Sagar Kamarthi, PhD
Professor, Mechanical and Industrial Engineering, Northeastern University
Sagar Kamarthi is a Professor of Industrial and Mechanical Engineering at Northeastern University, Boston. He is the founding director of MS in Data Analytics Engineering program at Northeastern. He teaches courses in manufacturing, data mining, and machine learning. Dr. Kamarthi received his PhD and MS degrees in Industrial Engineering from The Pennsylvania State University and a BS in Chemical Engineering from Sri Venkateswara University, India.
His research interests are in smart and sustainable manufacturing, predictive analytics for engineering and healthcare applications, and engineering education research. He has published more than 190 articles in internationally reputed journal and conference proceedings and has secured several grants from the National Science Foundation (NSF) and other federal agencies. Through his NSF funded education research grants he co-pioneered Engineering Based Learning (EBL) model (a structured version of project based learning), "Transform" curriculum model to train non-STEM graduates for manufacturing careers, and Mass Customized Instruction (MCI) model to enable personalized learning. Data analytics in engineering education is one of his current interests.
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Selected Publications
Ozturk, Arinc; Mohammadi, Ramin; Pierce, Theodore T; Kamarthi, Sagar; Dhyani, Manish; Grajo, Joseph R; Corey, Kathleen E; Chung, Raymond T; Bhan, Atul K; Chhatwal, Jagpreet; Samir, Anthony E
In: Ultrasound in medicine & biology, vol. 46, no. 4, pp. 972-980, 2020, ISSN: 1879-291X, ().
@article{Ozturk2020,
title = {Diagnostic Accuracy of Shear Wave Elastography as a Non-invasive Biomarker of High-Risk Non-alcoholic Steatohepatitis in Patients with Non-alcoholic Fatty Liver Disease.},
author = {Arinc Ozturk and Ramin Mohammadi and Theodore T Pierce and Sagar Kamarthi and Manish Dhyani and Joseph R Grajo and Kathleen E Corey and Raymond T Chung and Atul K Bhan and Jagpreet Chhatwal and Anthony E Samir},
url = {https://www.ncbi.nlm.nih.gov/pubmed/32005510},
doi = {10.1016/j.ultrasmedbio.2019.12.020},
issn = {1879-291X},
year = {2020},
date = {2020-04-01},
journal = {Ultrasound in medicine \& biology},
volume = {46},
number = {4},
pages = {972-980},
abstract = {In this study, we evaluated the diagnostic accuracy of shear wave elastography (SWE) for differentiating high-risk non-alcoholic steatohepatitis (hrNASH) from non-alcoholic fatty liver and low-risk non-alcoholic steatohepatitis (NASH). Patients with non-alcoholic fatty liver disease scheduled for liver biopsy underwent pre-biopsy SWE. Ten SWE measurements were obtained. Biopsy samples were reviewed using the NASH Clinical Research Network Scoring System and patients with hrNASH were identified. Receiver operating characteristic curves for SWE-based hrNASH diagnosis were charted. One hundred sixteen adult patients underwent liver biopsy at our institution for the evaluation of non-alcoholic fatty liver disease. The area under the receiver operating characteristic curve of SWE for hrNASH diagnosis was 0.73 (95% confidence interval: 0.61-0.84, p 0.001). The Youden index-based optimal stiffness cutoff value for hrNASH diagnosis was calculated as 8.4 kPa (1.67 m/s), with a sensitivity of 77% and specificity of 66%. SWE may be useful for the detection of NASH patients at risk of long-term liver-specific morbidity and mortality.},
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pubstate = {published},
tppubtype = {article}
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In this study, we evaluated the diagnostic accuracy of shear wave elastography (SWE) for differentiating high-risk non-alcoholic steatohepatitis (hrNASH) from non-alcoholic fatty liver and low-risk non-alcoholic steatohepatitis (NASH). Patients with non-alcoholic fatty liver disease scheduled for liver biopsy underwent pre-biopsy SWE. Ten SWE measurements were obtained. Biopsy samples were reviewed using the NASH Clinical Research Network Scoring System and patients with hrNASH were identified. Receiver operating characteristic curves for SWE-based hrNASH diagnosis were charted. One hundred sixteen adult patients underwent liver biopsy at our institution for the evaluation of non-alcoholic fatty liver disease. The area under the receiver operating characteristic curve of SWE for hrNASH diagnosis was 0.73 (95% confidence interval: 0.61-0.84, p 0.001). The Youden index-based optimal stiffness cutoff value for hrNASH diagnosis was calculated as 8.4 kPa (1.67 m/s), with a sensitivity of 77% and specificity of 66%. SWE may be useful for the detection of NASH patients at risk of long-term liver-specific morbidity and mortality.
Namin, Amir T.; Jalali, Mohammad S.; Vahdat, Vahab; Bedair, Hany S.; O'Connor, Mary I.; Kamarthi, Sagar; Isaacs, Jacqueline A.
Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants Journal Article
In: Value in Health, vol. 22, no. 4, pp. 423-430, 2019, ().
@article{RN64,
title = {Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants},
author = {Amir T. Namin and Mohammad S. Jalali and Vahab Vahdat and Hany S. Bedair and Mary I. O'Connor and Sagar Kamarthi and Jacqueline A. Isaacs},
url = {https://www.ncbi.nlm.nih.gov/pubmed/30975393},
doi = {10.1016/j.jval.2019.01.008},
year = {2019},
date = {2019-04-01},
urldate = {2019-04-01},
journal = {Value in Health},
volume = {22},
number = {4},
pages = {423-430},
abstract = {OBJECTIVES:
To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants and to compare patient outcomes and cost effectiveness of off-the-shelf and CIM implants.
