The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation.

BACKGROUND: Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation. RESEARCH QUESTION: Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models? STUDY DESIGN AND METHODS: Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots. RESULTS: Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23. INTERPRETATION: The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.

Low hospital mobility-resurgence of an old epidemic within a new pandemic and future solutions.

Low mobility during hospitalisation poses risks of functional decline and other poor outcomes for older adults. Given the pervasiveness of this problem, low mobility during hospitalisation was first described as 'dangerous' in 1947 and later described as an epidemic. Hospitals have made considerable progress over the last half-century and the last two decades in particular, however, the COVID-19 pandemic presents serious new challenges that threaten to undermine recent efforts and progress towards a culture of mobility. In this special article, we address the question of how to confront an epidemic of immobility within a pandemic. We identify four specific problems for creating and advancing a culture of mobility posed by COVID-19: social distancing and policies restricting patient movement, personnel constraints, personal protective equipment shortages and increased patient hesitancy to ambulate. We also propose four specific solutions to address these problems. These approaches will help support a culture of healthy mobility during and after hospitalisation and help patients to keep moving during the pandemic and beyond.

Improving the assessment and documentation of patient mobility using a quality improvement framework.

OBJECTIVE: To implement a system for assessing and documenting patient mobility in an inpatient geriatric unit using a quality improvement framework. METHODS: Whiteboards incorporating the Johns Hopkins Highest Level of Mobility scale were placed on each door of the unit. Staff were trained to assess and document patient mobility, and documentation compliance was measured. Nurses were surveyed to assess perceived burden of the system. Fall rates were calculated and analyzed for change from baseline. RESULTS: Median daily documentation rates reached 79% by the end of the project. Surveys indicated a low perceived burden of the system. Fall rates did not increase when compared to the previous year baseline (p = 0.80) and the analogous time frames during the previous two years (p = 0.84). CONCLUSION: A quality improvement framework may be used to improve mobility assessment and documentation in a geriatric unit without increasing patient falls or nursing burden.

The Impact of an Interventional Pulmonary Program on Nontherapeutic Lung Resections.

BACKGROUND: Pulmonary resection can concurrently diagnose and treat known or suspected lung cancer, but is not without risk. Benign resection rates range widely (9% to 40%). We evaluated the impact of an Interventional Pulmonology (IP) program and dedicated Pulmonary Nodule Clinic on surgical benign resection rates at a single institution. METHODS: An IP program was initiated in August 2010 that offered advanced diagnostic techniques and a dedicated Pulmonary Nodule Clinic was opened in August 2013. We retrospectively reviewed all patients who underwent resection for known or suspected lung cancer between 2005 and 2015 at our tertiary referral hospital. Demographics, preoperative tissue diagnoses, surgical procedure, final pathology, and staging were collected. Quarterly benign resection rates were calculated and plotted on a statistical quality control chart (P-Chart) to determine the impact of the IP program and Pulmonary Nodule Clinic on benign resection rates over time. RESULTS: Of 1112 resections, 209 (19%) were benign. Variation in quarterly benign resection rates decreased after introduction of the IP program in 2010, and a significant (P<0.05) sustained decrease in the quarterly benign resection rate occurred after introduction of the pulmonary nodule clinic in 2013 to a new baseline of 12% compared with 24% before 2010. After introduction of the IP program, mean quarterly preoperative tissue diagnostic rates increased from 45% to 58% (P<0.01). CONCLUSION: Integration of an IP program employing advanced diagnostic bronchoscopic techniques has improved preoperative diagnostic rates of suspicious pulmonary nodules and in combination with a pulmonary nodule clinic has resulted in fewer benign resections.

Assessment of Fluorodeoxyglucose F18-Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules.

Importance: Clinicians rely heavily on fluorodeoxyglucose F18-labeled positron emission tomography (FDG-PET) imaging to evaluate lung nodules suspicious for cancer. We evaluated the performance of FDG-PET for the diagnosis of malignancy in differing populations with varying cancer prevalence. Objective: To determine the performance of FDG-PET/computed tomography (CT) in diagnosing lung malignancy across different populations with varying cancer prevalence. Design, Setting, and Participants: Multicenter retrospective cohort study at 6 academic medical centers and 1 Veterans Affairs facility that comprised a total of 1188 patients with known or suspected lung cancer from 7 different cohorts from 2005 to 2015. Exposures: 18F fluorodeoxyglucose PET/CT imaging. Main Outcome and Measures: Final diagnosis of cancer or benign disease was determined by pathological tissue diagnosis or at least 18 months of stable radiographic follow-up. Results: Most patients were male smokers older than 60 years. Overall cancer prevalence was 81% (range by cohort, 50%-95%). The median nodule size was 22 mm (interquartile range, 15-33 mm). Positron emission tomography/CT sensitivity and specificity were 90.1% (95% CI, 88.1%-91.9%) and 39.8% (95% CI, 33.4%-46.5%), respectively. False-positive PET scans occurred in 136 of 1188 patients. Positive predictive value and negative predictive value were 86.4% (95% CI, 84.2%-88.5%) and 48.7% (95% CI, 41.3%-56.1%), respectively. On logistic regression, larger nodule size and higher population cancer prevalence were both significantly associated with PET accuracy (odds ratio, 1.027; 95% CI, 1.015-1.040 and odds ratio, 1.030; 95% CI, 1.021-1.040, respectively). As the Mayo Clinic model-predicted probability of cancer increased, the sensitivity and positive predictive value of PET/CT imaging increased, whereas the specificity and negative predictive value dropped. Conclusions and Relevance: High false-positive rates were observed across a range of cancer prevalence. Normal PET/CT scans were not found to be reliable indicators of the absence of disease in patients with a high probability of lung cancer. In this population, aggressive tissue acquisition should be prioritized using a comprehensive lung nodule program that emphasizes advanced tissue acquisition techniques such as CT-guided fine-needle aspiration, navigational bronchoscopy, and endobronchial ultrasonography.