Investigating Incidental Pulmonary Nodules in Underserved Communities
NCT ID: NCT05738031
Last Updated: 2023-11-18
Study Results
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Basic Information
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WITHDRAWN
OBSERVATIONAL
2023-10-31
2025-05-31
Brief Summary
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Detailed Description
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Outside of screening programs, lung nodules are commonly detected incidentally on imaging done for other reasons. Each year, more than 1.5 million patients are diagnosed with an incidental pulmonary nodule. Of these, 5-9% are estimated to represent cancers, higher than the malignancy rate noted in lung cancer screening programs. Guidelines exist for the follow-up of IPNs, however compliance is often poor. IPNs may be overlooked in the context of the other illnesses for which imaging is obtained. Tests may also be ordered by providers without continuity of care, as occurs in the emergency department (ED). Patients may thus be unaware of incidental findings or receive inadequate direction for follow-up when there is no clear chain of responsibility. Racially disparate populations are specifically at risk and often face barriers to accessing primary care providers (PCPs), leading to increased use of the ED. In one study, a higher proportion of Black and Hispanic patients (38.3% and 28.1%, respectively) had initial imaging identifying an IPN performed in the ED compared to White patients (10.7%), who were more likely to have outpatient scans.
Previous studies indicate that only 38% of patients receive guideline concordant care once diagnosed with an IPN. Lung cancer was diagnosed in 8% of these patients undergoing such care compared to only 1% in those who received less intensive evaluation. Similarly, the median time to diagnosis of a lung cancer was 1.3 months in the guideline concordant care group versus 12 months in the less intensive evaluation patients, again underscoring the importance of appropriate mechanisms of follow-up. The consequences of a missed nodule are clear.
Data suggest that racial and ethnic disparities exist in the follow-up of IPNs. In a study of 1,562 patients with an IPN requiring follow-up at a tertiary care center, only 49.1% of Hispanic patients and 55.1% of Black patients were notified of IPNs compared to 79.5% of White patients. Similarly, non-White patients had significantly lower rates of ordering and adherence to follow-up imaging and had an increased odds of delaying follow-up. While this discrepancy in care has been identified, few solutions exist to bridge the gap and underrepresented patients remain at higher risk of delayed diagnosis until advanced stages of disease.
Further compounding the difficulty in managing underserved patients with IPNs is the lack of programs for formalized follow-up, specifically in urban areas. In an advisory board meeting of major medical centers within New York City, only one formal nodule evaluation program associated with a center's ED was identified. New York Presbyterian Hospital Weill Cornell identified 539 patients with IPNs over a two-year period. After radiologic review, chest radiologists referred 289 patients for further consultation and of these 142 (26.3% of original population) were referred for evaluation by a pulmonologist or oncologist. While the results of this investigation and rates of cancer diagnoses are currently being tabulated, the large proportion of patients referred for concerning findings is quite notable.
Within New York City, the Montefiore Health System is uniquely positioned to conduct clinical research and bridge health care disparities by engaging underserved and underrepresented communities. The health system is comprised of 11 hospitals in the Bronx, Westchester, and the Hudson Valley in New York. The main campuses include two high volume EDs, including one of the five busiest in the country, and serve a diverse population of nearly 1.5 million residents in the Bronx. Previous data from Montefiore have demonstrated that of 855 primary lung cancers diagnosed between 2013 and 2016, only 417 (55%) were found in patients with an in-network PCP, illustrating the need for a better support system and for a systematic approach to identify and guide these patients. Furthermore, of the 175 of these patients who were eligible for LCS, only 33 had completed screening. Among screened patients, 64% were diagnosed with stage I/II non-small cell lung cancer, compared to only 29% of the lung cancers found outside of screening. In this latter group, 46% were diagnosed with metastatic disease. This demonstrates not only the value of screening in this at-risk population, but also emphasizes the need for prompt follow-up of incidentally detected lung abnormalities. A large proportion of this population in whom lung cancers were identified outside of screening was comprised of Black (46%) and Hispanic (34%) patients with a median per capita income of only approximately $20,000.12. The evaluation and implementation of a nodule detection program may thus extend care and improve the potential for survival in patients with reduced access to health care, where the ED may function as the primary care hub.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Group A
Aim 1 (Part A). To utilize natural language processing (NLP) to identify all ED patients with incidentally detected lung nodules found on chest radiographs or chest or abdominal CT scans, and to develop a standardized referral and notification process
Aim 1.1) Utilization of NLP to screen radiologic reports and identify nodules meeting criteria for follow-up per Fleischner Society Guidelines
Aim 1.2) Creation of an electronic medical record-based notification system to alert patients and providers of the identification of an IPN that requires follow-up, tracked by a dedicated patient navigator
Aim 1.3) Establishment of a multidisciplinary lung nodule management team, hereafter referred to as the lung nodule clinic, to ensure guideline-directed management of nodules with emphasis on high risk nodules as identified in subsequent aims
No interventions assigned to this group
Group B
Aim 2 (Part B). To clinically risk stratify patients with IPNs utilizing artificial intelligence (AI) processing of known clinical risks factors for pulmonary malignancy, such as age, smoking history, and history of malignancy, along with radiographic risk classifiers including nodule location, size, and imaging features.
