In multivariate analysis, patient’s origin did not predict treatment failure . Discussion: Higher rates of antibiotic failure and mortality in NHAP patients were explained by the presence of other risk factors such as comorbidities, more severe presentation, and age. Admission from a nursing home is not a sufficient condition to start broader-spectrum antibiotics.Purpose: Nursing home-acquired pneumonia (NHAP) patients are at higher risk of multi-drug resistant infection (MDR) than those with community-acquired pneumonia (CAP). Recent evidence suggests a single risk factor for MDR does not accurately predict the need for broad-spectrum antibiotics. The goal of this study was to compare the rate antibiotic failure between NHAP and CAP patients. Methods: Demographic characteristics, co-morbidities, clinical and laboratory variables, antibiotic therapy, and mortality data were collected retrospectively for all patients with pneumonia admitted to an Internal Medicine Service between April 2017 and April 2018. Results: In total, 313 of 556 patients had CAP and 243 had NHAP. NHAP patients were older, and were more likely to be dependent, to have recent antibiotic use, and to experience treatment failure (odds ratio (OR) 1.583; 95% CI 1.102–2.276;  Pneumonia is one of the major causes of death in Portugal, resulting in 57.7 deaths for every 100,000 habitants , with pnNHAP was incorporated into the concept of healthcare-associated pneumonia (HCAP) when it was introduced to the American Thoracic Society and the Infectious Diseases Society of America (ATS/IDSA) 2005 clinical practice guidelines for hospital-acquired, ventilator-associated, and healthcare-associated pneumonia in adults. The HCAP classification was based on two large U.S. studies [In the proposed classification, a patient was considered to have healthcare-associated pneumonia if they had at least one of the following risk factors : hospitaHowever, the concept of HCAP faced criticism as subsequent retrospective studies in other countries showed differing rates of MDR infections, with Spain and the UK reporting much lower incidences of MDR bacteria in HCAP patients than in the USA ,7. AdditHowever, it is clear that the incidence of MDR infection in non-hospitalized patients varies between countries , and theThe goal of this study was to compare the clinical presentation, outcomes, microbiological identification, and patterns of antibiotic resistance between patients with community-acquired pneumonia and those with nursing home-acquired pneumonia in order to determine if there currently exists a significant enough difference to justify a broader-spectrum of empirical coverage in these patients.In this retrospective observational study, all patients admitted to the Internal Medicine Service with a diagnosis of pneumonia from 01 April 2017 to 30 April 2018 were selected for inclusion. Patients with hospitalization for more than 48 h in the preceding 10 days were considered to have hospital-acquired pneumonia and were excluded. The patient population was divided into 2 groups: NHAP and CAP. Baseline demographic characteristics, comorbidities, functional status, clinical and laboratory markers of pneumonia severity at presentation, chosen course of empiric antibiotics and the need to change antibiotic therapy, and mortality and readmission outcomes were collected. The study protocol was reviewed and approved by the Ethics Committee of Coimbra Hospital and Universitary Centre (CHUC). Informed consent was waived due to the retrospective study design and complete anonymization of patient data.The patients were defined as having pneumonia if they presented with a clinical history compatible with the diagnosis of pneumonia  plus confirmation of a new pulmonary consolidation on chest radiography.The NHAP group included residents of nursing homes, residents of medium-term and rehabilitation units, and residents of long-term and maintenance units. Prior antibiotic use was defined as a prescribed antibiotic regimen in the previous 6 months.Pneumonia severity was determined using the CURB-65 score .Functional status was assessed through the Katz scale and patients were classified into 3 groups: independent (6 points), partially dependent (1–5 points), and dependent (0 points).Staphylococcus aureus (MRSA), Pseudomonas aeruginosa, and Enterococcus species. The antibiotics included in this group were vancomycin, ceftazidime, trimethoprim/sulfamethoxazole, fluoroquinolones, piperacillin-tazobactam, and meropenem.Empirical antibiotic regimens were defined as covering for MDR pathogens if they included an antibiotic drug known to cover for methicillin-resistant Antibiotic failure was defined as death during the course of antibiotic therapy or a change in antibiotic regimen due to a lack of clinical improvement.Venous blood samples obtained at admission were analyzed. Leukocyte count, neutrophil count, creatinine, urea, hematocrit, sodium, albumin, lactate dehydrogenase (LDH), glucose, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and C-reactive protein values were collected. Arterial blood gas was used to determine PaO2/FiO2, PaCO2, pH, and lactate values.Streptococcus pneumoniae and Legionella species.Data from blood and sputum cultures were collected, as well as urinary antigen testing for The primary outcome of interest was antibiotic failure. Readmission in the first 30 days, mortality during hospitalization, and mortality at day 30 after discharge were secondary outcomes.W-test was calculated to test for normal distribution of continuous variables. Parametric variables are presented as mean (± standard deviation), and non-parametric variables are presented as median and interquartile range (IQR). For inter-group comparisons where the data were normally distributed, continuous variable comparisons were made using Student’s t-test, otherwise the non-parametric Mann–Whitney U test was used.Shapiro–Wilk’s Categorical variables were compared using the chi-squared test or Fisher’s exact test if more than 20% of the expected cell frequencies were <5.Univariable logistic regression analysis was used to assess potential risk factors for antibiotic failure and multidrug resistance. Variables found to be predictive of antibiotic failure and variables found to be predictive of multidrug resistance in univariable analysis were inserted into a multivariable logistic regression model. The association of each risk factor with the outcome of interest was denoted by the odds ratio (OR) and corresponding 95% confidence intervals (CI).p < 0.05. Statistical analysis was performed using SPSS Statistics version 25.0 .Statistical significance was assumed for A total of 782 patients with a diagnosis of pneumonia admitted to the Internal Medicine Service were analyzed, of which 556 fulfilled selection criteria. CAP was found in 313 (56.3%), and the remaining 243 (43.6%) patients had NHAP. The baseline characteristics of the patients with CAP and NHAP are presented in p < 0.001), and they were also more likely to be dependent for activities of daily living on the basis of the Katz scale . There was no difference in prevalence of comorbidities between the two groups, with the exception of cerebrovascular disease (CVD), which was more common in NHAP , and chronic liver disease, which was more common in CAP . Patients admitted from nursing homes were more likely to have taken an antibiotic in the previous 6 months .There was no difference in sex distribution between the two groups. Compared with the CAP group, NHAP patients had a higher median age , with 66.3% of CAP patients compared to 82.3% of NHAP patients presenting with a score ≥3. This increased severity was also noted in a higher incidence of respiratory failure in the NHAP group . Median temperature and systolic blood pressures were higher in the CAP group. Although there was a trend towards more frequent vasoactive amine and non-invasive ventilation use in CAP compared to NHAP patients, this was not statistically significant.Clinical features and severity stratification at admission for CAP and NHAP patients are shown in p = 0.029). Albumin value was lower in this group . Other variables, including arterial blood gas analysis, NT-proBNP, and C-reactive protein showed no difference between groups.The distribution of laboratory variables at admission is also presented in p = 0.546) (p = 0.029). Pneumococcal and Legionella urinary antigen tests were more often requested in CAP patients .Blood culture was the most common test used in an attempt to establish an etiological diagnosis in both CAP and NHAP  . Sputum At least 1 microorganism was isolated in culture samples of 66 patients (11.9%), of which 32 had CAP and 34 had NHAP . In the Staphylococcus aureus, followed by Escherichia coli, in either group. Haemophilus influenzae was detected only in CAP patients. Pseudomonas species were more frequent in the NHAP group (The most frequently identified microorganism was AP group .p < 0.001)  . The prep = 0.014)  .p = 0.013), readmission in 30 days, and death until 30 days after discharge. The origin of the patient had a statistically significant relationship with therapeutic failure, with an OR of 1.583 .The clinical outcomes of patients with NHAP and CAP are shown in p = 0.696).Baseline characteristics of the patients that were found to predict therapeutic failure in univariate logistic regression were inserted into a multivariable logistic regression model, presented in p = 0.232), as seen in In a multivariable logistic regression model of culture-positive patients, NHAP also did not predict identification of MDR microorganisms when controlled for patient characteristics that predicted MDRO in univariate analysis , and had a higher prevalence of CVD than CAP patients. They presented clinically with more severe pneumonia, with a higher CURB-65 score and a higher rate of respiratory failure. A higher prevalence of CVD in NHAP patients has also been recorded by Kang et al. , who ideEnterobacteriaceae species , followed by Staphylococcus aureus, were the most common bacterial isolates in this study, with a low identification of Streptococcus pneumoniae and Haemophilus influenzae. This differs from expected values in the literature, as a meta-analysis by Chalmers et al. describes a predominance of Streptococcus pneumoniae in the CAP group and a predominance of Enterobacteriaceae in the HCAP group [Staphylococcus aureus with a very low rate of Streptococcus pneumoniae as well as a significant percentage of Escherichia coli isolates [Staphylococcus aureus and enteric Gram-negative bacilli [Streptococcus pneumoniae with a reciprocal higher than expected relative prevalence of Escherichia coli and Pseudomonas aeruginosa. While the promotion of vaccination against invasive pneumococcal disease in older people might explain in part the lower-than-expected identification of this pathogen in this population, the relatively low use of sputum cultures and urinary antigen testing are also likely to have affected the identification of Streptococcus pneumoniae and Haemophilus influenzae infections. Since the decision to draw cultures was based on the individual clinician’s assessment, it is also likely that the choice to not pursue etiological diagnosis in less severe patients might contribute to an underdetection of Streptococcus pneumoniae and Haemophilus influenzae.The proportion of patients with the causative pathogens identified was low (11.9% of patients) in comparison with previous studies that report a positive microbiological diagnosis in between 24 and 43% of patients ,22,23,24AP group . Howeverisolates , and in  bacilli . In the p = 0.032). In-hospital mortality was also 1.6 times higher in the NHAP group (p = 0.013). The association between NHAP and an increased risk for MDRO has consistently been reported in the literature [In our study, just over half of microorganisms isolated were multidrug- or extensively drug-resistant, and the patients in the NHAP group were at higher risk for MDRO than those in the CAP group  is higher when compared with other studies that showed mortality rates varying between 14.1 and 22.4% ,29,35,36The ability of NHAP, as well as other HCAP criteria, to predict MDRO infection and hence justify the use of broader spectrum antibiotics had been questioned since the adoption of the original HCAP recommendations, with reviews showing that the rate of MDRO infection in these patients varied significantly between countries . A recenA multicenter prospective study concluded that not giving empirical antibiotic therapy advised to healthcare-associated pneumonia was independently associated with higher mortality . Yet, a A few limitations need to be addressed. First, the data were collected retrospectively from a single institution. Because of this, the choice to take cultures and urinary antigens was left to the individual clinician, introducing a risk for bias towards identification of microorganisms only in severe patients. Particularly in CAP patients, the results may not be representative of the actual microbiological pattern. As blood cultures, which have an inherently low sensitivity in respiratory infections, were the most used method for isolation of microorganisms, it might have further contributed to a low percentage of identified microorganisms. To compensate for an expected low diagnostic yield, we defined the main study outcome as the presence of antibiotic failure, a combination of mortality during hospitalization, or a change in antibiotic treatment. This introduces a different possibility of bias, as more severe patients, such as those in the NHAP group, might be more likely to die independently of adequate antibiotic treatment. Lastly, prognostic scores such as the CURB-65 was used in this study to identify patients with more severe pneumonia. However, the utility of these scores is variable , with stThe present study demonstrates that nursing home residents with pneumonia are at higher risk for antibiotic failure and antibiotic resistance, but this increased risk can be accounted for by the presence of comorbidities, age, and previous antibiotic use. This is in line with the current IDSA recommendations, reinforcing the need for selective use of broad-spectrum antibiotics in patients with pneumonia that takes into account a combination of risk factors for MDRO, as admission from a nursing home is not a sufficient condition to initiate empirical broad-spectrum antibiotics.


