Prospective trial of Functional Lung Avoidance Radiation Therapy for Lung Cancer: Quality of Life Report

PURPOSE

A novel form of lung function imaging has been developed that uses 4DCT data to generate lung ventilation images (4DCT-ventilation). Functional avoidance uses 4DCT-ventilation to reduce doses to functional lung with the aim of reducing pulmonary side-effects. A 4DCT-ventilation functional avoidance, phase II, multi-center clinical trial was completed. The purpose of this work is to quantify patient reported outcomes (PRO) changes for patients treated with functional avoidance and to determine which metrics are predictive of PRO changes.

MATERIALS AND METHODS

Patients with locally advanced lung cancer receiving curative intent radiotherapy were accrued. Each patient had a 4DCT-ventilation image generated using 4DCT data and image processing. PRO instruments included the Functional Assessment of Cancer Therapy-Lung (FACT-L) questionnaire, administered pre-treatment, 3, 6, and 12 months post-treatment. FACT-TOI (Trial Outcome Index) and the FACT-LCS (Lung Cancer Subscale) percentage of clinically meaningful declines (CMD) were determined. A linear mixed-effects model was used to determine which patient, clinical, dose, and dose-function metrics were predictive of PRO decline.

RESULTS

59 patients completed baseline PRO surveys. 83% of patients had non-small-cell lung cancer, with 75% having stage III disease. The median dose was 60 Gy in 30 fractions. CMD FACT-TOI decline was 46.3%, 38.5%, and 26.8%, at 3, 6, and 12 months, respectively. CMD FACT-LCS decline was 33.3%, 33.3%, and 29.3%, at 3, 6, and 12 months, respectively. While an increase in most dose and dose-function parameters was associated with a modest decline in PROs, none of the results were significant (all p>0.053).

CONCLUSION

The current work provides an innovative combination of functional avoidance and PROs and is the first report of PROs for patients treated with prospective 4DCT-ventilation functional avoidance. Approximately 30% of patients had clinically significant decline in PROs at 12 months. The study provides additional data on outcomes with 4DCT-ventilation functional avoidance.

Achievement of Target Gain Larger than Unity in an Inertial Fusion Experiment

On December 5, 2022, an indirect drive fusion implosion on the National Ignition Facility (NIF) achieved a target gain G_{target} of 1.5. This is the first laboratory demonstration of exceeding "scientific breakeven" (or G_{target}>1) where 2.05 MJ of 351 nm laser light produced 3.1 MJ of total fusion yield, a result which significantly exceeds the Lawson criterion for fusion ignition as reported in a previous NIF implosion [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129, 075001 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.075001]. This achievement is the culmination of more than five decades of research and gives proof that laboratory fusion, based on fundamental physics principles, is possible. This Letter reports on the target, laser, design, and experimental advancements that led to this result.

Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database

PURPOSE

A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods.

METHODS AND MATERIALS

The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]).

RESULTS

Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively.

CONCLUSIONS

Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.

Reactogenicity, pregnancy outcomes, and SARS-CoV-2 infection following COVID-19 vaccination during pregnancy in Canada: A national prospective cohort study

OBJECTIVE

To describe self-reported reactogenicity, pregnancy outcomes, and SARS-CoV-2 infection following COVID-19 vaccination during pregnancy.

DESIGN

National, prospective cohort study.

SETTING

Participants across Canada were enrolled from July 2021 until June 2022.

POPULATION

Individuals pregnant during the COVID-19 pandemic, regardless of vaccination status, were included.

METHODS

The Canadian COVID-19 Vaccine Registry for Pregnant and Lactating Individuals (COVERED) was advertised through traditional and social media. Surveys were administered at baseline, following each vaccine dose if vaccinated, pregnancy conclusion, and every two months for 14 months. Changes to pregnancy or vaccination status, SARS-CoV-2 infections, or significant health events were recorded.

MAIN OUTCOME MEASURES

Reactogenicity (local and systemic adverse events, and serious adverse events) within 1 week post-vaccination, pregnancy and neonatal outcomes, and subsequent SARS-CoV-2 infection.

RESULTS

Among 2868 participants who received 1-2 doses of a COVID-19 vaccine during pregnancy, adverse events described included: headache (19.5-33.9%), nausea (4.8-13.8%), fever (2.7-10.2%), and myalgia (33.4-42.2%). Reactogenicity was highest after the 2nd dose of vaccine in pregnancy. Compared to 1660 unvaccinated participants, there were no statistically significant differences in adverse pregnancy or infant outcomes, aside from an increased risk of NICU admission ≥ 24 h among the unvaccinated group. During follow-up, there was a higher rate of participant-reported SARS-CoV-2 infection in the unvaccinated compared to the vaccinated group (18[47.4%] vs. 786[27.3%]).

CONCLUSIONS

Participant-reported reactogenicity was similar to reports from non-pregnant adults. There was no increase in adverse pregnancy and birth outcomes among vaccinated vs. unvaccinated participants and lower rates of SARS-CoV-2 infection were reported in vaccinated participants.

TWEETABLE ABSTRACT

No significant increase in adverse pregnancy or infant outcomes among vaccinated versus unvaccinated pregnant women in Canada.

Comprehensive Quality of Life Report from a Prospective Clinical Trial of 4DCT-Ventilation Functional Lung Avoidance Radiation Therapy

PURPOSE/OBJECTIVE(S)

Functional imaging has been developed that uses 4DCT images and image processing to generate lung ventilation maps (4DCT-ventilation). 4DCT-ventilation functional avoidance uses 4DCT-images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxicity. A 4DCT-ventilation functional avoidance, phase II, multi-center clinical trial was completed, and patient reported outcomes (PRO) measured. PROs are an essential measure of quality-of-life following radiotherapy. The purpose of this work is to quantify PRO changes for lung cancer patients treated with functional avoidance and to compare PROs against clinical pneumonitis.

