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.

Exploration of a Potential DOOR Endpoint for Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia Using Six Registrational Trials for Antibacterial Drugs.

BACKGROUND: Desirability of outcome ranking (DOOR) is an innovative approach to clinical trial design and analysis that uses an ordinal ranking system to incorporate the overall risks and benefits of a therapeutic intervention into a single measurement. Here, we derived and evaluated a disease-specific DOOR endpoint for registrational trials for hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia (HABP/VABP). METHODS: Through comprehensive examination of data from nearly 4,000 participants enrolled in six registrational trials for HABP/VABP submitted to the FDA between 2005-2022, we derived and applied a HABP/VABP specific endpoint. We estimated the probability that a participant assigned to the study treatment arm would have a more favorable overall DOOR or component outcome than a participant assigned to comparator. RESULTS: DOOR distributions between treatment arms were similar in all trials. DOOR probability estimates ranged from 48.3% to 52.9% and were not statistically different. There were no significant differences between treatment arms in the component analyses. Though infectious complications and serious adverse events occurred more frequently in ventilated participants compared to non-ventilated participants, the types of events were similar. CONCLUSIONS: Through a data-driven approach, we constructed and applied a potential DOOR endpoint for HABP/VABP trials. The inclusion of syndrome-specific events may help to better delineate and evaluate participant experiences and outcomes in future HABP/VABP trials and could help inform data collection and trial design.

Regulatory Considerations in the Approval of Rezafungin (REZZAYO) for the Treatment of Candidemia and Invasive Candidiasis in Adults.

On March 22, 2023, the FDA approved rezafungin (REZZAYO) for the treatment of candidemia and invasive candidiasis in adults with limited or no alternative treatment options. Rezafungin is an echinocandin that supports weekly dosing, enabling outpatient parenteral treatment that potentially avoids the need for a central venous catheter. Approval of rezafungin was based on a single adequate and well-controlled phase 3 study designed with a Day 30 all-cause mortality primary endpoint and 20% noninferiority margin, which demonstrated that rezafungin is noninferior to the comparator echinocandin. Nonclinical studies of rezafungin in non-human primates identified a neurotoxicity safety signal; however, rezafungin's safety profile in the completed clinical studies was similar to other FDA-approved echinocandins. Here we describe the rationale for this approval and important considerations during the review process for a flexible development program intended to expedite the availability of antimicrobial therapies to treat serious infections in patients with limited treatment options.

Confronting Health Misinformation Surrounding COVID-19 Vaccines in the State of Florida.

COVID-19 vaccination is estimated to have averted more than 2.4 million deaths globally. In the United States (U.S.) alone, more than 120,000 deaths and 700,000 hospitalizations are reportedly estimated to have been prevented during the first six months of the vaccine campaign. Despite the overwhelming evidence regarding the safety and efficacy of vaccination, COVID-19 vaccine hesitancy continues to pose a significant threat to public health. Notably, an unexpected source of vaccine misinformation has been the Surgeon General of the State of Florida, Dr. Joseph Ladapo. While both a tenured faculty member of the University of Florida, College of Medicine and the Surgeon General of Florida, Dr. Ladapo has delivered official Florida Department of Health statements regarding COVID-19 vaccines that run contrary to those of the U.S. Centers for Disease Control and Prevention (CDC). While tenure is designed to protect those with contrarian views, we believe that the University has an ethical obligation to condemn misleading statements that put public health at risk. Herein, we explore the challenges of managing misinformation disseminated by someone who is simultaneously a tenured professor at a public, state-supported university, and a politically appointed public health official.

Comprehensive geriatric assessment predicts listing for kidney transplant in patients with end-stage renal disease: a retrospective cohort study.