The analysis of the total numbers of falls showed a decreased likelihood of falls in patients who participated in exercise programs (p = 0.0008). Finally, the number of patients who fell at least once was significantly reduced in the intervention group (p = 0.02). Physical exercise is an effective treatment to improve balance and reduce fall rates in the elderly.The aims of this systematic review and meta-analysis were to evaluate the effects of physical exercise on static and dynamic balance in the elderly population, and to analyze the number of falls and fallers. A systematic literature search was conducted using PubMed–Medline, Cochrane Central, and Google Scholar to select randomized clinical trials that analyzed the role of exercise on balance and fall rate in patients aged 65 or older. Sixteen articles were included in this review. Applying the Cochrane risk-of-bias tool, three studies were determined to be at low risk of bias, nine at unclear risk of bias, and four at high risk of bias. The meta-analysis showed improvements in dynamic balance ( Increasingly, attention is being paid by the scientific community to aging, and especially to successful aging. Several interventions aimed to improve the physical and psychosocial status of the elderly have been developed . The decThis systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  and was The inclusion criteria were RCTs in the English language published in the past decade, which analyzed the effects of land-based or aquatic exercise on balance and falls rate on patients aged 65 or older, according to the World Health Organization (WHO) definition of the elderly. Exclusion criteria were studies that involved patients with Parkinson’s or Alzheimer’s disease, dementia, hemiplegia, cancer, fibromyalgia, or following a stroke, spinal cord injuries, or fractures. We excluded studies that evaluated activities such as tai chi, yoga, pilates, or dance.A systematic literature search was conducted using the following online databases: PubMed–Medline, Cochrane Central, and Google Scholar. We used the following search strings:  OR “balance”(All Fields)) AND (“exercise”(MeSH Terms) OR “exercise”(All Fields) OR  AND “activity”(All Fields)) OR “physical activity”(All Fields)) AND (“aged”(MeSH Terms) OR “aged”(All Fields) OR “elderly”(All Fields)). Moreover, the following filters were used: randomized controlled trial (article types); 2010/01/01 to 2020/05/01 (publication dates); aged: 65+ years (ages); English (languages); humans (species). The reference lists of the included RCTs were checked in order to select further studies for inclusion. After duplicates were removed, two reviewers (G.P. and S.V.) independently read the abstracts of studies appropriate for inclusion. Differences of opinion were resolved by discussion with the third review author (R.P.). Finally, the full articles were checked by two investigators (G.P. and S.V.) in order to choose the studies to be included in the review and meta-analysis.Two review authors (G.P. and S.V.) independently performed data extraction. The following data were extracted from the included studies: authors, year of publication, type of study, level of evidence, numbers of participants and their age and sex in both study and control groups, previous falls, follow-up, and intervention(s) in the experimental and in the control group. Many outcomes were analyzed for the assessment of static and dynamic balance, participants’ fear of falling, physical performance, quality of life, and risk of falls. Finally, total number of falls, number of fallers, and fall rate  were compared between exercise and control groups.The risk of bias of the included RCTs was independently assessed by two investigators (G.P. and S.V.) by the Cochrane risk-of-bias tool . This to2 test was used to evaluate heterogeneity. In the presence of low heterogeneity (I2 < 55%), we used a fixed-effect model; otherwise, a random-effect model was applied. The statistical significance of the results was set at p < 0.05A meta-analysis was conducted using the Review Manager (RevMan) software Version 5.3. Continuous outcomes were used to assess effects on static and dynamic balance, fear of falling, physical performance, quality of life, and risk of falls between the experimental and the control groups. Dichotomous outcomes were used to assess the total number of falls and the number of fallers between the two groups. Due to the use of different scores for the various outcomes, the continuous data are shown as standard mean difference (SMD) with 95% confidence intervals. Negative values of SMD indicate a benefit for the intervention group. Dichotomous data are presented as odds ratio (OR) with 95% confidence intervals. For the calculation of the weight of the samples of the trials, falls or fallers per month of follow-up were used instead of the total events. The IThe GRADE  assessment was used to evaluate the quality of the evidence of the outcomes and strength of recommendation . This ton = 17), not evaluating land-based or aquatic exercise (n = 11), patients aged below 65 years (n = 9), protocols of RCT (n = 7), and case reports (n = 2). Finally, 16 articles were included in this review and meta-analysis  were determined to be at low risk of bias (A), nine studies (56.25%) were at unclear risk of bias (B), and four studies (25%) were at a high risk of bias (C) . More spp = 0.008). BBS showed significant improvements in static balance for the exercise group . FES showed better fear-of-falling outcomes in patients who did physical exercise compared to controls , but no significant differences (p = 0.10). Balance confidence, assessed by ABC, showed significant differences in favor of the experimental group . Short-Form Health Survey results showed greater improvements in quality of life in the experimental groups , without significant differences (p = 0.08). SPPB showed no significant differences in physical performance between the two groups (p = 0.30), but better outcomes in the exercise group . Summarily, analyzing all the reported scores, a significant difference was shown in favor of the physical exercise group compared to the controls (p > 0.00001).The meta-analysis showed the effect of exercise on dynamic balance, static balance, participants’ fear of falling, balance confidence, quality of life, and physical performance compared to controls . TUG timp = 0.0008)  .p = 0.02)  .The GRADE was used to assess the quality of the evidence provided in the included trials . There wp = 0.0008 and p = 0,02). Furthermore, PE was more effective in reducing the total number of falls than the number of fallers, showing that improving muscle tropism and postural balance through specific protocol of exercises reduced the risk of falls, but it did not completely eradicate the risk of falling at least once. These data seem to strengthen the concept that PE represents a crucial aspect of prevention for reducing the risk of falls, which can lead to fractures and consequently to hospitalization, surgical procedures, and prolonged immobilization, with an increase in national healthcare costs. In contrast, a study by Lee et al. analyzed the effectiveness of exercise interventions on the rate of falls and number of fallers in care facilities, and they showed significant differences between all exercise interventions and control groups in the rate of falls, but they did not find differences in the number of fallers between all exercise interventions and control groups [The primary aim of this systematic review and meta-analysis was to evaluate the improvement of balance performance in the elderly population after various types of physical exercise. A secondary endpoint analyzed was the number of falls and/or fallers before and after an exercise program. Physical exercise was shown to be very beneficial for older people in terms of dynamic and static balance, fear of falling, balance confidence, quality of life, and physical performance, with a significant improvement reported for all the considered scores in patients who participated in a physical treatment compared to controls. The meta-analysis proved that the parameters for dynamic and static balance, such as TUG and BBS, demonstrated the best improvements after PE, with the most statistical significance compared to controls. In fact, balance training led to higher confidence in the participants’ ability to perform various daily activities without falling, better patient mobility and safety at speed, greater ability to perform balance-related tasks, and lessened difficulties with activities of daily living. PE seemed to be particularly useful in reducing falls via the increase of postural control, more than improving quality of life and physical performance in older people. Moreover, with the exception of SF-36/SF-12 which was low, all the other outcomes presented a moderate quality of evidence as assessed by GRADE, thus supporting a recommendation of physical exercise in the geriatric population with risk of falls. The number of falls was recorded using daily fall calendars, which were returned to the blinded investigators. Regarding the evaluation of the number of falls and fallers, almost all the studies showed great improvements in the patients who underwent PE. Furthermore, the data showed both high quality and strength of recommendation according to GRADE for the benefit of balance and postural control exercises to reduce the rate of falls in the elderly. Only one study  reportedl groups . Similarl groups  showed tl groups ,35, whicl groups  presentel groups ,38; thisl groups . Hewitt l groups  tested tl groups , is compl groups . The Lifl groups , is a hol groups , weight l groups , aerobicl groups , and prol groups . Finallyl groups  evaluatel groups , but thil groups  reportedl groups  demonstrl groups , in theil groups , the samThis systematic review proved that physical exercise is an effective treatment to improve static and dynamic balance and reduce the number of falls and fallers for patients aged 65 or over. The meta-analysis reports strong evidence that exercise programs can reduce fall rates in the geriatric population. Balance and postural exercises should be included in training protocols for the elderly in order to prevent the risk of falls, and they should be performed in the entire healthy population, not only as rehabilitation after stroke, fractures, or for patients affected by neurodegenerative disease. However, further large-scale trials with longer follow-up are needed to estimate the long-term effects of balance programs on decreasing the rate of falls. Moreover, more studies involving aquatic exercise or comparing aquatic versus land-based programs are necessary to promote innovative strategies to prevent falls in older people, which can be delivered by exercise trainers.


