Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

Objective: To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit from targeted treatment and management in order to preserve glucose metabolism and prevent adverse outcomes. The objective of this study was to utilize readily available laboratory data to train and test the performance of ML-based predictive risk models for progression from prediabetes to diabetes. Methods: The study population was composed of laboratory information services data procured from a large U.S. outpatient laboratory network. The retrospective dataset was composed of 15,029 adults over a 5-year period with initial hemoglobin A1C (A1C) values between 5.0% and 6.4%. ML models were developed using random forest survival methods. The ground truth outcome was progression to A1C values indicative of diabetes (i.e., ≥6.5%) within 5 years. Results: The prediabetes risk classifier model accurately predicted A1C ≥6.5% within 5 years and achieved an area under the receiver-operator characteristic curve of 0.87. The most important predictors of progression from prediabetes to diabetes were initial A1C, initial serum glucose, A1C slope, serum glucose slope, initial HDL, HDL slope, age, and sex. Conclusions: Leveraging readily obtainable laboratory data, our ML risk classifier accurately predicts elevation in A1C associated with progression from prediabetes to diabetes. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition, risk stratification, and optimal management for patients with prediabetes.

Outcomes after radioscapholunate arthrodesis for intra-articular malunion of distal radius fractures.

PURPOSE: To study the clinical, radiological, and functional outcomes after of radioscapholunate (RSL) fusion for intra-articular malunion of the distal radius. METHODS: This retrospective study included 26 patients (17 males and 9 females) with intra-articular malunion of distal radius fractures who underwent RSL arthrodesis using locked miniplates (without distal scaphoid excision) between 2012 and 2020. Their mean age was 43 years (range, 32-56). Patients were assessed radiographically for union and clinically for range of motion, grip strength, and pain (assessed by Visual Analogue Scale (VAS) for pain). Functional evaluation was performed by using the Mayo modified wrist score (MMWS) and the Disabilities for the Arm, Shoulder, and Hand (DASH) questionnaire. RESULTS: All patients showed complete healing at the fusion site after a mean of 8.7 weeks (range, 8-12). The mean follow-up period was 72 months (range, 60-84). The pinch strength improved from a mean of 6.2 kg (range, 3-12) to a mean of 9.8 kg (range, 5-18) which represents 80% of the contralateral side. The mean pinch strength was 7 kg (range, 5-18) which presents 80% of the other side. VAS for pain showed a mean improvement of 72.6%. The DASH score improved to a mean of 19.2 (range, 14-24). The MMWS improved to a mean of 68 (range, 45-86). At the final follow-up period, no degenerative changes were detected in the midcarpal joint. CONCLUSION: RSL arthrodesis (using locked miniplates without distal scaphoid excision) is a reliable surgical procedure to manage cases of radiocarpal OA after intra-articular malunion of distal radius fractures with good clinical and radiological outcomes. LEVEL OF EVIDENCE: Level IV- therapeutic.

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

PURPOSE: Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. MATERIALS AND METHODS: The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. RESULTS: Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. CONCLUSION: The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model.

Rationale & Objective: Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression. Study Design: Retrospective observational study. Setting & Participants: The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m2. Predictors: Patient demographic and laboratory characteristics. Outcomes: Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years. Analytical Approach: Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline. Results: The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex. Limitations: The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model. Conclusions: Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression. Plain-Language Summary: Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

A Cross-Sectional Evaluation of Parents' Awareness Towards Testicular Torsion and Their Response to a Potential Torsion: A Northern Saudi Study.

Background: Assessment of parents' awareness of testicular torsion (TT) is essential to plan for necessary awareness-raising campaigns by policymakers. Hence, the preventable loss of testis due to inadequate awareness can be avoided. We aimed to evaluate the awareness of TT amongst parents from the Aljouf region, KSA, and to assess their response to a potential torsion. Methods: We conducted a cross-sectional study among parents from the Aljouf region. The sample population was obtained using a consecutive sampling method. The present study used a pretested Arabic questionnaire. We used a statistical package for social science software for data analysis. Results: There were 320 parents who participated in different public places for the present study. Of the respondents, 10.6% of their children had sudden pain in the scrotum. More than half (52.2%) had never heard of testicular torsion, and 72.5% of parents agreed that they would seek immediate medical attention for severe testicular pain, but a low (42.5%) proportion of parents responded that they would seek help immediately. Nearly one-fourth of them responded that less than 6 hours is the critical time for repair. Parents who were knowledgeable at the critical time had more odds of presenting to a healthcare facility immediately for both mild (OR = 2.77, CI = 1.55-4.03, p = 0.001) and severe (OR = 1.92, CI = 1.03-3.63, p = 0.032). Conclusion: We found a lack of awareness of TT among Saudi parents. It is suggested to improve the knowledge among them through awareness-raising campaigns by the concerned health authorities through feasible methods. Furthermore, we recommend conducting a futuristic multicenter and exploratory study to find province-specific awareness.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Structural Racism, Social Determinants of Health, and Provider Bias: Impact on Brain Development in Critical Congenital Heart Disease.

Critical congenital heart disease (cCHD) has neurodevelopmental sequelae that can carry into adulthood, which may be due to aberrant brain development or brain injury in the prenatal and perinatal/neonatal periods and beyond. Health disparities based on the intersection of sex, geography, race, and ethnicity have been identified for poorer pre- and postnatal outcomes in the general population, as well as those with cCHD. These disparities are likely driven by structural racism, disparities in social determinants of health, and provider bias, which further compound negative brain development outcomes. This review discusses how aberrant brain development in cCHD early in life is affected by reduced access to quality care (ie, prenatal care and testing, postnatal care) due to divestment in non-White neighbourhoods (eg, redlining) and food insecurity, differences in insurance status, location of residence, and perceived interpersonal racism and bias that disproportionately affects pregnant people of colour who have fewer economic resources. Suggestions are discussed for moving forward with implementing strategies in medical education, clinical care, research, and gaining insight into the communities served to combat disparities and bias while promoting cultural humility.

Khalas date flavonoids inhibited cell viability, induced apoptosis and expression of the pro-autophagy LC3-B gene in human hepatocellular carcinoma cells (HepG2).

Autophagy is a protective mechanism important in human diseases as cancer. We evaluated the impact of khalas date extract (KDE) (20-60 mg/mL) on cell viability, morphological changes, DNA fragmentation and gene expression of LC3B-II associated with autophagosome on HepG2 cell line. The GC/MS identification of KDE showed its high content of flavonoids including quercetin, myricetin, kaempferol and catechol. KDE reduced cell viability of HepG2 with IC50 (31.52 mg/mL). Cells treated with KDE showed two band of DNA fragments at (30 and 40 mg) indicating that KDE induced DNA damage and apoptosis in HepG2. The analysis RT-PCR data showed a 0.2-fold increase in the expression of LC3-B in the cells treated with KDE versus control. We concluded that, KDE flavonoids such as quercetin, myricetin kaempferol exhibited anticancer properties manifested by inhibition of HepG2 cell viability and induction of apoptosis and upregulation of the pro-autophagy LC3-B gene.

Towards understanding various influences on mass balance of the Hoksar Glacier in the Upper Indus Basin using observations.

Mass balance is a good indicator of glacier health and sensitivity to climate change. The debris-covered Hoksar Glacier (HG) in the Upper Indus Basin (UIB) was studied using direct and geodetic mass balances. During the 5-year period from 2013 to 2018, the glacier's mean in situ mass balance (MB) was - 0.95 ± 0.39 m w.e. a-1. Similarly, the glacier's mean geodetic MB from 2000 to 2012 was - 1.20 ± 0.35 m w.e. a-1. The continuously negative MB observations indicated that the HG is losing mass at a higher rate than several other Himalayan glaciers. The glacier showed increased mass loss with increasing altitude, in contrast to the typical decreasing MB with increasing elevation, due to the existence of thick debris cover in the ablation zone, which thins out regularly towards the accumulation zone. Rising temperatures, depleting snowfall and increasing black carbon concentration in the region, indicators of climatic change, have all contributed to the increased mass loss of the HG. During the lean period, when glacier melt contributes significantly to streamflow, the mass loss of glaciers has had a considerable impact on streamflow. Water availability for food, energy, and other essential economic sectors would be adversely affected, if, glaciers in the region continued to lose mass due to climatic change. However, long-term MB and hydro-meteorological observations are required to gain a better understanding of glacier recession in the region as climate changes in the UIB.

Oral health knowledge, practice, and oral health status among rohingya refugees in Cox's Bazar, Bangladesh: A cross-sectional study.

Oral health is a critical component of human health but is sometimes forgotten, particularly during humanitarian crises. This research aimed to ascertain the state of oral health among Rohingya refugees living in one of the largest refugee camps and evaluate their knowledge and practice of oral health. A multicenter cross-sectional survey was conducted among 477 participants from July to September 2021 using a structured questionnaire. There were 34 Rohingya camps and out of those 14 camps were accessible for data collection. The study participants were between 18-82 years residing in the refugee camps under Cox's Bazar. The majority of participants (53.88%) were female and between the ages of 25 and 45. Around 46.12% of respondents did not have basic oral health knowledge, while 53.67% were in need of dental care. Nearly half of the participants demonstrated poor oral health practices. Participants' age and educational level were positively associated with oral health knowledge (p = 0.02 and p<0.001). Furthermore, the knowledge level was positively associated with oral health practice (p = .025). Participants with a history of teeth pain and discomfort in the last 12 months were ten times more likely to seek treatment (OR = 9.93, CI: 5.591-17.64). The study indicated a growing demand for dental care among Rohingya refugees staying in Bangladesh. To reduce the severity of oral health issues, use of minimally invasive restorative procedures can be suggested in camps. New oral health promotion campaigns should be emphasized and proper education, ideally in their original language, can be beneficial.

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling.

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

The diagnostic utility of unenhanced computed tomography of the brain and D-dimer levels in acute cerebral venous sinus thrombosis: A quantitative study.

Objectives: (1) To calculate the sensitivity and specificity of the Hounsfield Unit (HU), the HU to hematocrit (H:H) ratio, and the D-dimer level in the diagnosis of acute CVST. (2) To assess the D-dimer level's linear relationship with the HU and the H:H ratio. Materials and Methods: A single-center retrospective case-control study was conducted from 2005 to 2020. The inclusion criteria for the thrombosed and control groups were specified. A region of interest (ROI) was plotted on the respective sinuses to calculate the HU. The H:H ratio was calculated by dividing the HU value by the hematocrit value. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of the HU and the H:H ratio at different cutoff values. The Pearson correlation was used to assess the linear relationship between the D-dimer level and the HU and H:H ratio. Results: There were 19 patients in the thrombosed group and 28 patients in the control group. There were significant differences in the mean HU (71 ± 6.3 vs. 45 ± 4.8, P < 0.001) and the mean H:H ratio (2.11 ± 0.38 vs. 1.46 ± 0.63,P < 0.001). An optimal HU value of 56 yielded 100% sensitivity and specificity. An H:H value of 1.48 yielded a sensitivity of 100% and a specificity of 65%, an H:H ratio of 1.77 demonstrated a sensitivity of 85% and a specificity of 90%, and an H:H ratio of 1.88 yielded a sensitivity of 79% and a specificity of 93%. D-dimer levels had a 95% and 71% sensitivity and specificity, respectively. There was a significant moderately positive linear correlation between the D-dimer level and the HU (r = 0.52, P < 0.001) and the H:H ratio (r = 0.61, P < 0.001). Conclusion: Unenhanced CT of the brain can be a valuable objective diagnostic tool for acute CVST diagnosis. Hounsfield blood density and its normalized ratio with hematocrit are positively correlated with D-dimer levels, which may indicate active blood coagulation in a cerebral venous sinus.

Anthropogenic climate change drives melting of glaciers in the Himalaya.

The Himalayan glaciers provide water to a large population in south Asia for a variety of purposes and ecosystem services. As a result, regional monitoring of glacier melting and identification of the drivers are important for understanding and predicting future cryospheric melting trends. Using multi-date satellite images from 2000 to 2020, we investigated the shrinkage, snout retreat, thickness changes, mass loss and velocity changes of 77 glaciers in the Drass basin, western Himalaya, India. During this period, the total glacier cover has shrunk by 5.31 ± 0.33 km2. The snout retreat ranged from 30 to 430 m (mean 155 ± 9.58 m). Debris cover had a significant impact on glacier melting, with clean glaciers losing ~ 5% more than debris-covered glaciers (~ 2%). The average thickness change and mass loss of glacier have been - 1.27 ± 0.37 and - 1.08 ± 0.31 m w.e.a-1, respectively. Because of the continuous melting and the consequent mass loss, average glacier velocity has reduced from 21.35 ± 3.3 m a-1 in 2000 to 16.68 ± 1.9 m a-1 by 2020. During the observation period, the concentration of greenhouse gases (GHGs), black carbon (BC) and other pollutants from vehicular traffic near the glaciers increased significantly. Increasing temperatures, caused by a significant increase in GHGs, black carbon and other pollutants in the atmosphere, are driving glacier melting in the study area. If the current trend continues in the future, the Himalayan glaciers may disappear entirely, having a significant impact on regional water supplies, hydrological processes, ecosystem services and transboundary water sharing.

Serum heavy metals of passive smoker females and its correlation to bone biomarkers and risk of osteoporosis.

Serum total and free calcium reflect the status of the body health and disease. Smoking is risk factor for many diseases as cardiovascular, lung, and cancers. The goal of this work is to evaluate the correlation between serum lead, cadmium arsenate resulting from passive smoking, and bone status in females. This study was conducted on two hundred women (age 30-50 years) divided into four groups (each 50). Group I, control, included non-smoking healthy women. Group II included heavy smoker (>20 cigarettes/day). Group III, nonsmoker women with osteoporosis, have many fractures. Group IV, smoking women with osteoporosis, included heavy smokers (>20 cigarettes/day) with osteoporotic women and have many fractures. Data obtained showed that T-score of osteoporotic smokers was -3.5 that indicated reduced bone mineral density (BMD) while serum total and ionized calcium were statistically significant decreased in smokers with or without osteoporosis compared with nonsmokers (p < 0.001). A negative correlation between total and free calcium and cadmium levels in smokers was compared with nonsmokers (r =-0.65). The levels of C-terminal pro-peptide of pro-collagen type I (PICP) and N-terminal pro-peptide of procollagen type I (PINP) were higher in smoker osteoporotic women than nonsmokers. It was concluded that cadmium resulting from smoking may compete with absorption of calcium and reduced its level and BMD and increased incidence of osteoporosis. The elevated PICP and PINP indicated decreased rate of proto collagen I turnover in bone tissue and increased incidence of osteoporosis.

COVID-19 vaccine acceptance in South Asia: a multi-country study.

OBJECTIVES: With COVID-19 vaccination underway, this study aimed to understand belief, attitude and intention of the people in the South Asia region towards the vaccine. METHODS: We conducted a cross-sectional study using semi-structured questionnaires among 18201 individuals in four South Asian countries; Bangladesh, India, Pakistan, and Nepal between January 17 and February 2, 2021. We used the Health Belief Model (HBM) to identify the predictors related to vaccine acceptance. STATA (v16.1) was used for all analyses. RESULTS: The percentage of respondents willing to be vaccinated against COVID-19 was 65%, 66%, 72% and 74% for Bangladesh, India, Pakistan and Nepal, respectively. Perceived destructive impact of COVID-19, positive perception of vaccines and concern about possible side effects were significant in modifying respondents' intentions.. In multivariable logistic regression, age, sex, marital status, education, comorbidities, worry about getting infected, perceived COVID-19 impact, belief regarding vaccine efficacy, positive attitude towards mandatory measures, and vaccine availability were found to be associated with vaccine acceptance across countries. CONCLUSION: Nearabout two-third of the respondants were willing to take COVID-19 vaccine in the four South Asia countries.

Quality of life of COVID-19 recovered patients in Bangladesh.

Coronavirus Disease-2019 (COVID-19) quickly surged the whole world and affected people's physical, mental, and social health thereby upsetting their quality of life. Therefore, we aimed to investigate the quality of life (QoL) of COVID-19 positive patients after recovery in Bangladesh. This was a study of adult (aged ≥18 years) COVID-19 individuals from eight divisions of Bangladesh diagnosed and confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) from June 2020 to November 2020. Given a response rate of 60% in a pilot study, a random list of 6400 COVID-19 patients was generated to recruit approximately 3200 patients from eight divisions of Bangladesh and finally a total of 3244 participants could be recruited for the current study. The validated Bangla version of the World Health Organization Quality of Life Brief (WHOQOL-BREF) questionnaire was used to assess the QoL. Data were analyzed by STATA (Version 16.1) and R (Version 4.0.0). All the procedures were conducted following ethical approval and in accordance with the Declaration of Helsinki. The mean scores of QoL were highest for the physical domain (68.25±14.45) followed by social (65.10±15.78), psychological (63.28±15.48), and environmental domain (62.77±13.07). Psychological and physical domain scores among females were significantly lower than the males (p<0.001). The overall quality of life was lower in persons having a chronic disease. Participants over 45 years of age were 52% less likely to enjoy good physical health than the participants aged below 26 years (AOR: 0.48, CI: 0.28-0.82). The quality of life of employed participants was found 1.8 times higher than the unemployed (AOR: 1.80, CI: 1.11-2.91). Those who were admitted to hospitals during infection had a low QoL score in physical, psychological, and socials domains. However, QoL improved in all aspect except the psychological domain for each day passed after the diagnosis. These findings call for a focus on the quality of life of the COVID-19 affected population, with special emphasis given to females, older adults, unemployed, and people with comorbidities.

Leveraging patient-reported outcomes (PROs) in patients with pancreatic cancer: The Pancreatic Cancer Action Network (PanCAN) online patient registry experience.

BACKGROUND: The Pancreatic Cancer Action Network (PanCAN) Patient Registry is an online, pancreatic cancer-specific, global registry enabling patients to self-report sociodemographics, disease/management characteristics, and patient-reported outcomes (PROs). We sought to describe the creation, user experience, and research potential of the PanCAN Registry. METHODS: We obtained data to describe (1) the creation of the Registry (questionnaire development, marketing efforts, and regulatory considerations); (2) the user experience (user characteristics and interactions with the registry following inception); and (3) the research potential of the registry (comparing PROs and treatment patterns by age [±65 years] and treatment site [community or academic] for users with de novo metastatic disease). RESULTS: The Registry was conceived as part of PanCAN's strategic plan for a personalized therapy initiative. PanCAN staff and disease expert consultants developed questionnaires hosted on an electronic PRO platform. Users had the option to include their data in research efforts, and the Registry platform received institutional review board approval. From 7/2015 to 12/2020, 2187 patients visited the registry and 1697 (77.6%) completed at least one survey (median age = 64 years [range: 24-90], 47.9% women, 88.7% White, 34.0% metastatic disease). Among patients with metastatic disease (N = 567), 46.0% were ≥65 years old and 67.5% received treatment at community sites. Patients ≥65 years reported feeling less hopeful about the treatment plan (12.4% vs. 24.3%, p = 0.003), and patients treated at community sites reported more frequent treatment breaks of >2 weeks (58.2% vs. 28.1%, p < 0.001). CONCLUSIONS: Our findings demonstrate the feasibility, usability, and research potential of an online PRO registry for patients with cancer. This description of the PanCAN Registry should inform future registry-building efforts to facilitate standardized PRO reporting and provide a valuable research database. CLINICAL TRIAL REGISTRATION NUMBER: Not applicable.

Rheumatic Heart Disease in the United States: Forgotten But Not Gone: Results of a 10 Year Multicenter Review.

Background Recent evaluation of rheumatic heart disease (RHD) mortality demonstrates disproportionate disease burden within the United States. However, there are few contemporary data on US children living with acute rheumatic fever (ARF) and RHD. Methods and Results Twenty-two US pediatric institutions participated in a 10-year review (2008-2018) of electronic medical records and echocardiographic databases of children 4 to 17 years diagnosed with ARF/RHD to determine demographics, diagnosis, and management. Geocoding was used to determine a census tract-based socioeconomic deprivation index. Descriptive statistics of patient characteristics and regression analysis of RHD classification, disease severity, and initial antibiotic prescription according to community deprivation were obtained. Data for 947 cases showed median age at diagnosis of 9 years; 51% and 56% identified as male and non-White, respectively. Most (89%) had health insurance and were first diagnosed in the United States (82%). Only 13% reported travel to an endemic region before diagnosis. Although 96% of patients were prescribed secondary prophylaxis, only 58% were prescribed intramuscular benzathine penicillin G. Higher deprivation was associated with increasing disease severity (odds ratio, 1.25; 95% CI, 1.08-1.46). Conclusions The majority of recent US cases of ARF and RHD are endemic rather than the result of foreign exposure. Children who live in more deprived communities are at risk for more severe disease. This study demonstrates a need to improve guideline-based treatment for ARF/RHD with respect to secondary prophylaxis and to increase research efforts to better understand ARF and RHD in the United States.

The case for AI-driven cancer clinical trials - The efficacy arm in silico.

Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have made it possible to simulate the efficacy of cancer drug protocols in diverse patient populations, which could inform and improve clinical trial design. Here, we review the current and potential use of in silico trials and causal AI to increase the efficacy and safety of traditional clinical trials. We conclude that in silico trials using causal AI approaches can simulate control and efficacy arms, inform patient recruitment and regimen titrations, and better enable subgroup analyses critical for precision medicine.