BACKGROUND: Comprehensive geriatric assessment (CGA) involves a formal broad approach to assess frailty and creating a plan for management. However, the impact of CGA and its components on listing for kidney transplant in older adults has not been investigated. METHODS: We performed a single-center retrospective study of patients with end-stage renal disease who underwent CGA during kidney transplant candidacy evaluation between 2017 and 2021. All patients ≥ 65 years old and those under 65 with any team member concern for frailty were referred for CGA, which included measurements of healthcare utilization, comorbidities, social support, short physical performance battery, Montreal Cognitive Assessment (MoCA), and Physical Frailty Phenotype (FPP), and estimate of surgical risk by the geriatrician. RESULTS: Two hundred and thirty patients underwent baseline CGA evaluation; 58.7% (135) had high CGA ("Excellent" or "Good" rating for transplant candidacy) and 41.3% (95) had low CGA ratings ("Borderline," "Fair," or "Poor"). High CGA rating (OR 8.46; p < 0.05), greater number of CGA visits (OR 4.93; p = 0.05), younger age (OR 0.88; p < 0.05), higher MoCA scores (OR 1.17; p < 0.05), and high physical activity (OR 4.41; p < 0.05) were all associated with listing on transplant waitlist. CONCLUSIONS: The CGA is a useful, comprehensive tool to help select older adults for kidney transplantation. Further study is needed to better understand the predictive value of CGA in predicting post-operative outcomes.

Factors Predicting 90-Day Readmissions for US Older Adult Burn Patients From the 2016-2018 Nationwide Readmissions Database.

Older adults with burn injuries have a high risk of readmission after the initial burn admission. Frailty is associated with poor outcomes from the initial burn injury, however, it remains unknown if frailty impacts readmission in older adults after the initial index burn admission. Our study aims to examine patient, frailty, burn, surgical, and hospital factors that contribute to 90-day readmissions in older adults with an acute burn. Patients ≥ 50 years of age with an acute burn diagnosis in the Nationwide Readmissions Database (2016-2018) were included. Patients who died during index admissions were excluded from the readmission analysis. Patient, burn, hospital stay, and hospital characteristics were analyzed. Using variables significant from univariate logistic regression analysis, we performed a multivariable logistic regression analysis to identify factors associated with 90-day readmissions. A total of 41,500 weighted index burn admissions were included. The 90-day readmission rate was 25.6% (10,641/41,500). Almost all the readmissions were unplanned (96.5%). The most common primary readmission diagnoses based on Clinical Classifications Software Refined were burn injury and septicemia. Multivariable logistic regression analysis demonstrated an increased hospital frailty risk and lower-body-only burns were associated with an increased risk of readmission. Notably, surgical treatment of the burn injury during the index admission was associated with a decreased risk of readmission. This highlights the importance and challenges of surgical decision-making for older adults with high comorbidity burden. Future studies are needed to develop decision guides to help clinicians identify patients who would benefit from surgical treatment versus more conservative strategies in older adult patients with burns.

Towards trustworthy seizure onset detection using workflow notes.

A major barrier to deploying healthcare AI is trustworthiness. One form of trustworthiness is a model's robustness across subgroups: while models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal features, leading to errors in hidden subgroups. To take a step closer towards trustworthy seizure onset detection from EEG, we propose to leverage annotations that are produced by healthcare personnel in routine clinical workflows-which we refer to as workflow notes-that include multiple event descriptions beyond seizures. Using workflow notes, we first show that by scaling training data to 68,920 EEG hours, seizure onset detection performance significantly improves by 12.3 AUROC (Area Under the Receiver Operating Characteristic) points compared to relying on smaller training sets with gold-standard labels. Second, we reveal that our binary seizure onset detection model underperforms on clinically relevant subgroups (e.g., up to a margin of 6.5 AUROC points between pediatrics and adults), while having significantly higher FPRs (False Positive Rates) on EEG clips showing non-epileptiform abnormalities (+19 FPR points). To improve model robustness to hidden subgroups, we train a multilabel model that classifies 26 attributes other than seizures (e.g., spikes and movement artifacts) and significantly improve overall performance (+5.9 AUROC points) while greatly improving performance among subgroups (up to +8.3 AUROC points) and decreasing false positives on non-epileptiform abnormalities (by 8 FPR points). Finally, we find that our multilabel model improves clinical utility (false positives per 24 EEG hours) by a factor of 2×.

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.

Moving Beyond Mortality: Development and Application of a Desirability of Outcome Ranking (DOOR) Endpoint for Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia.

