Development of an international glossary for clinical guidelines collaboration

OBJECTIVES

Clinical practice guidelines (CPGs) are often created through collaboration among organizations. The use of inconsistent terminology may cause poor communication and delays. This study aimed to develop a glossary of terms related to collaboration in guideline development.

STUDY DESIGN AND SETTING

A literature review of collaborative guidelines was performed to develop an initial list of terms related to guideline collaboration. The list of terms was presented to the members of the Guideline International Network Guidelines Collaboration Working Group, who provided presumptive definitions for each term and proposed additional terms to be included. The revised list was subsequently reviewed by an international, multidisciplinary panel of expert stakeholders. Recommendations received during this pre-Delphi review were implemented to augment an initial draft glossary. The glossary was then critically evaluated and refined through two rounds of Delphi surveys and a virtual consensus meeting with all panel members as Delphi participants.

RESULTS

Forty-nine experts participated in the pre-Delphi survey, and 44 participated in the two-round Delphi process. Consensus was reached for 37 terms and definitions.

CONCLUSION

Uptake and utilization of this guideline collaboration glossary by key organizations and stakeholder groups may facilitate collaboration among guideline-producing organizations by improving communication, minimizing conflicts, and increasing guideline development efficiency.

Electrical impedance spectroscopy significantly enhances correct biopsy choice for pigmented skin lesions beyond clinical evaluation and dermoscopy

The purpose of this study was to investigate whether EIS technology can further improve correct biopsy choices beyond clinical and dermoscopic evaluation for melanoma (MM), severe dysplastic nevi (SDN) and benign PSLs. Images of 49 MMs, SDNs and benign PSLs were randomly selected from a prior study and were provided in a reader-type survey study to dermatologists to evaluate for biopsy. A total of 33,957 biopsy decisions were analyzed. Respondents significantly improved on the correct biopsy choice with the addition of dermoscopy versus clinical image alone for melanoma and severely dysplastic nevi. Respondents also showed a statistically significant improvement in correct biopsy choice beyond their dermoscopic evaluation when integrating the EIS score versus dermoscopy with clinical images for MM, SDN and benign lesions. Respondents also made fewer incorrect biopsy choices with the addition of the EIS score versus dermoscopy and clinical image for MM and benign lesions. Sub-analyses of biopsy choices were also conducted based on experience and practice type. The findings from this study demonstrate that the integration of EIS technology into PSL biopsy decisions has the potential to significantly improve the accuracy of lesion selection for biopsy beyond clinical and dermoscopic evaluation alone.

The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography

Low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, we evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed.  We reviewed primary scientific literature dating up to April 2020 using Pubmed and Google Scholar, with the search terms low dose CT, lung cancer screening, coronary artery calcium, EKG/cardiac gated CT, deep learning, machine learning, and AI. These publications were then independently evaluated by each member of our team. Overall, there was a consensus within these papers that LDCT for lung cancer screening plays a role in the evaluation of CAC. Most studies note the inherent problems with the evaluation of the density of coronary calcifications on LDCT to give an accurate numeric calcium or Agatston score. The current method of evaluating CAC on LDCT involves using a qualitative categorical system (none, mild, moderate, or severe). When performed by cardiac imaging experts, this method broadly correlates with traditional CAC score groups (0, 1 to 100, 101 to 400, and > 400). Furthermore, given the high sensitivity of a properly protocolled LDCT for coronary calcium, a negative study for CAC precludes the need for a dedicated gated CT assessment. However, qualitative methods are not as accurate or reproducible when performed by general radiologists. The implementation of AI in the LDCT screening process has the potential to give a quantifiable and reproducible numeric value to the calcium score, based on whole heart volume scoring of calcium. This more closely aligns with the Agatston score and serves as a better guide for treatment and risk assessment using current guidelines. We conclude that CAC should be assessed on all LDCT performed for lung cancer screening and that a qualitative categorical scoring system should be provided in the impression for each patient. Early studies involving AI for the assessment of CAC are promising, but more extensive studies are needed before a final recommendation for its use can be given. The implementation of an accurate, automated AI CAC assessment tool would improve radiologist compliance and ease of overall workflow. Ultimately, the potential end result would be improved turnaround time, better patient outcomes, and reduced healthcare costs by maximizing preventative care in this high-risk population.