bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of psychological distress, coping, and spirituality in patients with resected, non-metastatic vs unresectable advanced cancer. Data from the NEOcoping and NEOetic_SEOM studies
Authors: Velasco Veronica , Cruz-Castellanos Patricia , Hernandez Raquel , Rodriguez-Gonzalez Adan , Montes Ana Fernandez , Gallego Alejandro , Manzano-Fernandez Aranzazu , Sorribes Elena , Zafra Marta , Carmona-Bayonas Alberto , Calderon Caterina , Jiménez-Fonseca Paula 
Published: May 2022

ABSTRACTBackgroundCancer negatively affects patients’ physical and mental health. This work sought to analyze the sociodemographic and clinical variables that modulate psychological distress in individuals with resected vs unresectable cancer before and after receiving systemic antineoplastic treatment, and to evaluate how different coping strategies and spiritual well-being influence psychological distress in these subjects.MethodsObservational, prospective, consecutive study performed in 15 hospitals in Spain. The study consists of two cohorts: patients with resected locally and locally advanced cancer receiving adjuvant chemotherapy (NEOcoping study, 2016 and 2018) and those with unresectable locally advanced and advanced cancer, eligible for systemic treatment (NEOetic study, 2020 and 2021). Participants completed the following scales: BSI-18 (psychological distress) at baseline and after antineoplastic treatment; MINI-MAC (coping), and FACIT-sp (spirituality), before initiating systemic treatment.ResultsFourteen hundred and fifty patients were recruited; 941 had resected cancer and 509 had advanced cancer. The advanced cancer sample included more males, older, less educated, and more unemployed individuals. Before starting systemic treatment, the advanced, unresectable disease group suffered more psychological distress than those with resected cancer (66.9 vs. 63.7, p=0.001) and used more coping strategies, especially positive attitude (79.1 vs. 75.6, p=0.001) and cognitive avoidance (64.3 vs. 53.6, p=0.001). Participants with resected cancer displayed greater spiritual well-being (36.5 vs 33.2, p=0.001). After receiving systemic treatment, psychological distress decreased in cases of advanced cancer and increased in resected cancer, albeit still higher in subjects with advanced cancer, particularly anxiety (61.4 vs 60.9) and depression (61.0 vs 60.6), (p=0.001 in both cases).ConclusionPatients with advanced cancer exhibit greater psychological distress, use more coping strategies, and have less spiritual well-being, but their psychological distress improves with antineoplastic treatment.
Increasing the Value of Digital Phenotyping Through Reducing Missingness: A Retrospective Analysis
Authors: Currey Danielle , Torous John 
Published: May 2022

AbstractObjectivesDigital phenotyping methods present a scalable tool to realize the potential of personalized medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements. This requires a focus on the data quality of digital phenotyping and assessing the nature of the smartphone data used to derive clinical and health-related features.DesignRetrospective cohorts. Representing the largest combined dataset of smartphone digital phenotyping, we report on the impact of sampling frequency, active engagement with the app, phone type (Android vs Apple), gender, and study protocol features may have on missingness / data quality.SettingmindLAMP smartphone app digital phenotyping studies run at BIDMC between May 2019 and March 2022Participants1178 people who partook in mindLAMP studiesMain outcome measuresRates of missing digital phenotyping data.ResultsMissingness from sensors in digital phenotyping is related to active user engagement with the app. There are small but notable differences in missingness between phone models and genders. Datasets with high degrees of missingness can generate incorrect behavioral features that may lead to faulty clinical interpretations.ConclusionsDigital phenotyping data quality is a moving target that requires ongoing technical and protocol efforts to minimize missingness. Adding run-in periods, education with hands-on support, and tools to easily monitor data coverage are all productive strategies studies can utilize today.Strengths and Limitations of this Study○Methods are informed by a large sample of participants in digital phenotyping studies.○Methods can be replicated by others given the open-source nature of the app and code.○Methods are informed by only mindLAMP studies from one team which is a limitation.
Parameter Estimation for a Modified SEIR Model of the COVID-19 Dynamics in the Philippines using Genetic Algorithm
Authors: Santos Gabriel Lorenzo I. 
Published: May 2022

