Metabolic Syndrome increased the risk of developing WTC-lung injury (WTC-LI). We aim to attenuate the deleterious effects of WTC exposure through a dietary intervention targeting these clinically relevant disease modifiers. We hypothesize that a calorie-restricted Mediterranean dietary intervention will improve metabolic risk, subclinical indicators of cardiopulmonary disease, quality of life, and lung function in firefighters with WTC-LI. To assess our hypothesis, we developed the Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE), a randomized controlled clinical trial (RCT). Male firefighters with WTC-LI and a BMI > 27 kg/m2 will be included. We will randomize subjects (1:1) to either: (1) Low Calorie Mediterranean —an integrative multifactorial, technology-supported approach focused on behavioral modification, nutritional education that will include a self-monitored diet with feedback, physical activity recommendations, and social cognitive theory-based group counseling sessions; or (2) Usual Care. Outcomes include reduction in body mass index (BMI) (primary), improvement in FEV1, fractional exhaled nitric oxide, pulse wave velocity, lipid profiles, targeted metabolic/clinical biomarkers, and quality of life measures (secondary). By implementing a technology-supported LoCalMed diet our FIREHOUSE RCT may help further the treatment of WTC associated pulmonary disease. Fire Department of New York (FDNY) rescue and recovery workers exposed to World Trade Center (WTC) particulates suffered loss of forced expiratory volume in 1 s (FEV Metabolic Syndrome (MetSyn) is defined as having at least three of five risk factors  that increase the likelihood of cardiovascular disease . MetSyn 1) was an independent predictor of MetSyn [1 less than the lower limit of normal (LLN).Nearly half of chronic obstructive pulmonary disease (COPD) patients demonstrate the presence of MetSyn phenotypic characteristics ,13,14. Sf MetSyn ,11. Our Although one major contributor to poor lung mechanics is from mechanical load-induced stress secondary to abdominal obesity, our prior work shows near equivalent contribution from dyslipidemia in those with WTC-LI ,10,11. I1 and forced vital capacity (FVC) by as much as 22 percent in as little as 15 days [1 by 73 mL [2 also had increased FEV1 and FVC [To investigate the potential reversibility of WTC-LI by direct impact on MetSyn risk factors, we focus on calorie-restricted (CR) Mediterranean diets based on recent studies showing their ability to attenuate lipid levels ,23,24. M 15 days ,26. Usinby 73 mL . It has  and FVC .Technology-supported behavioral interventions have shown enhanced data collection and improved adherence to diet plans ,30. Mobi1, fractional nitric oxide (NO) concentration in exhaled breath (FeNO), pulse wave velocity (PWV), lipid profiles, targeted biomarkers and quality of life measures.In the context of our prior findings, as well as the previously discussed literature linking nutritional modification to health benefits, we have chosen to explore the therapeutic potential of ameliorating MetSyn phenotype in our WTC-exposed cohort ,10,11,212) FDNY firefighters with documented WTC-LI (defined as FEV1 < LLN post exposure). While overweight is defined as having a BMI of 25.0 to <30.0 kg/m2, and subjects are considered obese if there BMI is ≥30.0 kg/m2 [2. This was by design since our power analysis and primary outcome assessment was targeting at least 1–2 units of BMI change in the intervention group [FIREHOUSE is a 6-month randomized, controlled, two-arm, parallel, un-blinded, exploratory clinical trial, with 1:1 allocation, in overweight -Institutional Review Board, Protocol 17-00127.1 < LLN after exposure, Inclusion and exclusion criteria for study enrollment detailed in Participant recruitment will include: (1) direct mailings; (2) telephone contact; (3) email; (4) self-referral. Recruitment efforts will be supported by a study website that provides general information about the study and answers frequently asked questions. The website will also facilitate study activities during the intervention period.Participants will be randomized to either Usual Care (control arm) or LoCalMed diet plus technology-assisted nutrition education and behavior modification (intervention arm), N = 70. In addition to standard care from their WTC-HP primary provider, participants in the Usual Care group will receive newsletters every five weeks during the intervention from study staff related to: (1) mindfulness; (2) sleep; (3) staying healthy year-round; (4) disease self-management. As an added incentive for remaining in the study, Usual Care participants will be provided access to educational and behavioral materials upon completion of their 6-month measurement visit. The LoCalMed group will be enrolled in a 6-month, technology-supported, educational-behavioral program designed to promote a calorie-restricted, Mediterranean-style diet and regular, moderate-intensity physical activity. At baseline, participants will be provided in-person, group-based technology training, and will receive handouts with the following study goals: (1) weight loss of ≥seven percent at 6-months; (2) saturated fat intake ≤seven percent of kcal; (3) ≥150-min per week of moderate-intensity physical activity. After the initial technology training session, all intervention-related activities will be delivered remotely via smartphones. The LoCalMed program will consist of two main parts: (1) mobile self-monitoring with feedback; (2) remote group-based education and behavioral counseling.The Usual Care group serves as a control (reference) arm, with target enrollment of Participants will be provided a 6-month subscription for the commercial diet diary application MyNetDiary  and directed to record their body weight weekly, all food and drink consumption, and any physical activity as it occurs. If needed, participants are provided with a digital bathroom scale for monitoring body weight during the intervention.Self-monitoring of health behaviors  and outcomes  is thought to promote behavioral change in part by bringing these factors to individuals’ conscious attention, thereby enhancing vigilance. In addition to providing a digital platform for self-monitoring, MND generates real-time feedback on nutrient intakes across foods, meals, and meal-days, as well as daily and weekly summaries of recorded physical activities. The multi-level organization of data in MND assists with information processing, which may reduce information burden that can lead to oversimplified heuristics (aka “food rules”) and burnout. To further limit information burden, MND nutrient output will be restricted to track only calories and macronutrients . MND summary reports are updated with new entries, they will permit ecological momentary assessment (EMA) of behaviors at the time, which is thought to augment the effectiveness of self-monitoring.Adding to the automated feedback provided by MND, the study dietitian will review the participants’ MND accounts weekly for the first four weeks and every second week thereafter during the intervention, and they will provide feedback reports via the participant’s personal (or a study-provided) email account. Reports will contain three parts: (1) adherence to self-monitoring; (2) body weight; (3) saturated fat intake. Feedback on saturated fat is graded as green , yellow  or red , and it includes tailored advice on restricting saturated fat based on participants’ MND records. Within the program, reported body weights are plotted on a personalized weight loss trajectory line graph that is modeled with 95 percent confidence intervals to achieve a seven percent weight loss over six months . In our Participants will receive group-based education and behavioral counseling (~1-h) each week for the first four weeks, and every other week thereafter during the intervention via the videoconferencing application, WebEx . Group sessions are facilitated by the study dietitian, and anchored by brief animated videos , Education videos in Behavioral counseling and videos are based on social cognitive theory (SCT), which focuses on the roles of self-referent thought and self-efficacy  in adopting and maintaining healthy behaviors. According to SCT, self-efficacy can be derived from four factors: (1) mastery experiences: goal-setting, prior successes, and problem-solving; (2) social modeling: learning by observing others; (3) verbal persuasion: encouragement from others; (4) physiological benefits: improvements in health outcomes , 2 depending on the individual’s height.Primary Outcome Assessment is a seven percent decrease in total body weight with a subsequent drop in BMI of roughly 1–2 kg/m1 after trial intervention compared to control subjects, as well as an increased lean body mass relative to fat percentage on body-impedance analysis (BIA), reduction in PWV and FeNO, and improved perceived health status as measured by the Short Form 36 (SF-36) and the St. George’s Respiratory Questionnaire (SGRQ), which are both validated quality of life assessment tools available for public use. Details of the primary and secondary endpoints of the FIREHOUSE trial are shown in Secondary outcome measures include a 7–10 cc increase in FEVAdditionally, we will collect clinical measurements such as routine vital signs, electrocardiograms (ECGs), routine chemistries, complete blood counts , liver function tests, and blood lipid profiles. Furthermore, we will collect saliva and stool samples for genomic  and microbiomic analysis , respectively.Primary statistical modeling will be performed based on the change in outcomes from baseline to 6-months post-intervention between the two randomized groups. Descriptive analysis of all data collected, using typical graphic and numerical exploratory data techniques, is planned. An intention to treat approach will be used to avoid bias from noncompliance and missing outcomes for primary analyses of the treatment effect between two groups. Regression models will be used for modeling treatment effects while adjusting other covariates. Post hoc multiple comparisons tests are applied to adjust for pairwise comparisons.The primary objective is to compare the change in BMI in the two groups from pre-intervention to post-intervention after six months (intervention completion). The analysis for this primary objective will be based on the multivariate linear regression with adjustment for potential confounders. The difference for the change of BMI between the two groups will be estimated, and a corresponding 95 percent confidence interval will be calculated.2 and the means of two groups “Usual Care” and “LoCalMed” to be 0 and −1 kg/m2, respectively. Furthermore, 6-month interventions with similar calorie targets and behavioral modification with exercise produce a 7–10 percent weight