Cardiovascular disease (CVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random Forest. We used the multivariate regression model as a benchmark for performance evaluation . The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of CVD prediction and treatment in China. CVD is a disease that can be prevented and controlled, and early intervention can effectively control its progress2.Cardiovascular disease (CVD) is a series of diseases involving the circulatory system, including angina pectoris, myocardial infarction, coronary heart disease, heart failure, arrhythmia and else, which is generally related to atherosclerosis. With the social economy development, the population aging and the urbanization acceleration in China, some changes have taken place in national lifestyles, which leading to a rise of CVD prevalence. In 2016, there were more than 290 million cases of CVD in China, and 4.344 million deaths from it, including 2.098 million deaths from stroke and 1.736 million deaths from coronary heart disease, which bringing heavy social and economic burden4. Machine learning (ML) such as random forest (RF) can improve the performance of risk predictions by exploiting large data repositories to identify novel risk predictors and more complex interactions between them3.In recent years, many achievements have been made in the study of CVD risk prediction model, but the effect of epidemiological risk factors and biomarkers may be different in different populations, the CVD model has certain population specificity. In addition, there has been no study on CVD risk prediction model based on large cohort population in eastern China. At the same time, a large number of the existing CVD prediction models use multivariable regression method to build prediction models in a linear fashion, but it generally exhibit modest predictive performance, especially for certain sub-populationsIn this study, we conducted a CVD prediction model research based on a specific culture, lifestyle, behavior and genetic background in eastern China. From September 2014 to December 2016, a cohort of 25231 subjects with high-risk CVD were selected from 101056 people in Zhejiang province. Cardiovascular events were collected through regular follow-up using the electronic health record (EHR) system, and a CVD prediction model for 3-year risk assessment of CVD was constructed using the RF algorithm based on classification and regression tree (CART).5. The large program was patient-centered evaluative assessment of cardiac events including 1.7 million persons in China, and it is a population-cantered national screening initiative to detect populations at high risk of CVD.The project was one of the centers of the national high-risk screening program, and its design and population screening had been published in other journals6. Inclusion criteria: subjects aged ≥35 years, living in selected community, with normal cognitive function, high risk of CVD, and able to cooperate with investigation and relevant referral. Exclusion criteria: subjects with new cardiovascular and cerebrovascular disease events within half a year, including angina pectoris, stroke, acute myocardial infarction, coronary heart disease, heart failure, arrhythmia and else, subjects with severe dementia, severe liver and kidney dysfunction, subjects with acute critical illness, incoordination. All participants provided written informed consent.Our study was conducted in 6 geographically defined regions of Zhejiang province in China that began from Sept 15, 2014 and has continued by now. Subjects identified as being at high risk of CVD in community health center were moved to hospital to receive further assessment and follow-up care. Participants are considered at high risk of CVD if they meet at least one of criteria. The criteria are adapted from WHO guidelines for the assessment and management of cardiovascular riskFrom 2014 to 2016, a screening was conducted among more than 100,000 residents in Zhejiang province, mainly through questionnaire survey, physical examination, laboratory examination and else, to understand risk factors related CVD, evaluate CVD risk, determine high-risk CVD subjects, and a cohort was established conducting follow-up. Follow-up was conducted once a year to collect socio-economic information, and blood samples were collected every two years.7. If the difference between the 2 systolic blood pressure (SBP) readings was greater than 10 mmHg, a third measurement was obtained and the average of the last 2 readings was used. Participants were required to wear light clothes, no shoes while being measured for height and weight8.For each participant, blood pressure, lipid and blood glucose levels, height, and weight were measured at the initial screening. Blood pressure (BP) was measured twice on the right upper arm after 5 minutes of rest in a seated position with a standardized electronic blood pressure monitor (Omron HEM-7430)Trained nurses conducted standardized interviews among the participants to collect information on sociodemographic status , lifestyle , medical history, and medication use. The questionnaire is designed by Fuwai Hospital.The protocol of this study was approved by the National Center for Cardiovascular Disease (NCCD) and the Medical Ethics Committee of Zhejiang Hospital. The participants were informed about the objectives and methods of the study. They were informed that their participation was totally voluntary and that they could withdraw from the study at any time without citing any reason. Written and signed or thumb printed informed consent was obtained from those who agreed to participate, or from their guardians. The methods used in this research were carried out in accordance with the approved guidelines. Trial registration number: NCT02536456.11. The detailed information of definitions was shown in Table The definitions of variables were on the basis of Chinese recommendations from the Working Group on Obesity and Chinese guidelines on prevention and treatment of dyslipidemia in adults12. Then we used the confusion matrix to calculate the sensitivity and specificity of every important risk factor.All statistical analysis was conducted by using R version 3.2.5 . Enumeration data were expressed as percentages (%) and were compared using the χ2 test, and data were analyzed using Yates’s continuity correction or Fisher’s exact probability test as necessary. Univariate logistic regression analysis was performed to screen related risk factors of CVD. We found the point on the ROC curve that is closest  to the perfect model (with 100% sensitivity and 100% specificity), which was associated with the upper left corner of the plot13. P value of less than 0.05 indicated a statistically significant difference.The discrimination ability of the model was evaluated by using receiver operator characteristic (ROC) curve analysis. The AUC > 0.5 indicated better predictive values, the closer the AUC to 1, the better the model performance. Model calibration was checked by using the Hosmer–Lemeshow goodness-of-fit test to determine whether chance could explain the difference between the predicted and the observed event rateWe established a CVD risk prediction model suitable for population in Zhejiang province based on RF. Several methods were used to be compared with RF, including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees and Ada Boost. And the multivariate regression model was used as a benchmark for performance evaluation AUC=0.7143). The illustrative schematic for CVD prediction model conduction was shown in Fig. 43. The ipi, and the information entropy of the data set D was 12.The decision tree model was a tree structure composed of root node, branch node and leaf node, which reflected the mapping relationship between features and tags. Information gain was used to measure the influence of a certain feature on the classification result. Suppose the sample data set was D, which contained n types of data, and the sample proportion of the ith type of data in the total data set was 12.It was an implementation of decision tree. Generally, there were three implementations of decision tree, namely ID3 algorithm, CART algorithm and C4.5 algorithm. CART algorithm was a binary recursive segmentation technology, which divided the current sample into two sub-samples, so that each generated non-leaf node had two branches. Therefore, the decision tree generated by CART algorithm was a binary tree with simple structure. Since CART algorithm was a binary tree, it could only be “yes” or “no” in the decision of each step. Even if a feature had multiple values, it would divide the data into two parts. CART algorithm was mainly divided into two steps: recursively divide the samples for the tree building process, pruning with validation dataRF was an ensemble learning method based on decision tree. It adopted the re-sampling technique of bootstrap to repeatedly randomly select b samples from the original training sample set of N as the training set and the remaining samples as the test set. We adopted the method of random sampling in proportion, and generated a new training sample set from Linan (4533), Zhuji (3542), Anji population (4990). We randomly selected m feature sets from each training self-help sample, and then generated B decision trees according to the self-help sample set. When the decision tree was split, the optimal feature set was selected from m features. B decision trees constituted the random forest, and the classification results of new data were determined by the number of votes in the decision tree.12.Bayes’ Rule answers the question “based on