ABSTRACTThe Philippines has been under a series of different levels of community quarantine and this affected the dynamics of the COVID-19 spread in the country. Predicting the trajectory has been an interest of various research groups. To provide a more efficient method to estimate the parameters of the Age-Stratified, Quarantine-modified SEIR model with Nonlinear Incidence Rates (ASQ-SEIR-NLIR) other than the shooting method, a genetic algorithm approach is explored. By defining constraints for each parameter, the algorithm arrived at an acceptable optimal value for each parameter. The experiment is done on two regions of interest: the Philippines (country-level) and Quezon City, Metro Manila (city-level). The ASQ-SEIR-NLIR model, using the parameters generated by the genetic algorithm, is able to produce an average trajectory compared to the actual data, which may be deemed noisy. The dynamics of the COVID-19 spread between Quezon City and average country level is compared, showing that the city population is being exposed to the virus at a much faster rate than the country average and may have more asymptomatics not getting tested than the country average. Given the average trajectory, the peak daily infection projection is way lower at 0.0823% of the country population for the country projection and 0.1494% of the Quezon City population for the city projection, which is below than previous literature estimates of 3-10%.
A hybrid approach to predict COVID-19 cases using neural networks and inverse problem
Authors: Paul Subhendu , Lorin Emmanuel 
Published: May 2022

ABSTRACTWe derive a novel hybrid approach, a combination of neural networks and inverse problem, in order to forecast COVID-19 cases, and more generally any infectious disease. For this purpose, we extract a second order nonlinear differential equation for the total confirmed cases from a SIR-like model. That differential equation is the key factor of the present study. The neural network and inverse problems are used to compute the trial functions for total cases and the model parameters, respectively. The number of suspected and infected individuals can be found using the trial function of total confirmed cases. We divide the time domain into two parts, training interval (first 365/395 days) and test interval (first 366 to 395/ 396 to 450 days), and train the neural networks on the preassigned training zones. To examine the efficiency and effectiveness, we apply the proposed method to Canada, and use the Canadian publicly available database to estimate the parameters of the trial function involved with total cases. The trial functions of model parameters show that the basic reproduction number was closed to unity over a wide range, the first from 100 to 365 days of the current pandemic in Canada. The proposed prediction models, based on influence of previous time and social economic policy, show excellent agreement with the data. The test results revel that the single path prediction can forecast a period of 30 days, and forecasting using previous social and economical situation can forecast a range of 55 days.
Sequence Proven Reinfections with SARS-CoV-2 at a Large Academic Center
Authors: Morris C. Paul , Eldesouki Raghda E. , Fall Amary , Gaston David C. , Norton Julie M. , Gallagher Nicholas , Luo Chun Huai , Abdullah Omar , Klein Eili Y. , Mostafa Heba H. 
Published: May 2022

AbstractBackgroundIncreased reinfection rates with SARS-CoV-2 have recently been reported, with some locations basing reinfection on a second positive PCR test at least 90 days after initial infection.MethodsWe identified cases where patients had two positive tests for SARS-CoV-2 and evaluated which of these had been sequenced as part of our surveillance efforts, and evaluated sequencing and clinical data.Results750 patients (920 samples) had a positive test at least 90 days after the initial test. The median time between tests was 377 days, and 724 (79%) of the post 90-day positives were collected after the emergence of the Omicron variant in November 2021. Sequencing was attempted on 231 samples and successful in 127. Successful sequencing spiked during the Omicron surge and showed higher median days from initial infection compared to failed sequences (median 398 days compared to 276 days, p<0.0005). A total of 122 (98%) patients showed evidence of reinfection, 45 of which had sequence proven reinfection and 77 had inferred reinfections (later sequence showed a clade that was not circulating when the patient was initially infected). Children accounted for only 4% of reinfections. 43 (96%) of 45 infections with sequence proven reinfection were caused by the Omicron variant, 41 (91%) were symptomatic, 32 (71%), were vaccinated prior to the second infection, and 6 (13%) were Immunosuppressed. Only 2 (4%) were hospitalized, and both had underlying conditions.ConclusionSequence proven reinfections increased with the Omicron variant but generally caused mild infections.
Health Technology Assessment of Diagnostic Tests: A state of the art review of methods guidance from international organisations
Authors: Ferrante di Ruffano Lavinia , Harris Isobel M , Zhelev Zhivko , Davenport Clare , Mallett Sue , Peters Jamie , Takwoingi Yemisi , Deeks Jon , Hyde Chris 
Published: May 2022