loss [The sample size of the FIREHOUSE trial was estimated by the power analysis for the primary outcome of the change in BMI predicted between the start and end of the intervention, which was based on a two-sample test to compare the outcome between two groups under 1:1 randomization. According to the preliminary data, we assumed SD = 2 kg/mght loss ,38. A toThe interim analysis plan will incorporate two interim looks and one final look, with all looks named as formal interim analysis, Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and has since developed into a pandemic. During the FIREHOUSE RCT’s enrollment period, New York State emerged as an epicenter of the global health emergency ,47,48,49In response to this novel respiratory pathogen, NYU paused face-to-face clinical research for many studies in order to assess, address and minimize any potential COVID-19-related risks. This impacted our in-person baseline visits as well as final study visits. Meetings with our nutritionist continued for subjects already enrolled since these were designed to be on a video platform.Once safe to resume these activities, we implemented a COVID-19 pre-visit screening with subjects. This “Research Participant Information Sheet: COVID-19 Updates” informs both newly screened/enrolled subjects as well as subjects returning for their follow-up evaluation about the safeguards that have been put into place to protect against the risk of COVID-19 transmission while restarting certain in-person research activities.Specifically, subjects were informed that since some study visits need to be conducted in person, participation in these study activities may increase their risk of exposure to the COVID-19 virus. We also discussed that NYU, as with nearly all public venues, has implemented a number of safety policies to protect subjects and to reduce exposure risk. These policies include, among other requirements, screening all persons involved in research visits (staff and study subjects), use of Personal Protective Equipment (PPE), following Social Distancing guidelines when possible, and cleaning and sanitizing rooms and equipment. These protective measures are similar to those most doctors’ offices have implemented in response to COVID-19.This participant information document also informs subjects that their participation is completely voluntary. Additionally, subjects are provided with the Principal Investigator (PI)’s contact information and given the opportunity to ask any questions that may arise. A copy of this information sheet is also provided to the patient.Due to the pause, the final study visit timeframe, to the extent to which it could be performed, was extended for at least three additional months. Since spirometry and FeNO assessment may lead to aerosolization, we also suspended their assessment for the purposes of research during the COVID-19 pandemic.The impact on quality of life and financial burden of WTC-LI are public health concerns. Despite treatment, new cases continue to be identified, and those affected continue to experience morbidity. Our work fits into broader literature that explores the association of poor metabolic health and OAD in those exposed to pollution. Although the mechanisms that lead to lung disease in the context of pollution and metabolic syndrome remain poorly understood, we have shown that modifiable risks such as BMI and dyslipidemia can predict development of WTC-LI. In the FIREHOUSE RCT, we will investigate dietary intervention as a treatment of WTC-LI.Our prior investigations, from which the FIREHOUSE RCT stems, allowed us to discover and validate a metabolomic signature of WTC-LI, while investigating the effects of confounders found in the entire FDNY cohort such as smoking, obesity, and diet. Since dietary habits have a direct relationship to MetSyn and the metabolome, we propose to alter the subjects’ nutritional status using a Mediterranean style diet.1 [Our current WTC portfolio lacks insight into dietary patterns and quality of life measures as they pertain to metabolic risk. Furthermore, it lacks direct vascular injury measures and assessments of FeNO and PWV. In the context of our preliminary findings, and due to a clinical need, we propose an RCT to assess the effect of dietary modification on BMI, lung function, vascular injury, and quality of life measures. We measure PWV, a marker of central aortic stiffness, which has implications in cardiovascular disease ,52,53,541 ,60. We h1 . FeNO, a1 .We propose to sample, bank, and measure serum and plasma for biomarker assessment before and after the dietary intervention. Biomarkers are powerful predictors of disease that are useful in defining the efficacy of our intervention. This biorepository will not only allow us to complete and evaluate our current clinical trial but will be valuable to future epigenetic studies of WTC disease. Assaying biologically relevant biomarkers allows for the identification of relevant pathways involved in WTC-LI and helps guide therapies targeting these mediators. We will also obtain peripheral blood mononuclear cells from collected blood that will allow us to assess the genomic and microbiomic signatures pre/post intervention.There are limitations to this study, and a few will be discussed in this protocol paper. First, the study population is male and largely Caucasian, which may limit generalizability. To limit bias in this un-blinded study, we will randomize after visit one. However, there is a chance that the main research team (not including the nutritionist and technology support providers) will be privy to subject identities and experimenter bias. Given the tightly knit nature of the FDNY community, we also surmise that there could be some sharing of information regarding the protocol between participants in opposing treatment arms. Furthermore, as a behavioral and educational intervention, there is marked variability among individuals with regard to technologic savviness, educational background, willingness to alter diet and exercise, social support, economic means for a healthier diet, and physical ability to exercise. This variability will be equally prevalent in both groups. Similarly, subjects unwilling to participate fully will not pass the inclusion criteria.The FDNY cohort is highly motivated to improve their disease burden and quality of life. Rescue workers receive routine screening and have an excellent recall of at least 90 percent in prior clinical studies. In addition, there is a strong commitment at the FDNY for this proposed collaborative NYU/FDNY effort to improve the health of these brave individuals. Many participants are aware of our previous findings and have asked for ways to modify their lifestyle with hopes of changing disease outcomes. Hence, it is logical to provide this much-affected cohort with interventions that they may benefit from. This intervention, once validated, may yield meaningful findings for populations exposed to ambient and other occupational particulates.


Hypothesis. CTO in the non-IRA is an independent predictor of poor long-term prognosis in patients with AMI.  The prognostic significance of CTO in the non-IRA in patients with AMI has been under dispute. Relevant long-term follow-up studies are lacking.  We prospectively enrolled 2336 patients with AMI who received emergent percutaneous coronary intervention successfully from January 2006 to May 2011. Our primary endpoints included death from cardiovascular causes, recurrent myocardial infarction, stroke, and target-vessel revascularization. We adopted Cox regression analysis adjusted for confounders to analyze the impact of CTO in the non-IRA on long-term mortalities. P=0.004). CTO in the non-IRA independently predicted 11-year MACCE in the male AMI subgroup  and in the male NSTEMI subgroup . In the CTO group, three-vessel disease independently predicted 11 year MACCE .  We identified 628 (27.6%) subjects with CTO in the non-IRA among 2282 AMI patients. After a mean follow-up duration of 134.3 months, we found the CTO group had significantly higher MACCE rate than the group without CTO (30.4% versus 24.3%,  Our long-term observational study supported the association between CTO in the non-IRA and poorer prognosis in AMI patients undergoing primary PCI. We identified the group with the three-vessel disease as a high-risk subgroup in patients with CTO in the non-IRA. Acute myocardial infarction (AMI) is the primary cause of death in patients with coronary heart disease (CHD). The introduction of percutaneous coronary intervention (PCI) has identified chronic total occlusion (CTO) of the noninfarct related artery in about 10–30% patients with AMI . OpinionThe Ethics Committee in our institution has approved our study. From January 2006 till May 2011, we prospectively enrolled patients diagnosed with AMI who received emergent PCI within 24 hours after infarction successfully at our emergency department. We diagnosed STEMI if patients exhibited symptoms of myocardial ischemia with elevated serum troponin value ≥ two times the upper limit of normal and evolving electrocardiographic (ECG) ST-segment elevation more than 1 mm (0.1 mv) or new left bundle branch block in more than two contiguous leads. We considered NSTEMI when the patient manifested symptoms suggestive of myocardial ischemia with elevated serum troponin value ≥ two times the upper limit of the normal range. Furthermore, we confirmed the diagnosis of AMI through the emergent coronary angiography for all patients. We excluded patients with AMI with previous coronary bypass surgery to avoid the potentially confounding impact of different vessel anatomies. We also rejected patients with unsuccessful primary PCI or cardiogenic shock on admission because these factors could contribute to the mortality tremendously and might influence our analysis. Apart from signing standard informed consent regarding both the disease and the interventional procedure, all patients have agreed to participate in the study after they were fully informed about the follow-up plan. We recorded their baseline characteristics, including age, sex, ID number, contacts information, relevant risk factors, smoking habits, left ventricular ejection fraction measured by echocardiography, medications, and lab test results such as hemoglobin levels, cardiac enzymes, and liver and kidney functions both before and after the procedures. Before the intervention, we prescribed 300 mg aspirin and 300 mg clopidogrel or 180 mg ticagrelor to all patients as the loading doses.Experienced interventional cardiologists on duties performed the emergent coronary angiographies. They chose different arterial puncture sites, guidewires, catheters, inflation balloons, coronary stent types, and intravascular ultrasound or glycoprotein IIb/IIIa inhibitors based on the