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Background: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations.Methods: Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis.Results: Risk-based screening strategies requiring similar screens among individuals ages 55-80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%.Conclusions: Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages. [ABSTRACT FROM AUTHOR]

Background: Prehospital telemedicine triage systems combined with machine learning (ML) methods have the potential to improve triage accuracy and safely redirect low-acuity patients from attending the emergency department. However, research in prehospital settings is limited but needed; emergency department overcrowding and adverse patient outcomes are increasingly common., Objective: In this scoping review, we sought to characterize the existing methods for ML-enhanced telemedicine emergency triage. In order to support future research, we aimed to delineate what data sources, predictors, labels, ML models, and performance metrics were used, and in which telemedicine triage systems these methods were applied., Methods: A scoping review was conducted, querying multiple databases (MEDLINE, PubMed, Scopus, and IEEE Xplore) through February 24, 2023, to identify potential ML-enhanced methods, and for those eligible, relevant study characteristics were extracted, including prehospital triage setting, types of predictors, ground truth labeling method, ML models used, and performance metrics. Inclusion criteria were restricted to the triage of emergency telemedicine services using ML methods on an undifferentiated (disease nonspecific) population. Only primary research studies in English were considered. Furthermore, only those studies using data collected remotely (as opposed to derived from physical assessments) were included. In order to limit bias, we exclusively included articles identified through our predefined search criteria and had 3 researchers (DR, JS, and KS) independently screen the resulting studies. We conducted a narrative synthesis of findings to establish a knowledge base in this domain and identify potential gaps to be addressed in forthcoming ML-enhanced methods., Results: A total of 165 unique records were screened for eligibility and 15 were included in the review. Most studies applied ML methods during emergency medical dispatch (7/15, 47%) or used chatbot applications (5/15, 33%). Patient demographics and health status variables were the most common predictors, with a notable absence of social variables. Frequently used ML models included support vector machines and tree-based methods. ML-enhanced models typically outperformed conventional triage algorithms, and we found a wide range of methods used to establish ground truth labels., Conclusions: This scoping review observed heterogeneity in dataset size, predictors, clinical setting (triage process), and reported performance metrics. Standard structured predictors, including age, sex, and comorbidities, across articles suggest the importance of these inputs; however, there was a notable absence of other potentially useful data, including medications, social variables, and health system exposure. Ground truth labeling practices should be reported in a standard fashion as the true model performance hinges on these labels. This review calls for future work to form a standardized framework, thereby supporting consistent reporting and performance comparisons across ML-enhanced prehospital triage systems., (©Daniel Raff, Kurtis Stewart, Michelle Christie Yang, Jessie Shang, Sonya Cressman, Roger Tam, Jessica Wong, Martin C Tammemägi, Kendall Ho. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 11.09.2024.)

Background: The PLCO m2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCO m2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies., Methods: We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCO m2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCO m2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively., Results: Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCO m2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCO m2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCO m2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%)., Interpretation: In a cohort of Quebec smokers, the PLCO m2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution., Competing Interests: Competing interests: Nicole Ezer reports funding from the Canadian Institutes of Health Research and Rossy Cancer Network, a speaker fee from GSK, advisory board participation with GSK and receipt of study materials from COVIS Pharma. Martin Tammemägi developed the PLCOm2012 lung cancer risk prediction models (and related models), used in the current study. To date, he has not received any money for use of the model, nor does he anticipate any payments in the future. No other competing interests were declared., (© 2023 CMA Impact Inc. or its licensors.)

Background: Lung cancer is the leading cause of cancer mortality globally. Early detection through risk-based screening can markedly improve prognosis. However, most risk models were developed in North American cohorts of smokers, whereas less is known about risk profiles for never-smokers, which represent a growing proportion of lung cancers, particularly in Asian populations., Methods: Based on the China Kadoorie Biobank, a population-based prospective cohort of 512 639 adults with up to 12 years of follow-up, we built Asian Lung Cancer Absolute Risk Models (ALARM) for lung cancer mortality using flexible parametric survival models, separately for never and ever-smokers, accounting for competing risks of mortality. Model performance was evaluated in a 25% hold-out test set using the time-dependent area under the curve and by comparing model-predicted and observed risks for calibration., Results: Predictors assessed in the never-smoker lung cancer mortality model were demographics, body mass index, lung function, history of emphysema or bronchitis, personal or family history of cancer, passive smoking, and indoor air pollution. The ever-smoker model additionally assessed smoking history. The 5-year areas under the curve in the test set were 0.77 (95% confidence interval = 0.73 to 0.80) and 0.81 (95% confidence interval = 0.79 to 0.84) for ALARM-never-smokers and ALARM-ever smokers, respectively. The maximum 5-year risk for never and ever-smokers was 2.6% and 12.7%, respectively., Conclusions: This study is among the first to develop risk models specifically for Asian populations separately for never and ever-smokers. Our models accurately identify Asians at high risk of lung cancer death and may identify those with risks exceeding common eligibility thresholds who may benefit from screening., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: With advancing therapeutics, lung cancer (LC) survivors are rapidly increasing in number. Although mounting evidence suggests LC survivors have high risk of second primary lung cancer (SPLC), there is no validated prediction model available for clinical use to identify high-risk LC survivors for SPLC., Methods: Using data from 6325 ever-smokers in the Multiethnic Cohort (MEC) study diagnosed with initial primary lung cancer (IPLC) in 1993-2017, we developed a prediction model for 10-year SPLC risk after IPLC diagnosis using cause-specific Cox regression. We evaluated the model's clinical utility using decision curve analysis and externally validated it using 2 population-based data-Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST)-that included 2963 and 2844 IPLC (101 and 93 SPLC cases), respectively., Results: Over 14 063 person-years, 145 (2.3%) ever-smoking IPLC patients developed SPLC in MEC. Our prediction model demonstrated a high predictive accuracy (Brier score = 2.9, 95% confidence interval [CI] = 2.4 to 3.3) and discrimination (area under the receiver operating characteristics [AUC] = 81.9%, 95% CI = 78.2% to 85.5%) based on bootstrap validation in MEC. Stratification by the estimated risk quartiles showed that the observed SPLC incidence was statistically significantly higher in the 4th vs 1st quartile (9.5% vs 0.2%; P < .001). Decision curve analysis indicated that in a wide range of 10-year risk thresholds from 1% to 20%, the model yielded a larger net-benefit vs hypothetical all-screening or no-screening scenarios. External validation using PLCO and NLST showed an AUC of 78.8% (95% CI = 74.6% to 82.9%) and 72.7% (95% CI = 67.7% to 77.7%), respectively., Conclusions: We developed and validated a SPLC prediction model based on large population-based cohorts. The proposed prediction model can help identify high-risk LC patients for SPLC and can be incorporated into clinical decision making for SPLC surveillance and screening., (© The Author(s) 2021. Published by Oxford University Press.)

