Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.

BACKGROUND: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS: PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS: In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION: AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

Incidence, risk factors and outcome of postoperative acute kidney injury in China.

BACKGROUND: Postoperative acute kidney injury (AKI) is a common condition after surgery, however, the available data about nationwide epidemiology of postoperative AKI in China from large and high-quality studies are limited. This study aimed to determine the incidence, risk factors and outcomes of postoperative AKI among patients undergoing surgery in China. METHODS: This was a large, multicentre, retrospective study performed in 16 tertiary medical centres in China. Adult patients (≥18 years of age) who underwent surgical procedures from 1 January 2013 to 31 December 2019 were included. Postoperative AKI was defined by the Kidney Disease: Improving Global Outcomes creatinine criteria. The associations of AKI and in-hospital outcomes were investigated using logistic regression models adjusted for potential confounders. RESULTS: Among 520 707 patients included in our study, 25 830 (5.0%) patients developed postoperative AKI. The incidence of postoperative AKI varied by surgery type, which was highest in cardiac (34.6%), urologic (8.7%) and general (4.2%) surgeries. A total of 89.2% of postoperative AKI cases were detected in the first 2 postoperative days. However, only 584 (2.3%) patients with postoperative AKI were diagnosed with AKI on discharge. Risk factors for postoperative AKI included older age, male sex, lower baseline kidney function, pre-surgery hospital stay ≤3 days or >7 days, hypertension, diabetes mellitus and use of proton pump inhibitors or diuretics. The risk of in-hospital death increased with the stage of AKI. In addition, patients with postoperative AKI had longer lengths of hospital stay (12 versus 19 days) and were more likely to require intensive care unit care (13.1% versus 45.0%) and renal replacement therapy (0.4% versus 7.7%). CONCLUSIONS: Postoperative AKI was common across surgery type in China, particularly for patients undergoing cardiac surgery. Implementation and evaluation of an alarm system is important for the battle against postoperative AKI.

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China.

BACKGROUND: Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations. OBJECTIVE: This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health. METHODS: This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed. RESULTS: The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions. CONCLUSIONS: Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Unleashing the Potential of Internet Hospitals: An In-Depth Examination of Information Platform Functionality and Performance.

BACKGROUND: Internet hospitals (IHs) have rapidly developed as a promising strategy to address supply-demand imbalances in China's medical industry, with their capabilities directly dependent on information platform functionality. Moreover, a novel theory of "Trinity" smart hospital has provided advanced guidelines of IHs construction. OBJECTIVE: To explore the construction experience, construction models, and development prospects based on operational data from IHs. METHODS: Based on existing information systems and internet service functionalities, our hospital has built a "Smart Hospital Internet Information Platform (SHIIP)" for IHs operation, actively to expand online services, digitalize traditional healthcare, and explore healthcare services modes throughout the entire process and lifecycle. This article encompasses the platform architecture design, technological applications, patient service content and processes, healthcare professional support features, administrative management tools, and associated operational data. RESULTS: Our platform has presented a remarkable set of data, including 82,279,669 visits, 420,120 online medical consultations, 124,422 electronic prescriptions, 92,285 medication deliveries, 6,965,566 pre-diagnosis triages, 4,995,824 offline outpatient appointments, 2,025 medical education articles with a total of 15,148,310 views, and so on. These data demonstrate the significant role of IH as an indispensable component of our physical hospital services, with a deep integration between online and offline healthcare systems. CONCLUSIONS: Attributing to extreme convenience and improved efficiency, our IH has achieved a wide recognition and use from both the public and healthcare workers, and the upward trends in multiple data metrics suggest a promising outlook for its sustained and positive development in the future. Our pioneering exploration holds tremendous significance and serves as a valuable guiding reference for IHs construction and the progressive development of the internet healthcare sector.

Statin initiation and risk of incident kidney disease in patients with diabetes.