technical standards of the time and ongoing clinical conditions. We adopted the Thrombolysis in Myocardial Infarction (TIMI) scale for assessing coronary blood flow. IRA was determined according to the ECG manifestations and the typical angiographic images. We defined multiple vessel disease (MVD) as ≥70% diameter stenosis in one or more major epicardial arteries or major branches. We considered CTO in the non-IRA if the non-IRA was completely occluded (TIMI 0 blood flow) with angiographical features of chronically occluded arteries such as filling through collateral circulations. In all patients, only IRA was revascularized, and drug-eluted stents were implemented instead of bare-metal stents. We regarded the procedure's success if TIMI ≥ 2 was restored on the IRA and resolution of the patient's symptoms. The operators removed the vascular sheath after coagulation parameters normalized. Moreover, we collected their angiography results and procedure data while we further excluded the patients with unsuccessful intervention.After interventions, all individuals received guideline-based medical therapies during hospitalization and follow-up periods. We implemented one year of dual antiplatelet therapy, including aspirin and clopidogrel or ticagrelor. Beyond one year, we recommended daily aspirin for all patients. We also initiated high-dose statin, beta-blockers, angiotensin-converting-enzyme inhibitors, or angiotensin-receptor blockers for all patients unless contraindicated.Our study's primary endpoint was Major Adverse Cardiovascular and Cerebrovascular Events (MACCE) at 11-year follow-up, including death from cardiovascular causes, recurrent myocardial infarction, stroke, and target vessel revascularization. Cardiac death was defined as death due to an AMI, fatal arrhythmia, or exacerbated heart failure (HF). Having measured the in-hospital MACCE among these patients, we contacted the patients by telephone and home visits during the follow-up period.∗20/15%). Moreover, considering a 10% loss of follow-up, we determined our sample size of 2223 individuals. We consecutively recruited 2336 AMI patients.We determined our sample size by implementing the rule of thumb: matching observed variables with the observed study population. It mandated our enrollment number was 10 to 20 times larger than the included variables. Since we decided to include 15 variables in the regression analysis, and the 11-year mortality for AMI patients is around 15%, we estimated the included individuals of 2000 , smoking history, the severity of CAD , and major adverse cardiovascular events after initial coronary revascularization at the first hospitalization.We presented continuous variables as mean ± standard deviation if they were normally distributed or as median (interquartile range) if they were not. The Shapiro–Wilk test was used to assess the normality of distribution. A P < 0.001), more previous MI episodes , increased prevalence of three-vessel disease , and predominantly type C lesions  than patients without, while both groups shared similar profiles including age, sex, and risk factors for cardiovascular diseases. Moreover, the incidence of in-hospital MACCE was significantly higher in the CTO group , owing to the increased target-vessel revascularizations of IRA .From January 2006 till May 2011, we consecutively enrolled 2336 patients diagnosed with AMI (STEMI or NSTEMI) in our medical center's emergency department. They all underwent emergent revascularization of IRA within 24 hours after infarction. We excluded ten patients for unsuccessful interventions and further rejected seven patients who presented with cardiogenic shock on admission. We subsequently assessed the outcome events by outpatient visits or telephone calls if they declined to visit our outpatient center, and our final follow-up visit dates ranged from November 2019 to February 2020. Our mean follow-up interval was 134.3 months, during which we lost contact with 37 patients. We included 2282 AMI patients with their baseline characteristics and angiographic data in our final analysis and divided them into two groups: the group of STEMI patients and the group of NSTEMI patients. As noted in P=0.004). The incidence of deaths from cardiovascular causes was higher in the CTO group  than the group without CTO . Notably, the percentage of target vessel revascularizations was significantly higher in the group with CTO  than in the group without CTO . The incidence of recurrent AMI and strokes were also higher in the group with CTO without statistical significance. Within the group with CTO, we identified patients with the three-vessel disease or left main plus three-vessel disease had significantly higher rates of myocardial infarction, strokes, and target vascular revascularizations than patients with the two-vessel disease or single-vessel disease.During a mean follow-up interval of 134.3 months, we recorded a total of 125 deaths (5.4%) from cardiovascular causes in our 2282 AMI population. We also observed 157 recurrent AMI episodes, 48 strokes, and 397 target vessel revascularizations (including 60 non-IRA CTO revascularizations). Both the STEMI and NSTEMI groups shared similar characteristics on the aspect of primary outcomes. However, as detailed in P=0.004). We further discovered the MACCE-free rate difference between CTO group and group without CTO was more pronounced in NSTEMI subgroup  than in the STEMI subgroup .The MACCE-free rate at 11 years' follow-up was 77.5%  in the entire study population, 78.4%  in the STEMI group, and 75.3%  in the NSTEMI group. As illustrated in P=0.004). Prior MI , in-hospital MACCE , two-vessel disease  posed threats while LVEF served as a protective role . However, multivariable analysis in P=0.008), two-vessel disease , and three-vessel disease  were an independent predictor of 11-year MACCE in the entire study population. LVEF remained a protective factor for the 11-year primary endpoints .Monofactor regression analysis for the entire study population discovered CTO in the non-IRA as a strong factor affecting MACCE  and in the male NSTEMI subgroup .Regarding the possible role of CTO in the non-IRA in predicting the mortality in the subgroup population, we further performed multivariable regression analysis towards MACCE stratified by sex, STEMI, or NSTEMI. We discovered that CTO in the non-IRA was an independent predictor of 11-year MACCE in the male AMI subgroup .Finally, to identify risk factors in the AMI patients with CTO in the non-IRA, we conducted multivariable regression analysis within the CTO group. We concluded that three-vessel disease could independently predict 11-year MACCE for AMI patients with CTO in the non-IRA  we only enrolled AMI patients who had received successful emergent revascularization of IRA and excluded patients with cardiogenic shock art admission since we believe unsuccessful PCI and cardiogenic shock at presentation would contribute to the long-term MACCE, which could muddle the potential effects of CTO. (2) Our 11-year mortality rate is relatively lower than the previous studies, which corroborates with our enrollment process and indicates the relatively benign nature of our study population. A longer follow-up study of our population would witness increased mortalities and might generate different results. (3) Our study showed NSTEMI group had significantly decreased LVEF, increased episodes of previous MI, increased prevalence of the three-vessel disease, and predominantly type C lesions compared to STEMI patients. Although the prevalence of CTO in the non-IRA, in-hospital MACCE, and primary endpoints were similar between two groups, coronary atherosclerosis in NSTEMI patients was more severe. Furthermore, CTO in the non-IRA could exert more damage to the myocardium in this population, which could explain its independent predictive role of long-term MACCE in this subpopulation. (4) Only 60 patients received recanalization of CTO in the non-IRA. The long-term prognosis would be better if more patients had the CTO revascularized.Moreover, our study revealed that AMI patients with CTO in the non-IRA had a significantly higher prevalence of type C lesions in the IRA, even though the risk factors were similar between the two groups. We think this unique finding indicates a more severe state of coronary atherosclerosis in patients with CTO in the non-IRA, which could explain the increased in-hospital and long-term mortalities. We also identified the subgroups with the three-vessel disease or left main plus three-vessel disease were at significantly higher risks of MACCE in the CTO group. Last but not least, due to the CTO's chronic effect of worsened myocardial ischemia, CTO's predictive role of long-term mortality in the NSTEMI patients could be explained by the more severe state of coronary atherosclerosis in this subpopulation, which was indicated by our study. Likewise, our results also showed that patients with MVD were at increased risk of mortalities in the CTO subgroup, which indicates that aggressive interventions should be employed for these high-risk subgroups.Compared with the studies mentioned above, the present study merits the following aspects. (1) By far, our study is the most extensive follow-up study focusing on the association between CTO in the non-IRA and prognosis of AMI patients. The unprecedented 11-year follow-up data could provide valuable information for understanding the long-term prognostic role of CTO in the non-IRA. (2) Our sample size is the largest among the observational studies above, which makes our results more concrete. (3) Our dropout rate is 1.6% during the average follow-up length of 134.3 months. It is even lower than the relatively short follow-up studies above, which states our follow-up data's reliability. (4) The prevalence of CTO in the non-IRA and the baseline characteristics are similar to the previous studies, which could rule out the possibility of selection bias.Our study has several limitations. First, regardless of the most prolonged follow-up duration and large sample size, our study is single-center oriented. Second, as mentioned above, we excluded patients with cardiogenic shock or unsuccessful emergent revascularization. These two limits will decrease the external validity. However, our screening process aims to elucidate the effect of CTO. Third, nearly 84% of our study population were male. Although this might not reflect the real world, this phenomenon did prevail across other previous studies , 12, 20.Our prospective observational study discovered that CTO in the non-IRA was associated with increased MACCE and lower survival rates in AMI patients. It was also an independent predictor of 11-year mortalities in male AMI patients. The subgroup of MVD in the CTO group was at pronounced risks of MACCE and mortality.