the predictors that we have observed, what is the probability that the outcome is class Ce?” More mathematically, let Y be the class variable and X represent the collection of predictor variables. We were trying to estimate 12.Bagging for classification was a simple modification to bagging for regression. Specifically, the regression tree was replaced with an unpruned classification tree for modeling C classes. Since each model had equal weight in the ensemble, each model could be thought of as casting a vote for the class it thought the new sample belonged to. The total number of votes within each class were then divided by the total number of models in the ensemble (M) to produce a predicted probability vector for the sample. The new sample was then classified into the group that had the most votes, and therefore the highest probabilityTo summarize the algorithm, AdaBoost generated a sequence of weak classifiers, where at each iteration the algorithm found the best classifier based on the current sample weights. Samples that were incorrectly classified in the kth iteration received more weight in the (k + 1)st iteration, while samples that were correctly classified received less weight in the subsequent iteration. At each iteration, a stage weight was computed based on the error rate at that iteration. The overall sequence of weighted classifiers was combined into an ensemble and had a strong potential to classify better than any of the individual classifiers.The Framingham score is based on 7 core risk factors including of gender, age, systolic blood pressure, treatment for hypertension, smoking status, history of diabetes, and BMI. All of those variables were complete for the participants in the extracted cohort. The number of imputed datasets was selected via cross-validation.From September 2014 to December 2016, 29930 participants with high-risk of CVD were selected from 101056 people in Zhejiang province, and a cohort of population with high-risk CVD was established. The network management system was used for regular follow-up and blood sample collection. There were 25231 subjects with 976 cardiovascular disease events in the cohort until the end of 2016. A total of 15.7% of participants were lost to follow-up.The results showed that the incidence of CVD in this cohort was about 3.9% in three years and was expected to increase to about 5% by 2021. Overall, the mean (SD) age of participants at baseline was 58.1 (10.6) years, and 13528 (53.62%) were female, 23269 (92.22%) were married, 1495 (5.93%) had high school education or above, 9676 (38.35%) were overweight, 4420 (17.52%) were obesity, 11718 (46.44%) had hypertension and 2256 (8.94%) had diabetes.Using univariate logistic regression analysis, combined with the professional knowledge, we screened the important variables of CVD. The analysis revealed that nearly 30 indicators including male, older age, family income, smoking, excessive drinking, obesity, large waistline, abnormal cholesterol, low HDL-C, abnormal FPG, low ability to action were related to CVD .The important variables screened by univariate logistic regression model were used to build the multivariate prediction model for CVD based on random forest algorithm. We used split validation method that two-thirds of the samples were randomly selected as the training set, and the remaining as the test set. Meanwhile, we tried to reduce the difference between the positive rate in the training set and the one in the test set. In the training set, the random forest algorithm was used to establish the training model, and then the test set was used for prediction. The confusion matrix was shown in Table The prediction accuracies of the different models under consideration were shown in Table 16. Although the effect of primary health care in controlling CVD has been demonstrated before, controversies still exist on the benefits of applying risk prediction compared with those of the risk factor approach in population-based interventions18.CVD is the leading cause of death worldwide and a major public health concern. Its risk assessment is crucial to many existing treatment and control guidelines9. In the present study, data of a cohort of 25231 people with high risk of CVD and with 3 years’ follow-up were analyzed, and the results showed that nearly 30 indicators including of male, older age, smoking, excessive drinking, obesity, large waistline, abnormal TC, low HDL, abnormal FBG and low ability to action were related to CVD.The preliminary survey results of our research group showed that many epidemiological factors were closely related to the occurrence of CVD, including old age, male, living alone, rural area, low education level, high BMI, large waist circumference, family history and else19, mainly including age, gender, family history, high blood pressure. In the PCE model recommended by ACC/AHA of the United States, the AUC was 0.713  − 0.818 20. In addition, there were ABC-CHD model (C-index was 0.81), CVDPoRT model, Q-risk score model and so on23.CVD prediction was one of the most effective measures for CVD control. There were many successful CVD prediction models in the world. The Framingham study put forward the concept of CVD “risk factors” for the first timeet al. developed the lifelong risk assessment model of CVD and stroke in China24. Yang X et al. made use of large sample cohort data in prediction for ASCVD Risk in China to establish a China-PAR model for 10-year Risk and lifetime Risk assessment of CVD, and proposed a risk stratification standard suitable for Chinese people, C-statistics reached 0.794   and 0.811  25. In our study, the CVD prediction model for 3-year risk with an AUC of 0.787 for all, 0.823 for male and 0.675 for female.Wang Y 27 and the substantial changes in preventive treatments28, most published CVD risk prediction equations are now likely to be out-of-date because they are based largely on older cohorts29 such as the 2013 American College of Cardiology/American Heart Association PCEs30. Median predicted 5-year CVD risk using new PREDICT equations was only 2.3% in women and 3.2% in men, and so for the PCEs to markedly overestimate CVD risk was not surprising. Māori, Pacific, and Indian patients with high deprivation scores had predicted CVD risks that were twice as high as those of European or Chinese patients with low deprivation scores26.As a consequence of the major changes in rates of CVD events internationally in the past few decadesCVD prediction models improved as more mathematical models been used in the prediction in recent years. ML played an increasingly important role in classification prediction problems. Previous studies had shown that ML had relatively accurate results in classification problems of epidemiological data. RF was an Ensemble Learning method based on decision tree. It adopted the resampling technique of bootstrap and selected feature sets by random sampling and random selection. It was not easy to produce overfitting phenomenon and had good anti-noise ability. The established model was robust and could deal with nonlinear problems. In addition, RF could deal with the problem of certain data loss, and could give the important score of each characteristic variable while classifying, according to which the variables that played an important role in classification could be screened out. The results of this study also showed that the RF achieved a significant improvement over the benchmark of multivariate regression model, and was superior to other ML models including CART, Naïve Bayes, Bagged Trees, Ada Boost.There were several strengths of this study. Firstly, using the method of RF, a CVD risk prediction model that suitable for population in Zhejiang province of China was established with an AUC of 0.7871, which was one of the first CVD prediction models for large population in eastern China. Secondly, Random Forest used here had the advantages that unlike most ML algorithms, it could accept dirty data, and unlike some traditional regression models, it also could model nonlinear relations and accept both regression and classification problems at meanwhile.Despite these strengths, there were several limitations should be addressed. The main limitation of the study was that it lacked external validation. ML could be deemed as internal validation to some extent since it consisted of multiple data-oriented analyses through randomly splitting the data repeatedly. And the validation and optimization of current model needed to be performed in future study. Otherwise, we did not collect information on participants’ attitudes and knowledge regarding control of high CVD risk, primary care physicians’ assessment of CVD risk, or medical recommendations. Though 30000 patients were a large cohort for traditional methodologies, it might not be big enough for training a RF model. More data could offer better AUC results. And the follow-up time of 3 years was relatively short. Finally, the dataset used in this research was imbalanced, applying techniques to handle imbalanced datasets should be performed in the future work.We provided a CVD prediction model for 3-year risk assessment of CVD, which achieved a significant improvement over the benchmark of multivariate regression model, and was superior to other ML models including CART, Naïve Bayes, Bagged Trees, Ada Boost. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of CVD prediction and treatment in China. Further population-based studies of the CVD prediction model proposed in this study with more population, longer follow up time, covering more places in China with external validation are needed.Supplementary table.


