1
|
Wang Z, Kim Y, Mortani Barbosa EJ. Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program. Curr Probl Diagn Radiol 2024; 53:552-559. [PMID: 38658287 DOI: 10.1067/j.cpradiol.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE We developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables' value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program. MATERIALS AND METHODS 480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models. RESULTS For outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome. CONCLUSIONS We identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.
Collapse
Affiliation(s)
- Zhuoyang Wang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yohan Kim
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eduardo J Mortani Barbosa
- Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Ground Floor Founders Bldg, Philadelphia, PA 19104, USA.
| |
Collapse
|
2
|
Wang Z, Mortani Barbosa EJ. Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models. J Am Coll Radiol 2024:S1546-1440(24)00274-6. [PMID: 38461910 DOI: 10.1016/j.jacr.2024.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/19/2024] [Accepted: 02/02/2024] [Indexed: 03/12/2024]
Abstract
OBJECTIVE To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features. METHODS We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance. RESULTS On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840. CONCLUSION Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.
Collapse
Affiliation(s)
- Zhuoyang Wang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eduardo J Mortani Barbosa
- Director of CT Modality at the Thoracic Imaging Section, Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
| |
Collapse
|
3
|
Martin MD, Henry TS, Berry MF, Johnson GB, Kelly AM, Ko JP, Kuzniewski CT, Lee E, Maldonado F, Morris MF, Munden RF, Raptis CA, Shim K, Sirajuddin A, Small W, Tong BC, Wu CC, Donnelly EF. ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule. J Am Coll Radiol 2023; 20:S455-S470. [PMID: 38040464 DOI: 10.1016/j.jacr.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 08/22/2023] [Indexed: 12/03/2023]
Abstract
Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
Collapse
Affiliation(s)
- Maria D Martin
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
| | | | - Mark F Berry
- Stanford University Medical Center, Stanford, California; Society of Thoracic Surgeons
| | - Geoffrey B Johnson
- Mayo Clinic, Rochester, Minnesota; Commission on Nuclear Medicine and Molecular Imaging
| | | | - Jane P Ko
- New York University Langone Health, New York, New York; IF Committee
| | | | - Elizabeth Lee
- University of Michigan Health System, Ann Arbor, Michigan
| | - Fabien Maldonado
- Vanderbilt University Medical Center, Nashville, Tennessee; American College of Chest Physicians
| | | | - Reginald F Munden
- Medical University of South Carolina, Charleston, South Carolina; IF Committee
| | | | - Kyungran Shim
- John H. Stroger, Jr. Hospital of Cook County, Chicago, Illinois; American College of Physicians
| | | | - William Small
- Loyola University Chicago, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, Illinois; Commission on Radiation Oncology
| | - Betty C Tong
- Duke University School of Medicine, Durham, North Carolina; Society of Thoracic Surgeons
| | - Carol C Wu
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Edwin F Donnelly
- Specialty Chair, Ohio State University Wexner Medical Center, Columbus, Ohio
| |
Collapse
|
4
|
Steiling K, Kathuria H, Echieh CP, Ost DE, Rivera MP, Begnaud A, Celedón JC, Charlot M, Dietrick F, Duma N, Fong KM, Ford JG, Gould MK, Holguin F, Pérez-Stable EJ, Tanner NT, Thomson CC, Wiener RS, Wisnivesky J. Research Priorities for Interventions to Address Health Disparities in Lung Nodule Management: An Official American Thoracic Society Research Statement. Am J Respir Crit Care Med 2023; 207:e31-e46. [PMID: 36920066 PMCID: PMC10037482 DOI: 10.1164/rccm.202212-2216st] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
Background: Lung nodules are common incidental findings, and timely evaluation is critical to ensure diagnosis of localized-stage and potentially curable lung cancers. Rates of guideline-concordant lung nodule evaluation are low, and the risk of delayed evaluation is higher for minoritized groups. Objectives: To summarize the existing evidence, identify knowledge gaps, and prioritize research questions related to interventions to reduce disparities in lung nodule evaluation. Methods: A multidisciplinary committee was convened to review the evidence and identify key knowledge gaps in four domains: 1) research methodology, 2) patient-level interventions, 3) clinician-level interventions, and 4) health system-level interventions. A modified Delphi approach was used to identify research priorities. Results: Key knowledge gaps included 1) a lack of standardized approaches to identify factors associated with lung nodule management disparities, 2) limited data evaluating the role of social determinants of health on disparities in lung nodule management, 3) a lack of certainty regarding the optimal strategy to improve patient-clinician communication and information transmission and/or retention, and 4) a paucity of information on the impact of patient navigators and culturally trained multidisciplinary teams. Conclusions: This statement outlines a research agenda intended to stimulate high-impact studies of interventions to mitigate disparities in lung nodule evaluation. Research questions were prioritized around the following domains: 1) need for methodologic guidelines for conducting research related to disparities in nodule management, 2) evaluating how social determinants of health influence lung nodule evaluation, 3) studying approaches to improve patient-clinician communication, and 4) evaluating the utility of patient navigators and culturally enriched multidisciplinary teams to reduce disparities.