Gliomas are the most frequent primary brain tumors. According to World Health Organization guidelines, gliomas are graded into four groups (Group I-IV). This histological grading will determine prognosis and treatment of the patient. Morphological criteria are not always accurate. Tumor proliferation index is a potent quantitative marker for tumor behavior and prognosis, also it’s the basis of gliomagenesis. Ki-67 immunohistochemistry examination for determining proliferation index has been suggested as an ancillary marker in deciding the definitive grading of glioma. To analyze the correlation between Ki-67 labeling index and histopathological grading of glioma in Indonesian population. Ki-67 was detected using immunohistochemistry staining and the labeling index was counted. The association between Ki-67 labeling index and histopathological grading was analyzed. One hundred and six formalin fixed-paraffin embedded tissue of glioma patients were collected from 4 different hospitals. Expression of Age range of patient were 1-73-years old, with male predominance (55.70%). Glioblastoma was the most common diagnosis accounting for 41.51% of all samples. Ki-67 labeling index cut point of 6.35% was obtained and significantly sensitive and specific for determining low- or high-grade glioma (p<0.001). A significant association between Ki-67 labeling index and histopathological grading in Indonesian glioma patients has been revealed. The result of this study may be used to improve diagnostic and grading accuracy of glioma cases in Indonesia, especially in small biopsy specimens. Data from The Global Cancer Atlas showed that 5.323 people i n Indonesia were diagnosed with primary brain and central nervous system cancer in 2018. Gliomas are the most frequent among all primary brain tumors . Glioma histological grading classification based on World Health Organization (WHO) was divided into 4 grades (grade I-IV). This grading determines the prognosis and treatment for the patient but there are still high inter- and intraobserver variability . The basic of gliomagenesis was the glial cell proliferation. Tumor proliferation index is a potent quantitative marker for tumor behavior and prognosis. Therefore, it has been suggested as an ancillary marker in deciding the definitive grading of glioma. It can be examined with a few immunohistochemistry markers. Among them, Ki-67 has been established as an independent factor for tumor progression and survival in glioma patients . Skjulsvik et al., (2014) reported that high-grade gliomas (WHO grade III/IV) have significantly higher Ki-67 labeling index than low-grade gliomas (WHO grade I/II). Ki-67 labeling index 10% has been used as cut-off point for differentiating low-grade and high-grade glioma because of its significances for predicting patient’s survival. However, due to no precise guidelines for Ki-67 labeling index evaluation, there were overlapping values between histological grades. Also, the reported Ki-67 labeling index values varied between studies .This study is trying to establish standardized and reproducible approaches in evaluation of Ki-67 labeling index in glioma and analyzing the association between Ki-67 labeling index and histopathological grading of glioma in Indonesian population. This report is expected to reinforce the application of Ki-67 immunostaining as an ancillary tool in determining the accurate grading of glioma for appropriate management. Samples and data collectionWe retrospectively identified, by reviewing databases from four referral hospitals in Yogyakarta region, all patients between 1 January 2010 and 31 July 2019 with a confirmed diagnosis of glioma. Eligible patients were required to have their formalin-fixed paraffin-embedded (FFPE) tumor tissue blocks available. In total, 106 samples were collected. All pathological specimens were reviewed and reclassified according to WHO Classification of Tumors of the Central Nervous System 2016 by expert pathologists. Tumors were graded based on morphological criteria. Glial fibrillary acid protein immunostaining and IDH-1 mutation status examination were performed in all tumor tissue blocks. The study was approved by the Medical and Health Research Ethics Committee of the Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.Immunohistochemistry stainingFFPE samples were cut into 3µm thick slides for immunostaining examination. FFPE sections were incubated, deparaffinized, and rehydrated. Antigen retrieval was performed using a decloaking chamber . Mouse monoclonal antibody Ki-67/MIB 1  was diluted to 1:100 in phosphate buffer saline. Diamino-benzidine then applied for visualization of positive cells, continued with Hematoxylin staining as counterstain. Tonsil tissue was used as positive control for Ki-67 staining. Interpretation of Ki-67 immunostaining results Ki-67 positive cells were evaluated by an independent and experienced pathologist under a light microscope in high power field (HPF). Areas that will be evaluated were photographed. Evaluation of Ki-67 labeling index was using average method by 2 independent pathologists without knowing the tumor grading and results of the other pathologist’s Ki-67 interpretations. This photomicrograph method of Ki-67 was adapted from Leung et al., (2016). As much as 1,000 tumor cells were counted, maximum number of cells counted in 1 photomicrograph were 500 cells. Every tumor nucleus that stained brown in any intensity was considered positive. Number of positive nuclei were divided by total number of counted nuclei and the values were presented as percentage. The average values between 2 pathologists were calculated for the final Ki-67 labeling index. Statistical analysisThe association between Ki-67 labeling index and glioma grading were analyzed using Kruskal-Wallis and Chi-square test. A probability value of less than 0.05 was considered as statistically significant.Demographic and Clinicopathological features of glioma subjectsThe demographic and clinicopathological features of the research subjects are summarized in Immunohistochemistry Analysis of Ki-67Histopathological features of each grade of glioma and its Ki-67 labeling index in the tissue samples are shown in Ki-67 Labeling Index and Glioma GradingThe lowest Ki-67 labeling index was 0.15% and the highest was 73.55%. Distribution of Ki-67 labeling index for each grade of glioma was shown in Glioma is the most frequent primary brain tumor, especially glioblastoma. It is more prevalent in males compared to females. Incidence of high-grade glioma tends to increase with age . This was also described in our study with male patient predominance , the youngest mean age was in glioma grade I (14.71 years old) while the highest mean age was in grade IV (46.50 years old) and the most common diagnosis was Glioblastoma, IDH-wildtype, (31.13%). There are 4 histological gradings in glioma. The determination of the grade is a subject for interobserver variability. Most of the time, the distinction between grade II and III especially in diffuse glioma based solely on mitotic activity . Glioma then was separated into 2 large groups, low-grade glioma (grade I and II) and high-grade glioma (grade III and IV) with distinct differences in terms of prognosis and therapy of choice . Skjulsvik (2014) dan Byreddy (2018) stated that there was a significant increase in Ki-67 labeling index in high-grade glioma compared to low-grade glioma but there were overlapping values between grade III and IV. This is similar to our study, where the mean value of Ki-67 labeling index of grade III (23.02%) was very similar to grade IV (23.88%). Therefore, the Ki-67 labeling index has limitation in differentiating glioma per grade but useful in differentiating between low- and high-grade glioma.Establishing a Ki-67 cut-off value is also a topic of interest in many studies. This value should be able to distinguish between low-grade glioma and high-grade glioma. The most common cut-off value of Ki-67 labeling index used for glioma was 10%. Several studies showed that this value was clinically significant and associated with the survival of the patients . However, until now there is no standardized procedure for immunostaining and counting of Ki-67, causing great inter- and intra-observer variability. In this case, each pathology department should establish its Ki-67 labeling index cut-off value accompanied by regular adjustment with tumor grading and survival rate for laboratory quality assurance . In our study the cut-off value was 6.35% which was similar to Byreddy et al., (2018) which showed a cut-off value of 6%. This cut off value had high sensitivity and specificity in differentiating low-grade and high-grade glioma (92 % and 93% respectively). This is higher compared to previous studies which showed 77% sensitivity and 75% specificity in discrimination of grade II and III astrocytomas . Commonly used Ki67 cut off value of 10% was also statistically significant in determining low- or high-grade glioma in our patients. However, this cut off value resulted in a wider range of confident interval and lower sensitivity (data not shown).Glioma grading based only on morphology can be inaccurate especially in small biopsy specimens, usually due to heterogeneity-induced sampling errors. In cases where clinical and radiological parameters have lack of correlation with histopathological results and tumor grade, Ki-67 labeling index can be used as an ancillary test . High proliferation rate in histological benign lesion may indicate there was a higher-grade tumor or a reactive condition. To differentiate between reactive lesion and true neoplasm, analyses for isocitrate dehydrogenase and p53 mutation combined with clinical and radiological assessment can be helpful . If there is any suspicion for high-grade glioma, we should add the information of elevated Ki-67 labeling index in the diagnosis with the recommendation of close clinical follow-up. On the contrary, high-grade glioma with low proliferation should not be down-graded . Other factors also can contribute to the discrepancy between Ki-67 labeling index and glioma gradings such as preanalytical factors  and immunostaining procedure . But the most important factor is the quality of immunohistochemistry interpretation by the pathologist . There are no precise guidelines for estimating Ki-67 labeling index in gliomas, therefore intra- and inter-observer variability, also overlapping values between histological grades have been reported. Several counting methods to evaluate Ki-67 labeling index for gliomas have been proposed. Most common method for Ki-67 in gliomas is the manual hot-spot method but because gliomas can have tumor heterogeneity there is no certainty that the most intense hot spot exists in the biopsy section. Assessment of the whole slide with average method is more recommendable due to its reproducibility and accuracy but required a semi-automatic quantitative approach . Study from Leung et al. (2016) was trying to established a standardized scoring protocol for Ki-67 using calibration test . We adapted the protocol for counting Ki-67 from the study. Using this protocol combined with average counting method, we reach high interobserver agreement (Kappa index = 0.96). The Ki-67 counting results can be seen in This study has several strong points. One of them is the larger number of samples compared to previous studies. We also used the newest glioma classification according to the WHO Classification of Tumours of the Central Nervous System 2016 edition that used integration of histological grading and molecular parameters. The Ki-67 labeling index interpretation used average counting method that covered the heterogeneity nature of glioma. A more standardized scoring protocol was also used, which is more accurate and easily reproducible. This method also decreased the intra- and inter-observer variability, which was the main problem in Ki-67 labeling index studies. The weakness of this study is the distribution of glioma per grade was not equal, which may influence the results.In conclusions, in this study, a significant association between Ki-67 labeling index and histopathological grading in Indonesian glioma patients has been revealed. The Ki-67 labeling index is useful in differentiating between low- and high-grade glioma together with histopathological morphology, clinical and radiological parameters. The result of this study may be used to improve diagnostic and grading accuracy of glioma cases in Indonesia, especially in small biopsy specimens.


