Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study.

BACKGROUND: Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. METHODS: An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222). FINDINGS: 17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001). INTERPRETATION: PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes. FUNDING: National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.

Barriers and facilitators to smartwatch-based prehabilitation participation among frail surgery patients: a qualitative study.

BACKGROUND: For older, frail adults, exercise before surgery through prehabilitation (prehab) may hasten return recovery and reduce postoperative complications. We developed a smartwatch-based prehab program (BeFitMe) for older adults that encourages and tracks at-home exercise. The objective of this study was to assess patient perceptions about facilitators and barriers to prehab generally and to using a smartwatch prehab program among older adult thoracic surgery patients to optimize future program implementation. METHODS: We recruited patients, aged ≥50 years who had or were having surgery and were screened for frailty (Fried's Frailty Phenotype) at a thoracic surgery clinic at a single academic institution. Semi-structured interviews were conducted by telephone after obtaining informed consent. Participants were given a description of the BeFitMe program. The interview questions were informed by The Five "Rights" of Clinical Decision-Making framework (Information, Person, Time, Channel, and Format) and sought to identify the factors perceived to influence smartwatch prehab program participation. Interview transcripts were transcribed and independently coded to identify themes in for each of the Five "Rights" domains. RESULTS: A total of 29 interviews were conducted. Participants were 52% men (n = 15), 48% Black (n = 14), and 59% pre-frail (n = 11) or frail (n = 6) with a mean age of 68 ± 9 years. Eleven total themes emerged. Facilitator themes included the importance of providers (right person) clearly explaining the significance of prehab (right information) during the preoperative visit (right time); providing written instructions and exercise prescriptions; and providing a preprogrammed and set-up (right format) Apple Watch (right channel). Barrier themes included pre-existing conditions and disinterest in exercise and/or technology. Participants provided suggestions to overcome the technology barrier, which included individualized training and support on usage and responsibilities. CONCLUSIONS: This study reports the perceived facilitators and barriers to a smartwatch-based prehab program for pre-frail and frail thoracic surgery patients. The future BeFitMe implementation protocol must ensure surgical providers emphasize the beneficial impact of participating in prehab before surgery and provide a written prehab prescription; must include a thorough guide on smartwatch use along with the preprogrammed device to be successful. The findings are relevant to other smartwatch-based interventions for older adults.

Trends and In-Hospital Mortality for Perioperative Myocardial Infarction After the Introduction of a Diagnostic Code for Type 2 Myocardial Infarction in the United States Between 2016 and 2018.

BACKGROUND: The frequency of perioperative myocardial infarction has been declining; however, previous studies have only described type 1 myocardial infarctions. Here, we evaluate the overall frequency of myocardial infarction with the addition of an International Classification of Diseases 10th revision (ICD-10-CM) code for type 2 myocardial infarction and the independent association with in-hospital mortality. METHODS: A longitudinal cohort study spanning the introduction of the ICD-10-CM diagnostic code for type 2 myocardial infarction using the National Inpatient Sample (NIS) from 2016 to 2018. Hospital discharges that included a primary surgical procedure code for intrathoracic, intraabdominal, or suprainguinal vascular surgery were included. Type 1 and type 2 myocardial infarctions were identified using ICD-10-CM codes. We used segmented logistic regression to estimate change in frequency of myocardial infarctions and multivariable logistic regression to determine the association with in-hospital mortality. RESULTS: A total of 360,264 unweighted discharges were included, representing 1,801,239 weighted discharges, with median age 59 and 56% female. The overall incidence of myocardial infarction was 0.76% (13,605/1,801,239). Before the introduction of type 2 myocardial infarction code, there was a small baseline decrease in the monthly frequency of perioperative myocardial infarctions (odds ratio [OR], 0.992; 95% confidence interval [CI], 0.984-1.000; P = .042), but no change in the trend after the introduction of the diagnostic code (OR, 0.998; 95% CI, 0.991-1.005; P = .50). In 2018, where there was an entire year where type 2 myocardial infarction was officially a diagnosis, the distribution of myocardial infarction type 1 was 8.8% (405/4580) ST elevation myocardial infarction (STEMI), 45.6% (2090/4580) non-ST elevation myocardial infarction (NSTEMI), and 45.5% (2085/4580) type 2 myocardial infarction. STEMI and NSTEMI were associated with increased in-hospital mortality (OR, 8.96; 95% CI, 6.20-12.96; P < .001 and OR, 1.59; 95% CI, 1.34-1.89; P < .001). A diagnosis of type 2 myocardial infarction was not associated with increased odds of in-hospital mortality (OR, 1.11; 95% CI, 0.81-1.53; P = .50) when accounting for surgical procedure, medical comorbidities, patient demographics, and hospital characteristics. CONCLUSIONS: The frequency of perioperative myocardial infarctions did not increase after the introduction of a new diagnostic code for type 2 myocardial infarctions. A diagnosis of type 2 myocardial infarction was not associated with increased in-patient mortality; however, few patients received invasive management that may have confirmed the diagnosis. Further research is needed to identify what type of intervention, if any, may improve outcomes in this patient population.

