mah
- PediatricsUniversity of Arizona1501 N Campbell AveTucsonAZ85724
| | - James E Tooley
- PediatricsUniversity of Arizona1501 N Campbell AveTucsonAZ85724
| | - Hua Xu
- PediatricsUniversity of Arizona1501 N Campbell AveTucsonAZ85724
| | - Pawel R Kiela
- PediatricsUniversity of Arizona1501 N Campbell AveTucsonAZ85724
| | - Fayez K Ghishan
- PediatricsUniversity of Arizona1501 N Campbell AveTucsonAZ85724
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