Computerized assessments are already used to derive accurate and reliable measures of cognitive function. Web-based cognitive assessment could improve the accessibility and flexibility of research and clinical assessment, widen participation, and promote research recruitment while simultaneously reducing costs. However, differences in context may influence task performance.This study aims to determine the comparability of an unsupervised, web-based administration of the Cambridge Neuropsychological Test Automated Battery (CANTAB) against a typical in-person lab-based assessment, using a within-subjects counterbalanced design. The study aims to test (1) reliability, quantifying the relationship between measurements across settings using correlational approaches; (2) equivalence, the extent to which test results in different settings produce similar overall results; and (3) agreement, by quantifying acceptable limits to bias and differences between measurement environments.A total of 51 healthy adults  completed 2 testing sessions, which were completed on average 1 week apart (SD 4.5 days). Assessments included equivalent tests of emotion recognition (emotion recognition task [ERT]), visual recognition (pattern recognition memory [PRM]), episodic memory (paired associate learning [PAL]), working memory and spatial planning , and sustained attention . Participants were randomly allocated to one of the two groups, either assessed in-person in the laboratory first (n=33) or with unsupervised web-based assessments on their personal computing systems first (n=18). Performance indices  and median reaction times were extracted. Intraclass and bivariate correlations examined intersetting reliability, linear mixed models and Bayesian paired sample t tests tested for equivalence, and Bland-Altman plots examined agreement.Intraclass correlation (ICC) coefficients ranged from ρ=0.23-0.67, with high correlations in 3 performance indices . High ICC values were also seen for reaction time measures from 2 tasks . However, reaction times were slower during web-based assessments, which undermined both equivalence and agreement for reaction time measures. Performance indices did not differ between assessment settings and generally showed satisfactory agreement.Our findings support the comparability of CANTAB performance indices  in unsupervised, web-based assessments with in-person and laboratory tests. Reaction times are not as easily translatable from in-person to web-based testing, likely due to variations in computer hardware. The results underline the importance of examining more than one index to ascertain comparability, as high correlations can present in the context of systematic differences, which are a product of differences between measurement environments. Further work is now needed to examine web-based assessments in clinical populations and in larger samples to improve sensitivity for detecting subtler differences between test settings. Cognitive function is typically assessed during one-to-one administration of a neuropsychological test in a clinic or lab setting by a trained psychometrician . HoweverComputerized testing platforms and widespread access to fast and affordable internet has the potential to bring neuropsychological assessment into people’s homes -4. Web-bWeb-based automated assessments are inexpensive, are quick to conduct, and provide fewer restrictions on timing and location ,5-7. EviHowever, the joint position paper for the American Academy of Clinical Neuropsychology and the National Academy of Neuropsychology  highlighExaminer contact: Social demands created by the presence of an examiner may affect performance ; examineTesting environment: While the testing environment can be kept constant in the laboratory, it is uncontrolled elsewhere ,10. TherWorkstation: Differences in the performance of computer hardware, software, processing speed, and internet speed, as well as response input method (touch screen versus key stroke or mouse click), are likely to impact test measures, particularly those relating to response timing .Despite the key differences outlined earlier, web-based assessments have proven to be powerful for identifying age-related changes in cognitive processes , thus prHere, we examine the comparability of unsupervised web-based tests completed at home against in-person lab-based assessment in selected tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB). CANTAB is a widely used computerized assessment battery , publishThis study aimed to determine the comparability of unsupervised web-based assessment on CANTAB against a standard in-person assessment in a healthy adult population. The aim was to examine the consistency of assessment outcomes across these 2 settings, and by extension to inform whether web-based testing could be used as an alternative or as a complementary assessment method producing similar results. We selected 7 tests from CANTAB, which correspond to those most frequently used in academic and clinical research in the cognitive domain of interest.For web-based testing to show acceptable comparability, we required assessments to (1) show high levels of intersetting reliability, that is, the reproducibility of measures across settings , (2) shoThis study was powered to detect moderate-to-high intraclass correlations (ICCs) and moderate-to-large differences in test performance between test settings.α of .05. A sample of 45 would provide adequate power to detect an ICC that is indicative of fair reliability (ρ=0.40).Power calculations to detect ICCs indicating adequate reliability were completed using the R package ICC.Sample.Size ,25, a stα at .05), would require a sample of 52 in a paired sample test with normal distribution, and between 35 and 47 for the nonparametric equivalent, depending on the underlying distribution of data . An effect size of 0.4 has been reported as relatively typical within psychological sciences [The power to detect differences between testing platforms was examined using the program G*power 3 . This insciences ,30. Thissciences .Participants were approached via fliers and advertisements posted on Facebook, targeting Cambridge, United Kingdom, and the immediate surrounding areas. These directed potential participants to a web-based screening questionnaire, administered via SurveyMonkey , throughA total of 51 healthy adults were recruited into this study (32 women and 19 men), aged between 20 and 77 years, with a mean age of 36.8 (SD 15.6) years. Participants were highly educated, with 17.6% with school-level qualifications and 82.4% with university-level education, reflecting the demography of this region. All participants provided informed written consent to participate.Participants were allocated to one of the two groups (in-person first or web-based first), through randomization at the time of recruitment. However, where necessary, allocation from randomization was overridden, where participant availability or laboratory space constricted the timing of assessments. The allocation of test sessions was as follows: in-person testing first for 33 participants and web-based assessment first for 18 participants. Test sessions were completed on average 1 week apart , again with variation due to participant and laboratory availability.Measures), training trials are incorporated within the automatic test administration. The transition from training to tests proceeds automatically, as do transitions between tests. Responses were logged via the touch screen. A trained psychometrician was present, whose role was to provide technical support where needed or additional instructions where required as well as to log observations (distraction or problems) during task performance.In-person assessments were completed at Cambridge University, Cambridge, United Kingdom. Participants were seated in a quiet room and presented with CANTAB loaded onto an iPad . The CAWeb-based assessments were completed via the CANTAB Connect web-based testing feature . This deDistraction during web-based assessment was documented with inbuilt programming to log if tasks were completed in full-screen mode, or if the participant tabbed to another browser window during the task. Participants were also asked at the end of the testing session if they were distracted during testing, although the nature of the distraction was not queried. These different forms of distraction were logged, but not differentiated, in the study database during data collection.A total of 7 CANTAB tests  were admPaired associate learning (PAL)  is an 8-One touch stockings of Cambridge (OTS)  is a 10-Pattern recognition memory immediate (PRM-I)  is a 3-mSpatial working memory (SWM)  is a 4-mThe emotion recognition task (ERT)  is a 7-mPattern recognition memory delayed (PRM-D) is a 2-min test of delayed visual pattern recognition. Patterns displayed for PRM-I are revisited and recognition is probed in the same manner as described in (3) after delay. In this study, the delay between PRM-I and PRM-D was approximately 12 min. Key outcome variables include the percentage of correct responses and median latency of correct responses.Rapid visual information processing  (RVP) isCANTAB test structures are identical for each administration, across both in-person and web-based assessments. However, for most CANTAB tests , stimuli are allocated at random from a broader stimulus pool during each assessment, making it unlikely that participants complete the same problems more than once. For the SWM test, token locations are not fixed but instead programmed to respond to participants’ performance and selection strategy, reducing the risk of participants being able to learn the location of tokens from one assessment to the next. These adaptive features aim to reduce practice effects on repeat testing and also mean that there are no set variants of the tests that can be compared in a group-wise fashion.P≤.05 (two tailed). The Bayesian statistical analysis was carried out using JASP [Frequentist analyses including mixed models, regressions, correlational analysis, and ICCs were completed in SAS version 9.4. Statistical significance thresholds were set at ing JASP .Outliers were identified using the methods recommended by Aguinis et al , first tTo allow the comparison with test-retest reliabilities commonly reported in the literature ,5,18,42,However, although the correlational analysis reflects the degree to which paired observations follow a straight line, they do not inform regarding the slope of the line or whether the sets of observations capture the same metric or range of scores . ICCs weMixed effects models simultaneously investigated differences between the test settings (in-person vs web-based) and time (first vs second assessment). Mixed effects models can evaluate multiple factors that affect the structure of the data and allow longitudinal effects (practice and learning effects) to be straightforwardly incorporated into the statistical model . OutcomeThe normality of the distribution of residuals was examined, and where required data were transformed before data analysis. Transformations included log transformations for PAL Total Errors Adjusted, SWM Between Errors, OTS Problems Solved on First Choice, and OTS Median Latency to Correct response and square root transformation for PAL First