AbstractObjectivesTo identify which international HTA agencies are undertaking evaluations of medical tests, summarise commonalities and differences in methodological approach, and highlight examples of best practice.MethodsA methodological review incorporating: systematic identification of HTA guidance documents mentioning evaluation of tests; identification of key contributing organisations and abstraction of approaches to all essential HTA steps; summary of similarities and differences between organisations; and identification of important emergent themes which define the current state of the art and frontiers where further development is needed.ResultsSeven key organisations were identified from 216 screened. The main themes were: elucidation of claims of test benefits; attitude to direct and indirect evidence of clinical effectiveness (including evidence linkage); searching; quality assessment; and health economic evaluation. With the exception of dealing with test accuracy data, approaches were largely based on general approaches to HTA with few test-specific modifications. Elucidation of test claims and attitude to direct and indirect evidence are where we identified the biggest dissimilarities in approach.ConclusionsThere is consensus on some aspects of HTA of tests, such as dealing with test accuracy, and examples of good practice which HTA organisations new to test evaluation can emulate. The focus on test accuracy contrasts with universal acknowledgement that it is not a sufficient evidence base for test evaluation. There are frontiers where methodological development is urgently required, notably integrating direct and indirect evidence and standardising approaches to evidence linkage.
Association of Topiramate Prescribed for any Indication with Reduced Alcohol Consumption in Electronic Health Record Data
Authors: Kranzler Henry R. , Leong Shirley H. , Naps Michelle , Hartwell Emily E. , Fiellin David A. , Rentsch Christopher T. 
Published: May 2022

ABSTRACTBackground and AimsTopiramate is widely prescribed to treat a variety of conditions, including alcohol use disorder (AUD). We used electronic health record (EHR) data to examine topiramate’s effects on drinking in individuals differentiated by a history of AUD.DesignParallel-groups comparison of patients prescribed topiramate and a propensity-score matched comparison group.SettingA large U.S. integrated healthcare system.ParticipantsPatients with Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores prior to and after a minimum of 180 days of topiramate prescription for any indication and a propensity-score matched group. The sample included 5,918 patients with an electronic health record diagnosis of alcohol use disorder at any time (AUD-hx-pos) (1,738 topiramate exposed and 4,180 controls) and 23,614 patients with no EHR diagnosis of AUD (AUD-hx-neg) (6,324 topiramate exposed and 17,290 controls).MeasurementsRegression analyses compared difference-in-difference (DiD) estimates, separately by AUD history. DiD estimates represent exposure-group (i.e., topiramate vs. control) differences on the pre-post difference in AUDIT-C score. Effects of baseline AUDIT-C score and daily topiramate dosage were also tested.FindingsAmong AUD-hx-neg patients, those who received topiramate had a greater reduction in AUDIT-C score (−0.11) than matched controls (−0.04). This yielded a DiD score of -0.07 (95% CI= -0.11,-0.03; P =0.002), with the greatest effect among AUD-hx-neg patients with a baseline AUDIT-C score of 4+ (DiD = -0.35, 95% CI=-0.49, -0.21; P <0.0001) and those prescribed >150 mg/day of the medication (DiD = -0.15, 95%CI=-0.23, -0.07; P <0.001).DiscussionThe lack of an effect of topiramate on drinking levels in AUD-hx-pos patients contrasts with the robust reductions seen in topiramate clinical trials. Research is needed to ascertain whether AUDIT-C scores from EHR data accurately reflect medication effects on drinking and whether patient characteristics can be used to select patients most likely to reduce their drinking when treated with topiramate.
An Updated Meta-analysis of Studies Comparing Conventional to Underwater Endoscopic Mucosal Resection for Colorectal Polyps
Authors: Pamarthy Rahul , Ali Hassam , Bin Waqar Syed Hamza , Sarfraz Shiza 
Published: May 2022

AbstractBackgroundUnderwater endoscopic mucosal resection (UEMR) is an emerging alternative to conventional endoscopic mucosal resection (CEMR). We intended to compare both techniques for colorectal polyp resection.MethodsA comprehensive search of several databases to identify studies published until November 2021 was performed. Inclusion criteria included studies comparing UEMR to CEMR in adult patients. The calculation was done by standard meta-analysis methodology, and heterogeneity was assessed using the I2% statistics.Results1029 polyps were resected with the CEMR technique and 1078 polyps with UEMR. UEMR was associated with an increase in the rate of overall en-bloc resection (Odds ratios (OR) 1.77; 95% CI, 1.42-2.22; P < .0001; I2 = 20%). Subgroup analysis showed an increase in the rates of en-bloc resection in polyps greater than 20 mm (OR 1.62; 95% CI, 1.17-2.25; P = 0.004; I2 = 33%). There was a reduction in the recurrence rate of polyps (P < 0.0001) in the UEMR cohort. Post-procedural bleeding or risk of perforation was not increased in either group. Resection times were shorter in UEMR (Mean difference, -8.09; P = 0.006).ConclusionUEMR is associated with lower recurrence rates and shorter procedure duration. In the future, UEMR may become the standard technique for colorectal polypectomy.
Specialty Visits and Real-world Initiation of Cardioprotective Anti-hyperglycemic Medications Among US Adults with Type 2 Diabetes
Authors: Nargesi Arash A , Clark Callahan , Liu Mengni , Reddy Abraham , Amodeo Samuel , Khera Rohan 
Published: May 2022

ABSTRACTPrescription of sodium glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) among patients with guideline-directed indications remains limited with substantial inter-prescriber variability. In this prospective study of US adults, we used administrative claims database of individuals with type 2 diabetes and compelling indications for SGLT-2i and GLP-1RA to evaluate the impact of healthcare visits with certain specialty providers on the initiation of these medications. These specialties included family medicine, internal medicine, cardiology, endocrinology, and nephrology. Overall, 294,988 individuals eligible for SGLT-2i and 198,525 for GLP-1RA were identified. In 2019-2020, SGLT-2i and GLP-1RA were initiated in 10.4% and 16.7% of eligible individuals, respectively. After accounting for patient characteristics and comorbidities, healthcare visit with endocrinologists was associated with the highest rate of initiation of either drug across specialties (OR=2.16 [2.08-2.24] for SGLT-2i, and 2.76 [2.64-2.88] for GLP-1RA). Healthcare visits with cardiologists and with family medicine and internal medicine physicians were only modestly associated with initiation of SGLT-2i and GLP-1RA. The study highlights the need for broad education for expansion of the use of these medications rather than focus on dedicated specialty clinics.
A Predictive Model to Identify Complicated Clostridiodes difficile Infection
Authors: Berinstein Jeffrey A. , Steiner Calen A. , Rifkin Samara , Perry D. Alexander , Micic Dejan , Shirley Daniel , Higgins Peter D.R. , Young Vincent B. , Lee Allen , Rao Krishna 
Published: May 2022

AbstractBackgroundClostridioides difficile infection (CDI) is a leading cause of healthcare-associated infections and may result in organ dysfunction, colectomy, and death. We recently showed that published risk scores to predict severe complications from CDI demonstrate poor performance upon external validation. We hypothesized that building and validating a model using geographically and temporally distinct cohorts would more accurately identify patients at risk for complicated CDI.MethodsWe conducted a multi-center retrospective cohort study of adult subjects diagnosed with CDI in the US. After randomly partitioning the data into training/validation set, we developed and compared three machine learning algorithms (Lasso regression, random forest, stacked ensemble models) with 10-fold cross-validation that used structured EHR data collected within 48 hours of CDI diagnosis to predict disease-related complications from CDI (intensive care unit admission, colectomy, or death attributable to CDI within 30 days of diagnosis). Model performance was assessed using area under the receiver operating curve (AUC).ResultsA total of 3,762 patients with CDI were included of which 218 (5.8%) had complications. Lasso regression, random forest, and stacked ensemble models all performed well with AUC ranging between 0.89-0.9. Variables of importance were similar across models, including albumin, bicarbonate, change in creatinine, systolic blood pressure, non-CDI-related ICU admission, and concomitant non-CDI antibiotics. Sensitivity analyses indicated that model performance was robust even when varying derivation cohort inclusion and CDI testing approach.ConclusionUsing a large heterogeneous population of patients, we have developed and validated a prediction model based on structured EHR data that accurately estimates risk for complications from CDI.Key PointsMachine learning models using structured electronic health records can be leveraged to accurately predict risk of severe complications related to Clostridiodes difficile infection, including intensive care unit admission, colectomy, and/or death.
Survival and predictors of mortality after completion of TB treatment among people living with HIV
Authors: Lumu Ivan , Musaazi Joseph , Semeere Aggrey , Handel Ian , Castelnuovo Barbara 
Published: May 2022

