I2 = 0%); with no differences in adverse effects, it may be associated with a shorter development time of PUs and shorter hospital stays. The certainty of the evidence assessed by the GRADE approach was moderate and low. The topical application of olive oil is effective and safe in reducing the incidence of PUs compared to other treatments. These findings could provide new insights into olive oil as a preventive and alternative treatment for PUs as it is accessible and inexpensive compared to other products.The purpose was to identify and summarize the existing evidence on the efficacy and safety of the topical application of olive oil for preventing pressure ulcers (PUs). We included only randomized controlled trials (RCTs) involving patients at risk of developing PUs, testing the topical application of olive oil versus other products for PU prevention. We assessed the risk of bias using the RoB 2 tool, and the certainty of the evidence with GRADE. Four RCTs met the eligibility criteria. All studies were judged at a low risk of bias overall. The meta-analysis showed that the clinical efficacy of olive oil for prevention occurs by reducing the incidence of PUs (RR = 0.56, 95% CI = 0.30 to 0.79,  Pressure injuries, commonly referred to as pressure ulcers (PUs), are a common complication for people who are forced to lay in bed for long periods of time. and increase morbidity and mortality in people with chronic conditions ,2. TheirPeople at a high risk of developing PUs require early prevention measures . CurrentAn interesting alternative for PU prevention is olive oil. It contains essential fatty acids, such as linoleic and oleic acid, which improve hydration and cause a protective, emollient, and regenerating effect on the skin . The advTherefore, it is vital to understand the potentialities and uses of olive oil in order to improve current prevention strategies. Thus, this study aims to identify and summarize existing evidence on the efficacy and safety of the topical application of olive oil for preventing PUs.This systematic review (SR) and meta-analysis follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guidelines . The proWe searched for all potentially relevant studies published from inception until 14 December 2021. A systematic literature search was conducted using eight electronic bibliographic databases: PubMed, EMBASE (access via OVID), Cochrane Library, CINAHL, Web of Science, Scopus, Global Health, and LILACS. The search strategy was carried out by an experienced medical research librarian (DC) and validated by the research team. Search terms focused on these keywords: bedsore, pressure ulcer, pressure sore, decubitus ulcer, and olive oil see . In addiWe imported records obtained from all databases into the EndNote X9 reference management software to eliminate duplicate publications, following the procedures described by Bramer et al. .We used the PICOS framework  to guide our eligibility criteria .We included only RCTs meeting all the following inclusion criteria: (a) investigate patients at risk of developing PUs, in any care setting; (b) assess topical application of olive oil as the intervention for PU prevention; (c) compare other products for PU prevention. Studies that do not explicitly define the control group, or studies with a placebo as the control group, were also included. We excluded studies meeting any of the following criteria: (a) include hospitalized patients with pre-existing PUs; (b) published in a language other than English, Spanish, or Portuguese; (c) other types of publications such as review articles, case reports, book chapters, editorials, and letters.Two previously trained reviewers (FJVL and CBR) independently screened the titles and abstracts for relevant articles prior to appraising the full texts. A third reviewer (AHV) solved any disagreements. If necessary, the reviewers contacted the principal authors of the identified studies via email for details, but if no response was received within 2 weeks, the study was excluded. For all the selection processes, we used Rayyan, a free web app that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability .The primary outcome was the incidence of PUs, defined as the number or proportion of patients developing a PU on any part of the body that is grade 1 (nonblanchable erythema), 2 , 3 (full-thickness skin loss), or 4 (deep-tissue destruction) during their stay in the care setting . SecondaTwo reviewers (FJVL and CCR) extracted data independently from all included studies, using a predefined form previously piloted. A second reviewer (AHV) cross-checked this process. The study authors were contacted for missing data or clarification where appropriate. The types of data extracted included: first author, year of publication, country of study, language of publication, study design, setting, type of participants, type of intervention, control, outcomes, duration and follow-up, sample size, population characteristics, and funding/conflict of interest.Two reviewers (FJVL and CCR) assessed the risk of bias of each included RCT using the Cochrane Risk of Bias tool (RoB 2). A third reviewer (MS) cross-checked this assessment. RoB 2 is structured into a fixed set of domains of bias, focusing on different aspects of trial design, conduct, and reporting. We considered six domains for the assessment: (i) risk of bias arising from the randomization process, (ii) risk of bias due to deviations from the intended interventions, (iii) missing outcome data, (iv) risk of bias in the measurement of the outcome, (v) risk of bias in the selection of the reported result, and (vi) overall risk of bias. Within each domain, a series of questions  aim to elicit information about features of the trial that are relevant to the risk of bias. A proposed judgement about the risk of bias arising from each domain is generated by an algorithm, based on answers to the signaling questions. The judgement can be a “Low” or “High” risk of bias, or can express “Some concerns”.For dichotomous outcomes, we present a risk ratio (RR) with the 95% confidence interval (95% CI). When it was allowed, we performed a meta-analysis using random-effects models (inverse variance and Mantel–Haenszel method) to calculate a weighted summary effect estimate RR and a 95% CI. Meta-analyses were performed using the meta package in the R programming language and RStudio . ResultsI2 statistics, and we considered that heterogeneity might not be important when I2 < 40% [2 defined the between-studies variance. Publication bias was not assessed due to the number of studies pooled for each meta-analysis being less than ten [p-value < 0.05.We assessed heterogeneity using the I2 < 40% . Tau2 dethan ten . StatistWe performed a sensitivity analysis, excluding one study at a time to verify the stability of the results and sources of heterogeneity, by using the leave-one-out method; to assess the influence of small-study effects on the results of our meta-analysis, the fixed-effects and random-effects estimates of the intervention effect were compared.We assessed the certainty of the evidence for each outcome according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidance , and madOur study did not require institutional ethics approval because it was an analysis of publicly accessible aggregated secondary data. We did not collect deeply personal, sensitive, or confidential information from participants.Our initial systematic literature search identified 104 records. First, we excluded duplicate records, leaving a total of 45 articles. Second, we excluded 37 records   with a mean age ranging between 17.3 . Table 1One study reported the time to ulcer development , and oneAll studies were judged to have a low risk of bias overall ,18,19,20I2 = 0%) [I2 = 0%) and no difference in studies with HOFAs  as the control (I2 = 0%) ,19,20,21 control .I2 = 0%) [I2 = 0%) ,18.Only one study including 129 participants in the ICU suggested that olive oil may be associated with a longer number of days for PU development (olive oil: 10.45 +/− 5.20 days vs. control: 7.50 +/− 5.43) .Only one study with 70 participants in the ICU suggested that olive oil may be associated with an improvement in the average length of stay in days (olive oil: 23.25 ± 28.70 vs. control: 25.93 ± 30.63) .We performed sensitivity analyses based on the leave-one-out method, and the obtained results were consistent with the main analysis for the primary outcome .We systematically revised and summarized the existing evidence on the effects and safety of the topical application of olive oil for preventing PUs. Of all the databases explored, there are few RCTs of good quality. After meta-analyses of the data, it seems that olive oil has an effect in reducing the incidence of PUs, is safe, and may be associated with a longer development time of PUs and shorter hospital stays. However, the certainty of the evidence assessed by the GRADE approach is still moderate and low.The findings of our SR are directly in line with previous studies. For example, a SR from 2016, contrasting the efficacy of olive oil against hyperoxygenated fatty acids, reported that extra virgin olive oil reduces the incidence of PUs, being as effective as hyperoxygenated fatty acids in preventing these injuries . RegardiFrom the results of our SR, we propose olive oil as an effective and safe preventive alternative treatment in conjunction with other measures such as risk assessment, skin care, repositioning, and well-controlled blood sugars. Although there are other preventive measures for PUs in patients at risk of developing them, olive oil is shown as an economic and frequently used option. For example, in Spain, where the studies with the largest sample size were carried out, Oleicopiel is a product manufactured and distributed nationwide by a pharmacist; thus, it can be assumed that it is a frequently used product. Likewise, from the RCT of Lupiañez-Pérez, it was concluded that olive oil as a preventive treatment allows considerable savings in direct costs, being EUR 10,192 less expensive than hyperoxygenated fatty acids . In our The present study also involved certain limitations. First, we do not address other regional databases. However, an extensive literature search was conducted on several scientific bibliographic databases, including PubMed, EMBASE (access via OVID), Cochrane Library, CINAHL, Web of Science, Scopus, Global Health, and LILACS. Second, not all the studies evaluated presented information about the secondary outcomes of this study, and therefore, it is not possible to determine with certainty the effect of the application of olive oil on these outcomes. Nevertheless, the study synthesizes the available evidence about the main objective of our SR: the incidence of PUs. Third, only four studies conducted in three countries, of which two were from Spain, were included in this study. Considering the characteristics of the healthcare system of this country, it becomes difficult to extrapolate the results to low- and middle-income countries. Fourth, another relevant limitation of this study is the difference between the attention of the nurses in an intensive care unit and a nursing home. The number of nurses who take care of the patient is distinct in ratio. The literature does not specify a ratio for each category, but it has been demonstrated that the nurse–patient ratio is one of the determining factors of the patient outcome ,34. LikeThese findings could provide new insights into olive oil as a preventive and alternative treatment for PUs based on it being accessible and inexpensive compared to the other products available to prevent Pus, and as it is a widely available product. Although further evidence is needed, the present study contributes to a better understanding of the efficacy and safety of olive oil in the prevention of PUs.For future research, it would be important to consider this preventive method in different settings, such as immobilized, post-operative patients, or patients with certain comorbidities . In thisIn conclusion, the present study contributes to a better understanding of a preventive treatment little studied. We found that there is an effect of olive oil in reducing the incidence of PUs, and that it is safe. However, it should be taken into consideration that there is not much evidence and the certainty of the results is not the best. For that reason, future research could deepen our conclusions and new variables of interest could be addressed, such as patient product acceptability, adverse events, and economic assessments to help healthcare managers make rational decisions.