MATERIALS/METHODS

Patients with locally advanced lung cancer receiving curative intent radiotherapy (prescriptions of 45-75 Gy) and chemotherapy were accrued. Each patient had a 4DCT-ventilation image generated using 4DCT data. Favorable arc geometry and optimization techniques were used to generate functional avoidance plans. PRO instruments included the Functional Assessment of Cancer Therapy Lung (FACT-L) questionnaire and the Visual Analog Scale (VAS) administered pre-treatment and 3-, 6-, and 12-months post-treatment to gather data on physical, social, emotional, functional, and pulmonary well-being. The percentage of patients with clinically significant decline was calculated using the FACT-TOI (Trial Outcome Index), FACT-LCS (Lung Cancer Subscale), and VAS instruments. To evaluate the correlation between PROs and clinical toxicity, the percentage of clinically significant FACT-LCS decline was compared (Chi-square test) for patients who did or did not experience grade 2+ pneumonitis.

RESULTS

Fifty-nine patients completed baseline PRO surveys. Median age was 65, 83% of patients had non-small-cell lung cancer, with 75% having stage III disease. Clinically significant FACT-TOI decline at, 3, 6, and 12 months was 46.3%, 38.5%, and 26.8%, respectively. The percentage of patients with clinically significant FACT-LCS decline was 33.3%, 33.3%, and 29.3%, at 3 months, 6 months, and 12 months, respectively. The percentage of patients with clinically significant VAS decline at 3, 6, and 12 months was 18.9%, 20.0%, and 18.6%, respectively. Patients who experienced grade 2+ pneumonitis had a greater percentage of clinically significant decline at all time-points with the results reaching significance (p = 0.045) at 6 months.

CONCLUSION

The study presents the first comprehensive evaluation of PROs for patients treated with 4DCT-ventilation functional avoidance. The data show that 20-40% of patients had clinically significant decline and that PROs had a strong correlation with pneumonitis. The PRO data demonstrate that functional avoidance results in low rates of patient reported outcome clinical decline and provide seminal results to be used in phase III studies.

Evaluation of variables predicting PFT changes for lung cancer patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial

PURPOSE

Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional regions of the lung. A phase-II, multi-center, prospective study of 4DCT-ventilation functional avoidance was completed. Pre and post-treatment pulmonary function tests (PFTs) were acquired and assessed pulmonary function change. This study aims to evaluate which clinical, dose and dose-function factors predict PFT changes for patients treated with 4DCT-ventilation functional avoidance radiotherapy.

MATERIALS AND METHODS

56 patients with locally advanced lung cancer receiving radiotherapy were accrued. PFTs were obtained at baseline and three months following radiotherapy and included forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The ability of patient, clinical, dose (lung and heart), and dose-function metrics (metrics that combine dose and 4DCT-ventilation-based function) to predict PFT changes were evaluated using univariate and multivariate linear regression.

RESULTS

Univariate analysis showed that only dose-function metrics and the presence of chronic obstructive pulmonary disease (COPD) were significant (p<0.05) in predicting FEV1 decline. Multivariate analysis identified a combination of clinical (immunotherapy status, presence of thoracic comorbidities, smoking status, and age), along with lung dose, heart dose, and dose-function metrics in predicting FEV1 and FEV1/FVC changes.

CONCLUSION

The current work evaluated factors predicting PFT changes for patients treated in a prospective functional avoidance radiotherapy study. The data revealed that lung dose- function metrics could predict PFT changes, validating the significance of reducing the dose to the functional lung to mitigate the decline in pulmonary function and providing guidance for future clinical trials.

AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load-displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load-displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.

Cardiac metabolic changes on 18 F-positron emission tomography after thoracic radiotherapy predict for overall survival in esophageal cancer patients

PURPOSE

Heart doses have been shown to be predictive of cardiac toxicity and overall survival (OS) for esophageal cancer patients. There is potential for functional imaging to provide valuable cardiac information. The purpose of this study was to evaluate the cardiac metabolic dose-response using 18 F-deoxyglucose (FDG)-PET and to assess whether standard uptake value (SUV) changes in the heart were predictive of OS.

METHODS

Fifty-one patients with esophageal cancer treated with radiation who underwent pre- and post-treatment FDG-PET scans were retrospectively evaluated. Pre- and post-treatment PET-scans were rigidly registered to the planning CT for each patient. Pre-treatment to post-treatment absolute mean SUV (SUVmean) changes in the heart were calculated to assess dose-response. A dose-response curve was generated by binning each voxel in the heart into 10 Gy dose-bins and analyzing the SUVmean changes in each dose-bin. Multivariate cox proportional hazard models were used to assess whether pre-to-post treatment cardiac SUVmean changes predicted for OS.

RESULTS

The cardiac dose-response curve demonstrated a trend of increasing cardiac SUV changes as a function of dose with an average increase of 0.044 SUV for every 10 Gy dose bin. In multivariate analysis, disease stage and SUVmean change in the heart were predictive (p < 0.05) for OS.

CONCLUSIONS

Changes in pre- to post-treatment cardiac SUV were predictive of OS with patients having a higher pre- to post-treatment cardiac SUV change surviving longer.