METHODS:
A system dynamics simulation model was developed to study adoption dynamics of CIM and meet the research objectives. The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within 3 years of primary knee replacement implants were used as performance metrics.
RESULTS:
The simulation results indicate that by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time and cut down cumulative healthcare costs by approximately $38 billion.
CONCLUSIONS:
CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents the first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be used to consider the adoption of any emerging customized therapies for personalized medicine.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
OBJECTIVES:
To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants and to compare patient outcomes and cost effectiveness of off-the-shelf and CIM implants.
METHODS:
A system dynamics simulation model was developed to study adoption dynamics of CIM and meet the research objectives. The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within 3 years of primary knee replacement implants were used as performance metrics.
RESULTS:
The simulation results indicate that by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time and cut down cumulative healthcare costs by approximately $38 billion.
CONCLUSIONS:
CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents the first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be used to consider the adoption of any emerging customized therapies for personalized medicine.
To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants and to compare patient outcomes and cost effectiveness of off-the-shelf and CIM implants.
METHODS:
A system dynamics simulation model was developed to study adoption dynamics of CIM and meet the research objectives. The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within 3 years of primary knee replacement implants were used as performance metrics.
RESULTS:
The simulation results indicate that by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time and cut down cumulative healthcare costs by approximately $38 billion.
CONCLUSIONS:
CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents the first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be used to consider the adoption of any emerging customized therapies for personalized medicine.
Eschenfeldt, Patrick; Kartoun, Uri; Heberle, Curtis; Kong, Chung Yin; Nishioka, Norman S; Ng, Kenney; Kamarthi, Sagar; Hur, Chin
Analysis of factors associated with extended recovery time after colonoscopy. Journal Article
In: PloS one, vol. 13, no. 6, pp. e0199246, 2018, ISSN: 1932-6203, ().
@article{Eschenfeldt2018,
title = {Analysis of factors associated with extended recovery time after colonoscopy.},
author = {Patrick Eschenfeldt and Uri Kartoun and Curtis Heberle and Chung Yin Kong and Norman S Nishioka and Kenney Ng and Sagar Kamarthi and Chin Hur},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29927978},
doi = {10.1371/journal.pone.0199246},
issn = {1932-6203},
year = {2018},
date = {2018-06-01},
urldate = {2018-06-01},
journal = {PloS one},
volume = {13},
number = {6},
pages = {e0199246},
abstract = {A common limiting factor in the throughput of gastrointestinal endoscopy units is the availability of space for patients to recover post-procedure. This study sought to identify predictors of abnormally long recovery time after colonoscopy performed with procedural sedation. In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various "machine learning" techniques to see if better prediction could be achieved. Procedural data for 31,442 colonoscopies performed on 29,905 adult patients at Massachusetts General Hospital from 2011 to 2015 were analyzed to identify potential predictors of long recovery times. These data included the identities of hospital personnel, and the initial statistical analysis focused on the impact of these personnel on recovery time via multivariate logistic regression. Secondary analyses included more information on patient vitals both to identify secondary predictors and to predict long recoveries using more complex techniques. In univariate analysis, the endoscopist, procedure room nurse, recovery room nurse, and surgical technician all showed a statistically significant relationship to long recovery times, with p-value below 0.0001 in all cases. In the multivariate logistic regression, the most significant predictor of a long recovery time was the identity of the recovery room nurse, with the endoscopist also showing a statistically significant relationship with a weaker effect. Complex techniques led to a negligible improvement over simple techniques in prediction of long recovery periods. The hospital personnel involved in performing a colonoscopy show a strong association with the likelihood of a patient spending an abnormally long time recovering from the procedure, with the most pronounced effect for the nurse in the recovery room. The application of more advanced approaches to improve prediction in this clinical data set only yielded modest improvements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A common limiting factor in the throughput of gastrointestinal endoscopy units is the availability of space for patients to recover post-procedure. This study sought to identify predictors of abnormally long recovery time after colonoscopy performed with procedural sedation. In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various "machine learning" techniques to see if better prediction could be achieved. Procedural data for 31,442 colonoscopies performed on 29,905 adult patients at Massachusetts General Hospital from 2011 to 2015 were analyzed to identify potential predictors of long recovery times. These data included the identities of hospital personnel, and the initial statistical analysis focused on the impact of these personnel on recovery time via multivariate logistic regression. Secondary analyses included more information on patient vitals both to identify secondary predictors and to predict long recoveries using more complex techniques. In univariate analysis, the endoscopist, procedure room nurse, recovery room nurse, and surgical technician all showed a statistically significant relationship to long recovery times, with p-value below 0.0001 in all cases. In the multivariate logistic regression, the most significant predictor of a long recovery time was the identity of the recovery room nurse, with the endoscopist also showing a statistically significant relationship with a weaker effect. Complex techniques led to a negligible improvement over simple techniques in prediction of long recovery periods. The hospital personnel involved in performing a colonoscopy show a strong association with the likelihood of a patient spending an abnormally long time recovering from the procedure, with the most pronounced effect for the nurse in the recovery room. The application of more advanced approaches to improve prediction in this clinical data set only yielded modest improvements.