In the emergency room, clinicians spend a lot of time and are exposed to mental stress. In addition, fracture classification is important for determining the surgical method and restoring the patient's mobility. Recently, with the help of computers using artificial intelligence (AI) or machine learning (ML), diagnosis and classification of hip fractures can be performed easily and quickly. The purpose of this systematic review is to search for studies that diagnose and classify for hip fracture using AI or ML, organize the results of each study, analyze the usefulness of this technology and its future use value.PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used [All Fields] AND  OR "bone fractures"[All Fields] OR "fracture"[All Fields]). The following information was extracted from the included articles: authors, publication year, study period, type of image, type of fracture, number of patient or used images, fracture classification, reference diagnosis of fracture diagnosis and classification, and augments of each studies. In addition, AI name, CNN architecture type, ROI or important region labeling, data input proportion in training/validation/test, and diagnosis accuracy/AUC, classification accuracy/AUC of each studies were also extracted.In 14 finally included studies, the accuracy of diagnosis for hip fracture by AI was 79.3–98%, and the accuracy of fracture diagnosis in AI aided humans was 90.5–97.1. The accuracy of human fracture diagnosis was 77.5–93.5. AUC of fracture diagnosis by AI was 0.905–0.99. The accuracy of fracture classification by AI was 86–98.5 and AUC was 0.873–1.0. The forest plot represented that the mean AI diagnosis accuracy was 0.92, the mean AI diagnosis AUC was 0.969, the mean AI classification accuracy was 0.914, and the mean AI classification AUC was 0.933. Among the included studies, the architecture based on the GoogLeNet architectural model or the DenseNet architectural model was the most common with three each. Among the data input proportions, the study with the lowest training rate was 57%, and the study with the highest training rate was 95%. In 14 studies, 5 studies used Grad-CAM for highlight important regions.We expected that our study may be helpful in making judgments about the use of AI in the diagnosis and classification of hip fractures. It is clear that AI is a tool that can help medical staff reduce the time and effort required for hip fracture diagnosis with high accuracy. Further studies are needed to determine what effect this causes in actual clinical situations. In the emergency room, clinicians spend a lot of time and are exposed to mental stress . There aDiagnosis can be defined as determining the cause and characteristics of an individual patient's disease, and classification is mainly for creating a relatively homogeneous population through standardized criteria, which is mainly an important factor in disease research . In addiCurrently, most medical institutions use digital medical imaging systems, which overcomes the temporal and spatial limitations of access to image information. In additTherefore, the purpose of this systematic review is to search for studies that diagnose and classify for hip fracture using AI or ML, organize the results of each study, analyze the usefulness of this technology and its future use value.Studies were selected based on the following inclusion criteria: (1) studies using AI or ML techniques for diagnosis or classification of hip fracture; and (2) studies reporting on the type of imaging information used; and (3) studies reporting on statistical analysis of accuracy or area under the ROC (receiver operating characteristic) curve (AUC) for diagnosis or classification of hip fracture. Studies were excluded if they failed to meet the above criteria.PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used [All Fields] AND  OR "bone fractures"[All Fields] OR "fracture"[All Fields]). Manual search was also conducted for possibly related references. Two of us reviewed the titles, abstracts, and full texts of all potentially relevant studies independently, as recommended by the Cochrane Collaboration. Any disagreement was resolved by the third reviewer. We assessed full-text articles of the remaining studies according to the previously defined inclusion and exclusion criteria, and then selected eligible articles. The reviewers were not blinded to authors, institutions, or the publication.The following information was extracted from the included articles: authors, publication year, study period, type of image, type of fracture, number of patient or used images, fracture classification, reference diagnosis of fracture diagnosis and classification, and augments of each studies. In addition, AI name, CNN architecture type, ROI or important region labeling, data input proportion in training/validation/test, and diagnosis accuracy/AUC, classification accuracy/AUC of each studies were also extracted.The initial search identified 123 references from the selected databases and 4 references from manual searching. Eighty-two references were excluded by screening the abstracts and titles for duplicates, unrelated articles, case reports, systematic reviews, and non-comparative studies. The remaining 45 studies underwent full-text reviews, and subsequently, 31 studies were excluded. Finally, 14 studies are included in this study , 11–21. In 14 studies, the type of image used for AI learning was all X-ray. However, one study additionally used CT images and another additionally used CT and MRI , 18. FouThe accuracy of diagnosis for hip fracture by AI was 79.3–98%, and the accuracy of fracture diagnosis in AI aided humans was 90.5–97.1. The accuracy of human fracture diagnosis was 77.5–93.5. AUC of fracture diagnosis by AI was 0.905–0.99. The accuracy of fracture classification by AI was 86–98.5 and AUC was 0.873–1.0 Table . The forAmong the included studies, the architecture based on the GoogLeNet architectural model , 11, 18 As human lifespans prolong and the elderly population grows, the socioeconomic problems associated with hip fractures and postoperative care are public concerns worldwide . Early dIn this study, several CNN structures were used for radiograph image analysis in each study for hip fracture diagnosis. Among the included studies, CNNs using DenseNet or GoogLeNet architecture models were used the most. These two CNNs are inception architecture, which are deep CNNs with an architecture design composed of repeating components . GoogLenIn the results of the articles included in our study, the accuracy of diagnosis for hip fracture by AI algorithm was over 90%, except for the results of Beyaz et al., and AUC of fracture diagnosis was over 0.9, which was very high . Also, tThe increase in human dependence on hip fracture detection using AI algorithm may be another issue because it is difficult and time-consuming for doctors to make their own clinical judgments by synthesizing the results of examinations performed face-to-face with patients . To solvBecause deep learning of AI automatically and adaptively learn features from data, large and clean datasets are required . Better As AI can quickly process large amounts of patient information, it has incredible potential in diagnosing and classifying patients' diseases . EspeciaThere are several limitations in our study. First, we did not consider the type of AI algorism and degree of training of AI algorism. Second, we did not consider the quality of radiographs for deep learning. The selected images are likely to have high quality. Also, these images can only represent characteristics of a specific age and sex. Third, implants used for surgical treatment of hip fracture were not considered.We expected that our study may be helpful in making judgments about the use of AI in the diagnosis and classification of hip fractures. It is clear that AI is a tool that can help medical staff reduce the time and effort required for hip fracture diagnosis. Further studies are needed to determine what effect this causes in actual clinical situations.