BackgroundAfter completion of TB treatment patients may remain at-risk of complications and mortality. We determined the survival and predictors of all-cause mortality after completing TB treatment among ART experienced patients.MethodsThis was a retrospective cohort analysis of all ART experienced patients who completed TB treatment at a specialist HIV clinic in Uganda, between 2009 and 2014. The patients were followed for five years after TB treatment. We determined mortality rate, probability of death, and predictors of all-cause mortality after TB treatment using Poisson methods, Kaplan-Meier methods, and Cox proportional hazard models, respectively.ResultsA total 1,287 patients completed TB treatment between 2009 and 2014, of which 1,111 were included in the analysis. At TB treatment completion, the median age was 36 years (IQR: 31-42), 563 (50.7%) were males, and median CD4 count was 235 cells/mL (IQR: 139-366). The person time at risk was 4410.60 person-years. The all-cause mortality rate was 15.42 per 1000 person-years (95% CI: 12.14-19.59). The probability of death at five years was 6.9% (95%CI: 5.5%-8.8%). In the multivariable analysis, CD4 count<200 cells/mL was a predictor of all-cause mortality (aHR=1.81, 95%CI:1.06 - 3.11, p=0.03) alongside TB history (aHR=2.12, 95%CI: 1.16 - 3.85, p=0.01).ConclusionSurvival post TB treatment in ART experienced PLHIV is reasonably good and most deaths occur within two years of TB treatment completion. Patients with low CD4 and those with history of treatment have an increased risk of mortality which underscores the need for TB prophylaxis, detailed assessment, and close monitoring after TB treatment.What is already known on this topicTuberculosis is the leading cause of death in PLHIV and patients who complete treatment remain at risk of mortality. However, it is not clear what the mortality rate is, when it occurs, and what factors are associated with mortality in exclusively ART experienced patients.What this study addsMost deaths occur within two years after treatment completion decreasing drastically by year five. Patients with low CD4 count after TB treatment have an 81% increased risk of death and those with a history of TB have a 200% increased risk of mortality.How this study might affect research, practice or policyThe study provides a detailed understanding of post-TB survival in ART experienced PLHIV and underscores the need for programs and clinics to re-define TB treatment success and consider the use of enhanced prophylaxis after TB treatment.
B cells, BAFF and interferons in MIS-C
Authors: Klocperk Adam , Bloomfield Marketa , Parackova Zuzana , Aillot Ludovic , Fremuth Jiri , Sasek Lumir , David Jan , Fencl Filip , Skotnicova Aneta , Rejlova Katerina , Magner Martin , Hrusak Ondrej , Sediva Anna 
Published: May 2022

AbstractMultisystem Inflammatory Syndrome in Children associated with COVID-19 (MIS-C) is a late complication of pediatric COVID-19, which follows weeks after original SARS-CoV-2 infection, regardless of its severity. It is characterized by hyperinflammation, neutrophilia, lymphopenia and activation of T cells with elevated IFN-γ. Observing production of autoantibodies and parallels with systemic autoimmune disorders, such as systemic lupus erythematodes (SLE), we explored B cell phenotype and serum levels of type I, II and III interferons, as well as the cytokines BAFF and APRIL in a cohort of MIS-C patients and healthy children after COVID-19.We documented a significant elevation of IFN-γ, but not IFN-α and IFN-λ in MIS-C patients. BAFF was elevated in MIS-C patient sera and accompanied by decreased BAFFR expression on all B cell subtypes. The proportion of plasmablasts was significantly lower in patients compared to healthy post-COVID children. We noted the presence of ENA Ro60 autoantibodies in 4/35 tested MIS-C patients.Our work shows the involvement of humoral immunity in MIS-C and hints at parallels with the pathophysiology of SLE, with autoreactive B cells driven towards autoantibody production by elevated BAFF.Graphical abstractSummary sentenceElevated serum BAFF in children with MIS-C supports the state of polyclonal B cell activation and autoimmune phenomena characterizing this disease.