Technology-assisted behavioral intervention to encourage prehabilitation in frail older adults undergoing surgery: Development and design of the BeFitMe™ Apple Watch app.

Objective: Increasing the physical activity of frail, older patients before surgery through prehabilitation (prehab) can hasten return to autonomy and reduce complications postoperatively. However, prehab participation is low in the clinical setting. In this study, we re-design an existing prehab smartphone application (BeFitMe™) using a novel standalone Apple Watch platform to increase accessibility and usability for vulnerable patients. Methods: Design Science Research Methodology was used to (1) develop an approach to clinical research using standalone Apple Watches, (2) re-design BeFitMe™ for the Apple Watch platform, and (3) incorporate user feedback into app design. In phase 3, beta and user testers gave feedback via a follow-up phone call. Exercise data was extracted from the watch after testing. Descriptive statistics were used to summarize accessibility and usability. Results: BeFitMe™ was redesigned for the Apple Watch with full functionality without requiring patients to have an iPhone or internet connectivity and the ability to passively collect exercise data without patient interaction. Three study staff participated in beta testing over 3 weeks. Six randomly chosen thoracic surgery patients participated in user testing over 12 weeks. Feedback from beta and user testers was addressed with updated software (versions 1.0-1.10), improved interface and notification schemes, and the development of educational materials used during enrollment. The majority of users (5/6, 83%) participated by responding to at least one notification and data was able to be collected for 54/82 (68%) of the days users had the watches. The amount of data collected in BeFitMe™ Watch app increased from 2/11 (16%) days with the first patient tester to 13/13 (100%) days with the final patient tester. Conclusions: The BeFitMe™ Watch app is accessible and usable. The BeFitMe™ Watch app may help older patients, particularly those from vulnerable backgrounds with fewer resources, participate in prehab prior to surgery.

Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women.

BACKGROUND: Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. METHODS: We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40-74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. RESULTS: The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18-1.57), 0.85 (0.77-0.93) and 1.44 (1.26-1.66) for LIBRA and 1.44 (1.33-1.55), 0.81 (0.74-0.89) and 1.54 (1.34-1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2-5 years and 5-10 years after the baseline mammogram. CONCLUSION: Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.

AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.

Purpose: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. Materials and Methods: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test. Results: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively. Conclusion: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.

Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in "triple-negative" breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.

Comparative effectiveness of mobile health smoking cessation approaches among underserved patients in primary care: Study protocol for the PROMOTE-UP trial.

INTRODUCTION: Tobacco smoking is the leading cause of preventable disease, disability, and premature death in the United States. Recent advances have led to two efficacious mobile health (mHealth) treatments for smoking cessation: iCanQuit, an Acceptance and Commitment Therapy-based behavioral treatment promoting cessation through accepting triggers and committing to values; and Motiv8, a contingency management intervention promoting smoking cessation with financial incentives via biochemically verified abstinence. This study will evaluate the comparative effectiveness of the Florida Quitline, iCanQuit alone, and iCanQuit+Motiv8 in a pragmatic trial among patients who smoke in underserved primary care settings. METHODS: The study will be an individually-randomized controlled trial with three arms (Florida Quitline, iCanQuit alone, iCanQuit+Motiv8 combined) conducted in multiple primary care practices affiliated with the OneFlorida+ Clinical Research Consortium. Adult patients who smoke will be randomized to one of the 3 study arms (n = 444/arm), stratified by healthcare setting (academic vs. community). The primary outcome will be 7-day point prevalence smoking abstinence at 6 months post-randomization. Secondary outcomes will be 12-month smoking abstinence, patient satisfaction with the interventions, and changes in patient quality of life and self-efficacy. The study will also assess how and for whom the interventions help sub-group patients in achieving smoking abstinence by measuring theory-derived factors that mediate smoking outcome-specific baseline moderators. CONCLUSIONS: Results from this study will provide evidence for the comparative effectiveness of mHealth smoking cessation interventions in healthcare settings. Use of mHealth interventions can make smoking cessation resources more equitably accessible and have far-reaching impact on community and population health. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05415761, Registered 13 June 2022.