For the other components of MetS, no significant associations were observed in either sex. These findings suggest evidence of no association between UPF consumption and MetS; however, consumption of UPF was associated with increased WC and low HDL-c, but only in women.Longitudinal studies evaluating the relationship between UPF consumption and the incidence of Metabolic Syndrome (MetS) and its components are still scarce. This study aimed to evaluate the effect of UPF consumption on the incidence of MetS and its components in adults. A prospective study was conducted with 896 participants from the 1978/79 Ribeirão Preto cohort, São Paulo, Brazil. UPF consumption was evaluated in %kcal and %g at ages 23–25 years. Incidence of MetS and its components were estimated at ages 37–39 years, according to the Joint Interim Statement criteria. Poisson regression was used to assess associations, and interactions with sex were investigated. UPF consumption had no association with MetS . However, women with higher UPF consumption, in %kcal and %g, had a higher risk of abdominal obesity (%kcal:  According to the definition of the NOVA system of food classification, ultra-processed foods (UPF) are formulations of ingredients, mainly of exclusive industrial use, resulting from a series of industrial techniques and processes . UPF conParallel to the increase in UPF consumption, the prevalence of metabolic syndrome (MetS) increased in recent decades, also becoming an important public health problem ,5. MetS Evidence has shown that UPF consumption contributes significantly to adverse health effects, such as the increase in chronic non-communicable diseases (NCDs) and all-cause mortality ,8. In thIn view of the increasing UPF consumption and its potential negative health impacts, this study aimed to evaluate the effect of UPF consumption on the incidence of MetS and its components in adults in a Brazilian cohort.This was a prospective study with data from a birth cohort initiated in 1978/79 in the city of Ribeirão Preto, state of São Paulo, Southeast region of Brazil. Cohort data were used for birth (baseline), follow-up at 23–25 years (fourth phase) and follow-up at 37–39 years of age (fifth phase). At birth, 6973 births were evaluated in hospitals of mothers living in Ribeirão Preto. In the fourth phase of the cohort, a sample of one in three individuals was evaluated between 2002 and 2004, representing 31.8% of the original cohort. In the fifth phase, whose data collection occurred between 2016 and 2017, 1775 participants were evaluated. Details on the eligible individuals who were recruited and evaluated at each stage of this cohort are available in other publications ,14.n = 123) and implausible data on food consumption (±3 standard deviation of the mean daily energy intake: n = 6) at follow-up at 23–25 years were excluded, resulting in a sample of 896 participants in the main analyses. Subsequently, of the 896 participants considered in the main analyses, we excluded individuals diagnosed with each of these components at follow-up at 23–25 years  to estimate the incidences of each MetS component.In the 1978/79 Ribeirão Preto cohort, data of single births, evaluated in the fourth and fifth phase of the study, with collected information on food consumption in the fourth phase and on components of MetS in the fourth and fifth phase were available for 1025 participants. Of these 1025 participants, individuals diagnosed with MetS  and grams (%g) from the UPFs consumed by the participants in the daily total of calories and grams, respectively.Food consumption data were obtained by distributing a semi-quantitative Food Frequency Questionnaire (FFQ) regarding food consumption in the last 12 months. This FFQ was not validated, but was adapted for use in non-communicable chronic disease prevention programs aimed at adults, including the age group evaluated in this study .The FFQ consisted of 83 food items  and theiTo estimate food consumption in grams (g) per day, the consumption frequencies of each daily food item were obtained  and multiplied by the size of the portion. From the values in grams, the intake of nutrients and energy of each food was estimated by using food composition tables ,19,20. RFoods were grouped according to the NOVA classification as follows: in natura or minimally processed foods, processed culinary ingredients, and processed foods and ultra-processed foods. The group of in natura or minimally processed foods and processed culinary ingredients were considered jointly, being termed as “culinary preparations” .It is important to mention that since at the time of research planning the NOVA classification had not yet been published, the FFQ used in the study was not designed to classify food according to the degree of processing and consequently to accurately measure UPF intake. Thus, some considerations regarding the consumption estimates of some food items needed to be taken into account. When foods from different groups were grouped in the FFQ, the contribution of these foods was divided into more than one group, using as a parameter the consumption of said foods in the State of São Paulo, according to the estimates observed in the Brazilian Family Budget Survey (2002–2003) , which wFinally, the percentage contribution in grams (%g) and energy  of each food group was estimated for the total weight and total calories of the diet, respectively.The diagnosis of MetS and its components, in the study outcomes, was determined in the follow-ups at 23–25 years (baseline) and 37–39 years.Waist circumference (WC) was measured in centimeters using an inelastic fiberglass tape. To obtain WC measurement, the smallest perimeter between the ribs and the iliac crest was considered while the participant was standing with a relaxed abdomen at the end of a normal expiration. In the absence of natural waist, the measurement was performed at the level of the umbilical scar.To determine the biochemical components of MetS, in the 23–25 years follow-up, 40 mL of blood was collected after 12 h of fasting of the participant. In the 37–39 years follow-up, the same collection was performed in a non-fasting state. Blood glucose was measured by the human diagnostic calorimetric enzymatic method GOD/PAP with a coefficient of variation of 4.2%. High-density lipoprotein cholesterol (HDL-c) and triglycerides were determined by the same method using the Dade Behring XP device and Dade Behring Dimension reagents.® digital sphygmomanometer model 740 was used. Three checks were performed with 15 min intervals between each, and the individuals remained seated with their left arm at heart height. The mean between the last two measurements was considered to represent the blood pressure of the participants.To gauge blood pressure, the OmronIn the 23–25 year-old follow-up, the criterion for diagnosing MetS was considered to be a change in at least three components according to parameters established by the Joint Interim Statement (JIS) ,25.Data on pregnancy, delivery, and newborn, as well as demographic, socioeconomic, and maternal data, were obtained with a standardized questionnaire applied shortly after delivery by trained personnel at the time of birth in 1978/1979. In this study, the following variables were considered: maternal smoking during pregnancy (yes/no); number of prenatal consultations (0 consultations/1 to 5 consultations/≥6 consultations); gestational age (<37 weeks/≥37 weeks); type of delivery ; birth weight (<2500 g/≥2500 g); length at birth (<50 cm/≥50 cm); sex ; family income (in quartiles); parity (1 delivery/2 to 4 deliveries/≥5 deliveries); maternal age (<20 years/20 to 34 years/>35 years); maternal education (0 to 4 years of schooling/5 to 8 years of schooling/9 to 11 years of schooling/≥12 years of study); maternal marital status (with partner/without partner); maternal occupation .At the age of 23–25 years, demographic, socioeconomic, and lifestyle data were obtained by trained personnel through structured questionnaires. For this study, the following variables and respective categories were considered: sex ; self-reported skin color (white/black/mixed race/Asian or indigenous); age (23 years/24 years/25 years); schooling (0 to 8 years of study/9 to 11 years of study/≥12 years of study) marital status (with partner/without partner); family income (<5 minimum wages/5 to 9.9 minimum wages/≥10 minimum wages); alcohol consumption (yes/no); smoking (yes/no); and level of physical activity (high/moderate/low). The level of physical activity was evaluated and defined according to the guidelines of the International Physical Activity Questionnaire (IPAQ) .