Corynebacterium, Pseudomonadales, and Mitsuokella and increases in Alloprevotella, Bifidobacteriales, and Lactobacilli could be attributed to the supplementation of feed with coconut oil. Coconut oil treatment did not have a significant effect on the diversity index of rectal microbiomes, but an abundant increase in probiotics such as Bifidobacterium and Lactobacillus in the gastrointestinal tract is desirable in pig breeding. From this point of view, the addition of coconut oil to the feed of pigs is a good option for improving the microbiome in their gastrointestinal tracts.Looking for non-antibiotic substances that can enhance health by improving the gastrointestinal microbiome of animals is an ongoing task. Among other compounds, medium-chain fatty acids, such as lauric acid, can exert positive effects. Coconut oil is a rich source of lauric acid, and therefore, the aim of this study was to describe the effect of adding coconut oil to the feed of growing pigs on intestinal microbiome diversity and bacterial abundance. Rectal swab samples were analyzed to assess the intestinal microbiomes of pigs. Typically, growing pigs are characterized by continuously changing bacterial communities as a result of aging. However, a significant effect of coconut oil treatment was detected in the presented study. Decreases in rRNA high-throughput amplicon sequencing to evaluate the microbiome changes during the feeding experiment. A total of 446 operational taxonomic units (OTUs) were detected in the whole sample set. Shannon’s indices of bacterial diversity did not change significantly during the experiment. Changes in the bacterial community during the study period and in response to the coconut oil treatment were highly significant (p < 0.001). During the study, an increase in the abundance of Lactobacillus was detected in the group treated with coconut oil. An increase in Alloprevotella, Bifidobacteriales, and Lactobacillales and a decrease in Corynebacterium, Mitsuokella, Psychrobacter, and Pseudomonadales were attributed to the coconut oil treatment. Although the addition of coconut oil to pig feed did not affect Shannon’s index of diversity, it had a positive effect on the abundance of bacterial groups that are considered to be commensal and/or probiotic.Coconut oil has a high content of lauric acid, which has selective antibacterial activity. This study aimed to explore the effect of coconut oil ingestion on the gastrointestinal microbiomes of pigs. A 14-day-long feeding experiment included 19 pigs in two groups . At the start and end of the experiment, a rectal swab sample was taken from each pig in both groups, and total bacterial DNA was extracted. We used 16S  Lactobacillus spp. in increasing mucin production in the pig intestine. It is clear that the loss of diversity in the intestinal microbiome is related to an increased chance of GIT diseases. According to Fouhse et al. [Escherichia coli, Campylobacter, or Salmonella are detected as present or increased in the intestinal contents or in fecal samples.The gastrointestinal tract (GIT) is a multifactorial apparatus that assures the absorption of nutrients, water, and electrolytes and simultaneously protects the organism from pathogens and toxins . Bacterie et al. , the losClostridia and Bacteroidia and at the same time by an increase in facultative anaerobes such as species from Enterobacteriaceae [A weaned pig represents an excellent example of the very important role played by the gastrointestinal microbiome. Weaning is a key period in the life of piglets. It involves the separation of piglets from dams and also the transition from highly digestible milk to less digestible feed mixtures. During the weaning period, feed and water intakes also decrease, which results in structural and functional changes in the GIT, followed by the occurrence of diarrhea and increased mortality ,7,8. Diseriaceae .Staphylococcus aureus, Staphylococcus epidermidis, beta-hemolytic streptococci (group A and non-group A), group D Streptococcus, Bacillus subtilis, Sarcina lutea, Micrococcus spp., Nocardia asteroides, Corynebacterium spp., Pneumococcus, and Candida albicans. With respect to Clostridium perfringens, lauric acid showed the highest antimicrobial activity, followed by myristic, capric, oleic, and caprylic acid.The presence of short-chain fatty acids in the GIT of pigs seems to be beneficial for intestinal microbiota . HoweverThus, we hypothesized that coconut oil has a positive effect on the structure of intestinal microorganisms, which means increasing the diversity of the GIT microbiome while supporting probiotic microbes. Therefore, the aim of this study was to determine the diversity and composition of the microbiome in the rectums of pigs fed a diet supplemented with coconut oil.n = 10) and the group on a normal diet (n = 9) were randomly divided into two separate pens prior to the experiment. Both pens had straw bedding with daily manure removal. The microclimate during the experiment met the requirements for housing pigs. Pigs had ad libitum access to feed and water during the experiment. The length of the feeder allows the simultaneous intake of feed by all pigs in the pen. Before the start of this experiment, all pigs were housed in one pen and fed the same commercial feed mixture as that used during the experiment. Animal care throughout the whole experiment was in accordance with Directive 2010/63/EU on the protection of animals used for scientific purposes.A 14-day-long feeding experiment was realized at the Sheep and Pig Farm in Žirany , which belongs to a university farm in Kolíňany . In total, 19 pigs of the Large White breed were used to determine the effect of adding coconut oil to feed rations on the diversity and composition of the microbiome in the rectum. Pigs were 9 weeks old with an average live weight of 22.5 ± 3.03 kg. The group with coconut oil supplementation . Pigs in the oil group ingested feed rations that contained 99.7% commercial feed mixture and 0.3% commercial coconut oil for the duration of the experiment. The amount of coconut oil used represents a dose of 3 g/kg feed mixture, which was assessed according to previously published information about the addition of distilled coconut fatty acids (3 g/kg of pig feed) . This doNutritional parameters and the proportion of fatty acids in feed rations provided during the experiment were determined at the Laboratory of Quality and Nutritive Value of Feeds at the Department of Animal Nutrition  according to standard methods . In brieTM; Zymoresearch, Irvine, CA, USA). The Eppendorf tubes with rectal swab samples were transported on ice to the laboratory, where they were stored at −80 °C.Rectal swab sampling was used because it allowed us to compare changes in the microbiome over time within a single individual pig. The microbiome composition profiles provided by rectal swabs are similar to those obtained from fecal samples, according to Choudhury et al. . Three rg for 15 s was included in the protocol [The total DNA was extracted from each collected rectal swab using the QIAamp DNA Stool Mini Kit . Before extraction, swabs were washed in 1.4 mL of ASL lysis buffer. Then, a mechanical lysis step using the Precellys 24 homogenizer  at 6800× protocol  before h−1, 1.5 µL of primer 806R with a concentration of 5 pmol.µL−1, 7.5 µL of Q5 HS Mastermix , and 3.5 µL of MilliQ water. PCR consisted of the following thermal cycles: activation at 98 °C for 30 s, followed by 35 cycles , and final polymerization at 72 °C for 2 min.Hypervariable region V4 of the 16S rRNA gene was used for high-throughput sequencing analysis of pigs’ rectal microbiomes. Amplification of the selected region was carried out with a combination of primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), which were evaluated by Caporaso et al.  for use PCR products were purified by the Jena PCR purification kit , quantified using Qubit , and pooled together. The Nextera XT indexing kit  was used for sequencing library preparation and adapter attachment. The library was quantified by qPCR and sequenced on an Illumina Miseq using V3 kit 2 × 300 bp.The sequence editor SEED ver. 2.1  was usedThe determined OTUs were processed as a contingency table in MS Excel (MS Office 2013). A Venn diagram was constructed using the venn package in the R environment . The ShaIn total, 1,832,416 raw sequence reads were acquired for all sequenced samples . Quality filtration, length trimming, and chimera removal resulted in 1,215,613 sequences, representing an average of 10,663 sequences per single technical replicate, i.e., 31,989 sequences for each pig. Sequences were deposited in GenBank under BioProject no. PRJNA637986.Clustering resulted in 446 OTUs, the majority of which was shared by all evaluated pigs . Most ofFirmicutes and Bacteroidetes were the most abundant bacterial phyla in all groups of pigs , but the variance within some groups was considerably high , Porphyromonadaceae (increase), and Selenomonas (decrease) appeared in both treatments, and they can be attributed to the life stage. On the other hand, changes in Corynebacterium (decrease), Alloprevotella (increase), Mitsuokella (decrease), Pseudomonadales (decrease), Bifidobacteriales (increase), and certain lactobacilli (increase) can potentially be attributed to the supplementation of feed with coconut oil because their changes were opposite or very subtle in comparison with changes in the control group  increased mainly in the group fed the pure commercial diet, while it tended to decrease when coconut oil was added to the feed.For a better illustration of changes in the microbial community, the relative differences in the 35 most common genera are plotted in a heatmap . In thisFirmicutes and Bacteroidetes, followed by Proteobacteria and Actinobacteria. These results are in accordance with Choudhury et al. [rRNA sequences from 20 studies of pig intestinal microbiomes, and 85% of the total sequences within the whole intestinal tract were represented by the phyla Firmicutes and Bacteroidetes, while Prevotella, Clostridium, Alloprevotella, Ruminococcus, and RC9 groups were detected in 99% of feces samples. The observed diversity evaluated by Shannon’s index was in the typical range reported for rectal microbiomes in many studies [The most abundant phyla found in the rectal microbiomes of pigs were y et al. , who fouy et al. , some ba studies .Firmicutes and a decrease in Bacteroidetes, similar to the study of Choudhury et al. [Richards et al.  claimed y et al. .Megasphaera and a decrease in Prevotella in the microbiomes of pigs in both groups. This shift from Prevotella to Megasphaera did not have a negative effect on the health of the animals, as reported by Liao and Nyachoti [Prevotella bryantii and Megasphaera elsdenii to be probiotics commonly used in animal nutrition.We encountered a rise in Nyachoti . BajagaiNyachoti  considerBifidobacteriales, Lactobacillus, and Megasphaera together with a decrease in Prevotella was detected in both groups of pigs. Gresse et al. [Lactobacillus together with an increase in Clostridium, Prevotella, Proteobacteria, and E. coli. As we mainly observed the opposite changes in our study, we suppose that the pigs successfully adapted their microbiome to changed feeding conditions. The genus Prevotella, which had the second-highest abundance in this study, is connected to ingested saccharides in human nutrition [Clostridiales, such as Clostridium, Blautia, and Ruminococcus, which are similar to Prevotella and very common in the mammalian gastrointestinal tract [Prevotella is able to produce energy sources for butyrate-producing bacteria [During the experiment, an increase in e et al.  reportedutrition ,34. Membal tract , were mubacteria ,37. Butybacteria .Lactobacillus abundance was determined for both groups. However, it increased significantly only in the group fed a diet supplemented with coconut oil. Bacteria from this genus have been reported to have a favorable effect on the intestinal tract [Lactobacillus in the group that was fed coconut oil can be explained by the findings of Puyalto et al. [Enterobacteriaceae and increase that of Lactobacillus in the GIT of pigs at the same time.A positive trend in al tract . The inco et al. , who conBifidobacterium abundance in the group treated with coconut oil  despite pigs being randomized before the start of the experiment. Prominent inter-individual variation has also been reported in many other studies with pigs [Lactobacillus and Bifidobacterium increased in response to the addition of coconut oil to feed. According to Gueimonde et al. [E. coli strains, Lawsonia, or Salmonella [Salmonella and Brachyspira. These authors also claimed that medium-chain fatty acids could be used as antibiotics replacements.Homogeneity within each experimental group is crucial for the determination of the feed supplementation effect. However, it is difficult to ensure the homogeneity of pig microbiomes, as they change within a few days, especially after weaning . In our ith pigs ,42. Inteith pigs ,44. Subse et al. , such balmonella . Accordilmonella ,48,49, mFirmicutes and Bacteroidetes, while Megasphaera and Prevotella were the most abundant genera. During the experiment, microbiomes shifted toward a higher abundance of Firmicutes and a lower abundance of Bacteroidetes. Despite some differences in the microbiomes of pigs before treatment, we were able to prove that coconut oil addition mainly had a positive effect, which is probably connected to elevated levels of lauric acid. The addition of coconut oil increased the abundance of beneficial microorganisms such as Lactobacillus or Bifidobacterium while decreasing some unfavorable taxa. Positive shifts in the microbiome indicate that coconut oil has selective antimicrobial effects and can improve pig health, reduce the use of antibiotics, and result in higher pig performance. The exact amount of coconut oil that should be added to feed and its combination with other supplements need to be determined in further research.In the rectal swab samples of investigated pigs, the most abundant phyla were