Aim 2.1) Development of an integrated classifier based on automated scanning and data retrieval from the electronic medical record (EMR) to stratify patients with IPNs as low, intermediate or high risk for malignancy, with factor analysis to assess contributions of individual factors to the model
Aim 2.2) Prospective evaluation of the integrated classifier and comparison of automated integrated classifier to established manual risk calculators
No interventions assigned to this group
Group C
Aim 3 (Part C). To investigate biologic risk classifiers that may aid in the risk stratification of pulmonary nodules
Aim 3.1) Evaluation of a blood-based gene expression assay for risk stratification of pulmonary nodules using biobanked specimens
Aim 3.2) Prospective collection of plasma from patients enrolled in lung nodule clinic and evaluation of gene expression to assess malignancy risk
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Underwent an imaging study for any purpose other than lung cancer screening at a Montefiore Medical Center facility that identifies a pulmonary nodule
* Imaging results captured into the Epic electronic medical record
* Referred to the lung nodule clinic
* Underwent an in-person or telephone/telehealth encounter at a Montefiore Medical Center facility during the study period
* Lung cancers diagnosed at a Montefiore Medical Center facility between April 1, 2021 and March 31 2022 will also be included for retrospective review and comparison to the cohort of lung cancers diagnosed following the instillation of the IPN program herein described
* Above the age of 18
* Underwent an imaging study for any purpose other than lung cancer screening at a Montefiore Medical Center facility that identifies a pulmonary nodule
* Imaging results captured into the Epic electronic medical record
* Referred to the lung nodule clinic
* Underwent an in-person or telephone/telehealth encounter at a Montefiore Medical Center facility during the study period
The third part (Part C) of the study is a substudy examining plasma-based expression of markers associated with lung cancer in patients with lung nodules, and will include the following patients:
* Above the age of 18
* Underwent an imaging study, including imaging performed for lung cancer screening, at a Montefiore Medical Center facility that identifies a pulmonary nodule
* Imaging results captured into the Epic electronic medical record
* Referred to the lung nodule clinic
* Underwent an in-person or telephone/telehealth encounter at a Montefiore Medical Center facility during the study period
* Recommended follow-up by means of either continued imaging surveillance of nodule or procedure for diagnosis, including percutaneous transthoracic needle biopsy, endobronchial transbronchial needle aspiration, surgical biopsy or resection of pulmonary nodule
Exclusion Criteria
* No evidence of a lung nodule through imaging
18 Years
ALL
No
Sponsors
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Montefiore Medical Center
OTHER
Responsible Party
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Principal Investigators
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Neel Chudgar, MD
Role: PRINCIPAL_INVESTIGATOR
Montefiore Medical Center
Locations
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Montefiore Medical Center-Albert Einstein College of Medicine
The Bronx, New York, United States
Countries
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References
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Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021 Jan;71(1):7-33. doi: 10.3322/caac.21654. Epub 2021 Jan 12.
National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
US Preventive Services Task Force; Krist AH, Davidson KW, Mangione CM, Barry MJ, Cabana M, Caughey AB, Davis EM, Donahue KE, Doubeni CA, Kubik M, Landefeld CS, Li L, Ogedegbe G, Owens DK, Pbert L, Silverstein M, Stevermer J, Tseng CW, Wong JB. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021 Mar 9;325(10):962-970. doi: 10.1001/jama.2021.1117.