® . To minimize bias due to follow-up losses, weighting was performed by the inverse of the selection probability. For the weighting, only variables collected at birth were considered , as previously described.A statistical analysis was performed using the software Stata 14.0The continuous variables were described as measures of central tendency and dispersion (means and standard deviations) and categorical variables by absolute and relative frequencies.t and ANOVA tests. The frequencies of metabolic syndrome and each of its components at 37–39 years were also compared according to socioeconomic, demographic, and lifestyle characteristics at 23–25 years using Pearson’s chi-square or Fisher’s exact tests. For the statistical tests, a significance level of 5% was considered.The mean consumption of UPF in %g and %kcal at 23–25 years was compared according to socioeconomic, demographic, and lifestyle characteristics also at 23–25 years, by using the Student’s To evaluate the effect of UPF intake on the incidence of MetS and its components, Poisson regression models were adjusted with robust variance, with the variables MetS and each of its components used as an outcome. In each model, the crude and adjusted risk ratios and their respective 95% confidence intervals were estimated.For confounder control, the independent variables included in the adjusted model were identified by constructing the Directed Acyclic Graph (DAG)  in the Dp-value of lower than 0.10 included in the final model. Based on the adjusted regression models, the risk of MetS and each of its components were estimated according to UPF consumption. Marginal graphs were generated to visualize these interactions. To investigate nonlinear effects, regression models were also considered with the UPF consumption variable in the quadratic form.In each adjusted regression model, possible interactions of the sex variable with the UPF consumption variable were evaluated, with those with a The study was approved by the Research Ethics Committee of Hospital das Clínicas of the University of São Paulo’s School of Medicine of Ribeirão Preto . All participants signed the informed consent form.Of the 896 participants evaluated in the main analyses, at 23–25 years, 55.7% were female, most were white and had 9 to 11 years of schooling. Regarding lifestyle, 89.8% reported drinking alcohol and 14.8% were smokers. Regarding food consumption, the mean UPF consumption in %kcal and %g was 38.3% and 35.6%, respectively. For the UPF consumption in %kcal, we observed a significant difference regarding sex and skin color, with higher UPF consumption among women and individuals of black skin color. Considering the UPF consumption in %g, we observed a significantly higher UPF consumption among individuals who did not consume alcohol and had a low physical activity level .The incidence of MetS at 37–39 years was 35.4%. Considering each of the MetS components alone, the incidences were: 63.0% of increased WC; 27.5% of elevated triglycerides; 35.9% of low HDL-c; 28.3% of increased blood pressure; and 29.4% of high glucose. The incidence of MetS and its components showed some significant differences regarding the demographic, socioeconomic, and lifestyle variables of participants at 23–25 years. Regarding gender, men showed significantly higher incidences of MetS, elevated triglycerides, increased blood pressure, and increased glucose, whereas women showed a higher incidence of low HDL-c. Regarding skin color, the incidence of increased WC was significantly higher among individuals of black skin color. Individuals with lower schooling, in turn, showed significantly higher incidences of MetS, increased WC, and increased glucose. Regarding marital status, individuals who had a partner showed a significantly higher incidence of increased WC. Participants with lower income showed significantly higher incidences of MetS, increased WC, and low HDL-c. In addition, we observed significantly higher incidences of increased triglycerides and low HDL-c among smokers and those who consumed alcoholic beverages, respectively .p = 0.168 and Female: p = 0.030; %g: Male: p = 0.236 and Female: p = 0.003), whereas UPF consumption in %kcal was positively associated with low HDL-c , with both associations being significant only in women , abdominal obesity (62.5%), and low HDL-c (34.6%). The UPF consumption was not associated with MetS; however, the incidence of increased WC  and low HDL-c  was positively associated in women.Regarding the caloric contribution of UPF consumption in adults, the literature is quite heterogeneous. In Brazil, surveys conducted in several regions of the country reported lower means of the caloric proportion of UPF consumption in adults compared to those observed in our study ,29. In tSubstantially higher UPF consumption was observed in our study when compared to other studies carried out in Brazil, which may be related to the socioeconomic profile of the sample studied in the present study. Studies in Brazil already demonstrated that UPF consumption is associated with higher income and education ,31. RibeIn the international scenario, Julia et al.  reportedRegarding the contribution in grams of UPF consumption, to our knowledge, so far only one Brazilian study reported such estimates in adults. Silva et al. , when evRegarding the incidence of MetS and its components, comparisons with some studies in the literature are limited due to the various criteria for defining MetS used among the authors and the fact that most studies estimated prevalence rather than incidence. Regarding the definition criteria of the MetS, note that when compared with the criteria of the National Cholesterol Education Program Adult Treatment Panel-Adult Treatment Panel III (NCEP-ATPIII)  and the When comparing our findings with other studies that estimated incidences and considered the JIS criterion in the definition of MetS ,56,57,58The discrepancies in the MetS, abdominal obesity, and low HDL-c estimates, reported among the studies that evaluated such outcomes considering the JIS criteria can be explained, in part, by the differences in the age groups of the participants and the follow-up time for measuring new cases in each study, which varied widely.Regarding the association of UPF consumption and abdominal obesity in women, previous studies have reported similar findings; however, none of them evaluated the association longitudinally and estimated the incidences as our study did. In the study conducted by Sung et al. , with daThe differences between the sexes in the associations observed are still unclear; however, the literature suggests that women are more predisposed to the adverse metabolic effects of foods rich in carbohydrates with a high glycemic index and glycemic load than men . In addiPrevious studies investigated the association between UPF consumption  and low HDL-c in adults; however, none of them evaluated the outcome by longitudinally estimating its incidences. UPF consumption was positively associated with low HDL-c in the studies conducted by Lavigne-Robichaud et al.   in the subgroups of dairy products, margarines, breads, biscuits, cakes, sweets, and desserts was found in women compared to men in the sample excluding those with low HDL-c at 23–25 years in our study  and the higher incidence of low HDL-c observed only in women may be related to differences in food consumption between genders. According to the Update of the Brazilian guideline of dyslipidemia and prevention of atherosclerosis, 2017 , a diet ur study , which cRegarding the absence of a significant association of UPF consumption evaluated in %g and higher incidence of low HDL-c in females, we suggest that this may be related to the lower density (d = m/v) of some foods that may favor the reduction in HDL-c levels such as margarines, thereby impeding the analysis of UPF consumption considering the contribution in grams from capturing such an association.Some limitations of this study must be considered. At the time, the NOVA classification had not yet been published, so the FFQ used to evaluate food consumption was not developed to classify food according to the degree of processing and, thus, the limitation of this instrument for measuring AUP consumption needs to be taken into account. In addition, food consumption was only considered at one point in time (because the FFQs used in each cohort follow-up were very different), thus it would not be surprising if intake modified over a lifetime. Another limitation concerns the evaluation of biochemical components at 37–39 years without fasting requirements during the blood sample collection. However, the literature shows that glucose, regardless of fasting in normoglycemic individuals, is usually <100 mg/dL . FurtherOn the other hand, our study has strengths. Its longitudinal character allowed us to evaluate the effect of UPF consumption on the incidence of MetS and its components over 15 years of follow-up of a sample of Brazilian adults. Another strength is the use of DAG in the construction of the theoretical model to define the variables used in the adjustment as confounders, thus reducing unnecessary adjustments.In conclusion, our findings suggest no association between UPF consumption and MetS; however, the consumption of these foods was associated with two components of MetS, increased WC and low HDL-c only in women. The possible difference in dietary composition between men and women and its consequent impact on the development of cardiometabolic outcomes deserves further investigation. Thus, further studies on the topic are needed to elucidate this issue.