BACKGROUND: Hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) are frequently caused by multidrug-resistant organisms. Patient-centered endpoints in clinical trials are needed to develop new antibiotics for HABP/VABP. Desirability of outcome ranking (DOOR) is a paradigm for the design, analysis, and interpretation of clinical trials based on a patient-centered, benefit-risk evaluation. METHODS: A multidisciplinary committee created an infectious diseases DOOR endpoint customized for HABP/VABP, incorporating infectious complications, serious adverse events, and mortality. We applied this to 2 previously completed, large randomized controlled trials for HABP/VABP. ZEPHyR compared vancomycin to linezolid and VITAL compared linezolid to tedizolid. For each trial, we evaluated the DOOR distribution and probability, including DOOR component and partial credit analyses. We also applied DOOR in subgroup analyses. RESULTS: In both trials, the HABP/VABP DOOR demonstrated similar overall clinical outcomes between treatment groups. In ZEPHyR, the probability that a participant treated with linezolid would have a more desirable outcome than a participant treated with vancomycin was 50.2% (95% confidence interval [CI], 45.1%--55.3%). In VITAL, the probability that a participant treated with tedizolid would have a more desirable outcome than a participant treated with linezolid was 48.7% (95% CI, 44.8%-52.6%). The DOOR component analysis revealed that participants treated with tedizolid had a less desirable outcome than those treated with linezolid when considering clinical response alone. However, participants with decreased renal function had improved overall outcomes with tedizolid. CONCLUSIONS: The HABP/VABP DOOR provided more granular information about clinical outcomes than is typically presented in clinical trials. HABP/VABP trials would benefit from prospectively using DOOR.

Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans.

How the human cortex integrates ("binds") information encoded by spatially distributed neurons remains largely unknown. One hypothesis suggests that synchronous bursts of high-frequency oscillations ("ripples") contribute to binding by facilitating integration of neuronal firing across different cortical locations. While studies have demonstrated that ripples modulate local activity in the cortex, it is not known whether their co-occurrence coordinates neural firing across larger distances. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in the supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each other's firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during non-rapid eye movement sleep and waking, in temporal and Rolandic cortices, and at distances up to 16 mm (the longest tested). Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, indicating that it was not secondary to non-oscillatory activation. Co-rippling enhanced prediction was strongly modulated by ripple phase, supporting the most common posited mechanism for binding-by-synchrony. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and further enhanced when multiple sites co-ripple, supporting re-entrant facilitation. Together, these results support the hypothesis that trans-cortical co-occurring ripples increase the integration of neuronal firing of neurons in different cortical locations and do so in part through phase-modulation rather than unstructured activation.

Quantitative metrics of the LV trabeculated layer by cardiac CT and cardiac MRI in patients with suspected noncompaction cardiomyopathy.

OBJECTIVES: To compare cardiac computed tomography (CCT) and cardiac magnetic resonance (CMR) for the quantitative assessment of the left ventricular (LV) trabeculated layer in patients with suspected noncompaction cardiomyopathy (NCCM). MATERIALS AND METHODS: Subjects with LV excessive trabeculation who underwent both CMR and CCT imaging as part of the prospective international multicenter NONCOMPACT clinical study were included. For each subject, short-axis CCT and CMR slices were matched. Four quantitative metrics were estimated: 1D noncompacted-to-compacted ratio (NCC), trabecular-to-myocardial area ratio (TMA), trabecular-to-endocardial cavity area ratio (TCA), and trabecular-to-myocardial volume ratio (TMV). In 20 subjects, end-diastolic and mid-diastolic CCT images were compared for the quantification of the trabeculated layer. Relationships between the metrics were investigated using linear regression models and Bland-Altman analyses. RESULTS: Forty-eight subjects (49.9 ± 12.8 years; 28 female) were included in this study. NCC was moderately correlated (r = 0.62), TMA and TMV were strongly correlated (r = 0.78 and 0.78), and TCA had excellent correlation (r = 0.92) between CMR and CCT, with an underestimation bias from CCT of 0.3 units, and 5.1, 4.8, and 5.4 percent-points for the 4 metrics, respectively. TMA, TCA, and TMV had excellent correlations (r = 0.93, 0.96, 0.94) and low biases (- 3.8, 0.8, - 3.8 percent-points) between the end-diastolic and mid-diastolic CCT images. CONCLUSIONS: TMA, TCA, and TMV metrics of the LV trabeculated layer in patients with suspected NCCM demonstrated high concordance between CCT and CMR images. TMA and TCA were highly reproducible and demonstrated minimal differences between mid-diastolic and end-diastolic CCT images. CLINICAL RELEVANCE STATEMENT: The results indicate similarity of CCT to CMR for quantifying the LV trabeculated layer, and the small differences in quantification between end-diastole and mid-diastole demonstrate the potential for quantifying the LV trabeculated layer from clinically performed coronary CT angiograms. KEY POINTS: • Data on cardiac CT for quantifying the left ventricular trabeculated layer are limited. • Cardiac CT yielded highly reproducible metrics of the left ventricular trabeculated layer that correlated well with metrics defined by cardiac MR. • Cardiac CT appears to be equivalent to cardiac MR for the quantification of the left ventricular trabeculated layer.

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach.

Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.

Clinical outcome prediction using observational supervision with electronic health records and audit logs.

OBJECTIVE: Audit logs in electronic health record (EHR) systems capture interactions of providers with clinical data. We determine if machine learning (ML) models trained using audit logs in conjunction with clinical data ("observational supervision") outperform ML models trained using clinical data alone in clinical outcome prediction tasks, and whether they are more robust to temporal distribution shifts in the data. MATERIALS AND METHODS: Using clinical and audit log data from Stanford Healthcare, we trained and evaluated various ML models including logistic regression, support vector machine (SVM) classifiers, neural networks, random forests, and gradient boosted machines (GBMs) on clinical EHR data, with and without audit logs for two clinical outcome prediction tasks: major adverse kidney events within 120 days of ICU admission (MAKE-120) in acute kidney injury (AKI) patients and 30-day readmission in acute stroke patients. We further tested the best performing models using patient data acquired during different time-intervals to evaluate the impact of temporal distribution shifts on model performance. RESULTS: Performance generally improved for all models when trained with clinical EHR data and audit log data compared with those trained with only clinical EHR data, with GBMs tending to have the overall best performance. GBMs trained with clinical EHR data and audit logs outperformed GBMs trained without audit logs in both clinical outcome prediction tasks: AUROC 0.88 (95% CI: 0.85-0.91) vs. 0.79 (95% CI: 0.77-0.81), respectively, for MAKE-120 prediction in AKI patients, and AUROC 0.74 (95% CI: 0.71-0.77) vs. 0.63 (95% CI: 0.62-0.64), respectively, for 30-day readmission prediction in acute stroke patients. The performance of GBM models trained using audit log and clinical data degraded less in later time-intervals than models trained using only clinical data. CONCLUSION: Observational supervision with audit logs improved the performance of ML models trained to predict important clinical outcomes in patients with AKI and acute stroke, and improved robustness to temporal distribution shifts.

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.

Automated detection of immune effector cell-associated neurotoxicity syndrome via quantitative EEG.

OBJECTIVE: To develop an automated, physiologic metric of immune effector cell-associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor-T cell therapy. METHODS: We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor-T cell therapy with a CD19 or B-cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell-associated neurotoxicity syndrome score. RESULTS: The EEG immune effector cell-associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R2 of 0.69 (95% CI of 0.59-0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R2 0.24, p = 0.008), minimum platelets (R2 -0.29, p = 0.001), and dexamethasone usage (R2 0.42, p < 0.0001). The score significantly correlated with duration of neurotoxicity (R2 0.31, p < 0.0001). INTERPRETATION: The EEG immune effector cell-associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T-cell therapy.

Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans.

Synchronous bursts of high frequency oscillations ('ripples') are hypothesized to contribute to binding by facilitating integration of neuronal firing across cortical locations. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each-other's firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during NREM sleep and waking, in temporal and Rolandic cortices, and at distances up to 16mm. Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, and were strongly modulated by ripple phase. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and further enhanced when multiple sites co-ripple. Together, these results support the hypothesis that trans-cortical co-ripples increase the integration of neuronal firing of neurons in different cortical locations, and do so in part through phase-modulation rather than unstructured activation.