Use of Hotels as a Disposition Alternative to Hospital Admission for Undomiciled Patients Undergoing SARS-CoV-2 Testing

INTRODUCTION

The coronavirus 2019 (COVID-19) pandemic has presented various unprecedented challenges to healthcare systems globally, prompting society to adopt new preventative strategies to curb spread of the disease. Those experiencing homelessness have been particularly impacted because of barriers to practicing social distancing, inability to isolate, and poor access to care. Project Roomkey was established in California as a statewide measure to provide non-congregate shelter options for individuals experiencing homelessness to properly quarantine. On goal in this study was to analyze the effectiveness of hotel rooms as a safe disposition alternative to hospital admission for patients experiencing homelessness and who were also positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

This was a retrospective, observational study that included chart review of patients who were discharged to the hotel from March 2020-December 2021. We recorded demographic information, index visit details, number of emergency department (ED) visits both a month prior to and following the index visit, admission rates, and number of deaths.

RESULTS

During this 21-month study period, a total of 2,015 patients who identified as undomiciled were tested for SARS-COV-2 in the ED for various reasons. Of those patients, 83 were discharged from the ED to the hotel. Of the 83 patients, 40 (48.2%) ultimately tested positive for SARS-CoV-2 during their index visit. Two patients returned to the ED within seven days with COVID-19-related symptoms, and 10 patients within 30 days. Two patients required subsequent admission with COVID-19 pneumonia. No deaths were recorded within the 30-day follow-up period.

CONCLUSION

The availability of a hotel served as a safe alternative to hospital admission for patients experiencing homelessness and who were either suspected or confirmed to have COVID-19. It is reasonable to consider similar measures in the management of other transmissible diseases for patients experiencing homelessness who require isolation.

Perceptions and Practices of Cannabis Use Among Emergency Department Patients

BACKGROUND

Nationally, in states where cannabis has been legalized, increases in cannabis-related hospitalizations and emergency department (ED) visits have also been observed.

OBJECTIVES

This study aims to: 1) Describe the sociodemographic characteristics of cannabis users presenting to two academic EDs in California; 2) Assess cannabis-related behaviors; 3) Assess perceptions of cannabis; 4) Identify and describe reasons for cannabis-related ED utilization.

METHODS

This is a cross-sectional study of patients visiting one of two academic EDs between February 16, 2018 and November 21, 2020. Eligible participants completed a novel questionnaire developed by the authors. Basic descriptive statistics, Pearson correlation coefficients, and logistic regression were used for statistical analysis of responses.

RESULTS

The questionnaire was completed by 2577 patients. A quarter were categorized as Current Users (n = 628, 24.4%). Current, Regular Users were evenly divided across gender, were relatively younger (18-34 years, 48.1%), and were largely non-Hispanic Caucasian. Over half of all respondents believed that the use of cannabis was less harmful than tobacco or alcohol use (n = 1537, 59.6%). One in five Current Users (n = 123, 19.8%) reported driving while using cannabis in the past month. A small proportion (n = 24, 3.9%) of Current Users reported ever visiting the ED for a cannabis-related chief complaint.

CONCLUSIONS

Overall, many ED patients are currently using cannabis; few report utilizing the ED due to cannabis-related problems. Current, Irregular Users may represent the ideal target group for ED-based educational efforts aimed at improving knowledge of safe cannabis use.

Characterizing Pulmonary Function Test Changes for Patients With Lung Cancer Treated on a 2-Institution, 4-Dimensional Computed Tomography-Ventilation Functional Avoidance Prospective Clinical Trial

Purpose

Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy.

Methods and Materials

Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEV1), and forced vital capacity (FVC). PFT metrics were compared for patients who did and did not experience grade 2 or higher pneumonitis.

Results

Fifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEV1, and FVC was -11.6% ± 14.2%, -5.6% ± 16.9%, and -9.0% ± 20.1%, respectively. The mean change in DLCO was -15.4% ± 14.4% for patients with grade 2 or higher radiation pneumonitis and -10.8% ± 14.1% for patients with grade <2 radiation pneumonitis (P = .37). The mean change in FEV1 was -14.3% ± 22.1% for patients with grade 2 or higher radiation pneumonitis and -3.9% ± 15.4% for patients with grade <2 radiation pneumonitis (P = .09).

Conclusions

The current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV1. The presented data can help elucidate the potential pulmonary function improvement with functional avoidance radiation therapy.

Is individual perfusion dose-response different than ventilation dose-response for lung cancer patients treated with radiotherapy?

OBJECTIVE

Current ventilation and perfusion dose-response studies focus on single-modalities (ventilation or perfusion) and perform pulmonary-toxicity assessment related to radiotherapy on a population-based basis. This study aims at quantitative and clinical evaluation of intrapatient differences between ventilation and perfusion dose-responses among lung cancer patients treated with radiotherapy.

METHODS

20 patients enrolled on a prospective functional avoidance protocol underwent single photon emission computed tomography-CT ventilation and perfusion scans pre- and post-radiotherapy. Relative changes in pre- to post-treatment ventilation and perfusion in lung regions receiving ≥20 Gy were calculated. In addition, the slopes of the linear fit to the relative ventilation and perfusion changes in regions receiving 0-60 Gy were calculated. A radiologist read and assigned a functional defect score to pre- and post-treatment ventilation/perfusion scans.

RESULTS

25% of patients had a difference >35% between ventilation and perfusion pre- to post-treatment changes and 20-30% of patients had opposite directions for ventilation and perfusion pre- to post-treatment changes. Using a semi-quantitative scale, radiologist assessment showed that 20% of patients had different pre- to post-treatment ventilation changes when compared to pre- to post-treatment perfusion changes.

CONCLUSION

Our data showed that ventilation dose-response can differ from perfusion dose-response for 20-30% of patients. Therefore, when performing thoracic dose-response in cancer patients, it is insufficient to look at ventilation or perfusion alone; but rather both modes of functional imaging may be needed when predicting for clinical outcomes.

ADVANCES IN KNOWLEDGE

The significance of this study can be highlighted by the differences between the intrapatient dose-response assessments of this analysis compared to existing population-based dose-response analyses. Elucidating intrapatient ventilation and perfusion dose-response differences may be valuable in predicting pulmonary toxicity in lung cancer patients post-radiotherapy.