The aim of this study was to develop a nomogram based on early clinical features and treatment options for predicting in-hospital mortality in infective endocarditis (IE).We retrospectively analyzed the data of 294 patients diagnosed with IE in our hospital from June 01, 2012 to November 24, 2021, determined independent risk factors for in-hospital mortality by univariate and multivariate logistic regression analysis, and established a Nomogram prediction model based on these factors. Finally, the prediction performance of nomogram is evaluated by C-index, bootstrapped-concordance index, and calibration plots.Age, abnormal leukocyte count, left-sided IE, right-sided IE, and no surgical treatment were independent risk factors for in-hospital mortality in patients with IE, and we used these independent risk factors to construct a nomogram prediction model to predict in-hospital mortality in IE. The C-index of the model was 0.878 (95% CI: 0.824–0.931), and the internal validation of the model by bootstrap validation method showed a prediction accuracy of 0.852 and a bootstrapped-concordance index of 0.53.Our nomogram can accurately predict in-hospital mortality in IE patients and can be used for early identification of high-risk IE patients. Infective endocarditis (IE) refers to the inflammation of the heart valve or the inner lining of the ventricular wall caused by the invasion of bacteria, fungi or other microorganisms. It is a common inflammatory disease worldwide, with a global incidence of 1.8/100,000–9.7/100,000 . The proIn recent years, statistical prediction models have been widely used in the prognostic studies of clinical diseases. One of the prognostic prediction models is the nomogram. Its working method is to build a statistical prediction model to generate numerical probabilities of clinical events, then use the nomogram to display the outcomes of the statistical prediction model in simple graphics, and finally to make clinical decisions based on them. Nomogram is frequently utilized in the prognostic research of tumor patients due to its simplicity and convenience of usage. The effectiveness of the nomogram to predict prognosis in many tumors is much better than that of traditional TNM staging . The NomWe hope that by constructing a Nomogram prediction model based on patients' early clinical data, we will be able to use the probabilities generated by the model predictions to risk-stratify patients with IE in the clinic, allowing clinicians to identify patients with a poor prognosis earlier and take more aggressive and targeted treatment measures to reduce mortality during hospitalization in patients with IE.We used the hospital electronic medical record system to get clinical data from patients who visited our hospital and were diagnosed or suspected of having infective endocarditis between June 1, 2012, and November 24, 2021. A total of 694 cases were retrieved. The modified Duke diagnostic criteria were used to screen the patients, and 383 individuals with IE who satisfied the duke diagnostic criteria were included , transthoracic echocardiography (TTE) and transesophageal echocardiogram (TEE). In order to reflect the early clinical characteristics of the patients, the hematological examination of patients who visited our hospital directly and were not treated in other hospitals was based on the first blood test after admission, and the results of the tests before the patient received antibiotics were used for those patients who visited other hospitals and were later referred to our hospital. The echocardiographic examination results shall be based on the first examination after admission. If a patient has both TTE and TEE examinations, the TEE will take precedence. Cardiac vegetation is defined by cardiac echocardiography as thrombotic masses with chaotic echoes and unstable movements independent of valves . Left-siThe factors that may influence mortality during hospitalization in patients with IE were first screened using univariate logistic regression analysis, and then the statistically significant components were added to a multivariate logistic regression analysis model. Finally, the screened independent risk factors were used to create a nomogram. A concordance index and calibration plots with bootstrap samples were used to assess the nomogram's performance. A concordance index is a numerical assessment of discriminative capacity. Calibration plots are graphic evaluations of predictive ability that compare observed probabilities with nomogram-predicted probabilities .x ± s) or medians (interquartile range) depending on whether they conform to a normal distribution. Student's t-test or the Mann–Whitney U-test was used to evaluate the numerical data between groups. Categorical values are expressed as percentages. For compare groups of categorical data, the chi-square test or Fisher's exact strategy was applied. A P-value < 0.1 was considered statistically significant in univariate regression analysis. In multivariate regression analysis and data comparison between two groups, a P-value < 0.05 was deemed as statistically significant.Statistical analysis was performed using Social Package for the Social Sciences (SPSS) version 26 software. Kolmogorov–Smirnov and Shapiro–Wilk tests were used to determine whether the distribution of variables was normal. Numerical data are expressed as means ± standard deviations . The proportion of patients with prosthetic valve or cardiac device in the cured group was 13.6%, and that in the death group was 27.6%. There was a statistical difference in the proportion of patients with prosthetic valve or cardiac device in the two groups (P = 0.010). The cured group had a median leukocyte count of 9.13 (4.36) × 109/L, while the death group had a median leukocyte count of 11.83 (7.38) × 109/L, with a statistically significant difference in the distribution of leukocyte counts between the two groups . Left-sided endocarditis accounted for 62.7% in the cured group and 77.6% in the death group, according to the results of a cardiac ultrasound examination, and there was a statistical difference in the proportion of left-sided endocarditis between the two groups (P = 0.033). During hospitalization, 85.2% of patients in the cured group and 72.4% of patients in the death group were on antimicrobial drugs for more than 6 weeks, with a statistical difference in the proportion of time on antimicrobial drugs between the two groups (P = 0.022). The proportion of patients in the cure group who received surgery was 86.4%, and that in the death group was 24.4%. There was a statistically significant difference in the proportion of patients in the two groups who received surgery (P = 0.022).We compared the cured and death groups to see if there were any risk variables that affected the study's outcomes . PatientP < 0.1). The above variables were included in a multivariate logistic regression model, and the results indicated that age , Abnormal leukocyte count , left-sided infective endocarditis , right-sided infective endocarditis , and without surgery  were independent risk factors for mortality during hospitalization in patients with infective endocarditis (P < 0.05).We performed logistic regression by incorporating characteristics that differed significantly between the cured and death groups, as well as variables in the clinic which might influence mortality from infective endocarditis during hospitalization . Univari2 = 2.919, P = 0.232, indicating that the model fit was good.In the multivariate logistic regression analysis, factors that were statistically significant were used to create a risk prediction model for in-hospital mortality in IE, which was displayed as a nomogram . The areTo further verify the validity of the model, the calibration curve was established using R software , and theStaphylococcus aureus, Streptococcus griseus and Enterococcus are the most frequently seen in IE, accounting for about 70% of all , abnormal leukocyte count , left-sided IE , right-sided IE  and without surgery  were independent risk factors for mortality during hospitalization in patients with IE. We created a statistical prediction model based on the five variables listed above and displayed it as a nomogram . Internal validation of the prediction model using bootstrap validation method revealed that the prediction accuracy of the model was 0.852 and the kappa value was 0.53, which showed that the prediction accuracy of the model was high. In essence, the nomogram prediction model represents a scientific statistical method that can theoretically predict the probability of occurrence of any positive event in the clinic. Many oncologists currently use the nomogram prediction model to predict the prognosis of cancer patients and are also increasingly being used to predict the occurrence of clinical adverse events in non-oncology patients . One stuAmong the five independent risk factors obtained from multifactorial logistic regression analysis, the age factor was a common clinical variable affecting survival outcome, and patients with IE ≥50 years of age will have a higher risk of in-hospital death compared to those <50 years of age (OR = 2.28). As an infectious condition, IE is frequently accompanied by a rise in white blood cell counts, and individuals with severe infections will develop leukopenia as a result of bone marrow suppression. Blood screening parameters could predict adverse outcomes and in-hospital mortality in IE, according to a study by Li et al. The neutrophil-to-platelet ratio (NPR) from routine blood tests at IE patients' admission allowed risk stratification and prediction of in-hospital mortality in IE patients, and the results showed that the prediction model using NPR as the main variable performed well in predicting in-hospital mortality in IE patients. Evaluation with the ROC revealed an AUC of 0.832 . In our TTE was performed on all of the individuals, while TEE was performed on some of the patients. TTE was our major method of locating cardiac vegetation in our study. When there is cardiac vegetation on the valve, the valve function will be seriously affected, resulting in valve insufficiency and movement disorders. Furthermore, pathogens wrapped in cardiac vegetation can produce toxins and enzymes, and these toxins and enzymes can cause tissue disintegration and invasion, which can lead to serious complications, such as heart failure caused by valve reflux or fistula, and the damaged tissue cannot regenerate, resulting in irreversible heart damage . ExfoliaApproximately half of the patients with IE require surgical treatment due to severe complications . CurrentIn terms of the timing of surgery, the traditional view is that patients with IE need 4–6 weeks of regular anti-infection treatment with normal temperature and negative blood cultures before surgery to reduce the probability of postoperative recurrence. However, during this time, patients may experience severe structural damage such as valve regurgitation and perforation, and may also develop heart failure and other manifestations as a result. Furthermore, some patients may develop embolic events as a result of cardiac vegetation dislodgement during therapy, as well as an increase in-hospital mortality. As a result, a growing number of experienced groups are actively advocating for early surgery rather than waiting for complications