Jayson Marwaha, MD, MSc

All Publications

• Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION Marwaha, J. S., Downing, M., Halamka, J., Abernethy, A., Franklin, J. B., Anderson, B., Kohane, I., Wagholikar, K., Brownstein, J., Haendel, M., Brat, G. A. 2024; 12 (2): 100738

  Abstract

  The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues. The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses. Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.

  View details for DOI 10.1016/j.hjdsi.2024.100738

  View details for Web of Science ID 001246215000001

  View details for PubMedID 38531228

• Clinical prediction tool pitfalls and considerations: Data and algorithms. Surgery Choi, J., Marwaha, J. S. 2023

  Abstract

  In recent years, many surgical prediction models have been developed and published to augment surgeon decision-making, predict postoperative patient trajectories, and more. Collectively underlying all of these models is a wide variety of data sources and algorithms. Each data set and algorithm has its unique strengths, weaknesses, and type of prediction task for which it is best suited. The purpose of this piece is to highlight important characteristics of common data sources and algorithms used in surgical prediction model development so that future researchers interested in developing models of their own may be able to critically evaluate them and select the optimal ones for their study.

  View details for DOI 10.1016/j.surg.2023.08.009

  View details for PubMedID 37709646

• Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions. Annals of surgery Loftus, T. J., Altieri, M. S., Balch, J. A., Abbott, K. L., Choi, J., Marwaha, J. S., Hashimoto, D. A., Brat, G. A., Raftopoulos, Y., Evans, H. L., Jackson, G. P., Walsh, D. S., Tignanelli, C. J. 2023

  Abstract

  To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting.To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown.Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to PRISMA-ScR guidelines.Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2,000; seven of these eight articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic (AUROC) or accuracy. Overall, 29 articles (80.6%) performed internal validation only, five (13.8%) performed external validation, and two (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy.Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

  View details for DOI 10.1097/SLA.0000000000005853

  View details for PubMedID 36942574

• Appraising the Quality of Development and Reporting in Surgical Prediction Models. JAMA surgery Marwaha, J. S., Chen, H. W., Habashy, K., Choi, J., Spain, D. A., Brat, G. A. 2022

  View details for DOI 10.1001/jamasurg.2022.4488

  View details for PubMedID 36449299