Development and validation of a respiratory-responsive vocal biomarker-based tool for generalizable detection of respiratory impairment: independent case-control studies in multiple respiratory conditions including asthma, chronic obstructive pulmonary disease, and COVID-19

BACKGROUND

Vocal biomarker-based machine learning approaches have shown promising results in detecting various health conditions, including respiratory diseases such as asthma. In this study, we aim to validate a respiratory-responsive vocal-biomarker (RRVB) platform initially trained on an asthma and healthy volunteer dataset for its ability to differentiate, without modification, active COVID-19 infection vs. healthy volunteers in patients presenting to hospitals in the US and India.

OBJECTIVE

The objective of this study was to determine whether the RRVB model can differentiate patients with active COVID-19 infection vs. asymptomatic healthy volunteers by assessing its sensitivity, specificity, and odds ratio. Another objective was to evaluate whether the RRVB model outputs correlate with symptom severity in COVID-19.

METHODS

A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a dataset of about 1,700 patients with a confirmed asthma diagnosis vs. a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and cough. In the present study, a total of 497 participants (46% male, 54% female; 94% < 65 years, 6% >= 65 years; 51% Marathi, 45% English, 5% Spanish speakers) were enrolled across four clinical sites in US and India and provided voice samples and symptom reports on their personal smartphones. The participants included symptomatic COVID-19 positive and negative patients as well as asymptomatic healthy volunteers. The RRVB model performance was assessed by comparison with clinical diagnosis of COVID-19 confirmed by RT-PCR.

RESULTS

The RRVB model's ability to differentiate patients with respiratory conditions vs. healthy controls was previously demonstrated on validation data in asthma, COPD, ILD and cough with odds ratios of 4.3, 9.1, 3.1, and 3.9 respectively. The same RRVB model in the present study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and odds ratio of 4.64 (p<0.0001). Patients experiencing respiratory symptoms were detected more frequently than those not experiencing respiratory symptoms and completely asymptomatic patients (78.4% vs. 67.4% vs. 68.0%).

CONCLUSIONS

The RRVB model has shown good generalizability across respiratory conditions, geographies, and language. Results in COVID-19 demonstrate its meaningful potential to serve as a pre-screening tool for identifying subjects at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model could encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path to development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.

CLINICALTRIAL

ClinicalTrials.gov (NCT04582331.

Geriatric Falls: Patient Characteristics Associated with Emergency Department Revisits

Introduction

Falls are the leading cause of traumatic injury among elderly adults in the United States, which represents a significant source of morbidity and leads to exorbitant healthcare costs. The purpose of this study was to characterize elderly fall patients and identify risk factors associated with seven-day emergency department (ED) revisits.

Methods

This was a multicenter, retrospective, longitudinal cohort study using non-public data from 321 licensed, nonfederal, general, and acute care hospitals in California obtained from the Department of Healthcare Access and Information from January 1–December 31, 2017. Included were patients 65 and older who had a fall-related ED visit identified by International Classification of Diseases codes W00x to W19x. Primary outcome was a return visit to the ED within a seven-day window following the index encounter. Demographics collected included age, gender, ethnicity/race, patient payer status, Charlson Comorbidity Index (CCI), psychiatric diagnoses, and alcohol/substance use disorder diagnoses. We performed multivariate logistic regression to identify characteristics associated with seven-day ED revisit.

Results

We identified a total of 2,758,295 ED visits during the study period with 347,233 (12.6%) visits corresponding to fall-related injuries. After applying exclusion criteria, 242,572 index ED visits were identified, representing 206,612 patients. Of these, 24,114 (11.7%) patients returned to an ED within seven days (revisit). Within this revisit population, 6,161 (22.6%) presented to a facility that was distinct from their index visit, and 4,970 (18.2%) were ultimately discharged with the same primary diagnosis as their index visit. Characteristics with the largest independent associations with a seven-day ED revisit were presence of a psychiatric diagnosis (odds ratio [OR] 1.75; 95% confidence interval [CI] 1.69 to 1.80), presence of an alcohol or substance use disorder (OR 1.70; 95% CI 1.64 to 1.78), and CCI ≥ 3 (OR 2.79; 95% CI 2.68 to 2.90).

Conclusion

In this study we identified 24,114 elderly fall patients who experienced a seven-day ED revisit. Patients with multiple comorbidities, a substance use disorder, or a psychiatric diagnosis exhibited increased odds of experiencing a return visit to the ED within seven days of a fall-related index visit. These findings will help target at-risk elderly fall patients who may benefit from preventative multidisciplinary intervention during index ED visits to reduce ED revisits.

Lawson Criterion for Ignition Exceeded in an Inertial Fusion Experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion.

Results of a Multi-Institutional Phase 2 Clinical Trial for 4DCT-Ventilation Functional Avoidance Thoracic Radiation Therapy

PURPOSE

Radiation pneumonitis remains a major limitation in the radiation therapy treatment of patients with lung cancer. Functional avoidance radiation therapy uses functional imaging to reduce pulmonary toxic effects by designing radiation therapy plans that reduce doses to functional regions of the lung. Lung functional imaging has been developed that uses 4-dimensional computed tomography (4DCT) imaging to calculate 4DCT-based lung ventilation (4DCT-ventilation). A phase 2 multicenter study was initiated to evaluate 4DCT-ventilation functional avoidance radiation therapy. The study hypothesis was that functional avoidance radiation therapy could reduce the rate of grade ≥2 radiation pneumonitis to 12% compared with a 25% historical rate, with the trial being positive if ≤16.4% of patients experienced grade ≥2 pneumonitis.

