Association between ambient PM1 and the prevalence of chronic kidney disease in China: A nationwide study.

Particulate of diameter ≤ 1 µm (PM1) presents a novel risk factor of adverse health effects. Nevertheless, the association of PM1 with the risk of chronic kidney disease (CKD) in the general population is not well understood, particularly in regions with high PM1 levels like China. Based on a nationwide representative survey involving 47,204 adults and multi-source ambient air pollution inversion data, the present study evaluated the association of PM1 with CKD prevalence in China. The two-year average PM1, particulate of diameter ≤ 2.5 µm (PM2.5), and PM1-2.5 values were accessed using a satellite-based random forest approach. CKD was defined as estimated glomerular filtration rate < 60 ml/min/1.73 m2 or albuminuria. The results suggested that a 10 µg/m3 rise in PM1 was related to a higher CKD risk (odds ratio [OR], 1.13; 95% confidence interval [CI] 1.08-1.18) and albuminuria (OR, 1.11; 95% CI, 1.05-1.17). The association between PM1 and CKD was more evident among urban populations, older adults, and those without comorbidities such as diabetes or hypertension. Every 1% increase in the PM1/PM2.5 ratio was related to the prevalence of CKD (OR, 1.03; 95% CI, 1.03-1.04), but no significant relationship was found for PM1-2.5. In conclusion, the present study demonstrated long-term exposure to PM1 was associated with an increased risk of CKD in the general population and PM1 might play a leading role in the observed relationship of PM2.5 with the risk of CKD. These findings provide crucial evidence for developing air pollution control strategies to reduce the burden of CKD.

Heat exposure and hospitalizations for chronic kidney disease in China: a nationwide time series study in 261 major Chinese cities.

BACKGROUND: Climate change profoundly shapes the population health at the global scale. However, there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease (CKD). METHODS: In the present study, we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018. Standard time-series regression models and random-effects meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span, respectively. RESULTS: A total of 768,129 hospitalizations for CKD was recorded during the study period. The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD, especially in sub-tropical cities. With a 1 °C increase in daily mean temperature, the cumulative relative risks (RR) over lag 0-7 d were 1.008 [95% confidence interval (CI) 1.003-1.012] for nationwide. The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%. Stronger associations were observed among younger patients and those with obstructive nephropathy. Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days (RR = 1.116, 95% CI 1.069-1.166) above the effect of daily mean temperature. CONCLUSIONS: Short-term heat exposure may increase the risk of hospitalization for CKD. Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.

Visualization of Traditional Chinese Medicine Formulas: Development and Usability Study.

BACKGROUND: Traditional Chinese medicine (TCM) formulas are combinations of Chinese herbal medicines. Knowledge of classic medicine formulas is the basis of TCM diagnosis and treatment and is the core of TCM inheritance. The large number and flexibility of medicine formulas make memorization difficult, and understanding their composition rules is even more difficult. The multifaceted and multidimensional properties of herbal medicines are important for understanding the formula; however, these are usually separated from the formula information. Furthermore, these data are presented as text and cannot be analyzed jointly and interactively. OBJECTIVE: We aimed to devise a visualization method for TCM formulas that shows the composition of medicine formulas and the multidimensional properties of herbal medicines involved and supports the comparison of medicine formulas. METHODS: A TCM formula visualization method with multiple linked views is proposed and implemented as a web-based tool after close collaboration between visualization and TCM experts. The composition of medicine formulas is visualized in a formula view with a similarity-based layout supporting the comparison of compositing herbs; a shared herb view complements the formula view by showing all overlaps of pair-wise formulas; and a dimensionality-reduction plot of herbs enables the visualization of multidimensional herb properties. The usefulness of the tool was evaluated through a usability study with TCM experts. RESULTS: Our method was applied to 2 typical categories of medicine formulas, namely tonic formulas and heat-clearing formulas, which contain 20 and 26 formulas composed of 58 and 73 herbal medicines, respectively. Each herbal medicine has a 23-dimensional characterizing attribute. In the usability study, TCM experts explored the 2 data sets with our web-based tool and quickly gained insight into formulas and herbs of interest, as well as the overall features of the formula groups that are difficult to identify with the traditional text-based method. Moreover, feedback from the experts indicated the usefulness of the proposed method. CONCLUSIONS: Our TCM formula visualization method is able to visualize and compare complex medicine formulas and the multidimensional attributes of herbal medicines using a web-based tool. TCM experts gained insights into 2 typical medicine formula categories using our method. Overall, the new method is a promising first step toward new TCM formula education and analysis methodologies.