to appear , 3, 26. In our study, 218 (74.1%) of the 294 study participants had surgical treatment. Our cardiac surgeons all used a median sternotomy surgical technique under general anesthesia, created extracorporeal circulation on a regular basis, and chose different surgical procedures depending on the patient's situation. On the basis of entirely removing the infected tissue in the heart, the principle of surgery is to reconstruct the heart structure to the greatest extent possible and restore normal heart function. One hundred and sixty five patients (75.7%) had valve replacement surgery, 36 (16.5%) had valve repair surgery, and 17 (7.8%) had congenital heart disease correction surgery. In our statistical prediction model, the absence of surgical therapy (OR = 18.94) was an independent risk factor for in-hospital mortality in IE. Patients receiving surgery had a 24.1% in-hospital mortality rate, while patients without surgery had a 75.9% in-hospital mortality rate. We can make a dynamic nomogram  based onIn conclusion, the nomogram prediction model we developed performed well, with a C-index of 0.878 (95%CI: 0.824–0.931) and the internal validation of the model by bootstrap validation method showed a prediction accuracy of 0.852 and a bootstrapped-concordance index of 0.53. The development of dynamic nomogram could facilitate the use of statistical prediction models in clinical practice. The algorithm can accurately forecast IE patients' in-hospital death rate, and doctors can use the anticipated likelihood to make treatment decisions.First, some patients were transferred to our hospital after having care in another hospital, resulting in the loss of some clinical information. In addition, this is a retrospective study, and retrospective analysis inevitably results in bias in the study design and case selection. Finally, because this is a single-center study, the epidemiology and clinical characteristics of all IE in the region cannot be accurately represented. When applied to IE patients at other centers, the statistical prediction model built on clinical data of IE patients obtained in this center may have a lower prediction accuracy. To develop the statistical prediction model, we will need to collect clinical data from IE patients from several centers.Comprehensive assessment of early clinical characteristics of patients can help doctors identify high-risk IE patients early. In this study, we found that age, abnormal leukocyte count, cardiac vegetations and the absence of surgical treatment were independent predictors of in-hospital mortality in IE patients. This Nomogram prediction model, which is based on IE patients' early clinical features and surgical options, has good diagnostic performance and clinical application potential.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of Fujian Provincial Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.Z-JY and ZD drafted the manuscript. JL and Z-JN participated in the data collection. All authors were involved in the design of the study and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Hypertension, often known as increased blood pressure, is a worldwide public health concern. Globally, ~1 billion people have hypertension and 7.1 million die from this disease. It is disproportionately more prevalent in resource-poor nations, with inadequate health systems like Ethiopia. Moreover, information on the burden of disease from hypertension in the specific area, especially in the newly organized Wolkait Tegedie zone, is essential to develop effective prevention and control strategies. Therefore, this study aimed to assess the prevalence of hypertension and associated factors among adult patients evaluated at the outpatient department of the two district hospitals in the Wolkait Tegedie zone, Northwest Ethiopia.p-value < 0.05 from the multivariable analysis were considered to have a significant association with the outcome.An institution-based cross-sectional study was conducted from September to October 2021. A systematic random sampling technique was used to select a total of 449 participants. The data were collected and then entered using EPI-INFO version 7 and exported to STATA 14 for analysis. Bivariable and multivariable binary logistic regression analyses were performed. Adjusted odds ratio (AOR) with a 95% confidence interval (CI) was used as a measure of association. Variables having a The prevalence of hypertension among adult patients in this study was 44.91% , of which 63.92% were new diagnoses. Being >60 years , having poor physical exercise , consuming cruddy oil , having a family history of hypertension , and having a history of diabetes mellitus (DM)  had a statistically significant association with having hypertension.There is a relatively high prevalence of hypertension among adult patients in the outpatient department of the two primary hospitals, Northwest Ethiopia. Factors such as being of older age, having poor physical exercise behavior, cruddy oil consumption, and family history of DM and hypertension had a positive significant statistical association with being hypertensive. Community-based screening programs for hypertension should be designed and implemented to prevent this silent killer disease. Health education and promotion that focus on healthy nutrition and physical exercise should be delivered. Globally, non-communicable diseases (NCDs) account for approximately three-fourths of all deaths , 2. HypeIn low socioeconomic countries, in addition to the big challenge of curing communicable diseases such as malaria and HIV, the increasing problem of hypertension in this region makes a double burden and a serious consequence . In resoIncreased blood pressure is one of the principal risks of life-threatening complications on vital organs such as the heart, blood vessels, kidneys, and brain, which leads to premature disability and mortality . GloballStudies showed that too much salt and fat consumption , 6, alcoTreating the complications of hypertension such as cardiac bypass surgery, stroke management, and dialysis is a costly intervention that drains individual and government budget . SignifiAn institution-based cross-sectional study was conducted from 1 September to 30 October, 2021, among patients who come to the OPD at the two primary hospitals in the Wolkait Tegedie zone. The Wolkait Tegedie zone is a newly emerging and newly organized zone in the Amhara region formed in 2021. Currently, it has 5 primary hospitals and 19 health centers. From these, ~13,000 and 8,000 patients are expected to visit adult OPD per year in the Kiraker and Nigus Ketema primary hospitals, respectively.The source population for this study was all the adult patients who come to the OPD of the two primary hospitals. Those patients who were available at the time of data collection were considered as the study population. However, adult patients who were unable to communicate and those with severe psychiatric problems were excluded from the study.The sample size for studying the prevalence of hypertension and associated factors was determined using both the precision approach and the power approach. The precision approach was calculated using a single-population proportion formula by considering a prevalence of 28.3% from the study done in the nearest setting , 95% conn = 449).Therefore, from the above alternatives, the highest sample size means the sample derived based on the power approach Kelsey method for variable underweight was used . Then, using systematic random sampling and this kth interval, each hospital sample was selected. Finally, a total sample of 432 patients (96.1%) responded and were included in this study  were selected using a simple random sampling lottery method from the total of five primary hospitals found in the Wolkait Tegedie zone. The 2 months' (data collection period) expected case fellow status in the OPD of each hospital was 2,166 cases/2 months for the Nigus Ketema primary hospital and 1,334 case/2 months for the Kiraker primary hospital. Therefore, by using the proportional allocation method to select the representative samples from each hospital, 278 from Nigus Ketema primary hospital and 171 from Kiraker primary hospital were selected as the final candidate samples. Then, we calculated the interval for selecting the sample, is study .The dependent variable of this study was having hypertension. It is defined as when the patient has reported regular use of antihypertensive medication (s) or persistently high blood pressure (systolic blood pressure (SBP) ≥ 130 or diastolic blood pressure (DBP) ≥ 80 mmHg) . In contDefined as when the patient has reported regular use of antihypertensive medication (s) or having an average of two times measurement of high blood pressure (SBP ≥ 130 or DBP ≥ 85 mmHg) .Calculated as weight in kilograms divided by height in square meters and interpreted as underweight (BMI < 18.5), normal (18.5–24.9), overweight (25.0–29.9), and obese (≥ 30.0) .Data were collected using pretested and structured interviewer-administered questionnaire and from a chart review. The data were collected by nurses who have a BSc degree and working in the OPD and supervised by the principal investigator. The interview questionnaire was structured into three sections, namely, sociodemographic characteristics, behavioral factors and medical-related questions, and measurements.The participants' weight and height were measured and recorded by the interviewers after the interview by following the standard steps. Blood pressure was measured using a standard mercury sphygmomanometer BP cuff with the appropriate cuff size. It was measured two times in a sitting position after reassuring the participant no smoking or caffeine 30 min before measurement and rested for at least 5 min. The second measurement was taken 10 min after the first measurement. Finally, the average of two BP measurements was calculated to determine the BP status of the participant.The quality of the data was ensured through training of data collectors and supervisors, close supervision, and prompt feedback. The training consisted of instruction on interview techniques as per the prepared tool. We used standard instruments. The accuracy of the measurement instrument was checked before the beginning of each data collection session by the principal investigator and/or data collectors. The data were checked for any inconsistencies, coding errors, out of range, completeness, accuracy, clarity, missing values, and appropriate corrections were made by the principal investigator and the supervisor consistently on a daily basis.p-value of < 0.2 in the bivariable analysis were entered into the multivariable analysis. Both the crude odds ratio (COR) and adjusted odds ratio (AOR) with 95% CIs were estimated to show the strength of associations. Finally, a p-value of < 0.05 in the multivariable logistic regression analysis was used. For this study, the Hosmer and Lemeshow goodness-of-fit test was used to assess whether the necessary assumptions for the application of multiple logistic regression were fulfilled and whether it was non-significant.The survey data were entered into the EPI-INFO version 7 and analyzed using STATA 14 software. Descriptive statistics are presented using texts, graphs, and tables. A binary logistic regression model was used to identify the factors affecting the