METHODS AND MATERIALS

Lung cancer patients receiving curative-intent radiation therapy (prescription doses of 45-75 Gy) and chemotherapy were accrued. Patient 4DCT scans were used to generate 4DCT-ventilation images. The 4DCT-ventilation images were used to generate functional avoidance plans that reduced doses to functional portions of the lung while delivering the prescribed tumor dose. Pneumonitis was evaluated by a clinician at 3, 6, and 12 months after radiation therapy.

RESULTS

Sixty-seven evaluable patients were accrued between April 2015 and December 2019. The median prescription dose was 60 Gy (range, 45-66 Gy) delivered in 30 fractions (range, 15-33 fractions). The average reduction in the functional volume of lung receiving ≥20 Gy with functional avoidance was 3.5% (range, 0%-12.8%). The median follow-up was 312 days. The rate of grade ≥2 radiation pneumonitis was 10 of 67 patients (14.9%; 95% upper CI, 24.0%), meeting the phase 2 criteria.

CONCLUSIONS

4DCT-ventilation offers an imaging modality that is convenient and provides functional imaging without an extra procedure necessary. This first report of a multicenter study of 4DCT-ventilation functional avoidance radiation therapy provided data showing that the trial met phase 2 criteria and that evaluation in a phase 3 study is warranted.

Pharmacokinetic and pharmacodynamic properties of aerosolized ("vaped") THC in adolescent male and female rats

RATIONALE

Adolescent exposure to ∆9-tetrahydrocannabinol (THC), the psychotropic constituent of cannabis, might affect brain development, and in rodent models leads to long-term behavioral and physiological alterations. Yet, the basic pharmacology of this drug in adolescent rodents, especially when ingested via ecologically relevant routes like aerosol inhalation, commonly referred to as "vaping," is still poorly characterized. Moreover, sex differences exist in THC metabolism, kinetics, and behavioral effects, but these have not been rigorously examined after vapor dosing in adolescents.

OBJECTIVES

We investigated the pharmacokinetics and pharmacodynamics of aerosolized THC (30 min inhalation exposure, 25 or 100 mg/ml) in adolescent Wistar rats of both sexes.

METHODS

Liquid chromatography/mass spectrometry analysis of THC and its major metabolites was conducted on blood plasma and brain tissue at 5, 30, 60, and 120 min following a 30-min aerosol dosing session. Effects on activity in a novel environment for 120 min after aerosol, and temperature, were measured in separate rats.

RESULTS

We found sex-dependent differences in the pharmacokinetics of THC and its active (11-OH-THC) and inactive (11-COOH-THC) metabolites in the blood and brain, along with dose- and sex-dependent effects on anxiety-like and exploratory behaviors; namely, greater 11-OH-THC levels accompanied by greater behavioral effects in females at the low dose but similar hypothermic effects in both sexes at the high dose.

CONCLUSIONS

These results provide a benchmark for dosing adolescent rats with aerosolized (or "vaped") THC, which could facilitate adoption by other labs of this potentially human-relevant THC exposure model to understand cannabis effects on the developing brain.

Dynamic lung compliance imaging from 4DCT-derived volume change estimation

Background. Lung compliance (LC) is the ability of the lung to expand with changes in pressure and is one of the earliest physiological measurements to be altered in patients with parenchymal lung disease. Therefore, compliance monitoring could potentially identify patients at risk for disease progression. However, in clinical practice, compliance measurements are prohibitively invasive for use as a routine monitoring tool.Purpose. We propose a novel method for computing dynamic lung compliance imaging (LCI) from non-contrast computed tomography (CT) scans. LCI applies image processing methods to free-breathing 4DCT images, acquired under two different continuous positive airway pressures (CPAP) applied using a full-face mask, in order to compute the lung volume change induced by the pressure change. LCI provides a quantitative volumetric map of lung stiffness.Methods. We compared mean LCI values computed for 10 patients with idiopathic pulmonary fibrosis (IPF) and 7 non-IPF patients who were screened for lung nodules. 4DCTs were acquired for each patient at 5 cm and 10 cm H₂0 CPAP, as the patients were free breathing at functional residual capacity. LCI was computed from the two 4DCTs. Mean LCI intensities, which represent relative voxel volume change induced by the change in CPAP pressure, were computed.Results.The mean LCI values for patients with IPF ranged between [0.0309, 0.1165], whereas the values ranged between [0.0704, 0.2185] for the lung nodule cohort. Two-sided Wilcoxon rank sum test indicated that the difference in medians is statistically significant (pvalue = 0.009) and that LCI -measured compliance is overall lower in the IPF patient cohort.Conclusion. There is considerable difference in LC scores between patients with IPF compared to controls. Future longitudinal studies should look for LC alterations in areas of lung prior to radiographic detection of fibrosis to further characterize LCI's potential utility as an image marker for disease progression.

Moving the needle on fall prevention: A Geriatric Emergency Care Applied Research (GEAR) Network scoping review and consensus statement

BACKGROUND

Although falls are common, costly, and often preventable, emergency department (ED)-initiated fall screening and prevention efforts are rare. The Geriatric Emergency Medicine Applied Research Falls core (GEAR-Falls) was created to identify existing research gaps and to prioritize future fall research foci.

METHODS

GEAR's 49 transdisciplinary stakeholders included patients, geriatricians, ED physicians, epidemiologists, health services researchers, and nursing scientists. We derived relevant clinical fall ED questions and summarized the applicable research evidence, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews. The highest-priority research foci were identified at the GEAR Consensus Conference.

