Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly. METHODS: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results. RESULTS: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation. CONCLUSIONS: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition.

Pen needle use patterns in an insured population with diabetes: US retrospective claims analysis.

BACKGROUND: People with diabetes who inject insulin with pen devices may reuse the pen needles (PNs), a practice that can cause PN tip deformity, breakage, and contamination, and that is associated with lipohypertrophy and injection-related pain. OBJECTIVE: This retrospective study aimed to estimate the extent of PN reuse among people with diabetes in two insured populations in the United States. METHODS: Using claims data for Commercial Fully Insured (CFI) and Medicare Advantage (MA) populations from 1-Oct-2018 to 31-Dec-2022, we identified adults with type 1 or type 2 diabetes (T1D/T2D) who had ≥1 claim for PNs and ≥2 claims for insulin from 1-Jan-2019 to 31-Dec-2021, with continuous medical/pharmacy eligibility for 3 months before first claim and 1 year after (follow-up). Those receiving hospice or palliative care or using mail-order prescriptions were excluded. We compared actual annual fill rate of PNs with expected fill rate (assuming single use) according to prescribed insulin regimen. Whether the annual actual-to-expected ratio for PN numbers equaled 1 was evaluated using sign tests with 2-sided p-values. RESULTS: Median annual actual-to-expected ratios ranged from 0.41 (T1D basal+prandial cohort) to 0.82 (T2D basal cohort; all p<0.001) in the CFI population (N=10,854), and from 0.55 (TID basal+prandial) to 1.10 (T2D basal and basal+prandial; p=0.382 to <0.001) in the MA population (N=32,495); medians were 0.34 and 0.55 for four expected T2D basal+prandial injections/day in CFI and MA populations, respectively (p<0.001). Annual actual-to-expected ratios were <1 for 62% and 47% of CFI and MA populations, respectively. An estimated 2-27% and 0-17%, respectively, depending on insulin regimen, had inadequate supplies of PNs suggesting that PNs could have been used ≥5 times. CONCLUSIONS: These findings highlight the need for educating people with diabetes about reasons for avoiding PN reuse and the key role that pharmacists can play in providing this information and adequate supplies of PNs.

Assessment of retrospective collection of EQ-5D-5L in a US COVID-19 population.

BACKGROUND: It is imperative to evaluate health related quality of life (HRQoL) pre-COVID-19, but there is currently no evidence of the retrospective application of the EuroQol 5-Dimension, 5 level version (EQ-5D-5L) for COVID-19 studies. METHODS: Symptomatic patients with SARS-CoV-2 at CVS Health US test sites were recruited between 01/31/2022-04/30/2022. Consented participants completed the EQ-5D-5L questionnaire twice: a modified version where all the questions were past tense to retrospectively assess pre-COVID-19 baseline QoL, and the standard version in present tense to assess current HRQoL. Duncan's new multiple range test was adopted for post analysis of variance pairwise comparisons of EQ visual analog scale (EQ VAS) means between problem levels for each of 5 domains. A linear mixed model was applied to check whether the relationship between EQ VAS and utility index (UI) was consistent pre-COVID-19 and during COVID-19. Matching-adjusted indirect comparison was used to compare pre-COVID-19 UI and EQ VAS scores with those of the US population. Lastly, Cohen's d was used to quantify the magnitude of difference in means between two groups. RESULTS: Of 676 participants, 10.2% were age 65 or more years old, 73.2% female and 71.9% white. Diabetes was reported by 4.7% participants and hypertension by 11.2%. The estimated coefficient for the interaction of UI-by-retrospective collection indicator (0 = standard prospective collection, 1 = retrospective for pre-COVID-19), -4.2 (SE: 3.2), P = 0.197, indicates that retrospective collection does not significantly alter the relationship between EQ VAS and UI. After adjusting for age, gender, diabetes, hypertension, and percent of mobility problems, the predicted means of pre-COVID-19 baseline EQ VAS and UI were 84.6 and 0.866, respectively. Both means were close to published US population norms (80.4 and 0.851) compared to those observed (87.4 and 0.924). After adjusting for age, gender, diabetes, and hypertension, the calculated ES between pre-COVID-19 and COVID-19 for UI and EQ VAS were 0.15 and 0.39, respectively. Without retrospectively collected EQ-5D-5L, using US population norms tended to underestimate the impact of COVID-19 on HRQoL. CONCLUSION: At a group level the retrospectively collected pre-COVID-19 EQ-5D-5L is adequate and makes it possible to directly evaluate the impact of COVID-19 on HRQoL. ( ClinicalTrials.gov NCT05160636).

Impaired insight in schizophrenia: impact on patient-reported and physician-reported outcome measures in a randomized controlled trial.

BACKGROUND: Impaired insight poses a challenge in the treatment of patients with schizophrenia because of its potential to jeopardize therapeutic engagement and medication adherence. This study explored how insight impairment, graded from none to extreme, is related to patient-reported mental health status, depression, and neurocognition in schizophrenia. METHODS: In a post hoc analysis of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study (NCT00014001), insight was measured using the Positive and Negative Syndrome Scale (PANSS) Item G12 (lack of insight). Additional assessments for this analysis included the 12-Item Short-Form Health Survey (SF-12) Mental Component Summary (MCS), physician- and patient-reported Clinical Global Impression-Severity (CGI-S), MATRICS Consensus Cognitive Battery, and Calgary Depression Scale for Schizophrenia. Relationships between patient-reported outcomes and PANSS total and Item G12 ratings were evaluated. RESULTS: Among 1431 CATIE study participants in this analysis, increasingly impaired insight at baseline was significantly associated with better patient-reported quality of life (QoL), lower baseline depression, and greater divergence between physician- and patient-reported illness severity. Patients with more severely impaired insight reported milder illness compared with physician reports, particularly those with moderate-severe to extreme impairment (PANSS Item G12 rating ≥ 5), approximately 10% (138/1431) of CATIE participants. For the 90% of patients with PANSS Item G12 ratings < 5, patient-reported QoL decreased with increasing symptoms. SF-12 MCS scores were linearly related to baseline PANSS total score only in patients with PANSS total score < 90 (moderately ill or better), and better symptom scores were associated with higher QoL. No significant relationship between insight and neurocognition was observed. CONCLUSIONS: In the small subgroup (10%) of CATIE study patients with schizophrenia and PANSS Item G12 ratings ≥5, moderate-severe-severe/extreme insight impairment was associated with significantly more positive perception of QoL and illness severity by the patient versus the treating physician. This was not observed in the remaining 90% of patients with normal to moderately impaired insight, suggesting that poor insight as a threat to the validity of self-report is uncommon.

Long-term effect of aripiprazole lauroxil on health-related quality of life in patients with schizophrenia.

BACKGROUND: This post hoc analysis of clinical trial data evaluated long-term, self-reported mental and physical health-related quality of life (HRQoL) scores in schizophrenia patients receiving aripiprazole lauroxil (AL), an atypical long-acting injectable (LAI) antipsychotic approved for the treatment of schizophrenia in adults. METHODS: The study population included 291 stable schizophrenia outpatients enrolled in 2 consecutive long-term safety studies of AL given every 4 weeks for up to 124 weeks. HRQoL was measured using the SF-36v2® Health Survey (SF-36v2) over the course of the follow-up. The primary outcome was change in SF-36v2 mental component summary (MCS) and physical component summary (PCS) scores from baseline to 124 weeks. To contextualize these scores, descriptive analyses were conducted to compare the scores with available scores for the general population as well as for other populations with chronic medical (ie, hypertension and type 2 diabetes) or psychiatric (ie, depression) conditions. RESULTS: Results from this post hoc analysis indicated that the mean MCS score for patients continuing AL improved significantly from baseline over 124 weeks (P < .05, all timepoints), while mean PCS score showed little change over 124 weeks. At baseline, patients had lower (worse) MCS scores than the normed general population, but by week 124, patients had MCS scores comparable to those in the general population. This pattern of change was not observed with PCS scores. Comparison of study MCS scores with those associated with other diseases showed that this schizophrenia cohort had lower scores than those with chronic medical conditions but higher scores than those with depression. PCS scores were higher in the study population than published scores for all reference populations at baseline and week 124. CONCLUSIONS: In this post hoc analysis, outpatients with schizophrenia who continued the LAI antipsychotic AL showed gradual and sustained improvement in self-reported mental HRQoL over several years of follow-up, whereas self-reported physical HRQoL did not change. By the end of follow-up, mental health scores of study patients with schizophrenia were comparable to those of the general population and better than those of patients with depression. TRIAL REGISTRATION: ClinicalTrials.gov (NCT01626456 [trial registration date: June 15, 2012] and NCT01895452 [trial registration date: July 5, 2013]).

Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score.

BACKGROUND: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. METHODS: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model. RESULTS: There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722. CONCLUSIONS: Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.

Randomized trial outcomes of a TTM-tailored condom use and smoking intervention in urban adolescent females.

Smoking and sexual risk behaviors in urban adolescent females are prevalent and problematic. Family planning clinics reach those who are at most risk. This randomized effectiveness trial evaluated a transtheoretical model (TTM)-tailored intervention to increase condom use and decrease smoking. At baseline, a total of 828 14- to 17-year-old females were recruited and randomized within four urban family planning clinics. Participants received TTM or standard care (SC) computerized feedback and stage-targeted or SC counseling at baseline, 3, 6 and 9 months. Blinded follow-up telephone surveys were conducted at 12 and 18 months. Analyses revealed significantly more consistent condom use in the TTM compared with the SC group at 6 and 12, but not at 18 months. In baseline consistent condom users (40%), significantly less relapse was found in the TTM compared with the SC group at 6 and 12, but not at 18 months. No significant effects for smoking prevention or cessation were found, although cessation rates matched those found previously. This TTM-tailored intervention demonstrated effectiveness for increasing consistent condom use at 6 and 12 months, but not at 18 months, in urban adolescent females. This intervention, if replicated, could be disseminated to promote consistent condom use and additional health behaviors in youth at risk.

Household Influenza Transmission and Healthcare Resource Utilization Among Patients Treated with Baloxavir vs Oseltamivir: A United States Outpatient Prospective Survey.

INTRODUCTION: Influenza is a common, seasonal infectious disease with broad medical, economic, and social consequences. Real-world evidence on the effect of influenza treatment on household transmission and healthcare resource utilization is limited in outpatient settings in the USA. This study examined the real-world effectiveness of baloxavir vs oseltamivir in reducing influenza household transmission and healthcare resource utilization. METHODS: This prospective electronic survey on patient-reported outcomes was conducted between October 2022 and May 2023 via CVS Pharmacy in the USA. Adult participants (≥ 18 years old) were eligible if they filled a prescription for baloxavir or oseltamivir at a CVS Pharmacy within 2 days of influenza symptom onset. Participant demographics, household transmission, and all-cause healthcare resource utilization were collected. Transmission and utilization outcomes were assessed using χ2 and Fisher exact tests. RESULTS: Of 87,871 unique patients contacted, 1346 (1.5%) consented. Of 374 eligible patients, 286 (90 baloxavir- and 196 oseltamivir-treated patients) completed the survey and were included in the analysis. Mean age of participants was 45.4 years, 65.6% were female, and 86.7% were White. Lower household transmission was observed with baloxavir compared with oseltamivir therapy (17.8% vs 26.5%; relative risk = 0.67; 95% CI 0.41-1.11). Healthcare resource utilization, particularly emergency department visits (0.0% vs 4.6%), was also numerically lower in the baloxavir-treated group; no hospitalizations were reported in either cohort. CONCLUSIONS: The findings from this real-world study suggest that antiviral treatment of influenza with baloxavir may decrease household transmission and reduce healthcare resource utilization compared with oseltamivir.

Multimorbidity in Atherosclerotic Cardiovascular Disease and Its Associations With Adverse Cardiovascular Events and Healthcare Costs: A Real-World Evidence Study.

Background: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of mortality and disability in the United States and worldwide. Objective: To assess the multimorbidity burden and its associations with adverse cardiovascular events (ACE) and healthcare costs among patients with ASCVD. Methods: This is a retrospective observational cohort study using Aetna claims database. Patients with ASCVD were identified during the study period (1/1/2018-10/31/2021). The earliest ASCVD diagnosis date was identified as the index date. Qualified patients were ≥18 years of age and had ≥12 months of health plan enrollment before and after the index date. Comorbid conditions were assessed using all data available within 12 months prior to and including the index date. Association rule mining was applied to identify comorbid condition combinations. ACEs and healthcare costs were assessed using all data within 12 months after the index date. Multivariable generalized linear models were performed to examine the associations between multimorbidity and ACEs and healthcare costs. Results: Of 223 923 patients with ASCVD (mean [SD] age, 73.6 [10.7] years; 42.2% female), 98.5% had ≥2, and 80.2% had ≥5 comorbid conditions. The most common comorbid condition dyad was hypertension-hyperlipidemia (78.7%). The most common triad was hypertension-hyperlipidemia-pain disorders (61.1%). The most common quartet was hypertension-hyperlipidemia-pain disorders-diabetes (30.2%). The most common quintet was hypertension-hyperlipidemia-pain disorders-diabetes-obesity (16%). The most common sextet was hypertension-hyperlipidemia-pain disorders-diabetes-obesity-osteoarthritis (7.6%). The mean [SD] number of comorbid conditions was 7.1 [3.2]. The multimorbidity burden tended to increase in older age groups and was comparatively higher in females and in those with higher social vulnerability. The increased number of comorbid conditions was significantly associated with increased ACEs and increased healthcare costs. Discussion: Extremely prevalent multimorbidity should be considered in the context of clinical decision-making to optimize secondary prevention of ASCVD. Conclusions: Multimorbidity was extremely prevalent among patients with ASCVD. Multimorbidity patterns varied considerably across ASCVD patients and by age, gender, and social vulnerability status. Multimorbidity was strongly associated with ACEs and healthcare costs.

Effectiveness of BNT162b2 BA.4/5 Bivalent COVID-19 Vaccine against Long COVID Symptoms: A US Nationwide Study.

BACKGROUND: Long COVID has become a central public health concern. This study characterized the effectiveness of BNT162b2 BA.4/5 bivalent COVID-19 vaccine (bivalent) against long COVID symptoms. METHODS: Symptomatic US adult outpatients testing positive for SARS-CoV-2 were recruited between 2 March and 18 May 2023. Symptoms were assessed longitudinally using a CDC-based symptom questionnaire at Week 4, Month 3, and Month 6 following infection. The odds ratio (OR) of long COVID between vaccination groups was assessed by using mixed-effects logistic models, adjusting for multiple covariates. RESULTS: At Week 4, among 505 participants, 260 (51%) were vaccinated with bivalent and 245 (49%) were unvaccinated. Mean age was 46.3 years, 70.7% were female, 25.1% had ≥1 comorbidity, 43.0% prior infection, 23.0% reported Nirmatrelvir/Ritonavir use. At Month 6, the bivalent cohort had 41% lower risk of long COVID with ≥3 symptoms (OR: 0.59, 95% CI, 0.36-0.96, p = 0.034) and 37% lower risk of ≥2 symptoms (OR: 0.63, 95% CI, 0.41-0.96, p = 0.030). The bivalent cohort reported fewer and less durable symptoms throughout the six-month follow-up, driven by neurologic and general symptoms, especially fatigue. CONCLUSIONS: Compared with unvaccinated participants, participants vaccinated with the bivalent were associated with approximately 40% lower risk of long COVID and less symptom burden over the six-month study duration.

A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis.

BACKGROUND: Multidisciplinary Heart Teams (HTs) play a central role in the management of valvular heart diseases. However, the comprehensive evaluation of patients' data can be hindered by logistical challenges, which in turn may affect the care they receive. AIMS: This study aimed to explore the ability of artificial intelligence (AI), particularly large language models (LLMs), to improve clinical decision-making and enhance the efficiency of HTs. METHODS: Data from patients with severe aortic stenosis presented at HT meetings were retrospectively analysed. A standardised multiple-choice questionnaire, with 14 key variables, was processed by the OpenAI Chat Generative Pre-trained Transformer (GPT)-4. AI-generated decisions were then compared to those made by the HT. RESULTS: This study included 150 patients, with ChatGPT agreeing with the HT's decisions 77% of the time. The agreement rate varied depending on treatment modality: 90% for transcatheter valve implantation, 65% for surgical valve replacement, and 65% for medical treatment. CONCLUSIONS: The use of LLMs offers promising opportunities to improve the HT decision-making process. This study showed that ChatGPT's decisions were consistent with those of the HT in a large proportion of cases. This technology could serve as a failsafe, highlighting potential areas of discrepancy when its decisions diverge from those of the HT. Further research is necessary to solidify our understanding of how AI can be integrated to enhance the decision-making processes of HTs.

Development and validation of a mortality risk-adjustment model for patients hospitalized for exacerbations of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death. We sought to develop and validate a mortality risk-adjustment model to enhance hospital performance measurement and to support comparative effectiveness research. METHODS: Using a derivation cohort of 69,299 AECOPD admissions in 2005-2006 across 172 hospitals, we developed a logistic regression model with age, sex, laboratory results, vital signs, and secondary diagnosis-based comorbidities as covariates. We converted the model coefficients into a score system and validated it using 33,327 admissions from 2007. We used the c-statistic to assess model fit. RESULTS: In the derivation and validation cohorts, the median (interquartile range) age was 72 (range, 63-79) versus 71 (range, 62-79) years; 45.6% versus 45.9% were male; and in-hospital mortality rates were 3.2% versus 2.9%, respectively. The predicted probability of deaths for individuals ranged from 0.004 to 0.942 versus 0.001 to 0.933, respectively. The relative contribution of variables to the predictive ability of the derivation model was age (18.3%), admission laboratory results (39.9%), vital signs (14.7%), altered mental status (7.1%), and comorbidities (19.9%). The model c-statistic was 0.83 (95% CI: 0.82, 0.84) versus 0.84 (95% CI: 0.83, 0.85), respectively, with good calibration for both cohorts. CONCLUSIONS: A mortality prediction model combining clinical and administrative data that can be obtained from electronic health records demonstrated good discrimination among patients hospitalized for AECOPD. The addition of admission vital signs and laboratory results enhanced clinical validity and could be applied to future comparative effectiveness research and hospital profiling efforts.

Using enriched observational data to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients.

BACKGROUND: Growth and development in early childhood are associated with rapid physiological changes. We sought to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients using objective physiological variables on admission in addition to administrative variables. METHODS: Age-specific laboratory and vital sign variables were crafted for neonates (up to 30 d old), infants/toddlers (1-23 mo), and children (2-17 y). We fit 3 logistic regression models, 1 for each age group, using a derivation cohort comprising admissions from 2000-2001 in 215 hospitals. We validated the models with a separate validation cohort comprising admissions from 2002-2007 in 62 hospitals. We used the c statistic to assess model fit. RESULTS: The derivation cohort comprised 93,011 neonates (0.55% mortality), 46,152 infants/toddlers (0.37% mortality), and 104,010 children (0.40% mortality). The corresponding numbers of admissions (mortality rates) for the validation cohort were 162,131 (0.50%), 33,818 (0.09%), and 73,362 (0.20%), respectively. The c statistics for the 3 models were 0.94, 0.91, and 0.92, respectively, for the derivation cohort and 0.91, 0.86, and 0.93, respectively, for the validation cohort. The relative contributions of physiological versus administrative variables to the model fit were 52% versus 48% (neonates), 93% versus 7% (infants/toddlers), and 82% versus 18% (children). CONCLUSIONS: The thresholds for physiological determinants varied by age. Common physiological variables assessed on admission contributed significantly to predicting mortality for hospitalized pediatric patients. These models may have practical utility in risk adjustment for pediatric outcomes and comparative effectiveness research when physiological data are captured through the electronic medical record.

Cost-Effectiveness of AF Screening With 2-Week Patch Monitors: The mSToPS Study.

BACKGROUND: The mSToPS study (mHealth Screening to Prevent Strokes) reported screening older Americans at risk for atrial fibrillation (AF) and stroke using 2-week patch monitors was associated with increased rates of AF diagnosis and anticoagulant prescription within 1 year and improved clinical outcomes at 3 years relative to matched controls. Cost-effectiveness of this AF screening approach has not been explored. METHODS: We conducted a US-based health economic analysis of AF screening using patient-level data from mSToPS. Clinical outcomes, resource use, and costs were obtained through 3 years using claims data. Individual costs, survival, and quality-adjusted life years (QALYs) were projected over a lifetime horizon using regression modeling, US life tables, and external data where needed. Adjustment between groups was performed using propensity score bin bootstrapping. RESULTS: Screening participants (mean age, 74 years, 41% female, median CHA2DS2-VASC score 3) wore on average 1.7 two-week monitors at a mean cost of $614/person. Over 3 years, outpatient visits were more frequent for monitored than unmonitored individuals (difference 190 per 100 patient-years [95% CI, 82-298]), but emergency department visits (-8.3 [95% CI, -12.6 to -4.1]) and hospitalizations (-15.2 [CI, -22 to -8.6]) were less frequent. Total adjusted 3-year costs were slightly higher (mean difference, $1551 [95% CI, -$1047 to $4038]) in the monitoring group. In patient-level projections, the monitoring group had slightly greater quality-adjusted survival (8.81 versus 8.71 QALYs, difference, 0.09 [95% CI, -0.05 to 0.24]) and slightly higher lifetime costs, resulting in an incremental cost-effectiveness ratio of $36 100/QALY gained. With bootstrap resampling, the incremental cost-effectiveness ratio for monitoring was <$50 000/QALY in 64% of study replicates, and <$150 000/QALY in 91%. CONCLUSIONS: Using lifetime projections derived from the mSToPS study, we found that AF screening using 2-week patch monitors in older Americans was associated with high economic value. Confirmation of these uncertain findings in a randomized trial is warranted. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02506244.

Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging.

PURPOSE: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. METHODS: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. RESULTS: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26°. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. CONCLUSION: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams.

Impact of COVID-19 and effects of booster vaccination with BNT162b2 on six-month long COVID symptoms, quality of life, work productivity and activity impairment during Omicron.

BACKGROUND: Longitudinal estimates of long COVID burden during Omicron remain limited. This study characterized long-term impacts of COVID-19 and booster vaccination on symptoms, Health-Related Quality of Life (HRQoL), and Work Productivity Activity Impairment (WPAI). METHODS: Outpatients with ≥ 1 self-reported symptom and positive SARS-CoV-2 test at CVS Health United States test sites were recruited between 01/31 and 04/30/2022. Symptoms, EQ-5D and WPAI were collected via online surveys until 6 months following infection. Both observed and model-based estimates were analyzed. Effect sizes based on Cohen's d quantified the magnitude of outcome changes over time, within and between vaccination groups. Mixed models for repeated measures were conducted for multivariable analyses, adjusting for covariates. Logistic regression assessed odds ratio (OR) of long COVID between vaccination groups. RESULTS: At long COVID start (Week 4), 328 participants included 87 (27%) Boosted with BNT162b2, 86 (26%) with a BNT162b2 primary series (Primed), and 155 (47%) Unvaccinated. Mean age was 42.0 years, 73.8% were female, 26.5% had ≥ 1 comorbidity, 36.9% prior infection, and 39.6% reported ≥ 3 symptoms (mean: 3.1 symptoms). At Month 6, among 260 participants, Boosted reported a mean of 1.1 symptoms versus 3.4 and 2.8 in Unvaccinated and Primed, respectively (p < 0.001). Boosted had reduced risks of ≥ 3 symptoms versus Unvaccinated (observed: OR 0.22, 95% CI 0.10-0.47, p < 0.001; model-based: OR 0.36, 95% CI 0.15-0.87, p = 0.019) and Primed (observed: OR 0.29, 95% CI 0.13-0.67, p = 0.003; model-based: OR 0.59, 95% CI 0.21-1.65, p = 0.459). Results were consistent using ≥ 2 symptoms. Regarding HRQoL, among those with long COVID, Boosted had higher EQ-5D Utility Index (UI) than Unvaccinated (observed: 0.922 vs. 0.731, p = 0.014; model-based: 0.910 vs. 0.758, p-value = 0.038) and Primed (0.922 vs. 0.648, p = 0.014; model-based: 0.910 vs. 0.708, p-value = 0.008). Observed and model-based estimates for EQ-VAS and UI among Boosted were comparable with pre-COVID since Month 3. Subjects vaccinated generally reported better WPAI scores. CONCLUSIONS: Long COVID negatively impacted HRQoL and WPAI. The BNT162b2 booster could have a beneficial effect in reducing the risk and burden of long COVID. Boosted participants reported fewer and less durable symptoms, which contributed to improve HRQoL and maintain WPAI levels. Limitations included self-reported data and small sample size for WPAI.

Physiological, biochemical, and metabolic changes in diploid and triploid watermelon leaves during flooding.

Background: Flooding is a major stress factor impacting watermelon growth and production globally. Metabolites play a crucial role in coping with both biotic and abiotic stresses. Methods: In this study, diploid (2X) and triploid (3X) watermelons were investigated to determine their flooding tolerance mechanisms by examining physiological, biochemical, and metabolic changes at different stages. Metabolite quantification was done using UPLC-ESI-MS/MS and a total of 682 metabolites were detected. Results: The results showed that 2X watermelon leaves had lower chlorophyll content and fresh weights compared to 3X. The activities of antioxidants, such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), were higher in 3X than in 2X. 3X watermelon leaves showed lower O2 production rates, MDA, and hydrogen peroxide (H2O2) levels in response to flooding, while higher ethylene production was observed. 3X had higher levels of dehydrogenase activity (DHA) and ascorbic acid + dehydrogenase (AsA + DHA), but both 2X and 3X showed a significant decline in the AsA/DHA ratio at later stages of flooding. Among them, 4-guanidinobutyric acid (mws0567), an organic acid, may be a candidate metabolite responsible for flooding tolerance in watermelon and had higher expression levels in 3X watermelon, suggesting that triploid watermelon is more tolerant to flooding. Conclusion: This study provides insights into the response of 2X and 3X watermelon to flooding and the physiological, biochemical, and metabolic changes involved. It will serve as a foundation for future in-depth molecular and genetic studies on flooding response in watermelon.

Impact of Bivalent BA.4/5 BNT162b2 COVID-19 Vaccine on Acute Symptoms, Quality of Life, Work Productivity and Activity Levels among Symptomatic US Adults Testing Positive for SARS-CoV-2 at a National Retail Pharmacy.

Evidence on the impact of COVID-19 vaccination on symptoms, Health-Related Quality of Life (HRQoL) and Work Productivity and Activity Impairment (WPAI) is scarce. We analyzed associations between bivalent BA.4/5 BNT162b2 (BNT162b2) and these patient-reported outcomes (PROs). Symptomatic US adults testing positive for SARS-CoV-2 were recruited between 2 March and 18 May 2023 (CT.gov NCT05160636). PROs were assessed using four questionnaires measuring symptoms, HRQoL and WPAI (a CDC-based symptom survey, PROMIS Fatigue, EQ-5D-5L, WPAI-GH), from pre-COVID to Week 4 following infection. Multivariable analysis using mixed models for repeated measures was conducted, adjusting for several covariates. The study included 643 participants: 316 vaccinated with BNT162b2 and 327 unvaccinated/not up-to-date. Mean (SD) age was 46.5 years (15.9), 71.2% were female, 44.2% reported prior infection, 25.7% had ≥1 comorbidity. The BNT162b2 cohort reported fewer acute symptoms through Week 4, especially systemic and respiratory symptoms. All PROs were adversely affected, especially at Week 1; however, at that time point, the BNT162b2 cohort reported better work performance, driven by less absenteeism, and fewer work hours lost. No significant differences were observed for HRQoL COVID-19 negatively impacted patient outcomes. Compared with unvaccinated/not up-to-date participants, those vaccinated with bivalent BA.4/5 BNT162b2 reported fewer and less persistent symptoms and improved work performance.

Impact of COVID-19 Infection on Health-Related Quality of Life, Work Productivity and Activity Impairment by Symptom-Based Long COVID Status and Age in the US.

COVID-19 infection adversely impacts patients' wellbeing and daily lives. This survey-based study examined differences in patient-reported COVID-19 symptoms, Health-Related Quality of Life (HRQoL) and Work Productivity and Activity Impairment (WPAI) among groups of patients defined based on age and symptom-based long COVID status. Symptomatic, COVID-19-positive US outpatients were recruited from 31 January-30 April 2022. Outcomes were collected via validated instruments at pre-COVID, Day 3, Week 1, Week 4, Month 3 and Month 6 following infection, with changes assessed from pre-COVID and between groups, adjusting for covariates. EQ-5D-5L HRQoL and WPAI scores declined in all groups, especially during the first week. Long COVID patients reported significantly higher symptoms burden and larger drops in HRQoL and WPAI scores than patients without long COVID. Their HRQoL and WPAI scores did not return to levels comparable to pre-COVID through Month 6, except for absenteeism. Patients without long COVID generally recovered between Week 4 and Month 3. Older (>50) and younger adults generally reported comparable symptoms burden and drops in HRQoL and WPAI scores. During the first week of infection, COVID-19-related health issues caused loss of 14 to 26 work hours across the groups. These data further knowledge regarding the differential impacts of COVID-19 on clinically relevant patient groups.

First-line nivolumab plus ipilimumab with two cycles of chemotherapy versus chemotherapy alone (four cycles) in metastatic non-small cell lung cancer: CheckMate 9LA 2-year patient-reported outcomes.

BACKGROUND: In CheckMate 9LA (NCT03215706), first-line nivolumab plus ipilimumab with chemotherapy (2 cycles) significantly improved overall survival versus chemotherapy (4 cycles) in patients with metastatic non-small cell lung cancer and no known sensitising epidermal growth factor receptor/anaplastic lymphoma kinase alterations. We present exploratory patient-reported outcomes (PROs; minimum follow-up, 2 years). METHODS: In patients (N = 719) randomised 1:1 to nivolumab plus ipilimumab with chemotherapy or chemotherapy alone, disease-related symptom burden and health-related quality of life were assessed using the Lung Cancer Symptom Scale (LCSS) and 3-level EQ-5D (EQ-5D-3L). Treatment-phase changes in LCSS average symptom burden index (ASBI), LCSS three-item global index (3-IGI) and EQ-5D-3L visual analogue scale (VAS) and utility index (UI) over time were analysed descriptively and using mixed-effect model repeated measures. Time-to-deterioration/improvement analyses were conducted. RESULTS: Treatment-phase PRO questionnaire completion rates were >80%. Mean treatment-phase changes showed no deterioration from baseline in both arms for LCSS ASBI/3-IGI and EQ-5D-3L VAS/UI; however, minimally important differences were not met. Mixed-effect model repeated measures analyses showed overall reduction in symptom burden from baseline for both arms; changes from baseline for LCSS 3-IGI and EQ-5D-3L VAS/UI were numerically improved with nivolumab plus ipilimumab with chemotherapy versus chemotherapy, but minimally important differences were not met. Nivolumab plus ipilimumab with chemotherapy delayed time-to-definitive-deterioration versus chemotherapy (LCSS ASBI: hazard ratio, 0.62 [95% confidence interval, 0.45-0.87]); results were similar across PRO measures. CONCLUSIONS: At 2-year minimum follow-up, first-line nivolumab plus ipilimumab with chemotherapy reduced the risk of definitive deterioration in disease-related symptom burden and health-related quality of life versus chemotherapy and maintained QoL in patients with metastatic non-small cell lung cancer. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier, NCT03215706.