Jemal A, Fedewa SA. Lung Cancer Screening With Low-Dose Computed Tomography in the United States-2010 to 2015. JAMA Oncol. 2017 Sep 1;3(9):1278-1281. doi: 10.1001/jamaoncol.2016.6416.
Lake M, Shusted CS, Juon HS, McIntire RK, Zeigler-Johnson C, Evans NR, Kane GC, Barta JA. Black patients referred to a lung cancer screening program experience lower rates of screening and longer time to follow-up. BMC Cancer. 2020 Jun 16;20(1):561. doi: 10.1186/s12885-020-06923-0.
Wiener RS, Gould MK, Slatore CG, Fincke BG, Schwartz LM, Woloshin S. Resource use and guideline concordance in evaluation of pulmonary nodules for cancer: too much and too little care. JAMA Intern Med. 2014 Jun;174(6):871-80. doi: 10.1001/jamainternmed.2014.561.
Hanchate AD, Dyer KS, Paasche-Orlow MK, Banerjee S, Baker WE, Lin M, Xue WD, Feldman J. Disparities in Emergency Department Visits Among Collocated Racial/Ethnic Medicare Enrollees. Ann Emerg Med. 2019 Mar;73(3):225-235. doi: 10.1016/j.annemergmed.2018.09.007. Epub 2018 Oct 26.
Schut RA, Mortani Barbosa EJ Jr. Racial/Ethnic Disparities in Follow-Up Adherence for Incidental Pulmonary Nodules: An Application of a Cascade-of-Care Framework. J Am Coll Radiol. 2020 Nov;17(11):1410-1419. doi: 10.1016/j.jacr.2020.07.018. Epub 2020 Aug 7.
Su CT, Bhargava A, Shah CD, Halmos B, Gucalp RA, Packer SH, Ohri N, Haramati LB, Perez-Soler R, Cheng H. Screening Patterns and Mortality Differences in Patients With Lung Cancer at an Urban Underserved Community. Clin Lung Cancer. 2018 Sep;19(5):e767-e773. doi: 10.1016/j.cllc.2018.05.019. Epub 2018 Jun 5.
Dziadzko MA, Novotny PJ, Sloan J, Gajic O, Herasevich V, Mirhaji P, Wu Y, Gong MN. Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Crit Care. 2018 Oct 30;22(1):286. doi: 10.1186/s13054-018-2194-7.
Kammer MN, Massion PP. Noninvasive biomarkers for lung cancer diagnosis, where do we stand? J Thorac Dis. 2020 Jun;12(6):3317-3330. doi: 10.21037/jtd-2019-ndt-10.
Gould MK, Tang T, Liu IL, Lee J, Zheng C, Danforth KN, Kosco AE, Di Fiore JL, Suh DE. Recent Trends in the Identification of Incidental Pulmonary Nodules. Am J Respir Crit Care Med. 2015 Nov 15;192(10):1208-14. doi: 10.1164/rccm.201505-0990OC.
MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, Mehta AC, Ohno Y, Powell CA, Prokop M, Rubin GD, Schaefer-Prokop CM, Travis WD, Van Schil PE, Bankier AA. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017 Jul;284(1):228-243. doi: 10.1148/radiol.2017161659. Epub 2017 Feb 23.
Kang SK, Garry K, Chung R, Moore WH, Iturrate E, Swartz JL, Kim DC, Horwitz LI, Blecker S. Natural Language Processing for Identification of Incidental Pulmonary Nodules in Radiology Reports. J Am Coll Radiol. 2019 Nov;16(11):1587-1594. doi: 10.1016/j.jacr.2019.04.026. Epub 2019 May 24.
Mobiny A, Yuan P, Cicalese PA, Moulik SK, Garg N, Wu CC, Wong K, Wong ST, He TC, Nguyen HV. Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification. IEEE Trans Med Imaging. 2021 Oct;40(10):2869-2879. doi: 10.1109/TMI.2021.3051089. Epub 2021 Sep 30.
Silvestri GA, Tanner NT, Kearney P, Vachani A, Massion PP, Porter A, Springmeyer SC, Fang KC, Midthun D, Mazzone PJ; PANOPTIC Trial Team. Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial. Chest. 2018 Sep;154(3):491-500. doi: 10.1016/j.chest.2018.02.012. Epub 2018 Mar 1.
Other Identifiers
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2021-13475
Identifier Type: -
Identifier Source: org_study_id
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