RESULTS

We identified two clinical questions for our review (1) fall prevention interventions (32 studies) and (2) risk stratification and falls care plan (19 studies). For (1) 21 of 32 (66%) of interventions were a falls risk screening assessment and 15 of 21 (71%) of these were combined with an exercise program or physical therapy. For (2) 11 fall screening tools were identified, but none were feasible and sufficiently accurate for ED patients. For both questions, the most frequently reported study outcome was recurrent falls, but various process and patient/clinician-centered outcomes were used. Outcome ascertainment relied on self-reported falls in 18 of 32 (56%) studies for (1) and nine of 19 (47%) studies for (2).

CONCLUSION

Harmonizing definitions, research methods, and outcomes is needed for direct comparison of studies. The need to identify ED-appropriate fall risk assessment tools and role of emergency medical services (EMS) personnel persists. Multifactorial interventions, especially involving exercise, are more efficacious in reducing recurrent falls, but more studies are needed to compare appropriate bundle combinations. GEAR prioritizes five research priorities: (1) EMS role in improving fall-related outcomes, (2) identifying optimal ED fall assessment tools, (3) clarifying patient-prioritized fall interventions and outcomes, (4) standardizing uniform fall ascertainment and measured outcomes, and (5) exploring ideal intervention components.

Characterizing spatial differences between SPECT-ventilation and SPECT-perfusion in patients with lung cancer undergoing radiotherapy

This study investigates agreement between ventilation and perfusion for lung cancer patients undergoing radiotherapy. Ventilation-perfusion scans of nineteen patients with stage III lung cancer from a prospective protocol were compared using voxel-wise Spearman correlation-coefficients. The presented results show in about 25% of patients, ventilation and perfusion exhibit lower agreement.

Functional avoidance-based intensity modulated proton therapy with 4DCT derived ventilation imaging for lung cancer

The primary objective is to evaluate the potential dosimetric gains of performing functional avoidance‐based proton treatment planning using 4DCT derived ventilation imaging. 4DCT data of 31 patients from a prospective functional avoidance clinical trial were evaluated with intensity modulated proton therapy (IMPT) plans and compared with clinical volumetric modulated arc therapy (VMAT) plans. Dosimetric parameters were compared between standard and functional plans with IMPT and VMAT with one‐way analysis of variance and post hoc paired student t‐test. Normal Tissue Complication Probability (NTCP) models were employed to estimate the risk of two toxicity endpoints for healthy lung tissues. Dose degradation due to proton motion interplay effect was evaluated. Functional IMPT plans led to significant dose reduction to functional lung structures when compared with functional VMAT without significant dose increase to Organ at Risk (OAR) structures. When interplay effect is considered, no significant dose degradation was observed for the OARs or the clinical target volume (CTV) volumes for functional IMPT. Using fV20 as the dose metric and Grade 2+ pneumonitis as toxicity endpoint, there is a mean 5.7% reduction in Grade 2+ RP with the functional IMPT and as high as 26% in reduction for individual patient when compared to the standard IMPT planning. Functional IMPT was able to spare healthy lung tissue to avoid excess dose to normal structures while maintaining satisfying target coverage. NTCP calculation also shows that the risk of pulmonary complications can be further reduced with functional based IMPT.

Extubation Failure in Critically Ill COVID-19 Patients: Risk Factors and Impact on In-Hospital Mortality

PURPOSE

We sought to identify clinical factors that predict extubation failure (reintubation) and its prognostic implications in critically ill COVID-19 patients.

MATERIALS AND METHODS

Retrospective, multi-center cohort study of hospitalized COVID-19 patients. Multivariate competing risk models were employed to explore the rate of reintubation and its determining factors.

RESULTS

Two hundred eighty-one extubated patients were included (mean age, 61.0 years [±13.9]; 54.8% male). Reintubation occurred in 93 (33.1%). In multivariate analysis accounting for death, reintubation risk increased with age (hazard ratio [HR] 1.04 per 1-year increase, 95% confidence interval [CI] 1.02 -1.06), vasopressors (HR 1.84, 95% CI 1.04-3.60), renal replacement (HR 2.01, 95% CI 1.22-3.29), maximum PEEP (HR 1.07 per 1-unit increase, 95% CI 1.02 -1.12), paralytics (HR 1.48, 95% CI 1.08-2.25) and requiring more than nasal cannula immediately post-extubation (HR 2.19, 95% CI 1.37-3.50). Reintubation was associated with higher mortality (36.6% vs 2.1%; P < 0.0001) and risk of inpatient death after adjusting for multiple factors (HR 23.2, 95% CI 6.45-83.33). Prone ventilation, corticosteroids, anticoagulation, remdesivir and tocilizumab did not impact the risk of reintubation or death.

CONCLUSIONS

Up to 1 in 3 critically ill COVID-19 patients required reintubation. Older age, paralytics, high PEEP, need for greater respiratory support following extubation and non-pulmonary organ failure predicted reintubation. Extubation failure strongly predicted adverse outcomes.

Quantifying pulmonary perfusion from noncontrast computed tomography

Purpose

Computed tomography (CT)‐derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT‐Perfusion (CT‐P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.

Methods

CT‐Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT‐P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT‐P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT‐ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ. Spatial Spearman correlation between single photon emission CT perfusion (SPECT‐P) and the proposed CT‐P method was assessed in two patient cohorts via a parameter sweep of τ. The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT‐P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT‐P and 4DCT images acquired prior to radiotherapy. For each test case, CT‐P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT‐P and the resulting CT‐P images were computed.

Results

The median correlations between CT‐P and SPECT‐P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT‐P and SPECT‐P correlations across all 30 test cases ranged between 0.02 and 0.82. A one‐sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two‐sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One‐sample sign test was statistically significant with 96.5 % confidence interval: 0.20–0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one‐sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45–0.71, P < 0.00001.

Conclusion

CT‐Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT‐P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT‐P imaging.

Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021

Objectives

Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care.

Methods

Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables.

Results

Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79–0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8–96.3%), specificity of 20.0% (19.0–21.0%), negative likelihood ratio of 0.22 (0.19–0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points).