Attempt Memory Score. For most variables, transformations were successful and a linear mixed model was carried out (SAS command PROC MIXED). For PRM-I and PRM-D percentage correct, transformations were not successful. These data were reverse transformed  and were analyzed with mixed models with gamma error distributions and log links (SAS command PROC GLIMMIX).t tests were conducted, and Bayes factor test statistics were extracted, alongside effect sizes (δ) and their 95% credible intervals, contrasting the likelihood of data fitting under the null hypothesis (H0: no difference between test settings) with the alternate hypothesis (H1: that there is a difference between test settings). A default Chauchy prior width of r=0.707 was selected, and a Bayes factor robustness check was completed to examine if the qualitative conclusions changed with reasonable variations to the prior width. Bayes factors (BF10) were interpreted using a classification scheme adopted from Wagenmakers et al [H0), and Bayes factors above 1 seen as evidence for H1.Evidence in favor of the null hypothesis was examined using a Bayesian approach . The advrs et al : with BaA−B) versus the average across paired measures (A+B/2), along with 95% limits of agreement [Agreement between test settings was examined with Bland-Altman plots . These pgreement . The plogreement ,54. Agregreement . Proportgreement .Full test data were obtained from all participants with the exception of 2 individuals for whom the SWM test terminated early due a large number of errors made during web-based assessment. During in-person assessments, support from the examiner was required on 4 occasions . Distraction, either through self-report or due to participants tabbing away from the assessment window during web-based assessments, was noted for 16 participants for PAL, ERT, OTS, and PRM-I tests and for 17 participants during SWM, RVP, and PRM-D tests.P<.01). ICCs ranged from 0.23 to 0.67 (P≤.05). A total of 5 tests had ICC coefficients meeting the cutoff at ≥0.60, with PAL Total Errors Adjusted just meeting requirements , and above threshold coefficients for RVP A’, SWM Between Errors, PRM-I Median Latency, and ERT Median Correct Reaction Time. Partial correlations of test results across settings after controlling for the duration between tests produced very similar results. These are shown in Bivariate correlation coefficients and ICCs are shown in P=.10 to .54). However, 3 of the 5 reaction time measures showed differences across test settings , with web-based assessments yielding slower median response times (P<.001 to .03). Practice effects were seen for RVP and SWM performance indices, showing improvement on second administration (P<.01). Response latencies were faster on the second administration for OTS responses (P=.001).Descriptive statistics and results from the mixed model assessing fixed effects of test setting and time are presented alongside the Bayesian analysis results in F1,45=4.01, P=.051; for all other tests F statistic range 0.02-2.49; P=.12 to .90). Effects of distraction were nonsignificant for most tests, but reached or neared significance thresholds for certain reaction time measures .Additional fixed effects of age, an age-by-setting interaction effect, and distraction were incorporated into mixed models. Age effects on test performance, showing a decline in test performance with increasing age, were found for all outcome measures with the exception of RVP A’, the percentage of correct responses on PRM-I and PRM-D, and OTS Problems Solved on First Choice. No significant age-by-setting interactions were observed, indicating that test performance did not differ between in-person and web-based testing as a function of age, although there was a trend for slower reaction times on web-based testing for older participants on the PRM-I task (PRM-I Median Latency: H0: no difference between test settings) over the alternate hypothesis: BF10=0.161-0.54) for all performance indices. Applying the classification scheme adopted from Wagenmakers et al [Bayesian analyses supported the null hypothesis  and very strong for ERT (BF10=512557.32). Effect sizes were in the low-to-large range (0.04-1.69). Moderate support for the null hypothesis was seen for the RVP and OTS reaction time measures.The alternate hypothesis, reflecting a difference between test settings, was supported for 3 out of the 5 reaction time measures , with support being between anecdotal for the PRM measures , with lower mean scores being associated with greater difference between measurements .For reaction time measures, Bland-Altman plots reflected bias in test settings in PRM-I and PRM-D response latencies and ERT Median Correct Reaction Time eg, , confirmThis study examines the comparability of the widely used CANTAB administered unsupervised via the internet against a typical in-person lab-based assessment, using a counterbalanced within-subjects design. We imposed strict criteria for comparability, including satisfactory intersetting reliability, equivalence, and agreement across test settings. Overall, our results support the comparability of performance indices  acquired during web-based assessments. Reaction time measures show poorer comparability, with results revealing significant differences and poor agreement between test settings.Bivariate correlation coefficients between the 2 modes of test administration ranged between 0.39 and 0.73, broadly in keeping with previous research comparing in-person and web-based assessment of other cognitive tasks ,18,19. TICCs were higher for some tests than for others, with fair reliabilities (ICC ρ=0.40-0.49) seen for planning and executive function tasks (SWM Strategy and OTS performance measures). Previous research has shown that cognitive measures are subject to significant intraindividual variation . A meta-ICCs and Spearman correlations generally provided similar results, but showed greater discrepancy for reaction times, where there was a difference in the range and average between assessment settings. In these cases, ICCs typically presented a tempered correlation coefficient in comparison to Spearman correlations, reflecting that this statistic takes into account systematic error between assessments.Learning effects are likely to have had an impact on concordance between test settings . PracticTwo out of 9 performance indices met all predefined criteria for comparability between measures. PAL Total Errors Adjusted and RVP A’ test scores did not differ between test settings, showed good intersetting reliability, and showed acceptable agreement on Bland-Altman plots. Additionally, for SWM Between Errors, Bland-Altman analyses were not completed, but the intersetting reliability was good, and there was no evidence of performance differences between settings. These measures are therefore determined to have good overall comparability vis-à-vis typical in-person assessment .Overall, none of the 5 web-based reaction time measures met more than one of the predefined comparability criteria, indicating that response latency measures are less easily translated from the lab to the home. Acceptable correlations between in-person and web-based assessments were undermined by a lack of equivalence and agreement between the measures. Correlation coefficients examine the linear relationship and relative consistency between 2 variables  rather Differences between settings could be due to a variety of factors. First, web-based assessments were completed on laptop and desktop computers that participants had readily available to them at home or elsewhere. Differences in computing equipment across settings are likely to have had an impact on response times . Second,The use of a healthy, relatively young, and highly educated sample may limit the generalization of findings to lesser-educated, clinical, or old-age samples. This research suggests that for the examined CANTAB performance indices, web-based assessments are likely to be a suitable alternative for similar samples. Further examination of the comparability of web-based assessment is now required in populations of clinical interest. In the longer-term, participants and patient groups with access restrictions may be the ones who benefit most from remote testing.The study examined only the reliability of tests across settings and different devices, since all in-person tests were completed on touch screen iPads, and all web-based assessments on personal computers or laptops. Further research is required to examine whether reaction time data may be collected more consistently, where similar or the same devices are used across settings. Since the completion of this study, variance in workstation information is now routinely collected for CANTAB web-based tests, which allows for better determination of the effects of different workstations on test performance.It is not clear how computer/device experience may have interacted with our results because we did not collect this information. However, our participants were recruited via Facebook, screened for inclusion online, and tested at home using their personal computing system, so it is likely that they had at least modest computer experience. Discrepancies between lab-based and web-based remote testing may be amplified for individuals with less computer experience, who may need to rely on the support of study staff to a greater extent.The study was powered to detect moderate differences between test settings and was not adequately powered to identify subtle differences. Bayesian statistics were able to qualify the level of support for the null or alternate hypothesis, but much larger samples would be required to determine stronger evidence for the null hypothesis. Replication in a larger sample is now required to examine for the presence of any subtle differences between test settings.Further work is now required to examine test-retest reliability for web-based assessments to identify whether test reliabilities are similar to those obtained during repeated in-person assessments. Our data show intersetting reliabilities, which are similar to previously reported test-retest reliabilities obtained during in-person assessments. Automated test scoring of performance indices, standardized across test administration and testing platforms, circumvent problems with rater-based variances in reliability. However, differences in computer hardware and software can impact reaction time data, and this must be borne in mind during web-based neuropsychological assessments.This study compared web-based CANTAB tests with gold-standard in-person administered lab-based assessments. Performance indices obtained in person showed broad equivalence, good agreement, and significant linear relationships with those obtained during web-based assessments. Overall, this study provides evidence for the comparability of a range of performance outcome indices examined using web-based testing in a healthy adult sample. Certain performance indices showed better comparability than others and should therefore be preferable for use where comparability with typical in-person assessment is needed. Reaction time indices were not found to be comparable, and greater care is required in the interpretation of web-based latency results in relation to typical in-person assessments.


Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Based on these genes, we develop new across-tissue and tissue-specific age predictors. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Our results also indicate that both racial and tissue differences are associated with transcriptional age. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. RNAAgeCalc is available at  Aging is among the most complex phenotype and is a well-known risk factor for a myriad of diseases including cardiovascular, diabetes, arthritis, neurodegeneration and cancer . IncreasNumerous estimators have been developed to predict human aging from DNA methylation data profiled on the Illumina Infinium HumanMethylation450K BeadChip. Among the most widely used epigenetic age estimator are the DNA methylation age calculator of Horvath  and HannIn addition to DNA methylation, changes in gene expression have been shown to be associated with aging and aging-related outcomes –19. SpecBesides the de Magalhaes et al.  signaturTo the best of our knowledge, the largest meta-analysis to identify age related genes was conducted by Peters et al.  from whoUnlike DNA methylation in which several user-friendly software and computer programs are available for predicting epigenetic age across different tissues on the most widely utilized Illumina methylation platform, there were limited transcriptional age predictors and the existing predictors have several pitfalls. First, most of the human transcriptional age predictors were developed based on microarray data and/or limited to only a few tissues. Second, the only predictor constructed using RNA-Seq data was the ensemble LDA predictor . HoweverAn overview of the transcriptional age analysis pipeline and comparisons conducted in this study was given in We utilized the RNA-Seq data from the Genotype-Tissue Expression (GTEx) Program , a publiOne pitfall of individual gene expression studies for identifying age-related signatures was the low overlap between the gene lists across different tissues , 22. MetTo overcome the low overlap across tissues and to identify common age-related genes across tissues, we adapted the binomial test of de Magalhaes et al. . A totalThe list of genes which exhibited consistent positive association with age were enriched in GO terms related to plasma-membrane adhesion molecules, response to interferon-gamma, GTPase activity, and type I interferon. The enrichment analysis of genes negatively associated with age identified KEGG terms including proteasome, ribosome biogenesis, RNA transport in eukaryotes, citrate cycle, carbon metabolism, pyruvate metabolism, aminoacyl-tRNA biosynthesis as well as GO terms related to mitochondrial function, metabolic process, RNA processing, ribosome biogenesis, and purine metabolic process. Our results were consistent with the findings of Peters et al.  which shPrevious studies showed that age-related signatures were tissue-specific . In thisIn the elastic net model, the tissue-specific candidate feature sets based on DESeq2 and all genes outperformed the across-tissue candidate feature sets in most tissues. Another tissue-specific candidate feature set, namely Peters et al.  signaturTo better understand the generalizability of individual tissue-specific transcriptional age predictors to other tissues, we investigated the performance of across-tissue age prediction. Elastic net model was trained on samples from one tissue, and tested on the other tissues. Each gene signature discussed in the Methods section was applied and the Pearson correlation between predicted age and chronological age in test tissue was calculated. The correlation matrix was provided in r = 0.65 in GTExAge signature) due to the similarity in anatomic and function of these two tissues [The heatmap of Pearson correlation between predicted age and chronological age in each tissue based on GTExAge candidate feature was given in  tissues .To evaluate the effect of race on age prediction, we repeated the analysis of the meta-analysis, within-tissue prediction, and across-tissue prediction using only subsets of White samples which made up the racial majority in GTEx database. The predictors were trained on the White samples only and tested on White and non-White samples respectively.For the within-tissue prediction, the White samples were also divided into 50%-50% training-test set. The predictors were built on the training set and evaluated on the test set as well as on the non-White samples. p < 0.05).For the across-tissue prediction, elastic net model was constructed using White samples in each tissue. The model was then tested on the White and non-White samples of all the other tissues respectively. The correlation matrix on the White and non-White samples (based on GTExAge candidate feature) was provided in Both the within-tissue and across-tissue prediction suggested that transcriptional age predictor was racial-dependent. Thus, in our transcriptional age calculator, we provided the option for computing transcriptional age based on models trained on GTEx White samples only.Here we investigated the intersection between our transcriptional age signature to candidate genes identified from previous studies. First, we compared our tissue-specific signature to prior tissue-specific signatures as summarized in In general, our tissue specific signatures obtained from DESeq2 analyses were consistent with previous tissue-specific candidate age signatures, except for the comparison with Glass et al.  skin sigp < 0.05  to the prior across-tissue signatures, namely de Magalhaes , GenAge p < 0.05 , indicatSince both methylome and gene expression play important roles in aging, to assess whether they complement each other, we compared age-associated methylation to age-associated gene expression in different tissues. We utilized the DNA methylation data and gene expression data of The Cancer Genome Atlas (TCGA) consortium, a rich repository consisting of omics data for multiple types of cancers. The samples with matched DNA methylation data and RNASeq data were analyzed see  section.The number of mutations per cancer sample (mutation burden) was previously shown to be negatively correlated with DNAm age acceleration . Here, wWe evaluated whether transcriptional age was significantly associated with mortality risk in TCGA datasets. For each cancer type, two Cox proportional hazards models were fitted, namely the Cox regression on age acceleration adjusting for chronological age (Mod0a) and Cox regression on age acceleration adjusting for chronological age, stage, gender and race (Mod1a). In Mod1a, gender was not adjusted for breast (BRCA), ovary (OV), prostate (PRAD) and testis (TGCT) cancer. We further evaluated the association between age acceleration and cancer stage. Two linear regression models were fitted for each cancer type, namely regressing age acceleration on stage adjusting for chronological age (Mod0b) and regressing age acceleration on stage adjusting for chronological age, gender and race (Mod1b). Transcriptional age acceleration was marginally associated with stage . We showed that transcriptional age acceleration was associated with lower mutation burden and lower mortality risk in TCGA cancer samples, and offered complementary information to DNAm age. Our results also indicated that racial difference was associated with transcriptional age. As majority of the samples in GTEx were Whites, future work included extending RNAAgeCalc to non-White samples.RNAAgeCalc is available both as Bioconductor and Python packages as well as an interactive Shiny app. We anticipate that the calculator will be useful in the development of aging biomarker to advance our understanding on age-related diseases. These insights may ultimately inform development of novel treatments for age-related diseases.impute.knn function in Bioconductor package impute [Our software is capable of calculating tissue-specific transcriptional age for 26 types of tissues based on 8 choices of candidate feature sets described in this study. That is, users are able to specify the choice of tissue, aging signatures, as well as sample type for racial consideration . The software is implemented as follows. For each tissue, signature and sample type, we pre-trained the calculator using elastic net based on the GTEx samples. In the within-tissue age prediction section, we have demonstrated that elastic net model outperforms the other prediction algorithms. We saved the pre-trained model coefficients as internal data in the software. The software takes gene expression data as input and then match the input genes to the genes in the internal data automatically. If the genes in user input data do not fully cover all the genes in the signature, imputation will be performed automatically by the e impute .O(plogp) for each gene, where p is the number of genes provided [Our pre-trained models are computationally efficient because there is no need to re-train the elastic net model every time a new gene expression data is provided. In scenarios where the user input gene expression data covers the genes in the specified signature, transcriptional age computation only requires calculating the inner product of the coefficient vector and the gene expression. We take advantage of the vectorization in R and Python, which is computationally efficient. In scenarios where the user input gene expression data does not cover all the genes in the signature, the vast majority of computational time is in the imputation process. The time complexity of nearest neighbor imputation is provided . The comgetRPKM function in Bioconductor package recount [To facilitate integrated analysis and direct comparison of multiple datasets, we utilized recount2  version  recount . The ben recount  describe recount   less than one were filtered out. Differential expression analyses with respect to chronological age were performed on each tissue using DESeq2  based onn denotes the total number of tissues, k denotes the number of tissues in which the gene was positively (negatively) associated. The parameter p was estimated by the average proportion of positively (negatively) associated genes across tissues, resulting in value 13.86% (13.77%). The raw p-values in binomial tests were adjusted using FDR procedure, and statistically significant genes were identified at FDR < 0.05.We adapted the binomial test of de Magalhaes et al.  to identTo identify enriched pathways among our 1,616 age-related genes, 297 KEGG pathways  and 5,78For each individual tissue, we considered several machine learning models including elastic net , general(i)K differentially expressed genes ranked by the p-values from differential expression analysis. To investigate the influence of the number of genes on prediction accuracy, we considered within-tissue prediction using top 500, 1000, 1,500, 2,000 genes and compared their performances. Differentially expressed genes by DESeq2  (denoted(ii)Genes highly correlated with chronological age by Pearson correlation (denoted by Pearson). Before training the prediction models, Pearson correlations between the logarithm transformed FPKM and chronological age were calculated on the training set. The correlation coefficients were then sorted by its absolute value in decreasing order, and only the top correlated (either positive or negative) genes were used to train the prediction models. Similar to (i), we also considered top 1,000 correlated genes and a comparison of the number of genes in the model was given in (iii)t1-fold difference in expression between any two samples in the training set and at least one sample had expression level >t2 FPKM to be included in the prediction models. t1 and t2  were the thresholds to control the degree of deviance of the genes. In our analysis, we used t1 = t2 = 10 for most tissues. For some tissues with large sample size, in order to maximize the prediction accuracy while maintaining low computation cost, we increased t1 and t2 such that the number of genes retained in the model was between 2,000 and 7,000. An alternative way of selecting genes with high variability is based on the coefficient of variation (denoted by CVar), which is defined as the ratio of standard deviation to mean. Since genes with higher counts could have larger variance compared to genes with lower counts , selecting genes based on CVar could potentially reduce the bias toward high count genes. We compared these two approaches by performing within tissue transcriptional age prediction (Genes have large variance in expression across samples (denoted by Deviance). We adapted the gene selection strategy discussed in , in whicediction . To ensu(iv)The 1,497 age-related genes of  (denoted(v)All genes after filtering out low count genes. Specifically, genes with more than 20% samples having CPM less than one were filtered out.(vi)The 1,616 common age-related genes discussed in the meta-analysis section (denoted by GTExAge).(vii)The 73 common age-related genes of de Magalhaes et al.  .(viii)The 307 common age-related genes from the Ageing Gene Database (denoted by GenAge) .The models were evaluated by the Pearson and Spearman correlation between predicted age and chronological age, median absolute error (median error) and mean absolute error (mean error) on the test samples, averaging over the 10 repetitions.p < 0.05). The p-values of these prior genes in our DESeq2 analysis result is given in For the tissue-specific signatures, we extracted the genes reported in the original references and took their intersection with the genes available in GTEx. We investigated whether these prior aging candidate genes were significant in our DESeq2 analysis result . We thenp < 0.05 within each prior across-tissue candidate gene set.For each prior across-tissue candidate gene set, we first computed the p-value of each gene using the binomial test  as a summary measure of evidence the gene held as candidate common age-related gene. We then enumerated the proportion of genes which attained Illumina Human Methylation 450K annotation data were imported from the Broad GDAC Firehose and the DNAm age were obtained by analyzing the beta value using DNAm age calculator , 8, 10. S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S11 Table(PDF)Click here for additional data file.S12 Table(PDF)Click here for additional data file.S13 Table(PDF)Click here for additional data file.S14 Table(PDF)Click here for additional data file.S15 Table(PDF)Click here for additional data file.S16 Table(PDF)Click here for additional data file.S17 Table(CSV)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.