Introduction: Eligibility criteria for lung cancer screening based solely on age and smoking history are less sensitive than validated risk prediction models. The U.S. Preventive Services Task Force (USPSTF) has proposed new guidelines to improve the sensitivity for selecting high-risk individuals and to decrease race disparity. In this retrospective study, termed the Chicago Race Eligibility for Screening Cohort, we compare the sensitivity of the proposed USPSTF2020 criteria versus the PLCOm2012 risk prediction model for selecting a racially diverse lung cancer population with a smoking history for lung cancer screening., Methods: This Chicago Race Eligibility for Screening Cohort study applies the PLCOm2012 model with a risk threshold of 1.0%/6 years and the USPSTF2020 criteria (age 50-80 y, pack-years ≥ 20 y, quit-years ≤ 15 y) to 883 individuals with a smoking history diagnosed with having lung cancer., Results: The PLCOm2012 was more sensitive than the USPSTF2020 overall (79.1% versus 68.6%, p < 0.0001) in White (81.5% versus 75.4%, p  = 0.029) and in African American (82.8% versus 70.6% p < 0.0001) individuals. Of the total cohort, 254 (28.8%) would not have qualified owing to less than 20 pack-years, quit-time of more than 15 years, and age less than 50 years. Of these 254 cases, 40% would have qualified by the PLCOm2012 model. For the 20 pack-year criterion, of the 497 African American individuals, 19.3% did not meet this criterion, and of these, an additional 31.3% would have qualified by the PLCOm2012 model ( p  = 0.002)., Conclusions: Although more sensitive than USPSTF2013, the proposed USPSTF2020 draft guidelines still have a race disparity in eligibility for screening. This study provides "real world" evidence that use of the PLCOm2012 risk prediction model eliminates this race disparity., (© 2020 The Authors.)

Introduction: Smoking cessation activities incorporated into lung cancer screening programs have been broadly recommended, but studies to date have not exhibited increased quit rates associated with cessation programs in this setting. We aimed to determine the long-term effectiveness of smoking cessation counseling in smokers presenting for lung cancer screening., Methods: This was a randomized control trial of an intensive, telephone-based smoking cessation counseling intervention incorporating lung cancer screening results versus usual care (information pamphlet). This analysis reports on the long-term impact (24-mo) of the intervention on abstinence from smoking., Results: A total of 337 active smokers who participated in the screening study were randomized to active smoking cessation counseling (n = 171) or control arm (n = 174) and completed a 24-month assessment. The 30-day smoking abstinence rates at 24 months postrandomization was 18.3% and 21.4% in the control and intervention arms, respectively-a 3.1% difference (95% confidence interval: -5.4 to 11.6, p  = 0.48). No statistically significant differences in the 7-day abstinence, the use of pharmacologic cessation aids, nicotine replacement therapies, nor intent to quit in the following 30 days were noted ( p > 0.05). The abstinence rates at 24-months were higher overall than at 12-months (19.9% versus 13.3%, p < 0.001), and smoking intensity was lower than at baseline for ongoing smokers., Conclusions: A telephone-based smoking cessation counseling intervention incorporating lung cancer screening results did not result in increased long-term cessation rates versus written information alone in unselected smokers undergoing lung cancer screening. Overall, quit rates were high and continued to improve throughout participation in the screening program. (ClinicalTrials.govNCT02431962)., (© 2020 The Authors.)

Background: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations., Methods: Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis., Results: Risk-based screening strategies requiring similar screens among individuals ages 55-80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%., Conclusions: Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages., (© The Author(s) 2019. Published by Oxford University Press.)