Traditional Chinese Medicine (TCM) Domain Ontology: Current Status and Rethinking for the Future Development.

The past twenty years have seen the increasingly important role of ontology in traditional Chinese medicine (TCM). However, the development of TCM ontology faces many challenges. Since the epistemologies dramatically differ between TCM and contemporary biomedicine, it is hard to apply the existing top-level ontology mechanically. "Data silos" are widely present in the currently available terminology standards, term sets, and ontologies. The formal representation of ontology needs to be further improved in TCM. Therefore, we propose a unified basic semantic framework of TCM based on in-depth theoretical research on the existing top-level ontology and a re-study of important concepts in TCM. Under such a framework, ontologies in TCM sub-domains should be built collaboratively and be represented formally in a common format. Besides, extensive cooperation should be encouraged by establishing ontology research communities to promote ontology peer review and reuse.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.

BACKGROUND: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. RESULTS: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CONCLUSION: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Effects of ambient temperature on hospital admissions for obstructive nephropathy in Wuhan, China: A time-series analysis.

Under the background of global warming, it has been confirmed that heat exposure has a huge impact on human health. The current study aimed to evaluate the effects of daily mean ambient temperature on hospital admissions for obstructive nephropathy (ON) at the population level. A total of 19,494 hospitalization cases for ON in Wuhan, China from January 1, 2015 to December 31, 2018 were extracted from a nationwide inpatient database in tertiary hospitals according to the International Classification of Diseases (ICD)- 10 codes. Daily ambient meteorological and pollution data during the same period were also collected. A quasi-Poisson Generalized Linear Model (GLM) combined with a distributed lag non-linear model (DLNM) was applied to analyze the lag-exposure-response relationship between daily mean temperature and daily hospital admissions for ON. Results showed that there were significantly positive associations between the daily mean temperature and ON hospital admissions. Relative to the minimum-risk temperature (-3.4 â„ƒ), the risk of hospital admissions for ON at moderate hot temperature (25 â„ƒ, 75th percentile) occurred from lag day 4 and stayed to lag day 12 (cumulative relative risk [RR] was 1.846, 95 % confidence interval [CI]: 1.135-3.005, over lag 0-12 days). Moreover, the risk of extreme hot temperature (32 â„ƒ, 99th percentile) appeared immediately and lasted for 8 days (RR = 2.019, 95 % CI: 1.308-3.118, over lag 0-8 days). Subgroup analyses indicated that the middle-aged and elderly (≥45 years) patients might be more susceptible to the negative effects of high temperature, especially at moderate hot conditions. Our findings suggest that temperature may have a significant impact on the acute progression and onset of ON. Higher temperature is associated with increased risks of hospital admissions for ON, which indicates that early interventions should be taken in geographical settings with relatively high temperatures, particularly for the middle-aged and elderly.

Cost-effectiveness of medical migration for chronic kidney disease: a national cross-sectional study in China.