Hip fracture is common and carries high morbidity and mortality; thus, it has become a vital concern. We aim to analyse the present status, worldwide trends in hip fracture and state of clinical research.Publications from 2000 to 2019 were retrieved from the Web of Science database and analysed using a bibliometric methodology. VOSviewer software was utilised for analysis.Osteoporosis International has published the most papers on the topic. Leading researchers, contributing institutions, their cooperative relationships and scientific masterpieces have been identified. The publications can be divided into five clusters: ‘mortality’, ‘surgical management’, ‘rehabilitation’, ‘osteoporosis’ and ‘epidemiology’. A clear developing trend was described, which began with fracture epidemiology and prevention, transitioned to perioperative management, orthogeriatric care and patient safety and then to functional recovery, disease burden and national audits in recent times.In total, 6139 publications were included, and publications increased annually from 152 in 2000 to 592 in 2019. U.S. researchers have produced the most publications, the highest H-index and the greatest number of citations. Hip fractures result in conditions that extend far beyond orthopaedics concerning epidemiology and preventive medicine, internal medicine and endocrinology, as well as critical care and gerontology. Interest, research and publications are on the rise. With an ageing population around the world, hip fracture has become a vital concern. The number of hip fractures is anticipated to increase from 1.26 million in 1990 to 4.5 million by 2050. Although the age-standardised rate is slowly decreasing in many nations, the growing number of elderly is outpacing it [Amongst all osteoporotic fractures, hip fracture carries the highest morbidity and mortality . All-cauBibliometric analysis is a feasible strategy to summarise and anticipate the research trends qualitatively and quantitatively by evaluating the studies of major authors, journals, institutes and nations . AdditioWhilst many databases could satisfy the need for analysis at a global level , we seleThe search strategy was as follows: (title=hip AND title=fracture*). We excluded pathological fractures caused by bone tumours and fractures following any type of hip arthroplasty. We included publications from 2000 to 2019. On June 21, 2020, we identified and retrieved 355 reviews and 5784 articles.Information on all identified publications—including title, author, publication year, contributing nations, affiliations, journal, keywords and abstract—was downloaded. Two authors independently browsed and extracted data from the eligible publications.The basic characteristics of publications are an intrinsic function of WoS. The H-index is described as the value according to a scholar or scientist who has published H papers, each of which has been cited by other studies no less than H times . TherefoVOSviewer  is a programme for creating and visualising bibliometric networks . In thisCo-authorship analysis illustrates the connection amongst items in line with the number of co-authored papers, which is an effective tool to evaluate collaboration trends and to identify leading researchers, nations, and organisations . Co-occuIn total, 6139 publications (355 reviews and 5784 articles) were included in this study. Over the past two decades, the number of topical publications increased yearly, from 152 in 2000 to 592 in 2019, as shown in Table The total number of citations and the H-index reflect the quality of publications and academic impact of one country . The USAOsteoporosis International published 459 articles/reviews, outranking other journals with the most publications. Injury-International Journal of the Care of the Injured was second with 347 publications. There were 165 papers published in the Journal of Bone and Mineral Research, 157 in the Journal of Orthopaedic Trauma and 134 in the Journal of the American Geriatrics Society. The top 10 journals with the most publications are listed in Table The top 10 research orientations related to hip fracture are shown in Fig. The top 10 authors with the greatest number of publications are listed in Table As presented in Table New England Journal of Medicine (NEJM) by McClung et al. [NEJM by Lyles et al. [The mean number of citations per publication was 25.9. Table g et al. , with 13s et al. , ranked As presented in Fig. A total of 54 countries and regions with a minimum of 5 publications were identified. The top five countries and regions with the largest TLSs were as follows: the USA TLS = 683 times), the UK (TLS = 595 times), Canada (TLS = 341 times), Sweden (TLS = 287 times) and the Netherlands (TLS = 280 times), as shown in Fig.  times, tAs presented in Fig. Keywords utilised more than five times in the publications were recognised and analysed via VOSviewer. As presented in Fig. A repeated co-occurrence analysis using a second dataset based on the time period 2018-2020 demonstrated keywords with high occurrence in more recent years then compared them with those generated using the primary dataset. These comparisons are shown in Table In this study, the current status and global trends of hip fracture research were delineated. The annual quantity of publications has gradually increased. Leading researchers, institutions, countries and their cooperative relationships have been identified, and important publications with high numbers of citations have been highlighted.Utilising co-occurrence cluster analysis, we presented a network map of co-occurrence relationships by analysing keywords found in relevant studies. In total, five potential research orientations were identified. These results suggest that hip fractures, especially in older individuals, lead to conditions that extend far beyond the orthopaedic injury, with consequences in the aspects of epidemiology and preventive medicine, internal medicine and endocrinology, as well as critical care and gerontology. This is confirmed in Fig. Most of the highly cited studies were published before 2010, as prior studies inherently have more time to accumulate citations than more recently published manuscripts. These earlier studies mainly focused on osteoporosis management, fracture epidemiology and prevention, as well as perioperative management and patient safety.The most-cited report was published by McClung in 2001 describing the protective effects of risedronate, which substantially minimised hip fracture risk amongst older females with established osteoporosis . The resOther research centred on epidemiology. In the USA, hip fracture rates and consequent mortality amongst individuals 65 years and older decreased with the usage of bisphosphonates, whilst comorbidities amongst patients with hip fractures increased . An addiRegarding mortality, a meta-analysis reported that older adults have a five- to eightfold higher risk for all-cause mortality throughout the first 3 months after hip fracture. With the use of life-table methods, the investigators estimated that an American white woman who has a hip fracture at age 80 has an excess annual mortality of 8%, 11%, 18% and 22% at 1, 2, 5 and 10 years after injury, respectively. The corresponding figures for an American white man were 18%, 22%, 26% and 20% . Von FriA prospective cohort study reported that in elderly patients, the existence of three or more comorbidities would be the strongest CRF for mortality within the first month postoperatively. Pneumonia and heart failure were again the most common early postoperative complications and resulted in increased mortality. These groups offer an apparent target for specialised medical evaluation . On surgSince 2010, research has concentrated more on patient rehabilitation, national audit and registry studies.Fracture has a considerable impact on older individuals’ medium- to longer-term capabilities, physical function, quality of life and need for accommodations. Only 40% to 60% of patients recovered their pre-fracture level of mobility, whilst 40%-70% regained their level of independence for fundamental activities of daily living (ADL) . Only 24The great burden caused by hip fracture warrants action on a greater scale, i.e. country level. The UK National Hip Fracture Database was launched in 2007 as a national collaborative, clinician-led audit initiative to enhance hip fracture care quality, which was associated with significant improvements in the care and survival of aged individuals with hip fracture. From 2007 to 2011, the early surgery rate increased from 54.5 to 71.3% and remained stable from 2003-2007. Thirty-day mortality fell from 10.9 to 8.5%. The yearly relative decrease in adjusted 30-day mortality was 1.8% per year in the time period 2003-2007, compared with 7.6% per year over 2007-2011 .Analysis of secular trends in age-adjusted hip fracture rates worldwide showed differences between countries and continents. In the USA, Canada, Northern and Western Europe, Oceania, Hong Kong and Taiwan, the age-standardised fracture incidence or crude incidence is decreasing . In a stp = 0.002 and 0.001, respectively) [Regarding patient characteristics, investigators found that hip fracture patients are becoming older and increasingly frail . Accordictively) . In a rectively) . The risctively) . These rctively) .This research has identified a clear trend in hip fracture research over the past two decades, which began with fracture epidemiology and prevention, transitioned to perioperative management, orthogeriatric care and patient safety, and then to patient rehabilitation, disease burden and national audit studies in recent years. As the number of topical publications increases and a significant burden of hip fracture prevails, more vigorous studies can be expected. Through the results of the overlay visualisation map in co-occurrence analysis, ‘timing of surgery, registry, and patient mobility’ indicated that more and more studies after 2010 are focusing on patient safety, functional recovery and big data research. Additionally, we used a second data set from 2018 to 2020 in the co-occurrence analysis to identify keywords  with increasing frequency in recent years, as shown in Table This study inevitably has some limitations. First, there are intrinsic differences between the results of bibliometric analysis and real-world studies. For instance, some comparatively new publications of high quality may not attach sufficient attention due to lower citation frequency, whilst older articles have a tendency to accumulate more citations. A second limitation is the ‘obliteration by incorporation’ effect describing the bias created with citation analysis, which occurs when particular ideas become so accepted that the most original work is no longer cited . AdditioIn this study, with the usage of bibliometric and visualised analysis, hot topics in research and collaborative relationships amongst countries, authors and institutions were identified, and scientific masterpieces were reviewed. This information could provide investigators with a vivid general view within the academic field of hip fracture research. A time trend was depicted from its epidemiology, osteoporosis management and fracture prevention in the first decade of twenty-first century, to patient mortality and surgery timing in the later time period, to rehabilitation as well as national registry and audit research in the last period. This information could also guide stakeholders in prioritising funding and optimising the care of hip fracture.