prevalence of hypertension. Both bivariable and multivariable logistic regression models were carried out. Variables with a Ethical approval was obtained from the University of Gondar Institutional Ethical Review Board Committee. A support letter was obtained from the University of Gondar Research and Community Service for the respective hospitals, and permission was obtained from two executive directors of the hospitals. Participants were informed about the purpose, objectives, and their right to participate and not to participate in the study. Privacy and confidentiality of the study participants were ensured by not using a personal identifier, and data were collected in private conditions individually. Written informed consent was obtained from the study participants.2. One-tenth (10.19%) of the participants were overweight. Approximately 55 (12.76%) of patients had preexisting hypertension . About a quarter (25.23%) of study subjects were found to be aged > 60 years, with a median age of 46  years. More than three-fourths (78.24%) of the patients were from rural residences, and 304 (70.37%) patients had a low monthly income. The mean BMI of respondents was 20.63 (± 3.13 SD) kg/mrtension .The prevalence of hypertension among patients evaluated at the OPD in Kiraker and Nigus Ketema primary hospitals in northwest Ethiopia was 44.91% . Of the total of 194 patients with hypertension found in the two hospitals, 124 (63.92%) were newly diagnosed, whereas the remaining 79 (36.08%) were having preexisting hypertension.The prevalence of hypertension was more prevalent among old-age patients (age > 60) (57.8%), patients who consumed crude oil (60.09%), and patients who were doing poor physical exercise (53.95%) but did not have a significant difference between sex .p-value of ≤ 0.2 in the simple logistics regression analysis were considered for multiple logistics regression analysis. But variables such as the marital status of the patient with a p-value > 0.2 were excluded from multivariable analyses. Moreover, out of those variables considered for multivariate analysis, age of the patient, physical exercise behavior of the patient, types of dietary oil consumption, having a history of DM, and having a family history of hypertension were statistically significantly associated with having hypertension.All variables were analyzed using simple (bivariable) logistic regression analysis to assess the association between each variable having hypertension. Then, variables having a Patients >60 years old were likely to have nearly two times higher chance of hypertension compared to patients 18–40 years old . The odds of having hypertension among patients who were doing poor physical exercise were 1.74 times higher than in patients who were doing their physical exercise rigorously . Patients who consumed cruddy oil were 15.22 times higher to have hypertension compared to those patients who did not consume oil for dietary consumption .Patients with a family history of hypertension were 13 times more likely to be hypertensive compared to patients without a family history of hypertension . Moreover, patients with a history of DM were eight times more likely to have hypertension compared to those without a DM history  .Globally, hypertension is among the leading causes of mortality . It is tIn our study, patients aged >60 years were found to be with nearly two times higher chance of having hypertension compared to patients who were aged 18–40 years. This is supported by a systematic review and meta-analysis , a commuThe chance of having hypertension among patients doing poor physical exercise was higher than in patients doing physical exercise rigorously. This is supported by a community-based study in Gondar, Ethiopia . StudiesPatients who consumed cruddy oil were considered to have hypertension compared to those patients who did not consume oil for dietary consumption. This is because crude oil ingestion causes abnormality in lipid profile and the risk of incidence of hypertension . Crude oPatients with a family history of hypertension were more likely to be hypertensive compared to patients without a family history of hypertension. This is supported by a study in Gimbi, Ethiopia , southweMoreover, patients with a history of DM are more likely to have hypertension compared to those without a DM history. This is supported by a systematic review and meta-analysis , a commuThe strengths of this study come from the use of a relatively large sample size and two district hospitals compared to the previous study conducted in Ethiopia, which makes it representative of populations of study settings. Therefore, it can be generalized to all patients in the Wolkait Tegedie zone during the study period.This study has some limitations. Since it is a cross-sectional study, causality with explanatory variables cannot be ascertained. We did not include biochemical measurements such as serum glucose level and a 24 h urine sodium concentration.There is a relatively high prevalence of hypertension among adult patients in the Kiraker and Nigus Ketema primary hospitals, northwest Ethiopia. Factors such as being older age, having poor physical exercise behavior, having cruddy oil consumption, having diabetes mellitus, and having a family history of hypertension had a positive significant statistical association with being hypertensive. Community-based screening programs for hypertension should be designed and implemented to prevent this silent killer disease. Health education and promotion that focus on healthy nutrition and physical exercise should be delivered. Trained health coaches are necessary per household of each patient with hypertension in the prevention and continued risk for stroke.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of Gondar CMHS ERB. The patients/participants provided their written informed consent to participate in this study.Conceptualization, formal analysis, methodology, and supervision: AK, DB, HF, MM, and HA. Data curator and investigation: AK, DA, EM, and DB. Resources: AK, DA, EM, HF, MM, and HA. Software: AK, DB, HF, MM, HA, and EM. Validation: AK, DB, GT, EG, and EM. Visualization and writing original draft: AK, HF, MM, HA, GT, EG, and DB. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables  to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC).N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts.A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables  had a better prognostic performance than the clinical nomogram both in the training cohort  and in the validation cohort . The calibration curves demonstrated optimal alignment between the prediction and actual observation.Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients’ OS (HR = 3.913, The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting. Lung cancer is one of the most common types of cancer and a major cause of mortality worldwide for both men and women . Non-smaThe tumor, node, and metastasis (TNM) staging is the most commonly used and universally accepted staging  system for cancer. TNM staging is simple to apply and can be highly discriminatory for survival . TNM staRecently, some studies have demonstrated the relations between lung cancer-related genes of tumors and survival prognosis, which can be used to improve the predictions from the traditional TNM staging strategies . The dirNowadays, with the help of non-invasive techniques and the ability to extract high-precision information, medical imaging becomes a clinical routine for diagnosis and prognosis . As an eIn the case of lung cancer, medical images reflecting features of tumors usually rely on radiological data . At present, it is a common clinical practice for lung cancer patients to undergo CT examinations in order to identify tumor size and location. Features from CT can be used to predict the malignant potential of a nodule on a chest CT based on the correlations between them , 12. In p < 0.05)  and response to immunotherapy in NSCLC  . Khorramin NSCLC . Yang etNomograms are generally accepted as a useful and reliable tool to evaluate risk and predict individualized cancer prognosis . Our teaA total of 1,524 patients with clinical data and pretreatment chest CT images at the Weihai Municipal Hospital during January 2013 and May 2018 were identified in this study. The inclusion criteria were defined as follows: (1) patients were histopathologically confirmed with NSCLC either by surgical specimen or by preoperative biopsy, (2) patients who received non-contrast-enhanced CT scans during the diagnosis, (3) patients aged ≥18 years, and (4) patients not diagnosed with lung cancer or other types of malignant tumors in the past 5 years. The exclusion criteria were defined as follows: (1) CT images were too blurry to identify the patient’s tumor area, and (2) eligible variables were incomplete. The study protocol was conducted under approval by the Public Health Ethics Committee of Shandong University . The requirement for informed consent was waived because of the retrospective nature of the study.The exposure information consisted of two parts: the clinical data and the assessment of CT scans for each patient. In our study, clinical data were collected from electronic medical record and examination data by oncologists  and a data manager (ZW). The following variables were collected: the sociodemographics of the patients , the presence of any comorbidity , and pathologic data .Smoking status was categorized as never, former, and current smoking. Drinking status was categorized as yes and no. Comorbidities were defined according to the International Classification of Diseases, 10th Revision (ICD-10) code: CODP, J40–J44; hypertension, I10–I15; diabetes, E10–E14; CHD, I20–I25; and cerebrovascular disease, I60–I69. The location of the tumor was categorized as the right upper, right middle, right lower, left upper, and left lower. Pathologic staging was defined in accordance with the eighth edition of the American Joint Committee on Cancer TNM staging system (The radiomics workflow is presented in www.slicer.org), a free open-source software   . Each CTMD, USA) . An ICC https://www.python.org/) package that provides many options to customize extracting the radiomics features from CT images  first-order features, (2) shape features, and (3) texture features , a free open-source python  to the date of death, recorded We randomly assigned 70% of the patients to the training cohort, and 30% to the validation cohort. The training cohort was used to develop the model while the validation cohort was used to qualify the performance of the model. N, %) were reported for categorical variables. Quantitative baseline variables were compared with t-tests. Categorical variables were compared by performing the chi-square tests between groups.Means ± standard deviations (SDs) or medians [interquartile ranges (IQRs)] were reported for quantitative variables. Frequencies and proportions  Cox regression model, which was an effective attractive method for high-dimensional data in survival analysis, to select the optimal nonzero coefficient features in the training cohort in order to reduce model overfitting . The mos cohorts . In addip-value was less than 0.05 in the univariable analysis were evaluated using the Kaplan–Meier method and then were integrated into a multivariable Cox regression model to identify independent