Risk factors for complications and in-hospital mortality: An analysis of 19,834 open pelvic ring fractures

BackgroundOpen pelvic fractures are rare injuries, associated with high patient morbidity and mortality. Few studies have investigated the impact of patient demographics, comorbidities, and injury related factors on complication and mortality rates. The purpose of this study was to: (1) identify the overall incidence of complications and mortality after open pelvic fractures, (2) compare patient factors between those who did and did not develop complications, (3) identify perioperative independent risk factors for complications and mortality.MethodsA query was performed for patients with open pelvic fractures between 2007 and 2017 using the American College of Surgeons National Trauma Data Bank. Patient and injury specific variables were collected and complications were identified using International Classification of Disease Ninth and Tenth edition Codes. Patient demographic and perioperative data was compared using Fisher’s exact test and chi-square test for categorical variables, and Welch’s t-test for continuous variables. Using pooled data from multiple imputations, logistic regressions were used to calculate odds ratios and confidence intervals of independent risk factors for complications.ResultsA total of 19,834 open pelvic fracture cases were identified, with 9622 patients (48.5%) developing at least one complication. Patients who developed complications were older (35.0 vs 38.1 years), and had higher Injury Severity Scores (17.7 vs 26.5), lower Glasgow Coma Scores (14.2 vs 11.7), and a larger proportion presenting with hypotension (21% vs 6.9%). After pooled regression involving 19 factors, these were the strongest independent predictors of inpatient complication and mortality.ConclusionWe report a mortality rate of 14%, with an inclusive complication rate of 48.5%. Evaluating risk factors for morbidity and mortality for this devastating orthopaedic injury provides knowledge of an inherently sparse population.Level of EvidenceLevel II, Retrospective study.     

Impact of Guidelines for Antihypertensive Treatment on Clinical Practice

Many Guidelines and Consensus Statements were published in 1993 and 1994. The pressure to produce these guidelines comes from clinicians faced with rapid change, from a wish to provide an independent authoritative view rather than rely on commercial promotion of particular treatments, and from governments seeking assistance with the allocation of scarce resources.

Evaluation of the impact that guidelines exert on clinical practice is notoriously difficult, but has been attempted using the self-report questionnaire, or the more reliable but expensive analysis of actual practice data, before and after publication of the guidelines.     

Effects of differential privacy techniques: Considerations for end users

We study the effects of differentially private (DP) noise injection techniques in a survey data setting, using the release of cost of early care and education estimates from the National Survey of Early Care and Education as a motivating example. As an example of how DP noise injection affects statistical estimates, our analysis compares the relative performance of DP techniques in the context of releasing estimates of means, medians, and regression coefficients. The results show that for many statistics, basic DP techniques show good performance provided that the privacy budget does not need to be split over too many estimates. Throughout, we show that small decisions, such as the number of bins in a histogram or the scaling of a variable in a regression equation, can have sometimes dramatic effects on the end results. Because of this, it is important to develop DP techniques with an eye towards the most important aspects of the data for end users.     

《联邦数据战略与2020年行动计划》及其治理逻辑的分析与启示

系统梳理了《联邦数据战略与2020年行动计划》的主要内容，介绍和分析了其使命宣言、原则与实践和治理逻辑。通过比较分析中外开放政府数据情况，明确了我国目前政府数据治理的方向，并为我国数据战略的发展和智慧型政府的建设提出了我国应在重视数据资产的价值、提高数据标准化程度、探索高效的数据共享机制、建立健全具体法规制度、加快数据领域课程建设和人才培养等方面进行深入研究。     

Ultrasound Bone Fracture Sensing and Data Communication: Experimental Results in a Pig Limb Sample

Approximately 6.3 million fractures occur each year in the United States alone. Accurately monitoring the progression of fracture healing is essential to be able to advise patients when it is safe to return to normal activity. The most common method used to confirm and monitor fracture healing is the acquisition of multiple radiographic images over the many months required for healing. This imaging method uses large expensive equipment and exposes patients to high levels of ionizing radiation. In the study described here, we tested another technology for monitoring fracture healing that could minimize the need for multiple radiographic images. We tested a piezoelectric transducer fixed to the surface of a bone that uses electromechanical impedance spectroscopy to measure simulated fractures and transmits the measurement data to an acoustic receiver located externally on the skin surface.     

基于数据挖掘的中药辅助化疗治疗晚期结直肠癌的用药规律文献研究＊

目的 分析中药复方辅助化疗治疗晚期结直肠癌的用药规律。方法 借助中国知网、万方数据、重庆维普以及中国生物医学文献数据库数据平台，搜索晚期结直肠化疗并中药经方治疗等相关文献，进行质量评价，剔除低质量文献，罗列组方建立Excel数据库，进行高频药物及其性味归经的描述性统计，基于SPSS Statistics 26.0关联规则及因子分析，SPSS Modeler 18.0聚类分析，Cytoscape软件直观网络图，进行数据挖掘整理与分析。结果 研究纳入相关文献148篇，处方155首，药物191味，用药总频次1823次，核心中药27味（使用频率 ≥ 10%），以白术所用最广。性味以甘温为首，归经以脾经为最。以置信度95%、支持度15%、提升度 > 1进行关联规则分析得到常用三药组合17组。聚类分析以欧式平方距离24为标准得到潜在聚类方剂3个。结论 中药复方用药以清补健脾，甘温扶正；平解邪毒，调气畅腑；肝脾同调，肺胃同治为规律，正合晚期结直肠癌化疗后脾虚毒恋、痰瘀互结之证。研究归纳出中药辅助化疗治疗晚期结直肠癌的用药组方规律，予以临床一定的理论依据及借鉴意义。     

Trends in Use and Outcomes of Same-Day Discharge Following Elective Percutaneous Coronary Intervention

ObjectivesThe aims of this study were to describe trends and hospital variation in same-day discharge following elective percutaneous coronary intervention (PCI) and to evaluate the association between trends in same-day discharge and patient outcomes.BackgroundInsights on contemporary use of same-day discharge following elective PCI are limited.MethodsIn a sequential cross-sectional analysis of 819,091 patients undergoing elective PCI at 1,716 hospitals in the National Cardiovascular Data Registry CathPCI Registry from July 1, 2009, to December 31, 2017, overall and hospital-level trends in same-day discharge were assessed. Among the 212,369 patients who linked to Centers for Medicare and Medicaid Services data, the association between same-day discharge and 30-day mortality and rehospitalization was assessed.ResultsA total of 114,461 patients (14.0%) were discharged the same day as PCI. The proportion of patients with same-day discharge increased from 4.5% in the third quarter of 2009 to 28.6% in the fourth quarter of 2017. From 2009 to 2017, the rate of same-day discharge increased from 4.3% to 19.5% for femoral-access PCI and from 9.9% to 39.7% for radial-access PCI. Hospital-level variation in the use of same-day discharge persisted throughout (median odds ratio adjusted for year and radial access: 4.15). Risk-adjusted 30-day mortality did not change over time, while risk-adjusted rehospitalization decreased over time and more quickly for same-day discharge (P for interaction <0.001).ConclusionsIn the past decade, a large increase in the use of same-day discharge following elective PCI was not associated with worse 30-day mortality or rehospitalization. Hospital-level variation in same-day discharge may represent an opportunity to reduce costs without compromising patient outcomes.     

加州理工大学图书馆数据监管需求评估项目的启示和思考

介绍数据监管的内涵，阐述加州州立理工大学图书馆数据监管需求评估项目，分析项目成果，指出该项目对国内图书馆的启示，即开展科研数据服务营销推广、数据监管培训服务、通过合作实现高质量的数据监管服务等。     

Predicting protein-binding RNA nucleotides using the feature-based removal of data redundancy and the interaction propensity of nucleotide triplets

Several learning approaches have been used to predict RNA-binding amino acids in a protein sequence, but there has been little attempt to predict protein-binding nucleotides in an RNA sequence. One of the reasons is that the differences between nucleotides in their interaction propensity are much smaller than those between amino acids. Another reason is that RNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding RNA nucleotides is much harder than predicting RNA-binding amino acids. We developed a new method that removes data redundancy in a training set of sequences based on their features. The new method constructs a larger and more informative training set than the standard redundancy removal method based on sequence similarity, and the constructed dataset is guaranteed to be redundancy-free. We computed the interaction propensity (IP) of nucleotide triplets by applying a new definition of IP to an extensive dataset of protein-RNA complexes, and developed a support vector machine (SVM) model to predict protein binding sites in RNA sequences. In a 5-fold cross-validation with 812 RNA sequences, the SVM model predicted protein-binding nucleotides with an accuracy of 86.4%, an F-measure of 84.8%, and a Matthews correlation coefficient of 0.66. With an independent dataset of 56 RNA sequences that were not used in training, the resulting accuracy was 68.1% with an F-measure of 71.7% and a Matthews correlation coefficient of 0.35. To the best of our knowledge, this is the first attempt to predict protein-binding RNA nucleotides in a given RNA sequence from the sequence data alone. The SVM model and datasets are freely available for academics at http://bclab.inha.ac.kr/primer.     

Analyzing the attitude of Indian citizens towards COVID-19 vaccine – A text analytics study

Background and aimsThe government of India recently planned to start the process of the mass vaccination program to end the COVID-19 crises. However, the process of vaccination was not made mandatory, and there are a lot of aspects that arise skepticism in the minds of common people regarding COVID-19 vaccines. This study using machine learning techniques analyzes the major concerns Indian citizens voice out about COVID-19 vaccines in social media.MethodsFor this study, we have used social media posts as data. Using Python, we have scrapped the social media posts of Indian citizens discussing about the COVID- 19 vaccine. In Study 1, we performed a sentimental analysis to determine how the general perception of Indian citizens regarding the COVID-19 vaccine changes over different months of COVID-19 crises. In Study 2, we have performed topic modeling to understand the major issues that concern the general public regarding the COVID- 19 vaccine.ResultsOur results have indicated that 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine.ConclusionWith the positive sentiments regarding vaccine is just over 35%, the Indian government needs to focus especially on addressing the fear of vaccines before implementing the process of mass vaccination.