TM accelerometer was used to monitor PA and heart rate. Participants were divided into four quartile groups. Most sedentary boys and girls had less PA and showed a worse ratio of physical inactivity (PI)/PA than non-sedentary participants during recesses. In the after-school period, there were no significant differences. On school days, most sedentary boys and girls showed lower PA, a worse ratio of PI/PA, fewer steps·hour−1, and lower energy expenditure than their non-sedentary counterparts. Vigorous PA of ≥8 METs was reached by 48% of most sedentary boys (75% non-sedentary) and 47% of most sedentary girls (54% non-sedentary). Most sedentary adolescents do not compensate for their sedentary behavior in lessons with higher PA intensity or volume during recesses, after-school, or in overall daily PA.The traditional concept of education and school settings significantly contribute to the sedentary behavior of adolescents at secondary schools. The aim of this study is to identify the volume and intensity of physical activity (PA) that adolescent boys and girls engage in during recesses, after school, and during the day to compensate for sedentary behavior in lessons. The study was conducted at 29 Czech and 9 Polish schools. The study involved 868 girls and 409 boys aged 15–17 years. An ActiTrainer School time in relation to the increasing academic requirements ,2 and thPELs should incorporate at least 50% of moderate-to-vigorous physical activity (MVPA) lesson time ; howeverIn addressing the issue of SB in secondary schools it must be considered that MVPA decreases and SB increases with age ,29,30. IUnfortunately, existing studies on SB and PA do not provide any evidence of the compensation effect of PA on SB and cognitive load of students in school. It is assumed that any health-oriented PA in school and after school has a compensation effect. An increase in MVPA in school increases overall daily MVPA on schooldays, but there is no evidence whether or not MVPA decreases in the following days . Nor is It is assumed that the adoption of the habit of compensating for sitting by immediate PA in school irrespective of the volume or intensity of PA provides the basis for a future occupational and lifelong healthy lifestyle. This study focuses on adolescents who belong to the group with high-risk sedentary behavior, which affects their physical health.The aim of this study is to identify the volume and intensity of physical activity among adolescent boys and girls during recesses, after school, and during the day to compensate for sedentary behavior in lessons. Further to find differences in volume and intensity of PA between non-sedentary and most sedentary boys and girls. The research was conducted at 29 Czech and 9 Polish secondary schools during the fall season. The participating schools were selected according to the place of residence of the undergraduate students who were at the schools as student teachers at the time of the research or later. Proper habitual week without any disruption of regular school schedule was selected with the school management if the school agreed to participate in the study. The size of involved schools was about 50 to 80 students in every year of study. All the selected schools had a similar organization education process: classes usually start at 8 a.m. and end between 2 and 3 p.m. Each class lasts 45 min. The recess after the second lesson of the day lasts 20 min, the duration of other recesses varies from 5 to 10 min. In addition, if there was a recess for lunch, the recess lasted at least 30 min. During school recesses and lunch recesses, the participants could move freely on campus and had the opportunity to use school facilities for various unstructured physical activities. Students used those facilities very rarely. None of the schools had implemented a special PA program intended for recesses, lunch recesses, or for carrying out lessons in subjects other than PELs. None of the schools used special equipped rooms except PELs. One of the typical movement behaviors during the recesses was to move from one classroom to another, but all classrooms were in the same building. TM  accelerometer can precisely capture PI (defined as <100 counts per minute) and on top of that the total amount of PA captured by the accelerometer is more reliable than dividing the groups according to the particular levels of PA. For the purpose of this study we defined PI as a physical activity that is lower than 100 counts per minute. This definition differs from the definition of PI presented in Tremblay et al. [In total, 2702 students aged 15 to 17 years old participated in the study. Due to incomplete record sheets or insufficient wear time, 959 participants were excluded. Inclusion criteria were set as follows: for at least 15 min before classes, at least 180 min at school (excluding PEL), at least 120 min after school, and at least 600 min per day. An additional 466 participants were excluded from the study because of having PEL in their daily school schedule. School days with PEL were excluded because participation in PELs could negatively affect the character of movement behavior during recess before and after PELs. Simultaneously, results that could be distorted when students who do not participate in PELs from various reasons are included in the data sample. No swimming was reported in the participants’ daily PA. Finally, 868 girls and 409 boys (response rate 47.26%) met the criteria to participate in the study . Only 6.The study was approved by the Institutional Research Ethics Committee of the Palacký University Olomouc (decision no. 24/2012).TM accelerometer was used to objectively assess PA. This device is a lightweight and compact triaxial accelerometer  designed to measure the amount and frequency of human activity: activity counts and vector magnitude, energy expenditure, steps taken, activity intensity levels, and subject position. The device also collects heart rate (HR) data when the PolarTM chest strap (type S610 was used in this study) is worn across the sternum. Our department verified the device’s validity of step count in field conditions. The correlation values expressing the relationships between actual and device-measured steps ranged from 0.96 to 0.97 [TM, Pensacola, FL, USA) was used for device initialization and data downloading. The epoch duration was set at 15 s.The ActiTrainer to 0.97 . Acceler to 0.97 ,40,41. ATM weight scale  and height using Leicester height measure during this session. PA monitoring starts in the morning of the following day. After three or five weeks from the end of the monitoring, all participants received the following feedback: time data on PA and PI, energy expenditure, HR, and step counts. The feedback also included information on load in METs and HR zones, together with clear energy expenditure curves, and HR curves. Individual and group results were analyzed as a part of biology lessons at schools.Participants used the accelerometer to monitor their PA and HR for three consecutive school days of the same week from morning (after morning hygiene) throughout the day to the evening (hygiene before bedtime). Participants wore the device on their right hip and the chest strap across the sternum and took it on and off when changing their clothes. They were not allowed to wear the device during water activities . Individual resting HR was measured in accordance with the exact instructions in the morning after waking up, repeatedly three times in 15-s intervals. The measured values for each measurement were converted to the number of beats per minute and recorded on the record sheet. Individually measured HR resting values were used to calculate the mean value that was, in the case of higher values, corrected according to the lowest daily HR value as measured by the accelerometer. During the day, participants recorded the following times: putting the devices on, arrival at school, school hours including school breaks, departure from school, participation in organized PA, and taking the devices off before bed. All instructions were provided by the research team during the initial session. Researchers obtained participant’s date of birth, body weight using TanitaTM accelerometer followed the same procedure as previous studies that used the same methods [https://upol.cz/fileadmin/userdata/FTK/Fakulta/Verejnost/Navod_IntPA13.pdf; SoftWareCentrum, Olomouc, Czech Republic) was used to process the 15-s epoch accelerometer data. The software enables us to evaluate data in different parts of the day: before school, at school, after school, during recesses, in PELs, and over the whole day. The ranges of load were determined according to performed steps, HR in 30%–100% HRmax in 10% intervals and in METs as a metabolic equivalent in 1 MET interval. The intensity zones were classified individually as follows: low , moderate , or vigorous PA . The sedentary  cut points were set to <25 counts per 15 s. Physical inactivity is understood as unrecorded change of the “center of gravity” of the body, with up to 100 counts per minute, simply as a “stable body position” in sitting, lying, or a different body position [Data processing from the ActiTrainer methods ,18,42. AFurther, processed accelerometry data were compared with guidelines for the physically active lifestyle of adolescents based on the present published literature ,43,44,45In compliance with the main aim of the study, the before school segment is not presented in this paper, but the differences between the groups were not significant in this segment.ŋ2 and r effect size coefficients were calculated. The effect size coefficients were interpreted as follows: small effect size ŋ2 = 0.01–0.059, r = 0.1–0.29; medium effect size ŋ2 = 0.06–0.139, r = 0.30–0.49; large effect size ŋ2 ≥ 0.14, r > 0.50. Significance was set at p < 0.05.In Statistica version 13  and SPSS version 25  programs, we used descriptive statistics for basic characteristics of the sample. Crosstabulations were used to undertake a group assessment of whether the recommendations for PA were met. Kruskal–Wallis ANOVA was used to ascertain the differences among groups with different proportions of PI and PA (quartile groups). Further to compare chosen pairs, multiple comparisons of means rank was used for all groups as a post-hoc test. Binary logistic regression with the enter method  was used to find out the odds ratio in meeting PA recommendations between the non-sedentary and sedentary adolescents. Further, the p < 0.001, resp. p = 0.021) during recesses than their sedentary pears . Further, non-sedentary boys had a significantly higher number of steps (p = 0.020) and caloric expenditure (p = 0.001) than sedentary boys. The differences in physical load according to HR and time spend in MVPA according to METs were not significant between non-sedentary and sedentary participants. During recesses, only 7% of non-sedentary boys (7% of sedentary) and 5% of non-sedentary girls (2% of sedentary) reached the load of ≥85% HRmax. The physical load of ≥8 METs was achieved by 18% of non-sedentary boys (6% of sedentary) and 8% of non-sedentary girls (5% of sedentary). Furthermore, we found that there are significant differences between non-sedentary boys and non-sedentary girls according to the time spent in LPA (p = 0.012) and in the ratio of PI/PA (p = 0.002). Finally, non-sedentary boys spent 9.0% of the recess time in MVPA (7.1% of sedentary), and non-sedentary girls spent 7.5% of their time in MVPA (5.7% of non-sedentary).Non-sedentary boys and girls spent significantly more time in LPA according to METs (ry pears . Higher p = 0.012) and had a significantly better ratio of PI/PA (p = 0.017) in comparison to sedentary boys. We found no significant differences between non-sedentary and sedentary boys and girls in other