Conclusion

Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

An assessment of the correlation between robust CT-derived ventilation and pulmonary function test in a cohort with no respiratory symptoms

OBJECTIVE

To evaluate CT-ventilation imaging (CTVI) within a well-characterized, healthy cohort with no respiratory symptoms and examine the correlation between CTVI and concurrent pulmonary function test (PFT).

METHODS

CT scans and PFTs from 77 Caucasian participants in the NORM dataset (clinicaltrials.gov NCT00848406) were analyzed. CTVI was generated using the robust Integrated Jacobian Formulation (IJF) method. IJF estimated total lung capacity (TLC) was computed from CTVI. Bias-adjusted Pearson's correlation between PFT and IJF-based TLC was computed.

RESULTS

IJF- and PFT-measured TLC showed a good correlation for both males and females [males: 0.657, 95% CI (0.438-0.797); females: 0.667, 95% CI (0.416-0.817)]. When adjusting for age, height, smoking, and abnormal CT scan, correlation moderated [males: 0.432, 95% CI (0.129-0.655); females: 0.540, 95% CI (0.207-0.753)]. Visual inspection of CTVI revealed participants who had functional defects, despite the fact that all participant had normal high-resolution CT scan.

CONCLUSION

In this study, we demonstrate that IJF computed CTVI has good correlation with concurrent PFT in a well-validated patient cohort with no respiratory symptoms.

ADVANCES IN KNOWLEDGE

IJF-computed CTVI's overall numerical robustness and consistency with PFT support its potential as a method for providing spatiotemporal assessment of high and low function areas on volumetric non-contrast CT scan.

Association of anticoagulation dose and survival in hospitalized COVID-19 patients: A retrospective propensity score-weighted analysis

BACKGROUND

Hypercoagulability may contribute to COVID-19 pathogenicity. The role of anticoagulation (AC) at therapeutic (tAC) or prophylactic doses (pAC) is unclear.

OBJECTIVES

We evaluated the impact on survival of different AC doses in COVID-19 patients.

METHODS

Retrospective, multi-center cohort study of consecutive COVID-19 patients hospitalized between March 13 and May 5, 2020.

RESULTS

A total of 3480 patients were included (mean age, 64.5 years [17.0]; 51.5% female; 52.1% black and 40.6% white). 18.5% (n = 642) required intensive care unit (ICU) stay. 60.9% received pAC (n = 2121), 28.7% received ≥3 days of tAC (n = 998), and 10.4% (n = 361) received no AC. Propensity score (PS) weighted Kaplan-Meier plot demonstrated different 25-day survival probability in the tAC and pAC groups (57.5% vs 50.7%). In a PS-weighted multivariate proportional hazards model, AC was associated with reduced risk of death at prophylactic (hazard ratio [HR] 0.35 [95% confidence interval {CI} 0.22-0.54]) and therapeutic doses (HR 0.14 [95% CI 0.05-0.23]) compared to no AC. Major bleeding occurred more frequently in tAC patients (81 [8.1%]) compared to no AC (20 [5.5%]) or pAC (46 [2.2%]) subjects.

CONCLUSIONS

Higher doses of AC were associated with lower mortality in hospitalized COVID-19 patients. Prospective evaluation of efficacy and risk of AC in COVID-19 is warranted.

Severity of radiation pneumonitis, from clinical, dosimetric and biological features: a pilot study

BACKGROUND AND OBJECTIVE

Radiation pneumonitis (RP) could be a lethal complication of lung cancer treatment. No reliable predictors of RP severity have been recognized. This prospective pilot study was performed to identify early predictors of high grade lung toxicity and to evaluate clinical, biological or dosimetric features associated with different grades of toxicity.

METHOD

Sixteen patients with non-small cell lung cancer with indication of concurrent chemoradiotherapy using 60 Gy/2 Gy/fraction starting at cycle one of platinum based chemotherapy were included. Bronchoalveolar lavage (BAL), pulmonary function testing (PFT), and 18F-2-fluoro-2-deoxy-D-glucose positron-emission tomography was performed before radiotherapy (RT), after three weeks of treatment, and two months post-RT. For analysis, patients were grouped by grade (low [G1-G2] vs. high [G3-G5]). The two groups were compared to identify predictors of RP. Protein expression BAL and lung tissue metabolism was evaluated in two patients (RP-G1 vs. RP-G3). Categorical variables such as comorbidities, stages and locations were summarized as percentages. Radiation doses, pulmonary function values and time to RP were summarized by medians with ranges or as means with standard deviation. Longitudinal analysis PFT was performed by a T-test.

RESULTS

All 16 patients developed RP, as follows: G1 (5 pts; 31.3%); G2 (5 pts; 31.3%); G3 (5 pts; 31.3%); and G5 (1 pts; 6.1%). Patients with high grade RP presented significant decrease (p = 0.02) in diffusing lung capacity for carbon monoxide (DLCO) after three weeks of RT. No correlation between dosimetric values and RP grades was observed. BAL analysis of the selected patients showed that CXCL-1, CD154, IL-1ra, IL-23, MIF, PAI-1 and IFN-γ were overexpressed in the lungs of the RP-G3 patient, even before treatment. The pre-RT SUVmax value in the RP-G3 patient was non-significantly higher than in the patient with RP-G1.

CONCLUSIONS

RT induces some degree of RP. Our data suggest that decrease in DLCO% is the most sensitive parameter for the early detection of RP. Moreover, we detect biological differences between the two grades of pneumonitis, highlighting the potential value of some cytokines as a prognostic marker for developing high grade lung toxicity. Further multicenter studies with larger sample size are essential to validate these findings.

