Family and personal history of cancer in the All of Us research program for precision medicine.

The All of Us (AoU) Research Program is making available one of the largest and most diverse collections of health data in the US to researchers. Using the All of Us database, we evaluated family and personal histories of five common types of cancer in 89,453 individuals, comparing these data to 24,305 participants from the 2015 National Health Interview Survey (NHIS). Comparing datasets, we found similar family cancer history (33%) rates, but higher personal cancer history in the AoU dataset (9.2% in AoU vs. 5.11% in NHIS), Methodological (e.g. survey-versus telephone-based data collection) and demographic variability may explain these between-data differences, but more research is needed.

Comparative Safety and Effectiveness of Biologic Therapy for Crohn's Disease: A CA-IBD Cohort Study.

BACKGROUND & AIMS: We compared the safety and effectiveness of tumor necrosis factor α (TNF-α) antagonists vs vedolizumab vs ustekinumab in patients with Crohn's disease (CD) in a multicenter cohort (CA-IBD). METHODS: We created an electronic health record-based cohort of adult patients with CD who were initiating a new biologic agent (TNF-α antagonists, ustekinumab, vedolizumab) from 5 health systems in California between 2010 and 2017. We compared the risk of serious infections (safety) and all-cause hospitalization and inflammatory bowel disease-related surgery (effectiveness) between different biologic classes using propensity score (PS) matching. RESULTS: As compared with TNF-α antagonists (n = 1030), 2:1 PS-matched, ustekinumab-treated patients with CD (n = 515) experienced a lower risk of serious infections (hazard ratio [HR], 0.36; 95% CI, 0.20-0.64), without any difference in the risk of hospitalization (HR, 0.99; 95% CI, 0.89-1.21) or surgery (HR, 1.08; 95% CI, 0.69-1.70). Compared with vedolizumab (n = 221), 1:1 PS-matched, ustekinumab-treated patients with CD (n = 221) experienced a lower risk of serious infections (HR, 0.20; 95% CI, 0.07-0.60), without significant differences in risk of hospitalization (HR, 0.76; 95% CI, 0.54-1.07) or surgery (HR, 1.42; 95% CI, 0.54-3.72). Compared with TNF-α antagonists (n = 442), 2:1 PS-matched, vedolizumab-treated patients with CD (n = 221) had a similar risk of serious infections (HR, 1.53; 95% CI, 0.84-2.78), hospitalization (HR, 1.32; 95% CI, 0.98-1.77), and surgery (HR, 0.63; 95% CI, 0.27-1.47). High comorbidity burden, concomitant opiate use, and prior hospitalization were associated with serious infections and hospitalization in biologic-treated patients with CD. CONCLUSION: In a multicenter cohort of biologic-treated patients with CD, ustekinumab was associated with a lower risk of serious infections compared with TNF-α antagonists and vedolizumab, without any differences in risk of hospitalization or surgery. The risk of serious infections was similar for TNF-α antagonists vs vedolizumab.

Effectiveness and Safety of Biologic Therapy in Hispanic Vs Non-Hispanic Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study.

BACKGROUND & AIMS: There are limited data on outcomes of biologic therapy in Hispanic patients with inflammatory bowel diseases (IBDs). We compared risk of hospitalization, surgery, and serious infections in Hispanic vs non-Hispanic patients with IBD in a multicenter, electronic health record-based cohort of biologic-treated patients. METHODS: We identified adult patients with IBD who were new users of biologic agents (tumor necrosis factor α [TNF-α] antagonists, ustekinumab, vedolizumab) from 5 academic institutions in California between 2010 and 2017. We compared the risk of all-cause hospitalization, IBD-related surgery, and serious infections in Hispanic vs non-Hispanic patients using 1:4 propensity score matching and survival analysis. RESULTS: We compared 240 Hispanic patients (53% male; 45% with ulcerative colitis; 73% TNF-α antagonist-treated; 20% with prior biologic exposure) with 960 non-Hispanic patients (51% male; 44% with ulcerative colitis; 67% TNF-α antagonist-treated; 27% with prior biologic exposure). After propensity score matching, Hispanic patients were younger (37 ± 15 vs 40 ± 16 y; P = .02) and had a higher burden of comorbidities (Elixhauser index, >0; 37% vs 26%; P < .01), without any differences in patterns of medication use, burden of inflammation, and hospitalizations. Within 1 year of biologic initiation, Hispanic patients had higher rates of hospitalizations (31% vs 23%; adjusted hazard ratio [aHR], 1.32; 95% CI, 1.01-1.74) and IBD-related surgery (7.1% vs 4.6%; aHR, 2.00; 95% CI, 1.07-3.72), with a trend toward higher risk of serious infections (8.8% vs 4.9%; aHR, 1.74; 95% CI, 0.99-3.05). CONCLUSIONS: In a multicenter, propensity score-matched cohort of biologic-treated patients with IBD, Hispanic patients experienced higher rates of hospitalization, surgery, and serious infections. Future studies are needed to investigate the biological, social, and environmental drivers of these differences.

Effect of Obesity on Risk of Hospitalization, Surgery, and Serious Infection in Biologic-Treated Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study.

INTRODUCTION: Obesity is variably associated with treatment response in biologic-treated patients with inflammatory bowel diseases (IBD). We evaluated the association between obesity and risk of hospitalization, surgery, or serious infections in patients with IBD in new users of biologic agents in a large, multicenter, electronic health record (EHR)-based cohort (CA-IBD). METHODS: We created an EHR-based cohort of adult patients with IBD who were new users of biologic agents (tumor necrosis factor [TNF-α] antagonists, ustekinumab, and vedolizumab) between January 1, 2010, and June 30, 2017, from 5 health systems in California. Patients were classified as those with normal body mass index (BMI), overweight, or obese based on the World Health Organization classification. We compared the risk of all-cause hospitalization, IBD-related surgery, or serious infections among patients with obesity vs those overweight vs those with normal BMI, using Cox proportional hazard analyses, adjusting for baseline demographic, disease, and treatment characteristics. RESULTS: Of 3,038 biologic-treated patients with IBD (69% with Crohn's disease and 76% on TNF-α antagonists), 28.2% (n = 858) were overweight, and 13.7% (n = 416) were obese. On a follow-up after biologic initiation, obesity was not associated with an increased risk of hospitalization (adjusted hazard ratio [aHR] vs normal BMI, 0.90; [95% confidence interval, 0.72-1.13]); IBD-related surgery (aHR, 0.62 [0.31-1.22]); or serious infection (aHR, 1.11 [0.73-1.71]). Similar results were observed on stratified analysis by disease phenotype (Crohn's disease vs ulcerative colitis) and index biologic therapy (TNF-α antagonists vs non-TNF-α antagonists). DISCUSSION: In a multicenter, EHR-based cohort of biologic-treated patients with IBD, obesity was not associated with hospitalization, surgery, or serious infections. Further studies examining the effect of visceral obesity on patient-reported and endoscopic outcomes are needed.

Modification of Alginates to Modulate Their Physic-Chemical Properties and Obtain Biomaterials with Different Functional Properties.

Modified alginates have a wide range of applications, including in the manufacture of dressings and scaffolds used for regenerative medicine, in systems for selective drug delivery, and as hydrogel materials. This literature review discusses the methods used to modify alginates and obtain materials with new or improved functional properties. It discusses the diverse biological and functional activity of alginates. It presents methods of modification that utilize both natural and synthetic peptides, and describes their influence on the biological properties of the alginates. The success of functionalization depends on the reaction conditions being sufficient to guarantee the desired transformations and provide modified alginates with new desirable properties, but mild enough to prevent degradation of the alginates. This review is a literature description of efficient methods of alginate functionalization using biologically active ligands. Particular attention was paid to methods of alginate functionalization with peptides, because the combination of the properties of alginates and peptides leads to the obtaining of conjugates with properties resulting from both components as well as a completely new, different functionality.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.

PURPOSE: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research. DESIGN: Development and evaluation of machine learning models. METHODS: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall. RESULTS: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests). CONCLUSIONS: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

BACKGROUND: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment. RESEARCH QUESTION: Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance? STUDY DESIGN AND METHODS: We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio2, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value. RESULTS: We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943. INTERPRETATION: A transparent deep learning algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.

Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations.

There is an urgent need to answer questions related to COVID-19's clinical course and associations with underlying conditions and health outcomes. Multi-center data are necessary to generate reliable answers, but centralizing data in a single repository is not always possible. Using a privacy-protecting strategy, we launched a public Questions & Answers web portal (https://covid19questions.org) with analyses of comorbidities, medications and laboratory tests using data from 202 hospitals (59,074 COVID-19 patients) in the USA and Germany. We find, for example, that 8.6% of hospitalizations in which the patient was not admitted to the ICU resulted in the patient returning to the hospital within seven days from discharge and that, when adjusted for age, mortality for hospitalized patients was not significantly different by gender or ethnicity.

Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

IMPORTANCE: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment. OBJECTIVE: To develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19. DESIGN: Observational cohort study SETTING: Two academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts). PARTICIPANTS: Over 31,000 admissions to the intensive care units (ICUs) at two hospitals. Additionally, 777 patients with COVID-19 patients were used for prospective validation. Patients who were placed on mechanical ventilation within four hours of their admission were excluded. MAIN OUTCOME(S) and MEASURE(S): Electronic health record (EHR) data were extracted on an hourly basis, and a set of 40 features were calculated and passed to an interpretable deep-learning algorithm to predict the future need for mechanical ventilation 24 hours in advance. Additionally, commonly used clinical criteria (based on heart rate, oxygen saturation, respiratory rate, FiO2 and pH) was used to assess future need for mechanical ventilation. Performance of the algorithms were evaluated using the area under receiver-operating characteristic curve (AUC), sensitivity, specificity and positive predictive value. RESULTS: After applying exclusion criteria, the external validation cohort included 3,888 general ICU and 402 COVID-19 patients. The performance of the model (AUC) with a 24-hour prediction horizon at the validation site was 0.882 for the general ICU population and 0.918 for patients with COVID-19. In comparison, commonly used clinical criteria and the ROX score achieved AUCs in the range of 0.773 - 0.782 and 0.768 - 0.810 for the general ICU population and patients with COVID-19, respectively. CONCLUSIONS AND RELEVANCE: A generalizable and transparent deep-learning algorithm improves on traditional clinical criteria to predict the need for mechanical ventilation in hospitalized patients, including those with COVID-19. Such an algorithm may help clinicians with optimizing timing of tracheal intubation, better allocation of mechanical ventilation resources and staff, and improve patient care.

Patient Perspectives About Decisions to Share Medical Data and Biospecimens for Research.

Importance: Patients increasingly demand transparency in and control of how their medical records and biospecimens are shared for research. How much they are willing to share and what factors influence their sharing preferences remain understudied in real settings. Objectives: To examine whether and how various presentations of consent forms are associated with differences in electronic health record and biospecimen sharing rates and whether these rates vary according to user interface design, data recipients, data and biospecimen items, and patient characteristics. Design, Setting, and Participants: For this survey study, a data and biospecimen sharing preference survey was conducted at 2 academic hospitals from May 1, 2017, to September 31, 2018, after simple randomization of patients to 1 of 4 options with different layout and formats of indicating sharing preferences: opt-in simple, opt-in detailed, opt-out simple, and opt-out detailed. Interventions: All participants were presented with a list of data and biospecimen items that could be shared for research within the same health care organization or with other nonprofit or for-profit institutions. Participating patients were randomly asked to select the items that they would share (opt-in) or were asked to select items they would not share (opt-out). Patients in these 2 groups were further randomized to select only among 18 categories vs 59 detailed items (simple vs detailed form layout). Main Outcomes and Measures: The primary end points were the percentages of patients willing to share data and biospecimen categories or items. Results: Among 1800 eligible participants, 1246 (69.2%) who completed their data sharing survey were included in the analysis, and 850 of these patients (mean [SD] age, 51.1 [16.7] years; 507 [59.6%] female; 677 [79.6%] white) responded to the satisfaction survey. A total of 46 participants (3.7%) declined sharing with the home institution, 352 (28.3%) with nonprofit institutions, and 590 (47.4%) with for-profit institutions. A total of 836 (67.1%) indicated that they would share all items with researchers from the home institution. When comparing opt-out with opt-in interfaces, all 59 sharing choice variables (100%) were associated with the sharing decision. When comparing simple with detailed forms, only 14 variables (23.7%) were associated with the sharing decision. Conclusions and Relevance: The findings suggest that most patients are willing to share their data and biospecimens for research. Allowing patients to decide with whom they want to share certain types of data may affect research that involves secondary use of electronic health records and/or biosamples for research.

Chitosan and Its Derivatives - Biomaterials with Diverse Biological Activity for Manifold Applications.

Derived from chitin, chitosan is a natural polycationic linear polysaccharide being the second most abundant polymer next to cellulose. The main obstacle in the wide use of chitosan is its almost complete lack of solubility in water and alkaline solutions. To break this obstacle, the structure of chitosan is subjected to modification, improving its physic-chemical properties and facilitating application as components of composites or hydrogels. Derivatives of chitosan are biomaterials useful for different purposes because of their lack of toxicity, low allergenicity, biocompatibility and biodegradability. This review presents the methods of chemical modifications of chitosan which allow to obtain tailor- made properties required for a variety of biomedical applications. Selected pharmaceutical and biomedical applications of chitosan derivatives are also highlighted. Possibility to manage waste from arthropod and crab processing is also emphasized.