External validation of the HIGH-2-LOW model: A predictive score for venous thromboembolism after allogeneic transplant.

In patients undergoing hematopoietic cell transplantation (HCT), venous thromboembolism (VTE) remains a serious complication that lacks validated risk assessment models (RAMs) to guide thromboprophylaxis. To address this dilemma, we performed a temporal and external validation study of the recently derived HIGH-2-LOW RAM. We selected adult patients undergoing allogeneic HCT from Fred Hutchinson Cancer Research Center (FHCRC) and MD Anderson Cancer Center (MDACC). Patients who died, received anticoagulation, or did not engraft platelets by day 30 were excluded. Primary outcomes were defined as overall VTE and pulmonary embolism ± lower-extremity deep venous thromboembolism (PE/LE-DVT) by day 180. Covariates were weighted according to the original model, except that grade 2-4 GVHD was substituted for grade 3-4. Discrimination and calibration were assessed. A total of 765 patients from FHCRC and 954 patients from MDACC were included. Incident VTE by day 180 was 5.1% at FHCRC and 6.8% at MDACC. The HIGH-2-LOW score had a c-statistic of 0.67 (0.59-0.75) for VTE and 0.75 (0.64-0.81) for PE/LE-DVT at FHCRC and 0.62 (0.55-0.70) for VTE and 0.70 (0.56-0.83) for PE/LE-DVT at MDACC. Twenty-five percent and 23% of patients were classified as high risk (2+ points) in the two cohorts, respectively. High versus low-risk was associated with odds ratio (OR) of 2.80 (1.46-5.38) for VTE and 4.21 (1.82-9.77) for PE/LE-DVT at FHCRC and OR of 3.54 (2.12-5.91) for VTE and 6.82 (2.30-20.16) for PE-LE-DVT at MDACC. The HIGH-2-LOW RAM identified allogeneic HCT recipients at high risk for VTE in both validation cohorts. It can improve evidence-based decision-making for thromboprophylaxis post-transplant.

Bidirectional association between knee osteoarthritis and depressive symptoms: evidence from a nationwide population-based cohort.

BACKGROUND: Both knee osteoarthritis (KOA) and depressive symptoms (DS) are major public health issues affecting the quality of life. This study aimed to examine the association between KOA and DS. METHODS: Data were gathered from the China Health and Retirement Longitudinal Study in 2011-2015 which surveyed middle-aged to elderly individuals and their spouses in 28 provinces in China. An adjusted Cox proportional hazards regression model was used to estimate hazard ratios (HRs). RESULTS: The analysis for baseline KOA and the subsequent risk of DS was based on 2582 participants without baseline DS. During the follow-up, KOA patients were more likely to have DS than non-KOA participants (adjusted HR = 1.38: 95% CI = 1.23 to 1.83). The analysis for baseline DS and the subsequent risk of KOA was based on 4293 participants without baseline KOA, those with DS were more likely to develop KOA than non-DS participants (adjusted HR = 1.51: 95% CI = 1.26 to 1.81). Subgroup analysis showed sex and age had no significant moderating effect on the KOA-DS association. CONCLUSIONS: Our results provide evidence that the association between KOA and DS is bidirectional. Therefore, primary prevention and management of KOA and DS should consider this relationship.

Silver Nanoflakes-Enhanced Anisotropic Hybrid Composites for Integratable Pressure Sensors.

Flexible pressure sensors based on polymer elastomers filled with conductive fillers show great advantages in their applications in flexible electronic devices. However, integratable high-sensitivity pressure sensors remain understudied. This work improves the conductivity and sensitivity of PDMS-Fe/Ni piezoresistive composites by introducing silver flakes and magnetic-assisted alignment techniques. As secondary fillers, silver flakes with high aspect ratios enhance the conductive percolation network in composites. Meanwhile, a magnetic field aligns ferromagnetic particles to further improve the conductivity and sensitivity of composites. The resistivity of the composite decreases sharply by 1000 times within a tiny compression strain of 1%, indicating excellent sensing performance. On the basis of this, we demonstrate an integratable miniature pressure sensor with a small size (2 × 2 × 1 mm), high sensitivity (0.966 kPa-1), and wide sensing range (200 kPa). Finally, we develop a flexible E-skin system with 5 × 5 integratable sensor units to detect pressure distribution, which shows rapid real-time response, high resolution, and high sensitivity.

Development of a model for predicting the 4-year risk of symptomatic knee osteoarthritis in China: a longitudinal cohort study.

OBJECTIVES: We aimed to develop a model for predicting the 4-year risk of knee osteoarthritis (KOA) based on survey data obtained via a random, nationwide sample of Chinese individuals. METHODS: Data was analyzed from 8193 middle-aged and older adults included in the China Health and Retirement Longitudinal Study (CHARLS). The incident of symptomatic KOA was defined as participants who were free of symptomatic KOA at baseline (CHARLS2011) and diagnosed with symptomatic KOA at the 4-year follow-up (CHARLS2015). The effects of potential predictors on the incident of KOA were estimated using logistic regression models and the final model was internally validated using the bootstrapping technique. Model performance was assessed based on discrimination-area under the receiver operating characteristic curve (AUC)-and calibration. RESULTS: A total of 815 incidents of KOA were identified at the 4-year follow-up, resulting in a cumulative incidence of approximately 9.95%. The final multivariable model included age, sex, waist circumference, residential area, difficulty with activities of daily living (ADLs)/instrumental activities of daily living (IADLs), history of hip fracture, depressive symptoms, number of chronic comorbidities, self-rated health status, and level of moderate physical activity (MPA). The risk model showed good discrimination with AUC = 0.719 (95% confidence interval [CI] 0.700-0.737) and optimism-corrected AUC = 0.712 after bootstrap validation. A satisfactory agreement was observed between the observed and predicted probability of incident symptomatic KOA. And a simple clinical score model was developed for quantifying the risk of KOA. CONCLUSION: Our prediction model may aid the early identification of individuals at the greatest risk of developing KOA within 4 years.

Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018.

OBJECTIVES: Given the increased ageing population and frequent epidemic challenges, it is vital to have the nurse workforce of sufficient quantity and quality. This study aimed to demonstrate the trends, composition and distribution of nurse workforce in China. DESIGN: Secondary analysis using national public datasets in China from 2003 to 2018. SETTING/PARTICIPANTS: National population, nurse workforce and physician workforce. PRIMARY AND SECONDARY OUTCOME MEASURES: Frequency and proportion were used to demonstrate: (1) the longitudinal growth of nurse workforce; (2) the diversity of nurse workforce in gender, age, work experience and education level; and (3) the distribution of nurse workforce among provinces, rural-urban areas and hospital/community settings. The Gini coefficient and Theil L index were used to measure the inequality trends of nurse workforce. RESULTS: The total number of nurses increased from 1.3 million to 4.1 million and the density increased from 1 to 2.94 per 1000 population over 2003-2018. The nurses to physician ratio changed from 0.65:1 to 1.14:1. The majority of the nurse workforce was female, under 35 years old, with less than 30 years of work experience, with an associate's degree and employed within hospitals. Central and eastern regions had more nurses and there were 5.08 nurses per 1000 population in urban areas while less than two in rural areas in 2018. The Gini coefficient and between-provincial Theil index experienced a consistent decline. Within-province inequality accounted for overall inequality has risen from 52.38% in 2010 to 71.43% in 2018 suggested that the differences of distribution are mainly reflected in urban and rural areas. CONCLUSION: Chinese nurse workforce has been changed significantly in the past 15 years that may be associated with the reformations of policy, nursing education in China. Our study suggests current features in the nurse workforce and can be used to strengthen future health services.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances to the utilization of these analytics tools, which must be well understood by clinicians seeking to take advantage of these innovative research strategies. One must recognize technical challenges to NLP, such as unintended search outcomes and variability in the expression of human written texts. Other caveats include dealing written texts in image formats, which may ultimately be handled with transformation to text format by OCR, though this technology is still under development. IoT is beginning to be used in cardiac monitoring, medication adherence alerts, lifestyle monitoring, and saving traditional labs from equipment failure catastrophes. These technologies will become more prevalent in the future research landscape, and cardiothoracic surgeons should understand the advantages of these technologies to propel our research to the next level. Experience and understanding of technology are needed in building a robust NLP search result, and effective communication with the data management team is a crucial step in successful utilization of these technologies. In this second installment of the series, we provide examples of published investigations utilizing the advanced analytic tools introduced in Part I. We will explain our processes in developing the research question, barriers to achieving the research goals using traditional research methods, tools used to overcome the barriers, and the research findings.

Enhancing Case Capture, Quality, and Completeness of Primary Melanoma Pathology Records via Natural Language Processing.

PURPOSE: Medical records contain a wealth of useful, informative data points valuable for clinical research. Most data points are stored in semistructured or unstructured legacy documents and require manual data abstraction into a structured format to render the information more readily accessible, searchable, and generally analysis ready. The substantial labor needed for this can be cost prohibitive, particularly when dealing with large patient cohorts. METHODS: To establish a high-throughput approach to data abstraction, we developed a novel framework using natural language processing (NLP) and a decision-rules algorithm to extract, transform, and load (ETL) melanoma primary pathology features from pathology reports in an institutional legacy electronic medical record system into a structured database. We compared a subset of these data with a manually curated data set comprising the same patients and developed a novel scoring system to assess confidence in records generated by the algorithm, thus obviating manual review of high-confidence records while flagging specific, low-confidence records for review. RESULTS: The algorithm generated 368,624 individual melanoma data points comprising 16 primary tumor prognostic factors and metadata from 23,039 patients. From these data points, a subset of 147,872 was compared with an existing, manually abstracted data set, demonstrating an exact or synonymous match between 90.4% of all data points. Additionally, the confidence-scoring algorithm demonstrated an error rate of only 3.7%. CONCLUSION: Our NLP platform can identify and abstract melanoma primary prognostic factors with accuracy comparable to that of manual abstraction (< 5% error rate), with vastly greater efficiency. Principles used in the development of this algorithm could be expanded to include other melanoma-specific data points as well as disease-agnostic fields and further enhance capture of essential elements from nonstructured data.