Technical Note: On the spatial correlation between robust CT-ventilation methods and SPECT ventilation

Purpose

The computed tomography (CT)‐derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion‐induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT‐ventilation images and single photon emission CT‐ventilation (SPECT‐V).

Methods

Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ, was conducted using the 4DCT and SPECT‐V images acquired for 15 non‐small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT‐ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0.01,0.25. Voxel‐wise Spearman correlation between the SPECT‐V and the resulting CT‐ventilation images was computed.

Results

The median correlations between MCVC and SPECT‐V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT‐V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ=0.07, the IJF and SPECT‐V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ=0.03, the MCVC and SPECT‐V correlations across all 15 test cases ranged between −0.06 and 0.84.

Conclusion

The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT‐V, with the transformation‐based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ∈[0.03,0.15], indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.

Therapeutic Anticoagulation Delays Death in COVID-19 Patients: Cross-Sectional Analysis of a Prospective Cohort

A hypercoagulable state has been described in coronavirus disease 2019 (COVID-19) patients. Others have reported a survival advantage with prophylactic anticoagulation (pAC) and therapeutic anticoagulation (tAC), but these retrospective analyses have important limitations such as confounding by indication. We studied the impact of tAC and pAC compared with no anticoagulation (AC) on time to death in COVID-19. We performed a cross-sectional analysis of 127 deceased COVID-19 patients and compared time to death in those who received tAC ( n  = 67), pAC ( n  = 47), and no AC ( n  = 13). Median time to death was longer with higher doses of AC (11 days for tAC, 8 days for pAC, and 4 days for no AC, p  < 0.001). In multivariate analysis, AC was associated with longer time to death, both at prophylactic (hazard ratio [HR] = 0.29; 95% confidence interval [CI]: 0.15 to 0.58; p  < 0.001) and therapeutic doses (HR = 0.15; 95% CI: 0.07 to 0.32; p  < 0.001) compared with no AC. Bleeding rates were similar among tAC and remaining patients (19 vs. 18%; p  = 0.877). In deceased COVID-19 patients, AC was associated with a delay in death in a dose-dependent manner. Randomized trials are required to prospectively investigate the benefit and safety of higher doses of AC in this population.

Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning

PURPOSE

Segmentation of organs-at-risk (OARs) is an essential component of the radiation oncology workflow. Commonly segmented thoracic OARs include the heart, esophagus, spinal cord, and lungs. This study evaluated a convolutional neural network (CNN) for automatic segmentation of these OARs.

METHODS

The dataset was created retrospectively from consecutive radiotherapy plans containing all five OARs of interest, including 22,411 CT slices from 168 patients. Patients were divided into training, validation, and test datasets according to a 66%/17%/17% split. We trained a modified U-Net, applying transfer learning from a VGG16 image classification model trained on ImageNet. The Dice coefficient and 95% Hausdorff distance on the test set for each organ was compared to a commercial atlas-based segmentation model using the Wilcoxon signed-rank test.

RESULTS

On the test dataset, the median Dice coefficients for the CNN model vs. the multi-atlas model were 71% vs. 67% for the spinal cord, 96% vs. 94% for the right lung, 96%vs. 94% for the left lung, 91% vs. 85% for the heart, and 63% vs. 37% for the esophagus. The median 95% Hausdorff distances were 9.5  mm vs. 25.3 mm, 5.1  mm vs. 8.1 mm, 4.0  mm vs. 8.0 mm, 9.8  mm vs. 15.8 mm, and 9.2 mm vs. 20.0 mm for the respective organs. The results all favored the CNN model (P < 0.05).

CONCLUSIONS

A 2D CNN can achieve superior results to commercial atlas-based software for OAR segmentation utilizing non-domain transfer learning, which has potential utility for quality assurance and expediting patient care.

Evaluating Positron Emission Tomography-Based Functional Imaging Changes in the Heart After Chemo-Radiation for Patients With Lung Cancer

PURPOSE

Studies have noted a link between radiation dose to the heart and overall survival (OS) for patients with lung cancer treated with chemoradiation. The purpose of this study was to characterize pre- to posttreatment cardiac metabolic changes using fluorodeoxyglucose/positron emission tomography (FDG-PET) images and to evaluate whether changes in cardiac metabolism predict for OS.

METHODS AND MATERIALS

Thirty-nine patients enrolled in a functional avoidance prospective study who had undergone pre- and postchemoradiation FDG-PET imaging were evaluated. For each patient, the pretreatment and posttreatment PET/CTs were rigidly registered to the planning CT, dose, and structure set. PET-based metabolic dose-response was assessed by comparing pretreatment to posttreatment mean standardized uptake values (SUVmean) in the heart as a function of dose-bin. OS analysis was performed by comparing SUVmean changes for patients who were alive or had died at last follow-up and by using a multivariate model to assess whether pre- to posttreatment SUVmean changes were a predictor of OS.

RESULTS

The dose-response curve revealed increasing changes in SUV as a function of cardiac dose with an average SUVmean increase of 1.7% per 10 Gy. Patients were followed for a median of 437 days (range, 201-1131 days). SUVmean change was significantly predictive of OS on multivariate analysis with a hazard ratio of 0.541 (95% confidence intervals, 0.312-0.937). Patients alive at follow-up had an average increase of 17.2% in cardiac SUVmean while patients that died had an average decrease in SUVmean decrease of 13.5% (P = .048).

CONCLUSIONS

Our data demonstrated that posttreatment SUV changes in the heart were significant indicators of dose-response and predictors of OS. The present work is hypothesis generating and must be validated in an independent cohort. If validated, our data show the potential for cardiac metabolic changes to be an early predictor for clinical outcomes.

Robust HU-based CT ventilation from an integrated mass conservation formulation

Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel.

PURPOSE

We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing.

METHODS

Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method.

RESULTS

The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11).

CONCLUSION

While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms.