Biren Reddy

Introduction: Peritoneal metastases (PM) are associated with poor prognosis in patients with colorectal cancer (CRC) or appendiceal adenocarcinoma (AA), yet detection of PM is unreliable using current circulating DNA technology. Leveraging novel 5hmC-seal technology to detect ultra-low amounts of DNA in plasma, we demonstrate the feasibility of 5-hydroxymethylcytosine (5hmC) signatures derived from circulating cell-free DNA (cfDNA) as biomarkers for PM. Methods: Using a highly sensitive and robust 5hmC sequencing approach on genomic DNA isolated from peripheral blood samples, we developed predictive models to identify biomarkers for peritoneal metastases. Results: We obtained genome-wide 5hmC profiles from 71 CRC/AA patients with PM, 41 without PM, and 73 non-cancer controls. Predictive models trained on genomic region 5hmC levels in patients with cancer could distinguish PM status with high sensitivity and moderate specificity (AUC 0.827, sensitivity 92.4%, specificity 46.1%). Pathway enrichment analysis identified epigenetically dysregulated cancer, cell migration, adhesion, and immune-related pathways in PM. Conclusion: Novel 5hmC-Seal technology based 5hmC signatures can detect patients with peritoneal metastases from CRC and AA, albeit with reduced specificity. This study lays a foundation for future clinical assay development for PM. Statement of significance: We demonstrate high-sensitivity detection of peritoneal metastasis in colorectal and appendiceal adenocarcinomas using 5hmC-Seal of plasma cfDNA. Earlier detection of this condition could expand curative treatments in ∼20,000 affected U.S. patients.

Background: Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are self-reporting tools that can measure important information about patients, such as health priorities, experience, and perception of outcome. The use of traditional objective measures such as vital signs and lab values can be supplemented with these self-reported patient measures to provide a more complete picture of a patient’s health status. Machine learning, the use of computer algorithms that improve automatically through experience, is a powerful tool in health care that often does not use subjective information shared by patients. However, machine learning has largely been based on objective measures and has been developed without patient or public input. Algorithms often do not have access to critical information from patients and may be missing priorities and measures that matter to patients. Combining objective measures with patient-reported measures can improve the ability of machine learning algorithms to assess patients’ health status and improve the delivery of health care. Objective: The objective of this scoping review is to identify gaps and benefits in the way machine learning is integrated with patient-reported outcomes for the development of improved public and patient partnerships in research and health care. Methods: We reviewed the following 3 questions to learn from existing literature about the reported gaps and best methods for combining machine learning and patient-reported outcomes: (1) How are the public engaged as involved partners in the development of artificial intelligence in medicine? (2) What examples of good practice can we identify for the integration of PROMs into machine learning algorithms? (3) How has value-based health care influenced the development of artificial intelligence in health care? We searched Ovid MEDLINE(R), Embase, PsycINFO, Science Citation Index, Cochrane Library, and Database of Abstracts of Reviews of Effects in addition to PROSPERO and the ClinicalTrials website. The authors will use Covidence to screen titles and abstracts and to conduct the review. We will include systematic reviews and overviews published in any language and may explore additional study types. Quantitative, qualitative, and mixed methods studies are included in the reviews. Results: The search is completed, and Covidence software will be used to work collaboratively. We will report the review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and Critical Appraisal Skills Programme for systematic reviews. Conclusions: Findings from our review will help us identify examples of good practice for how to involve the public in the development of machine learning systems as well as interventions and outcomes that have used PROMs and PREMs. International Registered Report Identifier (IRRID): DERR1-10.2196/36395

BACKGROUND Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are self-reporting tools that can measure important information about patients, such as health priorities, experience, and perception of outcome. The use of traditional objective measures such as vital signs and lab values can be supplemented with these self-reported patient measures to provide a more complete picture of a patient’s health status. Machine learning, the use of computer algorithms that improve automatically through experience, is a powerful tool in health care that often does not use subjective information shared by patients. However, machine learning has largely been based on objective measures and has been developed without patient or public input. Algorithms often do not have access to critical information from patients and may be missing priorities and measures that matter to patients. Combining objective measures with patient-reported measures can improve the ability of machine learning algorithms to assess patients’ health status and improve the delivery of health care. OBJECTIVE The objective of this scoping review is to identify gaps and benefits in the way machine learning is integrated with patient-reported outcomes for the development of improved public and patient partnerships in research and health care. METHODS We reviewed the following 3 questions to learn from existing literature about the reported gaps and best methods for combining machine learning and patient-reported outcomes: (1) How are the public engaged as involved partners in the development of artificial intelligence in medicine? (2) What examples of good practice can we identify for the integration of PROMs into machine learning algorithms? (3) How has value-based health care influenced the development of artificial intelligence in health care? We searched Ovid MEDLINE(R), Embase, PsycINFO, Science Citation Index, Cochrane Library, and Database of Abstracts of Reviews of Effects in addition to PROSPERO and the ClinicalTrials website. The authors will use Covidence to screen titles and abstracts and to conduct the review. We will include systematic reviews and overviews published in any language and may explore additional study types. Quantitative, qualitative, and mixed methods studies are included in the reviews. RESULTS The search is completed, and Covidence software will be used to work collaboratively. We will report the review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and Critical Appraisal Skills Programme for systematic reviews. CONCLUSIONS Findings from our review will help us identify examples of good practice for how to involve the public in the development of machine learning systems as well as interventions and outcomes that have used PROMs and PREMs. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/36395