BACKGROUND: The phenomenon of medical migration is common in China. Due to the limited capacity and substantial geographical variation in medical practice, patients with chronic kidney disease (CKD) travel more frequently to seek medical care. We aimed to assess the cost-effectiveness of medical migration for CKD patients in China and provide real-world evidence for the allocation of CKD resources. METHODS: Records of patients with CKD between January 2014 and December 2018 were extracted from a large national database. A patient is defined as a medical migrant if she travelled across the provincial border to a non-residential province to be admitted for inpatient care. The propensity score matching method is used to estimate the effect of medical migration on medical expenditure, length of hospital stay, and in-hospital mortality. The cost-effectiveness is evaluated by comparing the estimated cost per life saved with contemporaneous estimates of the value of a statistical life. RESULTS: Among 4,392,650 hospitalizations with CKD, medical migrants accounted for 4.9% in 2018. Migrant patients were estimated to incur a 26.35% increase in total medical expenditure, experience a 0.24-percentage-points reduction in in-hospital mortality rates, and a 0.49-days reduction in length of hospital stay compared to non-migrant patients. Overall, medical migration among CKD patients incurred an average of 1 million yuan per life saved, which accounted for 20-40% of contemporaneous estimates of the value of a statistical life. Compared with migrant patients with self-payment and commercial insurance, migrant patients with public health insurance (urban basic medical insurance and new rural co-operative medical care) incurred lower cost per life saved. Cost per life saved for CKD patients was similar between female and male, lower among older population, and varied substantially across regions. CONCLUSIONS: The medical care seeking behaviors of CKD patients was prominent and medical resources of kidney care were unevenly allocated across regions. Medical migration led to a reduction in mortality, but was associated with higher medical expenditure. It is imperative to reduce the regional disparity of medical resources and improve the clinical capacity. Our study shows that it is imperative to prioritize resource allocation toward improving kidney health and regional health care planning.

Profiling COVID-19 Vaccine Adverse Events by Statistical and Ontological Analysis of VAERS Case Reports.

Since the beginning of the COVID-19 pandemic, vaccines have been developed to mitigate the spread of SARS-CoV-2, the virus that causes COVID-19. These vaccines have been effective in reducing the rate and severity of COVID-19 infection but also have been associated with various adverse events (AEs). In this study, data from the Vaccine Adverse Event Reporting System (VAERS) was queried and analyzed via the Cov19VaxKB vaccine safety statistical analysis tool to identify statistically significant (i.e., enriched) AEs for the three currently FDA-authorized or approved COVID-19 vaccines. An ontology-based classification and literature review were conducted for these enriched AEs. Using VAERS data as of 31 December 2021, 96 AEs were found to be statistically significantly associated with the Pfizer-BioNTech, Moderna, and/or Janssen COVID-19 vaccines. The Janssen COVID-19 vaccine had a higher crude reporting rate of AEs compared to the Moderna and Pfizer COVID-19 vaccines. Females appeared to have a higher case report frequency for top adverse events compared to males. Using the Ontology of Adverse Event (OAE), these 96 adverse events were classified to different categories such as behavioral and neurological AEs, cardiovascular AEs, female reproductive system AEs, and immune system AEs. Further statistical comparison between different ages, doses, and sexes was also performed for three notable AEs: myocarditis, GBS, and thrombosis. The Pfizer vaccine was found to have a closer association with myocarditis than the other two COVID-19 vaccines in VAERS, while the Janssen vaccine was more likely to be associated with thrombosis and GBS AEs. To support standard AE representation and study, we have also modeled and classified the newly identified thrombosis with thrombocytopenia syndrome (TTS) AE and its subclasses in the OAE by incorporating the Brighton Collaboration definition. Notably, severe COVID-19 vaccine AEs (including myocarditis, GBS, and TTS) rarely occur in comparison to the large number of COVID-19 vaccinations administered in the United States, affirming the overall safety of these COVID-19 vaccines.

Renin-angiotensin system inhibitor is associated with the reduced risk of all-cause mortality in COVID-19 among patients with/without hypertension.

Consecutively hospitalized patients with confirmed coronavirus disease 2019 (COVID-19) in Wuhan, China were retrospectively enrolled from January 2020 to March 2020 to investigate the association between the use of renin-angiotensin system inhibitor (RAS-I) and the outcome of this disease. Associations between the use of RAS-I (angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB)), ACEI, and ARB and in-hospital mortality were analyzed using multivariate Cox proportional hazards regression models in overall and subgroup of hypertension status. A total of 2771 patients with COVID-19 were included, with moderate and severe cases accounting for 45.0% and 36.5%, respectively. A total of 195 (7.0%) patients died. RAS-I (hazard ratio (HR)= 0.499, 95% confidence interval (CI) 0.325-0.767) and ARB (HR = 0.410, 95% CI 0.240-0.700) use was associated with a reduced risk of all-cause mortality among patients with COVID-19. For patients with hypertension, RAS-I and ARB applications were also associated with a reduced risk of mortality with HR of 0.352 (95% CI 0.162-0.764) and 0.279 (95% CI 0.115-0.677), respectively. RAS-I exhibited protective effects on the survival outcome of COVID-19. ARB use was associated with a reduced risk of all-cause mortality among patients with COVID-19.

Influence of doctors' perception on the diagnostic status of chronic kidney disease: results from 976 409 individuals with electronic health records in China.

BACKGROUND: The diagnostic status of chronic kidney disease (CKD) and its underlying reasons provide evidence that can improve CKD management. However, the situation in developing countries remains under-investigated. METHODS: Adults with electronic health records (EHRs; 2008-19) in Yinzhou, China were included. The gold standard for CKD was defined as having persistently reduced estimated glomerular filtration rate (eGFR), albuminuria/proteinuria, haematuria or a history of CKD. CKD stages (G1-G5) were defined by eGFR. Clinical diagnosis of CKD in the real world setting was evaluated using International Classification of Diseases (ICD)-10 codes related to primary cause or stages of CKD. The specialty of doctors who administered the serum creatinine (SCr) tests and who made the primary-cause/CKD-staging diagnoses was analysed. The accuracy of CKD-staging codes was assessed. RESULTS: Altogether, 85 519 CKD patients were identified from 976 409 individuals with EHRs. Of them, 10 287 (12.0%) having persistent urinary abnormalities or labelled with CKD-related ICD codes did not receive SCr tests within 12 months before or after the urine tests. Among 75 147 patients who received SCr tests, 46 150 (61.4%) missed any CKD-related codes, 6857 (35.7%) were merely labelled with primary-cause codes, and only 2140 (2.9%) were labelled with CKD-staging codes. The majority of CKD patients (51.6-91.1%) received SCr tests from non-nephrologists, whereas CKD-staging diagnoses were mainly from nephrologists (52.3-64.8%). Only 3 of 42 general hospitals had nephrologists. The CKD-staging codes had high specificity (>99.0%) but low sensitivity (G3-G4: <10.0%). CONCLUSIONS: Under-perception of CKD among doctors, rather than unsatisfactory health-seeking behaviour or low detection rates, was the main cause of under-diagnosis of CKD in China. Intensification of CKD education among doctors with different specialties might bring about immediate effective improvement in the diagnosis and awareness of CKD.

Development of the International Classification of Diseases Ontology (ICDO) and its application for COVID-19 diagnostic data analysis.

BACKGROUND: The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology. METHODS: An ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO. RESULTS: We have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10. CONCLUSIONS: ICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.

Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context.

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept "knowledge unit" and "knowmetrics" for measuring knowledge. However, the definition on "computable knowledge object" remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.

Long-Term Exposure to Ambient PM2.5, Sunlight, and Obesity: A Nationwide Study in China.

Background: Accumulated researches revealed that both fine particulate matter (PM2.5) and sunlight exposure may be a risk factor for obesity, while researches regarding the potential effect modification by sunlight exposure on the relationship between PM2.5 and obesity are limited. We aim to investigate whether the effect of PM2.5 on obesity is affected by sunlight exposure among the general population in China. Methods: A sample of 47,204 adults in China was included. Obesity and abdominal obesity were assessed based on body mass index, waist circumference and waist-to-hip ratio, respectively. The five-year exposure to PM2.5 and sunlight were accessed using the multi-source satellite products and a geochemical transport model. The relationship between PM2.5, sunshine duration, and the obesity or abdominal obesity risk was evaluated using the general additive model. Results: The proportion of obesity and abdominal obesity was 12.6% and 26.8%, respectively. Levels of long-term PM2.5 ranged from 13.2 to 72.1 µg/m3 with the mean of 46.6 µg/m3. Each 10 µg/m3 rise in PM2.5 was related to a higher obesity risk [OR 1.12 (95% CI 1.09-1.14)] and abdominal obesity [OR 1.10 (95% CI 1.07-1.13)]. The association between PM2.5 and obesity varied according to sunshine duration, with the highest ORs of 1.56 (95% CI 1.28-1.91) for obesity and 1.66 (95% CI 1.34-2.07) for abdominal obesity in the bottom quartile of sunlight exposure (3.21-5.34 hours/day). Conclusion: Long-term PM2.5 effect on obesity risk among the general Chinese population are influenced by sunlight exposure. More attention might be paid to reduce the adverse impacts of exposure to air pollution under short sunshine duration conditions.

Early versus late acute kidney injury among patients with COVID-19-a multicenter study from Wuhan, China.

BACKGROUND: Acute kidney injury (AKI) is an important complication of coronavirus disease 2019 (COVID-19), which could be caused by both systematic responses from multi-organ dysfunction and direct virus infection. While advanced evidence is needed regarding its clinical features and mechanisms. We aimed to describe two phenotypes of AKI as well as their risk factors and the association with mortality. METHODS: Consecutive hospitalized patients with COVID-19 in tertiary hospitals in Wuhan, China from 1 January 2020 to 23 March 2020 were included. Patients with AKI were classified as AKI-early and AKI-late according to the sequence of organ dysfunction (kidney as the first dysfunctional organ or not). Demographic and clinical features were compared between two AKI groups. Their risk factors and the associations with in-hospital mortality were analyzed. RESULTS: A total of 4020 cases with laboratory-confirmed COVID-19 were included and 285 (7.09%) of them were identified as AKI. Compared with patients with AKI-early, patients with AKI-late had significantly higher levels of systemic inflammatory markers. Both AKIs were associated with an increased risk of in-hospital mortality, with similar fully adjusted hazard ratios of 2.46 [95% confidence interval (CI) 1.35-4.49] for AKI-early and 3.09 (95% CI 2.17-4.40) for AKI-late. Only hypertension was independently associated with the risk of AKI-early. While age, history of chronic kidney disease and the levels of inflammatory biomarkers were associated with the risk of AKI-late. CONCLUSIONS: AKI among patients with COVID-19 has two clinical phenotypes, which could be due to different mechanisms. Considering the increased risk for mortality for both phenotypes, monitoring for AKI should be emphasized during COVID-19.

Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research.

The effective use of EHR data for clinical research is challenged by the lack of methodologic standards, transparency, and reproducibility. For example, our empirical analysis on clinical research ontologies and reporting standards found little-to-no informatics-related standards. To address these issues, our study aims to leverage natural language processing techniques to discover the reporting patterns and data abstraction methodologies for EHR-based clinical research. We conducted a case study using a collection of full articles of EHR-based population studies published using the Rochester Epidemiology Project infrastructure. Our investigation discovered an upward trend of reporting EHR-related research methodologies, good practice, and the use of informatics related methods. For example, among 1279 articles, 24.0% reported training for data abstraction, 6% reported the abstractors were blinded, 4.5% tested the inter-observer agreement, 5% reported the use of a screening/data collection protocol, 1.5% reported that team meetings were organized for consensus building, and 0.8% mentioned supervision activities by senior researchers. Despite that, the overall ratio of reporting/adoption of methodologic standards was still low. There was also a high variation regarding clinical research reporting. Thus, continuously developing process frameworks, ontologies, and reporting guidelines for promoting good data practice in EHR-based clinical research are recommended.

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data.