Preoperative ß-Blocker Therapy and Stroke or Major Adverse Cardiac Events in Major Abdominal Surgery: A Retrospective Cohort Study.

BACKGROUND: Perioperative ß-blocker therapy has been associated with increased risk of stroke. However, the association between ß-blocker initiation before the day of surgery and the risk of stroke is unknown. The authors hypothesized there would be no association between preoperative ß-blocker initiation within 60 days of surgery or chronic ß-blockade (more than 60 days) and the risk of stroke in patients undergoing major abdominal surgery. METHODS: Data on elective major abdominal surgery were obtained from the IBM (USA) Truven Health MarketScan 2005 to 2015 Commercial and Medicare Supplemental Databases. Patients were stratified by ß-blocker dispensing exposure: (1) ß-blocker-naïve, (2) preoperative ß-blocker initiation within 60 days of surgery, and (3) chronic ß-blocker dispensing (more than 60 days). The authors compared in-hospital stroke and major adverse cardiac events between the different ß-blocker therapy exposures. RESULTS: There were 204,981 patients who underwent major abdominal surgery. ß-Blocker exposure was as follows: perioperative initiation within 60 days of surgery for 4,026 (2.0%) patients, chronic ß-blocker therapy for 45,424 (22.2%) patients, and ß-blocker-naïve for 155,531 (75.9%) patients. The unadjusted frequency of stroke for patients with ß-blocker initiation (0.4%, 17 of 4,026) and chronic ß-blocker therapy (0.4%, 171 of 45,424) was greater than in ß-blocker-naïve patients (0.2%, 235 of 155,531; P < 0.001). After propensity score weighting, patients initiated on a ß-blocker within 60 days of surgery (odds ratio, 0.90; 95% CI, 0.31 to 2.04; P = 0.757) or on chronic ß-blocker therapy (odds ratio, 0.86; 95% CI, 0.65 to 1.15; P = 0.901) demonstrated similar stroke risk compared to ß-blocker-naïve patients. Patients on chronic ß-blocker therapy demonstrated lower adjusted risk of major adverse cardiac events compared to ß-blocker-naïve patients (odds ratio, 0.81; 95% CI, 0.72 to 0.91; P = 0.007), despite higher unadjusted absolute event rate (2.6% [1,173 of 45,424] vs. 0.6% [872 of 155,531]). CONCLUSIONS: Among patients undergoing elective major abdominal surgery, the authors observed no association between preoperative ß-blocker initiation within 60 days of surgery or chronic ß-blocker therapy and stroke.

Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.

PURPOSE: To assess the performance of a machine learning model trained with contrast-enhanced CT-based radiomics features in distinguishing benign from malignant solid renal masses and to compare model performance with three abdominal radiologists. METHODS: Patients who underwent intra-operative ultrasound during a partial nephrectomy were identified within our institutional database, and those who had pre-operative contrast-enhanced CT examinations were selected. The renal masses were segmented from the CT images and radiomics features were derived from the segmentations. The pathology of each mass was identified; masses were labeled as either benign [oncocytoma or angiomyolipoma (AML)] or malignant [clear cell, papillary, or chromophobe renal cell carcinoma (RCC)] depending on the pathology. The data were parsed into a 70/30 train/test split and a random forest machine learning model was developed to distinguish benign from malignant lesions. Three radiologists assessed the cohort of masses and labeled cases as benign or malignant. RESULTS: 148 masses were identified from the cohort, including 50 benign lesions (23 AMLs, 27 oncocytomas) and 98 malignant lesions (23 clear cell RCC, 44 papillary RCC, and 31 chromophobe RCCs). The machine learning algorithm yielded an overall accuracy of 0.82 for distinguishing benign from malignant lesions, with an area under the receiver operating curve of 0.80. In comparison, the three radiologists had significantly lower accuracies (p = 0.02) ranging from 0.67 to 0.75. CONCLUSION: A machine learning model trained with CT-based radiomics features can provide superior accuracy for distinguishing benign from malignant solid renal masses compared to abdominal radiologists.

Racial differences in phenotypic frailty assessment among general thoracic surgery patients.

Objectives: The American Association for Thoracic Surgery recommends using frailty assessments to identify patients at higher risk of perioperative morbidity and mortality. We evaluated what patient factors are associated with frailty in a thoracic surgery patient population. Methods: New patients aged more than 50 years who were evaluated in a thoracic surgery clinic underwent routine frailty screening with a modified Fried's Frailty Phenotype. Differences in demographics and comorbid conditions among frailty status groups were assessed with chi-square and Student t tests. Logistic regressions performed with binomial distribution assessed the association of demographic and clinical characteristics with nonfrail, frail, prefrail, and any frailty (prefrail/frail) status. Results: The study population included 317 patients screened over 19 months. Of patients screened, 198 (62.5%) were frail or prefrail. Frail patients undergoing thoracic surgery were older, were more likely single or never married, had lower median income, and had lower percent predicted diffusion capacity of the lungs for carbon monoxide and forced expiratory volume during 1 second (all P < .05). More non-Hispanic Black patients were frail and prefrail compared with non-Hispanic White patients (P = .003) and were more likely to score at least 1 point on Fried's Frailty Phenotype (adjusted odds ratio, 3.77; P = .02) when controlling for age, sex, number of comorbidities, median income, diffusion capacity of the lungs for carbon monoxide, and forced expiratory volume during 1 second. Non-Hispanic Black patients were more likely than non-Hispanic White patients to score points for slow gait and low activity (both P < .05). Conclusions: Non-Hispanic Black patients undergoing thoracic surgery are more likely to score as frail or prefrail than non-Hispanic White patients. This disparity stems from differences in activity and gait speed. Frailty tools should be examined for factors contributing to this disparity, including bias.

Forecasting immune effector cell-associated neurotoxicity syndrome after chimeric antigen receptor t-cell therapy.

BACKGROUND: Immune effector cell-associated neurotoxicity syndrome (ICANS) is a clinical and neuropsychiatric syndrome that can occur days to weeks following administration chimeric antigen receptor (CAR) T-cell therapy. Manifestations of ICANS range from encephalopathy and aphasia to cerebral edema and death. Because the onset and time course of ICANS is currently unpredictable, prolonged hospitalization for close monitoring following CAR T-cell infusion is a frequent standard of care. METHODS: This study was conducted at Brigham and Women's Hospital from April 2015 to February 2020. A cohort of 199 hospitalized patients treated with CAR T-cell therapy was used to develop a combined hidden Markov model and lasso-penalized logistic regression model to forecast the course of ICANS. Model development was done using leave-one-patient-out cross validation. RESULTS: Among the 199 patients included in the analysis 133 were male (66.8%), and the mean (SD) age was 59.5 (11.8) years. 97 patients (48.7%) developed ICANS, of which 59 (29.6%) experienced severe grades 3-4 ICANS. Median time of ICANS onset was day 9. Selected clinical predictors included maximum daily temperature, C reactive protein, IL-6, and procalcitonin. The model correctly predicted which patients developed ICANS and severe ICANS, respectively, with area under the curve of 96.7% and 93.2% when predicting 5 days ahead, and area under the curve of 93.2% and 80.6% when predicting the entire future risk trajectory looking forward from day 5. Forecasting performance was also evaluated over time horizons ranging from 1 to 7 days, using metrics of forecast bias, mean absolute deviation, and weighted average percentage error. CONCLUSION: The forecasting model accurately predicts risk of ICANS following CAR T-cell infusion and the time course ICANS follows once it has begun.Cite Now.

A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions.

We describe a publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) and 513 studies without PET-positive malignant lesions (negative controls)) acquired between 2014 and 2018 were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner. The imaging protocol consisted of a whole-body FDG-PET acquisition and a corresponding diagnostic CT scan. All FDG-avid lesions identified as malignant based on the clinical PET/CT report were manually segmented on PET images in a slice-per-slice (3D) manner. We provide the anonymized original DICOM files of all studies as well as the corresponding DICOM segmentation masks. In addition, we provide scripts for image processing and conversion to different file formats (NIfTI, mha, hdf5). Primary diagnosis, age and sex are provided as non-imaging information. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT data and provide the trained deep learning model.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity.

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69-74) years, with a body mass index of 31 (27-32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102-114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108-118) versus 106 (96-114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden's index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

Use of Transcranial Doppler as a Biomarker of CAR T Cell-Related Neurotoxicity.

Background and Objectives: To examine the relationship between transcranial Doppler (TCD) mean flow velocity (MFV) and the severity and temporal onset of neurotoxicity after chimeric antigen receptor (CAR) T-cell therapy in patients with relapsed lymphoma. Methods: We identified a cohort of 165 patients with relapsed or refractory B-cell lymphoma who received CAR T-cell therapy. TCDs were performed at baseline, treatment day 5, and throughout hospitalization based on development of neurologic symptoms. We assessed the percent change in velocity from baseline in each of the 6 major supratentorial arteries and the relationship of these values to development and timing of neurotoxicity. Results: Our cohort was 30% female with an average age of 60 years. Of patients with TCDs performed, 63% developed neurotoxicity, and 32% had severe neurotoxicity. The median time of neurotoxicity onset was day 7. Higher maximum percent change in MFV across all vessels was significantly associated with likelihood of developing neurotoxicity (p = 0.0002) and associated with severe neurotoxicity (p = 0.0421). We found that with increased percent change in MFV, the strength of correlation between day of TCD velocity change and day of neurotoxicity onset increased. There was no single vessel in which increase in MFV was associated with neurotoxicity. Discussion: Our study demonstrates an association between increase in TCD MFV and the development of neurotoxicity, as well as timing of neurotoxicity onset. We believe that TCD ultrasound may be used as a bedside functional biomarker in CAR T-cell patients and may guide immunologic interventions to manage toxicity in this complex patient group.

Perioperative Ischemic Optic Neuropathy after Cardiac Surgery: Development and Validation of a Preoperative Risk Prediction Model.

OBJECTIVE: Previous studies identified risk factors for ischemic optic neuropathy (ION) after cardiac surgery; however, there is no easy-to-use risk calculator for the physician to identify high-risk patients for ION before cardiac surgery. The authors sought to develop and validate a simple-to-use predictive model and calculator to assist with preoperative identification of risk and informed consent for this rare but serious complication. DESIGN: Retrospective case-control study. SETTING: Hospital discharge records. PATIENTS: A total of 5,561,177 discharges in the National Inpatient Sample >18 years of age, with procedure codes for coronary artery bypass grafting, heart valve repair/replacement, or left ventricular assist device insertion. INTERVENTIONS: All patients had undergone cardiac surgery. MEASUREMENTS AND MAIN RESULTS: Known preoperative risk factors for ION after cardiac surgery were assessed to develop a risk score and prediction model. This model was validated internally using the split-sample method. There were 771 cases of ION among 5,561,177 patients in the National Inpatient Sample. The risk factors for ION used in the model were carotid artery stenosis, cataract, diabetic retinopathy, macular degeneration, glaucoma, male sex, and prior stroke; whereas uncomplicated diabetes decreased risk. With the internal validation, the predictive model had an area under the receiver operating characteristic curve of 0.66. A risk score cutoff ≥3 had 98.4% specificity. CONCLUSIONS: This predictive model, based on previously identified preoperative factors, predicted risk of perioperative ION with a fair area under the receiver operating characteristic curve. This predictive model could enable screening to provide a more accurate risk assessment for ION, and consent process for cardiac surgery.

Accuracy of Electronic Medical Record Follow-Up Data for Estimating the Survival Time of Patients With Cancer.

PURPOSE: For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data. MATERIALS AND METHODS: Patients diagnosed with metastatic cancer from 2008 to 2014 were included in this retrospective study. Patients who were diagnosed with cancer or received their initial treatment within our system were included in the institutional cancer registry and this study. Overall survival was calculated using the Kaplan-Meier method. Survival curves were generated in two ways: using EMR follow-up data alone and using EMR data supplemented with data from the Stanford Cancer Registry/California Cancer Registry. RESULTS: Four thousand seventy-seven patients were included. The median follow-up using EMR + Cancer Registry data was 19.9 months, and the median follow-up in surviving patients was 67.6 months. There were 1,301 deaths recorded in the EMR and 3,140 deaths recorded in the Cancer Registry. The median overall survival from the date of cancer diagnosis using EMR data was 58.7 months (95% CI, 54.2 to 63.2); using EMR + Cancer Registry data, it was 20.8 months (95% CI, 19.6 to 22.3). A similar pattern was seen using the date of first systemic therapy or date of first hospital admission as the baseline date. CONCLUSION: Using EMR data alone, survival time was overestimated compared with EMR + Cancer Registry data.