Coarctation of the aorta (CoA) accounts for up to 10% of all congenital heart defects. Open repair with surgical reconstruction has been the main form of treatment and more recently, aortic stenting has been used as a less invasive technique without the need for cardiac bypass. The long term success and impact of repair on the structure of the aorta is important with regard to morbidity and management of these patients.We used cardiac magnetic resonance imaging (CMR) to assess the long term success of repair of CoA with respect to structural complications and residual stenosis.CMR studies performed in patients with repaired CoA from January 2005 to September 2008 were reviewed. Details of age at, date and type of repair were obtained from patient medical notes. Multi-slice HASTE, cine and turbo-spine echo T2 images, and aortic in-plane and through-plane flow studies were analysed for complications of repair  and residual stenosis. Aneurysmal dilatation was defined as localised widening at the repair site in comparison to the diameter of the pre- and post-aortic segments. Residual stenosis was defined as constriction at the repair site with flow acceleration on in-plane flow analysis, ± peak recorded velocity on through-plane measurement of >1.5 m/s.Analyses were done on 281 studies . Average time between surgical repair and balloon dilatation, and CMR imaging 18 ± 7 years, and between aortic stenting and CMR imaging 4 ± 0.8 years. Types of repair: resection and end-to-end anastomosis , subclavian flap repair , aortic stenting , interposition graft repair , balloon dilatation , dacron patch repair , and bypass graft . Structural complications were most marked in patch repair , and subclavian flap repair . There were no direct structural complications from resection and end-to-end anastomosis, but 57/94 had residual stenosis. Balloon dilatation had 17/24 residual stenosis and mild aneurysmal dilatation in 4/24. Aortic stenting showed less residual stenosis (6/43), with minimal structural complications . Reasonably good long term results were observed in graft repairs: interposition grafts  and bypass grafts . Main complications of the types of repair are in Table Patch and flap aneurysms are frequent complications of patch and subclavian flap repair of aortic coarctation, and the rate of residual stenosis with resection and end-to-end anastomosis was high. Balloon dilatation had minimal complications, but frequent residual stenosis. Graft and bypass graft repair are the most favourable surgical repairs with limited complications and residual stenosis. Aortic stenting also appears quite successful with few complications and residual stenosis but long term follow-up is not yet available.


To compare the mortality rates between culture-positive and culture-negative sepsis in complicated intra-abdominal infections (cIAI) and investigate the predictors of culture-positivity and their causative microorganisms.The medical records of 1581 adult patients who underwent emergency gastrointestinal surgery between January 2013 and December 2018 were reviewed retrospectively. A total of 239 patients with sepsis or septic shock who were admitted to an emergency department, underwent emergency surgery for cIAI, and needed postoperative intensive care unit care were included and divided into two groups according to their initial blood and peritoneal culture results.P = .001) and 30-day mortality  than the culture-negative group. Colon involvement  and higher Sequential Organ Failure Assessment (SOFA) score  were shown to be the predictors of culture-positive sepsis for cIAI. Regarding antibiotic sensitivity, 31.6% of the gram-positive bacteria were methicillin-resistant and 42.1% of the gram-negative bacteria were extended spectrum β-lactamase-producing Enterobacteriaceae.Among the 239 patients, 200 were culture-negative and 39 were culture-positive. The culture-positive group had higher in-hospital  contains supplementary material, which is available to authorized users. Sepsis is one of the most common causes of mortality in hospitalized patients , 2. FromIdentification of the sepsis origin and the causative microorganisms followed by appropriate selection of empirical antibiotics and prompt source control is critical for its treatment , 7. AccoNannan Panday et al.  reportedThere are not many studies comparing the mortality rates between the culture-positive and culture-negative patient groups with complicated intra-abdominal infections (cIAI). Therefore, the primary aim of this study to evaluate the association between culture results and the mortality rates in these patients. The secondary aims were to investigate the predictors for culture-positive sepsis in cIAI and their causative microorganisms with their resistance to antibiotics.Electronic medical records of 1581 patients who underwent emergency gastrointestinal (GI) surgery for intra-abdominal infection from January 2013 to December 2018 in a tertiary medical center were reviewed. 239 patients with sepsis or septic shock were included for analysis with following inclusion criteria: patients who admitted via emergency department (ED), received no antibiotics prior to the ED admission, required postoperative intensive care unit (ICU) care, and underwent surgery for cIAI. These patients were classified into culture-negative and culture-positive groups according to the initial blood or peritoneal culture results  were collected. Parameters reflecting their initial status such as the Acute Physiology and Chronic Health Evaluation II (APACHE II), American Society of Anesthesiologists (ASA), Quick SOFA (qSOFA), and SOFA scores, systemic inflammatory response syndrome (SIRS), initial systolic blood pressure (SBP), respiration rate, mental status, the presence of preoperative shock, and use of vasopressors at ED were obtained. Quick Sequential Organ Failure Assessment and SIRS were defined as positive when they fulfilled two requisite criteria. Initial postoperative parameters of the population and data regarding postoperative complications, length of treatment, and mortality rates were also collected.There were many causes resulted the patients to have cIAI. These causes were mentioned under “Diagnosis” category Table . PatientPulmonary consolidation and pleural effusion were recorded according to the official reports from the chest radiology specialists. Pre-existing pulmonary consolidations were excluded and those newly developed during the postoperative care were included for analysis. Infectious complications were also reviewed which is defined as newly occurred infections except for pulmonary consolidations during postoperative care based on the consensus conference definitions of infection in the ICU .This study was approved by the Ajou University Institutional Review Board (AJIRB-MED-MDB-19-314), and informed consent was waived due to the retrospective design of the study.t-test or the Mann–Whitney U test, as appropriate. Categorical variables, presented as percentages, were evaluated with chi-squared test or Fisher’s exact test. To evaluate risk factors for positive cultures, multivariate logistic regression analysis with maximum likelihood method and backward stepwise selection was performed. Variables for multivariate logistic regression analysis included variables with P value < 0.05 in univariate analysis. Patient data with any missing variables were excluded from the analysis. The findings were considered statistically significant at P values < 0.05. Statistical analysis was performed using SPSS® Statistics 25.0 .For all variables, data normality was tested using the Shapiro–Wilk test. Continuous variables, presented as means ± standard deviations or medians [interquartile range] depending on the data normality, were compared using the Student P = 0.031), their diagnoses that caused cIAI (P = 0.023), and their infection origins (P = 0.012). Among infection origins, patients in the culture-positive group had a significantly higher rate of colon involvement  , SOFA score , SIRS , SBP , respiration rate , and proportion of patients who used vasopressors in ED , the patients in the culture-positive group showed higher severity than those in the culture-negative group (Table According to the ASA score (P = 0.025) and needed percutaneous drainage . The survived patients in the culture-positive group stayed in the hospital  and ICU longer . Patients in the culture-positive group needed more days to wean from vasopressors  and mechanical ventilators  than those in the culture-negative group.More patients in the culture-positive group developed pleural effusion  and 30-day mortality rates  were also higher in the culture-positive group than the culture-negative group (Table P = 0.031).In-hospital  and higher SOFA score  were associated with a higher risk of positive culture (Table P = 0.003), higher SOFA score , and lower SBP  were independently associated with 30-day mortality (Supplementary Table 1).Multivariate logistic regression analysis revealed that involvement of the colon . Other microorganisms identified in the cultures were microorganisms from the Klebsiella genus and Candida species. Anaerobes, gram-positive and gram-negative rods were also identified from the cultures  followed by es Table .Table 5MResistance to methicillin in gram-positive bacteria was observed in 25.0 and 40.0% in blood and peritoneal cultures, respectively. Furthermore, half of methicillin resistant bacteria also had resistance to vancomycin. Among gram-negative bacteria from blood cultures and peritoneal cultures, 28.6 and 50.0% were ESBL-producing Enterobacteriaceae, respectively. One of them was resistant to carbapenem . Other microorganisms identified in the cultures were from the Klebsiella genus and Candida species. Anaerobes, gram-positive and gram-negative rods were also identified from the cultures. In previous studies, similar results were shown in which gram-positive cocci, E. coli, and Klebsiella were the most common [Gram-positive cocci were the most common microorganisms identified in both blood and peritoneal cultures (44.44 and 33.33%) followed by t common , 15, 22.Traditionally, a combination of third-generation cephalosporin and metronidazole had been used as first-line antibiotic regimen for intra-abdominal infection in our center, which is within the guidelines of the World Society of Emergency Surgery . The ratThe limitations of this study include the relatively small number of culture-positive patients compared with a modest total number of patients from a single surgical ICU in a tertiary medical center. Furthermore, the cause-effect relationship is not clear between the variables since this is a retrospective study. As shown in Table However, there were few attempts to conduct a study to identify predictors for culture positivity in patients with cIAI-originated sepsis who underwent emergency surgery within 24 h of their ED arrival. Thus, this study could contribute to the understanding of the characteristics of sepsis originating from cIAI, which is different from the results of studies conducted in patients with sepsis in medical ICUs. More studies in this subject on a larger number of patients with prospectively collected data are necessary to clarify our findings.To summarize, in our cohort with identifiable intra-abdominal infection who underwent emergency surgery for source control, culture-positive patients showed higher mortality rates. They also showed longer length of hospital stay, ICU stay, and mechanical ventilator use. The patients with colon involvement were 4.2 times more likely to be culture-positive, and as SOFA score increased 1 point, the risk of culture-positivity increased by 1.2 times. We suggest using antibiotics susceptible to pathogens with wide range of resistance in a selected group of patients with high risk of mortality who have colon involvement and higher SOFA score, which could be translated to a high risk of culture positivity and antibiotic resistance.Supplementary file1 (DOCX 21 kb)Supplementary file1 (DOCX 17 kb)Below is the link to the electronic supplementary material.