BACKGROUND: The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. OBJECTIVE: The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). METHODS: We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. RESULTS: The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly. CONCLUSIONS: We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations. Detecting phenotypic relevance is an important task when translating precision medicine into clinical practice, especially for patient stratification tasks based on deep phenotyping. In our previous work, we developed node embeddings for the Human Phenotype Ontology (HPO) to assist in phenotypic relevance measurement incorporating distributed semantic representations. However, the derived HPO embeddings hold only distributed representations for IS-A relationships among nodes, hampering the ability to fully explore the graph. METHODS: In this study, we developed a framework, HPO2Vec+, to enrich the produced HPO embeddings with heterogeneous knowledge resources (i.e., DECIPHER, OMIM, and Orphanet) for detecting phenotypic relevance. Specifically, we parsed disease-phenotype associations contained in these three resources to enrich non-inheritance relationships among phenotypic nodes in the HPO. To generate node embeddings for the HPO, node2vec was applied to perform node sampling on the enriched HPO graphs based on random walk followed by feature learning over the sampled nodes to generate enriched node embeddings. Four HPO embeddings were generated based on different graph structures, which we hereafter label as HPOEmb-Original, HPOEmb-DECIPHER, HPOEmb-OMIM, and HPOEmb-Orphanet. We evaluated the derived embeddings quantitatively through an HPO link prediction task with four edge embeddings operations and six machine learning algorithms. The resulting best embeddings were then evaluated for patient stratification of 10 rare diseases using electronic health records (EHR) collected at Mayo Clinic. We assessed our framework qualitatively by visualizing phenotypic clusters and conducting a use case study on primary hyperoxaluria (PH), a rare disease, on the task of inferring relevant phenotypes given 22 annotated PH related phenotypes. RESULTS: The quantitative link prediction task shows that HPOEmb-Orphanet achieved an optimal AUROC of 0.92 and an average precision of 0.94. In addition, HPOEmb-Orphanet achieved an optimal F1 score of 0.86. The quantitative patient similarity measurement task indicates that HPOEmb-Orphanet achieved the highest average detection rate for similar patients over 10 rare diseases and performed better than other similarity measures implemented by an existing tool, HPOSim, especially for pairwise patients with fewer shared common phenotypes. The qualitative evaluation shows that the enriched HPO embeddings are generally able to detect relationships among nodes with fine granularity and HPOEmb-Orphanet is particularly good at associating phenotypes across different disease systems. For the use case of detecting relevant phenotypic characterizations for given PH related phenotypes, HPOEmb-Orphanet outperformed the other three HPO embeddings by achieving the highest average P@5 of 0.81 and the highest P@10 of 0.79. Compared to seven conventional similarity measurements provided by HPOSim, HPOEmb-Orphanet is able to detect more relevant phenotypic pairs, especially for pairs not in inheritance relationships. CONCLUSION: We drew the following conclusions based on the evaluation results. First, with additional non-inheritance edges, enriched HPO embeddings can detect more associations between fine granularity phenotypic nodes regardless of their topological structures in the HPO graph. Second, HPOEmb-Orphanet not only can achieve the optimal performance through link prediction and patient stratification based on phenotypic similarity, but is also able to detect relevant phenotypes closer to domain expert's judgments than other embeddings and conventional similarity measurements. Third, incorporating heterogeneous knowledge resources do not necessarily result in better performance for detecting relevant phenotypes. From a clinical perspective, in our use case study, clinical-oriented knowledge resources (e.g., Orphanet) can achieve better performance in detecting relevant phenotypic characterizations compared to biomedical-oriented knowledge resources (e.g., DECIPHER and OMIM).

A chronic kidney disease patient awareness questionnaire: Development and validation.

BACKGROUND: With the advance of medical care, chronic non-communicable diseases, like chronic kidney disease (CKD), have become the predominant diseases around the world. With heavy society and economy burden, we shall make full use of chronic disease management, including precision therapies. And the prerequisite for implementing precision medicine is to fully understand the characteristics of patients. Being the basis of the Knowledge-Attitude-Practice Model, patient's awareness is essential to conduct individualized treatments. However, there have been no validated questionnaires specific to the awareness of patients with CKD. Therefore, this study aims to develop and validate an awareness questionnaire for patients with CKD. METHODS: From March 2013 to September 2014, a cross-sectional study was conducted at Guangdong Provincial Hospital of Chinese Medicine. Age 18 or above were enrolled in the study. After signing the informed consent, they received a self-developed questionnaire to evaluate their CKD-related awareness. Then we collected their demographic data for further analyses. We also conducted item analyses/ validity and reliability analysis to filter out improper items and to retain the eligible ones. RESULTS: We totally distributed 110 copies of the questionnaires and 100 of them were returned. After item analyses, 2 items were excluded because of Cronbach's Alpha analysis. In total, 18 items were retained, comprising the final set of the questionnaire. For validity analysis, 4 components could explain the cumulative 73.966% extraction sums of the squared loadings; for reliability analysis, the Guttman Split-Half coefficient was 0.918. CONCLUSIONS: This awareness questionnaire has favorable validity and reliability. It is a sound method for evaluating and measuring levels of disease-related awareness in CKD patients.

Self-management interventions for chronic kidney disease: a systematic review and meta-analysis.