Collapse
|
5
|
Douthit BJ, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic TA, Lee MA, Pruinelli L, Schultz MA, Wieben A, Jeffery AD. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Appl Clin Inform 2022; 13:161-179. [PMID: 35139564 PMCID: PMC8828453 DOI: 10.1055/s-0041-1742218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.
Collapse
Affiliation(s)
- Brian J. Douthit
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Rachel L. Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia P. Coviak
- Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Thompson Forbes
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Grace Gao
- Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States
| | - Theresa A. Kapetanovic
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Mikyoung A. Lee
- College of Nursing, Texas Woman's University, Denton, Texas, United States
| | - Lisiane Pruinelli
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
| | - Mary A. Schultz
- Department of Nursing, California State University, San Bernardino, California, United States
| | - Ann Wieben
- School of Nursing, University of Wisconsin-Madison, Wisconsin, United States
| | - Alvin D. Jeffery
- School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States,Address for correspondence Alvin D. Jeffery, PhD, RN-BC, CCRN-K, FNP-BC 461 21st Avenue South, Nashville, TN 37240United States
| |
Collapse
|
6
|
Schut RA, Mortani Barbosa EJ. Racial/Ethnic Disparities in Follow-Up Adherence for Incidental Pulmonary Nodules: An Application of a Cascade-of-Care Framework. J Am Coll Radiol 2020; 17:1410-1419. [PMID: 32771492 DOI: 10.1016/j.jacr.2020.07.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/14/2020] [Accepted: 07/15/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE The aim of this study was to evaluate racial/ethnic disparities in follow-up adherence for incidental pulmonary nodules (IPNs) using a cascade-of-care framework, representing the multistage pathway from IPN diagnosis to timely follow-up adherence. METHODS A cohort of 1,562 patients diagnosed with IPNs requiring follow-up in a tertiary health care system in 2016 were retrospectively identified. Racial/ethnic disparities in follow-up adherence were examined by developing a multistep cascade-of-care model (provider communication, follow-up examination ordering and scheduling, adherence) to identify where patients were most likely to fall off the path toward adherence. Racial/ethnic adherence disparities were measured using descriptive statistics and multivariate modeling, controlling for sociodemographic, communication, and health characteristics. RESULTS Among 1,562 patients whose IPNs required follow-up, unadjusted results showed that nonwhite patients were less likely to meet each step on the cascade than White patients: for provider-patient IPN communication, 55% among Black patients and 80% among White patients; for follow-up ordering and scheduling, 42% and 41% among Black patients and 66% and 64% among White patients; and for timely adherence, 29% among Black patients and 54% among White patients. Adjusting for provider communication, sociodemographic, and health characteristics, Black patients had increased odds of never adhering to and delaying follow-up compared with White patients (odds ratios, 1.30 [95% confidence interval, 0.90-1.89] and 2.51 [95% confidence interval, 1.54-4.09], respectively). CONCLUSIONS These findings demonstrate substantial racial/ethnic disparities in IPN follow-up adherence that persist after adjusting for multiple characteristics. The cascade of care demonstrates where on the adherence pathway patients are at risk for falling off, enabling specific targets for health policy and clinical interventions. Radiologists can play a key role in improving IPN follow-up via increased patient care involvement.
Collapse
Affiliation(s)
- Rebecca A Schut
- Population Studies Center, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Eduardo J Mortani Barbosa
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| |
Collapse
|