BACKGROUND: The role of statin therapy in the development of kidney disease in patients with type 2 diabetes mellitus (DM) remains uncertain. We aimed to determine the relationships between statin initiation and kidney outcomes in patients with type 2 DM. METHODS: Through a new-user design, we conducted a multicentre retrospective cohort study using the China Renal Data System database (which includes inpatient and outpatient data from 19 urban academic centres across China). We included patients with type 2 DM who were aged 40 years or older and admitted to hospital between Jan. 1, 2000, and May 26, 2021, and excluded those with pre-existing chronic kidney disease and those who were already on statins or without follow-up at an affiliated outpatient clinic within 90 days after discharge. The primary exposure was initiation of a statin. The primary outcome was the development of diabetic kidney disease (DKD), defined as a composite of the occurrence of kidney dysfunction (estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m2 and > 25% decline from baseline) and proteinuria (a urinary albumin-to-creatinine ratio ≥ 30 mg/g and > 50% increase from baseline), sustained for at least 90 days; secondary outcomes included development of kidney function decline (a sustained > 40% decline in eGFR). We used Cox proportional hazards regression to evaluate the relationships between statin initiation and kidney outcomes, as well as to conduct subgroup analyses according to patient characteristics, presence or absence of dyslipidemia, and pattern of dyslipidemia. For statin initiators, we explored the association between different levels of lipid control and outcomes. We conducted analyses using propensity overlap weighting to balance the participant characteristics. RESULTS: Among 7272 statin initiators and 12 586 noninitiators in the weighted cohort, statin initiation was associated with lower risks of incident DKD (hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.62-0.83) and kidney function decline (HR 0.60, 95% CI 0.44-0.81). We obtained similar results to the primary analyses for participants with differing patterns of dyslipidemia, those prescribed different statins, and after stratification according to participant characteristics. Among statin initiators, those with intensive control of high-density lipoprotein cholesterol (LDL-C) (< 1.8 mmol/L) had a lower risk of incident DKD (HR 0.51, 95% CI 0.32-0.81) than those with inadequate lipid control (LDL-C ≥ 3.4 mmol/L). INTERPRETATION: For patients with type 2 DM admitted to and followed up in academic centres, statin initiation was associated with a lower risk of kidney disease development, particularly in those with intensive control of LDL-C. These findings suggest that statin initiation may be an effective and reasonable approach for preventing kidney disease in patients with type 2 DM.

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom.

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries.

OBJECTIVE: Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. METHODS: A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization. RESULTS: We studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%). CONCLUSION: Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.

Access and Unmet Needs of Orphan Drugs in 194 Countries and 6 Areas: A Global Policy Review With Content Analysis.

OBJECTIVES: Three hundred million people living with rare diseases worldwide are disproportionately deprived of in-time diagnosis and treatment compared with other patients. This review provides an overview of global policies that optimize development, licensing, pricing, and reimbursement of orphan drugs. METHODS: Pharmaceutical legislation and policies related to access and regulation of orphan drugs were examined from 194 World Health Organization member countries and 6 areas. Orphan drug policies (ODPs) were identified through internet search, emails to national pharmacovigilance centers, and systematic academic literature search. Texts from selected publications were extracted for content analysis. RESULTS: One hundred seventy-two drug regulation documents and 77 academic publications from 162 countries/areas were included. Ninety-two of 200 countries/areas (46.0%) had documentation on ODPs. Thirty-four subthemes from content analysis were categorized into 6 policy themes, namely, orphan drug designation, marketing authorization, safety and efficacy requirements, price regulation, incentives that encourage market availability, and incentives that encourage research and development. Countries/areas with ODPs were statistically wealthier (gross national income per capita = $10 875 vs $3950, P < .001). Country/area income was also positively correlated with the scope of the respective ODP (correlation coefficient = 0.57, P < .001). CONCLUSIONS: Globally, the number of countries with an ODP has grown rapidly since 2013. Nevertheless, disparities in geographical distribution and income levels affect the establishment of ODPs. Furthermore, identified policy gaps in price regulation, incentives that encourage market availability, and incentives that encourage research and development should be addressed to improve access to available and affordable orphan drugs.

Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China.

BACKGROUND: Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. OBJECTIVE: The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. METHODS: The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. RESULTS: HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. CONCLUSIONS: The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

Application of plasma clearance of iohexol in evaluating renal function in chinese children with chronic kidney disease.

OBJECTIVE: To evaluate the accuracy of plasma clearance of iohexol (PCio) for glomerular filtration rate (GFR) measurement in Chinese children with chronic kidney disease (CKD) and assess the feasibility of single-blood-sample method or dried capillary blood spots in determining the PCio. METHODS: Totally 45 CKD children were included,in whom the (99m) Technetium-diethylenetriaminepentaacetic acid ((99m)Tc-DTPA) plasma clearance and iohexol plasma clearance were simultaneously determined. Blood samples were obtained 2,4,and 5 hours after injection. In addition, we also evaluated the efficacy of single blood sample method and dried blood spots method in iohexol plasma clearance. RESULTS: Forty-five CKD children completed the iohexol plasma clearance and thirty-six children completed the (99m)Tc-DTPA plasma clearance at the same time among them. Thirteen children finished the iohexol dried blood spot clearance. The correlation coefficient between (99m)Tc-DTPA plasma clearance and iohexol plasma clearance was 0.941 and the bias was (6.53 ± 11.6) ml/ (min·1.73 m²), and the intraclass correlation coefficient (ICC) was high (ICC=0.947). The correlation between iohexol single-sample plasma clearance and double samples was also strong (r=0.958), with the bias being (4.26 ± 9.06)ml/(min·1.73 m²) and the ICC being 0.970. The iohexol clearance by dried blood spots showed a good correlation with the serum iohexol clearance (r=0.950), with the bias still being small [(0.48 ± 10.89)ml/(min·1.73 m²)]. CONCLUSIONS: Iohexol plasma clearance has satisfactory agreement with (99m)Tc-DTPA plasma clearance and can be used as an ideal method to measure GFR in CKD children. The single-sample method and dried blood spots method make iohexol plasma clearance more convenient and practical.

Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region.

Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.

Inverse association between serum chloride levels and the risk of atrial fibrillation in chronic kidney disease patients.

Background: Electrolyte abnormalities are common symptoms of chronic kidney disease (CKD), but previous studies have mainly focussed on serum potassium and sodium levels. Chloride is an important biomarker for the prognosis of various diseases. However, the relationship between serum chloride levels and atrial fibrillation (AF) in CKD patients is unclear. Objective: In this study, we sought to determine the association between serum chloride homeostasis and AF in CKD patients. Methods: In this retrospective cohort study, we included patients who met the diagnostic criteria for CKD in China between 2000 and 2021. Competing risk regression for AF was performed. The associations of the baseline serum chloride concentration with heart failure (HF) and stroke incidence were also calculated by competing risk regression. The association of baseline serum chloride levels with all-cause death was determined by a Cox regression model. Results: The study cohort comprised 20 550 participants. During a median follow-up of 350 days (interquartile range, 123-730 days), 211 of the 20 550 CKD patients developed AF. After multivariable adjustment, every decrease in the standard deviation of serum chloride (5.02 mmol/l) was associated with a high risk for AF [sub-hazard ratio (sHR) 0.78, 95% confidence interval (CI) 0.65-0.94, P = .008]. These results were also consistent with those of the stratified and sensitivity analyses. According to the fully adjusted models, the serum chloride concentration was also associated with a high risk for incident HF (sHR 0.85, 95% CI 0.80-0.91, P < .001), a high risk for incident stroke (sHR 0.87, 95% CI 0.81-0.94, P < .001), and a high risk for all-cause death [hazard ratio (HR) 0.82, 95% CI 0.73-0.91, P < .001]. Conclusion: In this CKD population, serum chloride levels were independently and inversely associated with the incidence of AF. Lower serum chloride levels were also associated with an increased risk of incident HF, stroke, and all-cause death.

Dipeptidyl peptidase 4 inhibitors vs. metformin for new-onset dementia: a propensity score-matched cohort study.

BACKGROUND: Hypoglycemic pharmacotherapy interventions for alleviating the risk of dementia remains controversial, particularly about dipeptidyl peptidase 4 (DPP4) inhibitors versus metformin. Our objective was to investigate whether the initiation of DPP4 inhibitors, as opposed to metformin, was linked to a reduced risk of dementia. METHODS: We included individuals with type 2 diabetes over 40 years old who were new users of DPP4 inhibitors or metformin in the Chinese Renal Disease Data System (CRDS) database between 2009 and 2020. The study employed Kaplan-Meier and Cox regression for survival analysis and the Fine and Gray model for the competing risk of death. RESULTS: Following a 1:1 propensity score matching, the analysis included 3626 DPP4 inhibitor new users and an equal number of metformin new users. After adjusting for potential confounders, the utilization of DPP4 inhibitors was associated with a decreased risk of all-cause dementia compared to metformin (hazard ratio (HR) 0.63, 95% confidence interval (CI) 0.45-0.89). Subgroup analysis revealed that the utilization of DPP4 inhibitors was associated with a reduced incidence of dementia in individuals who initiated drug therapy at the age of 60 years or older (HR 0.69, 95% CI 0.48-0.98), those without baseline macrovascular complications (HR 0.62, 95% CI 0.41-0.96), and those without baseline microvascular complications (HR 0.67, 95% CI 0.47-0.98). CONCLUSION: In this real-world study, we found that DPP4 inhibitors presented an association with a lower risk of dementia in individuals with type 2 diabetes than metformin, particularly in older people and those without diabetes-related comorbidities.

Real-world effectiveness of early insulin therapy on the incidence of cardiovascular events in newly diagnosed type 2 diabetes.

Early insulin therapy is capable to achieve glycemic control and restore ß-cell function in newly diagnosed type 2 diabetes (T2D), but its effect on cardiovascular outcomes in these patients remains unclear. In this nationwide real-world study, we analyzed electronic health record data from 19 medical centers across China between 1 January 2000, and 26 May 2022. We included 5424 eligible patients (mean age 56 years, 2176 women/3248 men) who were diagnosed T2D within six months and did not have prior cardiovascular disease. Multivariable Cox regression models were used to estimate the associations of early insulin therapy (defined as the first-line therapy for at least two weeks in newly diagnosed T2D patients) with the incidence of major cardiovascular events including coronary heart disease (CHD), stroke, and hospitalization for heart failure (HF). During 17,158 persons years of observation, we documented 834 incident CHD cases, 719 stroke cases, and 230 hospitalized cases for HF. Newly diagnosed T2D patients who received early insulin therapy, compared with those who did not receive such treatment, had 31% lower risk of incident stroke, and 28% lower risk of hospitalization for HF. No significant difference in the risk of CHD was observed. We found similar results when repeating the aforesaid analysis in a propensity-score matched population of 4578 patients and with inverse probability of treatment weighting models. These findings suggest that early insulin therapy in newly diagnosed T2D may have cardiovascular benefits by reducing the risk of incident stroke and hospitalization for HF.

Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data.

BACKGROUND: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. OBJECTIVE: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. DESIGN, SETTING, AND PARTICIPANTS: From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. RESULTS AND LIMITATIONS: The most common comorbidities were hypertension (35-73%), obesity (9.2-54%), and type 2 diabetes (11-28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12-25%) and emergency department visits (10-14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. CONCLUSIONS: Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. PATIENT SUMMARY: Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.

Effect of picric acid and enzymatic creatinine on the efficiency of the glomerular filtration rate predicator formula.

BACKGROUND: To investigate the impact of serum creatinine measurement on the applicability of glomerular filtration rate (GFR) evaluation equations. METHODS: 99mTc-DTPA plasma clearance rate was used as GFR reference (rGFR) in patients with chronic kidney disease (CKD). Serum creatinine was measureded using enzymatic or picric acid creatinine reagent. The GFR of the patients were estimated using the Cockcroft-Gault equation corrected for body surface area, simplified Modification of Diet in Renal Disease (MDRD) equation, simplified MDRD equation corrected to isotopes dilution mass spectrometry, the CKD epidemiology collaborative research equation, and two Chinese simplified MDRD equations. RESULTS: Significant differences in the eGFR results estimated through enzymatic and picric acid methods were observed for the same evaluation equation. The intraclass correlation coefficient (ICC) of eGFR when the creatinine was measured by the picric acid method was significantly lower than that of the enzymatic method. The assessment accuracy of every equation using the enzymatic method to measure creatinine was significantly higher than that measured by the picric acid method when rGFR was > or = 60 mL/min/1.73m2. CONCLUSIONS: A significant difference was demonstrated in the same GFR evaluation equation using the picric acid and enzymatic methods. The enzymatic creatinine method was better than the picric acid method.

Prevalence and risk factors for chronic kidney disease among HIV-infected antiretroviral therapy-naïve patients in mainland China: a multicenter cross-sectional study.

AIM: The aim of the study was to evaluate the prevalence and risk factors of chronic kidney disease (CKD) among HIV-infected antiretroviral therapy (ART)-naïve patients in Mainland China. METHODS: In this multicenter cross-sectional study, glomerular filtration rate (GFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equation. CKD was defined as GFRMDRD < 60 mL/min per 1.73 m(2) and/or isolated proteinuria (≥1 + on urine dipstick) that persisted at month 3 after the baseline assessment. Risk factors associated with CKD were examined using univariate analysis and multivariate logistic regression analysis. RESULTS: In total, 538 HIV-infected ART-naïve patients were included in this study. There were 399 male and 139 female patients. The mean age was 36.5 ± 10.0 years. The prevalence of hypertension, glycometabolism abnormities, and CKD were 3.2%, 3.0%, and 16.1%, respectively. Thirteen (2.4%) patients had estimated GFR (eGFR) < 60 mL/min per 1.73 m(2), while 73 (13.7%) patients had proteinuria. Using univariate analysis, CKD was found to be significantly (P < 0.05) associated with age, hypertension, HCV co-infection, and plasma HIV-1 viral load ≥ 100 000 copies/mL. In the multivariate logistic regression model, older age (increased by an interval of 10 years; P = 0.002), HCV co-infection (P = 0.039), and plasma HIV-1 viral load ≥ 100 000 copies/mL (P = 0.011) were significantly associated with CKD. CONCLUSION: The incidence of CKD is high in Chinese HIV-infected ART-naïve patients. Traditional risk factors for renal disease, such as advancing age, HCV co-infection, and higher plasma viral load were correlated with CKD in the present patient samples.

The "Trinity" smart hospital construction policy promotes the development of hospitals and health management in China.

Recently, in order to comprehensively promote the development of medical institutions and solve the nationwide problems in the healthcare fields, the government of China developed an innovative national policy of "Trinity" smart hospital construction, which includes "smart medicine," "smart services," and "smart management". The prototype of the evaluation system has been established, and a large number of construction achievements have emerged in many hospitals. In this article, the summary of this field was performed to provide a reference for medical workers, managers of hospitals, and policymakers.

A bibliometric analysis of worldwide cancer research using machine learning methods.

With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, "Support Vector Machine," "classification," and "deep learning" have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.