BACKGROUND: Self-management intervention aims to facilitate an individual's ability to make lifestyle changes. The effectiveness of this intervention in non-dialysis patients with chronic kidney disease (CKD) is limited. In this study, we applied a systematic review and meta-analysis to investigate whether self-management intervention improves renoprotection for non-dialysis chronic kidney disease. METHODS: We conducted a comprehensive search for randomized controlled trials addressing our objective. We searched for studies up to May 12, 2018. Two reviewers independently evaluated study quality and extracted characteristics and outcomes among patients with CKD within the intervention phase for each trial. Meta-regression and subgroup analyses were conducted to explore heterogeneity. RESULTS: We identified 19 studies with a total of 2540 CKD patients and a mean follow-up of 13.44 months. Compared with usual care, self-management intervention did not show a significant difference for risk of all-cause mortality (5 studies, 1662 participants; RR 1.13; 95% CI 0.68 to 1.86; I2 = 0%), risk of dialysis (5 studies, 1565 participants; RR 1.35; 95% CI 0.84 to 2.19; I2 = 0%), or change in eGFR (8 studies, 1315 participants; SMD -0.01; 95% CI -0.23 to 0.21; I2 = 64%). Moreover, self-management interventions were associated with a lower 24 h urinary protein excretion (4 studies, 905 participants; MD - 0.12 g/24 h; 95% CI -0.21 to - 0.02; I2 = 3%), a lower blood pressure level (SBP: 7 studies, 1201 participants; MD - 5.68 mmHg; 95%CI - 9.68 to - 1.67; I2 = 60%; DBP: 7 studies, 1201 participants; MD - 2.64 mmHg, 95% CI -3.78 to - 1.50; I2 = 0%), a lower C-reactive Protein (CRP) level (3 studies, 123 participants; SMD -2.8; 95% CI -2.90 to - 2.70; I2 = 0%) and a longer distance on the 6-min walk (3 studies, 277 participants; SMD 0.70; 95% CI 0.45 to 0.94; I2 = 0%) when compared with the control group. CONCLUSIONS: We observed that self-management intervention was beneficial for urine protein decline, blood pressure level, exercise capacity and CRP level, compared with the standard treatment, during a follow-up of 13.44 months in patients with CKD non-dialysis. However, it did not provide additional benefits for renal outcomes and all-cause mortality.

Telehealth for the management of blood pressure in patients with chronic kidney disease: A systematic review.

BACKGROUND: Most patients with chronic kidney disease (CKD) fail to achieve blood pressure (BP) management as recommended. Meanwhile, the effects of promising intervention and telehealth on BP control in CKD patients remain unclear. We aimed to evaluate the efficacy of telehealth for BP in CKD non-dialysis patients. METHODS: Databases including MEDLINE, EMBASE, CENTRAL, CNKI, Wanfang, VIP and CBM were systematically searched for randomised controlled trials or quasi-randomised controlled trials on telehealth for BP control of CKD3-5 non-dialysis patients. We analysed systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), serum creatinine, and estimated glomerular filtration rate (eGFR) with a fixed-effects model. RESULTS: Three studies, with total 680 subjects, were included in our systematic review and two were included for meta-analysis. Pooled estimates showed decreased SBP (pooled mean difference (MD), -5.10; 95% confidence interval (CI), -11.34, 1.14; p > 0.05, p = 0.11), increased DBP (pooled MD, 0.45; 95% CI, -4.24, 5.13; p > 0.05, p = 0.85), decreased serum creatinine (pooled MD, -0.38; 95% CI, -0.83, 0.07; p > 0.05, p = 0.10) and maintained eGFR (pooled MD, 4.72; 95% CI, -1.85, 11.29; p > 0.05, p = 0.16) in the telehealth group. There was no significant difference from the control group. MAP (MD, 0.6; 95% CI, -6.61, 7.81; p > 0.05, p = 0.87) and BP control rate ( p > 0.05, p = 0.8), respectively, shown in two studies also demonstrated no statistical significance in the telehealth group. CONCLUSIONS: There was no statistically significant evidence to support the superiority of telehealth for BP management in CKD patients. This suggests further studies with improved study design and optimised intervention are needed in the future.