characteristics of after-school PA. An HRmax load of ≥85% was achieved by 38% of non-sedentary boys (27% of sedentary) and by 32% of non-sedentary girls (32% of sedentary). The load of ≥8 METs was detected in 63% non-sedentary boys (39% of sedentary) and in 37% non-sedentary girls (41% of sedentary).In the after-school segment, non-sedentary boys spent significantly more time in LPA according to METs , a better ratio of PI/PA , performed a higher number of steps , and had higher energy expenditure  than their sedentary peers. Results also showed that sedentary boys spent significantly more minutes in MVPA than sedentary girls (p = 0.014). In this study, 50% of non-sedentary boys (37% of sedentary) and 44% of non-sedentary girls (37% of sedentary) achieved a load of ≥85% HRmax during the day. The load of ≥8 METs was reached by 75% of non-sedentary boys (48% of sedentary) and 54% of non-sedentary girls (47% of sedentary). In total, the characteristics of daily PA showed that non-sedentary young people compensate for PI in classes more than the sedentary participants. Also, the daily results of the HR record correspond with the PA record. However, the differences between non-sedentary and sedentary participants were not significant.Over the whole day, non-sedentary boys and girls showed more time spent in LPA according to METs  and during recesses in girls . Meeting the recommendations for PA according to the time and intensity of PA , and over the whole day . No significant differences were found among the groups in the after-school segment.Significant differences in meeting the steps recommendations  were fouty of PA  correspoOR = 2.38, CI = 1.30–4.34, p < 0.001, resp. OR = 2.47, CI = 1.64–3.71, p < 0.001) with adjusting for age, BMI, and country .Non-sedentary boys and girls have higher odds ratio to meet the daily step recommendation  than the country . Non-sedIn the agreement with the aim of the study, we identified the volume and intensity of PA between boys and girls with different levels of SB in recesses, after school and in the whole day. The most important finding of the research is that boys and girls with the highest proportion of sedentary behavior do not compensate for sitting in lessons by means of PA volume or shorter higher-intensity PA in immediately subsequent recesses more than those who are less sedentary. The same applies to delayed PA compensation after school and to overall PA and PI indicators for the whole day. According to previous findings in similar conditions, students do not compensate for sedentary behavior either in school or at the weekend . This emSchools have the greatest chance to answer the simple but apt question according to Bouchard, Blair, and Katzmarzyk : “Less sThe achievement of PA recommendations expressed by steps in recesses, after school, and over the whole day confirms insufficient compensation for SB in the most sedentary boys and girls. The determination of these recommendations was based on internationally acknowledged PA guidelines ,45 takinThe fact that, according to SB, non-sedentary boys and girls in school lessons have a greater chance of achieving the recommendation on daily PA present a challenge to reduce sitting for all students in the process of education, especially the most sedentary ones. Moreover, it is important to support the transfer of students’ school PA to other segments of the school day and weekly PA structure, including weekend days . This reFurther research on SB and PA in high schools should be more integrated into the changes of the education system and should respect students’ opinions more  and actiThe study is one of the few that monitors whole-day PA in adolescents in METs and by HR, except sleep time, involving a higher number of students in natural school conditions. Monitoring whole-day HR using chest belts in the school environment is increasingly difficult; therefore, it was not possible to realize the random sample selection. The high number of excluded participants was mainly caused by insufficient wear time in observed day segments and by losing the appropriate contact between the chest belt HR monitor and skin of the participants’ body for several minutes. In the future it will be necessary to improve the quality of HR monitoring by using wrist wearables. Another positive aspect of the study is accurate time monitoring of the school program with minute accuracy, which on the other hand requires individual time records kept by all participants. Given the complexity of the research, the respondents’ physical fitness was not examined and HRmax was determined according to universal formulas.Boys and girls in high schools do not compensate for SB by means of school PA in recesses or after school. It has not been confirmed that boys or girls use shorter VPA to compensate for SB. School managers and teachers should as much as possible try to support the adoption of adolescents’ habit of compensating for sitting by any PA, even short PA. Any interruptions in sitting periods in lessons are of crucial importance to the negative implications of SB on adolescents’ health.


The AIP was positively correlated with the Atlanta classification . In multivariate analysis, the AIP was found to be an independent predictive factor for SAP . Conclusions: The AIP is a potential biomarker for the prediction of SAP in clinical practice. This result provides that impaired lipid metabolism is associated with the severity of pancreatitis.Background: The atherogenic index of plasma (AIP) reflects the levels of triglycerides (TG) and high-density lipoprotein (HDL) cholesterol. The purpose of this study was to assess the relationship between the AIP and severe acute pancreatitis (SAP). Materials and methods: Patients with acute pancreatitis (AP) were prospectively enrolled from March 2015 to June 2019. The severity of AP was classified according to the 2012 revised Atlanta classification. Mild and moderately severe AP were categorized as non-SAP. The AIP is calculated as log(TG/HDL). Results: A total of 323 patients were enrolled. The etiologies of AP were gallstone in 171 patients (52.9%), alcohol in 122 patients (37.8%), and hypertriglyceridemia in 30 patients (9.3%). Twenty-four patients (7.4%) were classified as SAP. The AIP was significantly higher in the SAP group compared to the non-SAP group ( Acute pancreatitis (AP) is an inflammatory disease of the pancreas which can cause local injury, systemic inflammatory response syndrome (SIRS), and organ failure (OF). The incidence of AP is increasing, with an annual incidence of 5–30 per 100,000 individuals . The oveThe atherogenic index of plasma (AIP) reflects the levels of triglycerides (TG) and high-density lipoprotein (HDL) cholesterol, and is calculated as log(TG/HDL). The AIP has been used to quantify blood lipid levels and as a robust biomarker of dyslipidemia and atherogenicity. It is also considered an indicator of metabolic syndrome and coronary syndrome ,12. In tHowever, the relationship between the AIP and the severity of AP remains unknown. Therefore, we determined the relationship between the AIP and the severity of AP and evaluated the predictive ability of the AIP for the severity of AP compared with other scoring systems.Patients diagnosed with AP at Yonsei University Wonju College of Medicine between March 2015 and June 2019 were enrolled in this prospective observational study. The study protocol was approved by the Internal Review Board for Human Research of Yonsei University Wonju College of Medicine . This study was performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all patients.The diagnosis of AP was based on the presence of two of the following three criteria: (1) upper abdominal pain, (2) serum amylase and/or lipase ≥3 times the upper normal limit, and (3) characteristic imaging on a radiological study . The etiClinical data and blood samples were obtained upon the patients’ diagnoses of AP. The presence of hypertension, diabetes mellitus, alcohol consumption, and smoking was recorded. The patients’ body mass index (BMI) was calculated using their height and weight. Hemoglobin, hematocrit, white blood cell (WBC) count, blood urea nitrogen, creatinine, lactate dehydrogenase, aspartate aminotransferase, CRP, procalcitonin, amylase, and lipase were recorded at the time of admission. Abdominal computed tomography (CT) scans were performed to diagnose AP and differentiate AP from other diseases. Once AP was diagnosed, the TG and HDL levels were measured as soon as possible. Additionally, scoring systems such as the Ranson score, BISAP, and CTSI were calculated. The AIP was defined as log (TG/HDL). The severity of AP was evaluated according to the 2012 revised Atlanta classification and recorded as mild, moderately severe, or severe AP . Mild APp value < 0.05 was considered statistically significant. Analyses were performed using SPSS version 23 .Categorical variables were presented as frequencies and percentages. Continuous variables were presented as means (± standard deviation) or medians with ranges. The Pearson rank method was used to evaluate the correlation between the AIP and the Atlanta classification. After adjustments for confounding factors, the odds ratios for SAP were evaluated using univariate and multivariate logistic regression analyses in order to determine the predictive factors of SAP among all the patients with AP and the predictive factors for intensive care unit (ICU) admission. The accuracy of the prediction power for SAP was determined by the area under the curve (AUC) using receiver operating characteristic (ROC) curves. AUC values were used to compare the predictive abilities of SAP using the AIP and scoring systems. A p < 0.001). The number of ICU admissions, mortality, and the duration of hospital stays were significantly higher in the SAP group compared to the non-SAP group (p < 0.001). The levels of CRP, procalcitonin, HDL, and TG were not different between the two groups, but the level of the AIP was significantly higher in the SAP group .A total of 323 patients were included in this study . Table 1R = 0.146; p = 0.009), CRP , and BMI  .We calculated the AUCs of various scoring systems and the AIP for predicting SAP . The AUC= 0.016) . Furtherp = 0.008) and AIP  were related to SAP. In the multivariate analysis, the AIP , alcohol , and procalcitonin  were independent predictive factors for SAP (p < 0.001), alcohol, and procalcitonin were independent predictive factors for ICU admission.In the univariate analysis, the procalcitonin . Hypertriglyceridemia is a common etiology of AP, and elevated serum TG is independently associated with persistent OF ,30. HypeThere are several limitations to our study. First, the number of SAP cases was small, and the study was conducted at a single institution. However, we enrolled sufficient numbers of all AP severities with different etiologies. Second, as the design of the study was cross-sectional, we cannot determine a cause-and-effect relationship between the AIP and the severity of AP. Finally, inflammatory cytokines such as tumor necrosis factor-α and interleukin-6 were not measured. However, this study is the first study to demonstrate that the AIP is related to SAP. A large-scale study is needed to establish the clinical role of the AIP for predicting the severity of AP and providing prognoses.In conclusion, we found that the AIP is significantly associated with SAP and that the AIP is a superior predictive factor to TG or HDL cholesterol level alone for predicting the risk of SAP. Our findings provide robust evidence regarding the association of impaired lipid metabolism and the severity of AP, thereby suggesting that the AIP is a simple and novel biomarker for predicting SAP. As the AIP can be easily measured in a convenient manner, it can be applied in clinical practice.