prognostic factors. The final multivariable Cox regression model was constructed using the stepwise backward variable selection process based on the maximum Akaike information criterion (AIC)  . The corFinally, the radiomics nomogram for predicting the OS was established based on the multivariable Cox regression model by combining the radiomics signatures and all the independent clinical risk factors derived from the clinical model. To test the robustness of the risk factors in the radiomics nomogram, we also did the stepwise Cox regression with all the risk factors  to see if the same risk factors remained in this time. The C-index and calibration curve were calculated to evaluate the validity of the established radiomics nomogram . To furtThe incremental value of the radiomics signatures to the clinical factors was evaluated by comparing the performance of the radiomics nomogram derived in this study with the clinical model in respect of discrimination (C-index) and calibration . The calibration curves were constructed to show accordance between nomogram-predicted survival probability with the observed survival probability using 1,000 bootstrap resamples . The calhttp://www.r-project.org) and a two-tailed p-value less than 0.05 was regarded as statistically significant. The LASSO Cox regression was performed using the R package glmnet. Stepwise AIC was implemented using the R function “step”, and the nomogram was built using the “rms” package.All statistical analyses were performed using the R software  and squamous cell carcinoma (N = 235) accounted for approximately 98% of patients, while other histology types, such as adenosquamous carcinoma and large cell lung cancer, were present in 2% of patients. Stage distribution of patients was listed as follows: stage IA = 666, stage IB = 220, stage IIA = 42, stage IIB = 125, stage IIIA = 167, stage IIIB = 71, stage IIIC = 15, and stage IV = 174. The observed numbers of deaths were 397 of 1,480 patients. Median follow-up time was 4.06 years . The median age at diagnosis was 61 years. Approximately one-third of the patients had a history of smoking and COPD. We randomly divided the data into training (N = 1036) and validation cohorts (N = 444). The clinical characteristics are summarized in Among 1,524 patients, 44 patients (2.89%) were excluded because they were missing T stage (23 patients) and N stage (37 patients), respectively. Overall, there were 1,480 patients histologically confirmed with NSCLC with complete information in both clinical and CT image data in our study. All the 1,480 patients enrolled had complete follow-up information. Among them, adenocarcinoma  and the validation cohort . Additionally, the performance of radiomics signatures for predicting OS for NSCLC was evaluated using the Cox regression model. The radiomics signature yielded a C-index of 0.808 (95% CI: 0.784–0.831) on the training cohort and 0.820 (95% CI: 0.786–0.853) on the validation set . The consensus result was found in the validation cohort.Furthermore, the patients were stratified into low-risk and high-risk groups in terms of the median value of the radiomics signature (−0.716). The Kaplan–Meier method was performed in the training and validation cohorts to analyze the association of the radiomics signature with OS in NSCLC patients   and 0.854 (95% CI: 0.824–0.884) in the training cohort and validation cohort, respectively. The C-index from the radiomics nomogram yielded 0.861 (95% CI: 0.843–0.879) in the training cohort and 0.868 (95% CI: 0.841–0.896) in the validation cohort Table 2.Lung cancer is the world’s leading cause of cancer death. Screening for lung cancer by low-dose computed tomography reduces mortality. The NSCLC TNM staging system was developed by the International Association for the Staging of Lung Cancer (IASLC) Lung Cancer Staging Project by a coordinated international effort to develop data-derived TNM classifications with significant survival differences. Based on these TNM groupings, current 5-year survival estimates in NSLCC range from 73% in stage IA disease to 13% in stage IV disease. TNM stage remains the most important prognostic factor in predicting recurrence rates and survival times, followed by tumor histologic grade, and patient sex, age, and performance status . HoweverN = 1,036) and validated in the test dataset (N = 444). The model showed good discrimination, which was indicated by the C-index over 0.86 in both the training cohort and the validation cohort. The calibration curves for probability of OS demonstrated good agreement between the prediction by the radiomics nomogram and the actual observations. Risk group stratification further guaranteed the prediction power of the established model, and also confirmed the reliability of our results.We have developed a radiomics nomogram for predicting the OS of patients with NSCLC in an Eastern Chinese population. The prediction model included the radiomics signatures derived from the CT images using the LASSO Cox regression, and six traditional clinical factors, namely, age, sex, COPD, T stage, N stage, and M stage. The model was developed in the training cohort (p < 0.001), and could be applied to a wider range of NSCLC. Moreover, researchers have found that COPD could be a driving factor in lung cancer by increasing oxidative stress and causing DNA damage, inhibiting DNA repair mechanisms, and increasing cell proliferation  were extracted from CT images than the previous study, which led to deeper mining of medical imaging; (2) these studies usually employed methods such as the Kaplan–Meier method to clarify the correlation between radiomics features and prognosis. In this study, we used the tenfold cross-validation LASSO Cox model to select the optimal features from 1,288 radiomics features, which contains first-order statistical features, shape-based features, statistical-based texture features, and Gaussian and wavelet information that could improve the stability of the radiomics model. Eleven features were used to construct the radiomics signatures. Our team had successfully established the radiomics nomogram as a preoperative prediction tool for malignant pulmonary nodule diagnosis. The validation results showed that the nomogram has good discrimination (C-index = 0.809) and calibration capacities, which indicated its clinical application in the early screening of lung cancer  was larger than the previous study; (4) the clinical information was thorough compared with the previous study. We included the sociodemographics of the patients , the presence of any comorbidity , and pathologic data  to obtain the optimal prediction clinical variables. Six independent prognostic factors  were identified by the multivariate Cox model based on the AIC criteria for the best combination to predict the OS and entered into the nomogram; (5) the radiomics signature was an independent prognostic factor and outperformed clinical features in predicting OS of NSCLC patients. Our model yielded a higher C-index of 0.868 (95% CI: 0.841–0.896) in the validation cohort. The performance of our model was measured in a number of ways including calibration and the risk stratification analysis.The advantages of our model compared with the other models in predicting NSCLC are listed as follows (1): more comprehensive quantitative features (g cancer . (3) TheIn this retrospective study, the data were collected from the Weihai Municipal Hospital located in East China. The characteristics of this Eastern Chinese lung cancer population differ considerably from other, particularly Western, lung cancer populations. On average, people from East China are richer than those in Central and West China, and thanks to widespread screening programs, residents are more willing to undergo routine physical examinations, which is helpful for the early diagnosis of lung cancer. There is a substantial portion of our patients (~60%) who belong to stage I, which inevitably decreases the death rate for the case mix. As shown in p-value = 0.002 in validation cohort), the improvement of OS prediction was not very large. Considering that, to some extent, the important prognostic factors  have already been defined. On the other hand, the prediction accuracy of the radiomics signature alone is just slightly lower than the clinical model (C-index: 0.820 vs. 0.854). Therefore, the radiomics signature could also serve as an alternative or auxiliary methodology for the clinical model and confer benefits for the oncologist’s decisions.In our study, though the radiomics nomogram that integrated the radiomics signature and clinical variables outperformed the clinical model alone  and obtain a more precise performance of our model. Secondly, we did not include the treatment variables  in our study. We hope to include these variables to improve the model performance in a future study. Our model performs well in the absence of treatment information (C-index over 0.86), which also proves the importance of radiomics. Third, the follow-up time was not long enough to obtain each patient’s end point, indicating the heterogeneity of tumor development. Further efforts on patient follow-up are encouraged to improve our model. Finally, this study collected data from a single center; although we divided the validation set to evaluate the stability of model, it is obvious that data from multicenter cohorts and different populations are better. Therefore, further multicenter studies are encouraged to promote the model generalization and improvement. Future prognostication of outcomes in NSCLC will likely be based on a combination of general condition, radiomics, TNM stage, treatment, and molecular tumor profiling, yielding more precise, individualized survival estimates and treatment algorithms.In summary, we have developed a radiomics nomogram that combines the optimal radiomics signature from CT images with the TNM staging and other clinical information , showing a significant improvement in predicting OS compared with clinical predictors alone. This nomogram should be validated in other Eastern Chinese populations with NSCLC and in other localities, as this work indicates that the radiomics signature increases the precision of survival prediction.The original contributions presented in the study are included in the article/The study protocol was conducted under approval by the Public Health Ethics Committee of Shandong University , The requirement for informed consent was waived because of the retrospective nature of the study. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.LW and AL drafted this manuscript. AL, ZW, NX, DZ, GL, and XG collected the data. LW, ZW, and TQ analyzed the data. WC and FX conceived the study and participated in its design and coordination. JW and FY contributed to the interpretation. All authors contributed to the article and approved the submitted version.This work was supported by Key R & D project of Shandong Province (2018GSF118152), the National Key Research and Development Program of China (2020YFC2003500), the National Natural Science Foundation of China (81773547), and the Natural Science Foundation of Shandong Province (ZR2019ZD02).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.We would like to thank all authors, reviewers, and editors for their critical discussion of this manuscript, and apologize to those not mentioned due to space limitations.