Specifically, EVs were quantified and phenotypically characterized by flow cytometry and cell-specific monoclonal antibodies. We observed that even in well medically controlled cHF patients  there are higher number of blood annexin-V+ (phosphatidylserine+)-EVs carrying activated immunity cell-epitopes in the circulation than in controls . Particularly, EVs shed by monocytes and neutrophils (innate immunity) and by T-lymphocytes and natural-killer cells (adaptive immunity) are significantly higher in cHF patients. Additionally, EVs-shed by activated leukocytes/neutrophils , and T-lymphocytes  were positively correlated with cHF disease severity (NYHA classification). Interestingly, cHF patients with ischemic etiology had the highest levels of EVs shed by lymphocytes and neutrophils . In summary, in cHF patients there is a significant immune cell activation shown by high-release of EVs that is accentuated by clinical severity of cHF. These activated innate and adaptive immunity cell messengers may contribute by intercellular communication to the progression of the disease and to the common affectation of distant organs in heart failure (paracrine regulation) that contribute to the clinical deterioration of cHF patients.Leukocyte-shed extracellular vesicles (EVs) can play effector roles in the pathophysiological mechanisms of different diseases. These EVs released by membrane budding of leukocytes have been found in high amounts locally in inflamed tissues and in the circulation, indicating immunity cell activation. These EVs secreted by immune cell subsets have been minimally explored and deserve further investigation in many areas of disease. In this study we have investigated whether in heart failure there is innate and adaptive immune cell release of EVs. Patients with chronic heart failure (cHF) ( Leukocyte-shed extracellular vesicles (EVs) can play effector roles in the pathophysiological mechanisms of different diseases. These EVs released by membrane budding of leukocytes have been found in high amounts locally in inflamed tissues and in the circulation, indicating immunity cell activation. These EVs secreted by immune cell subsets have been minimally explored and deserve further investigation in many areas of disease . Heart fThe systemic inflammation of these patients represents a chronic and non-resolving health impairment with the presence of elevated levels of circulating cytokines or pro-inflammatory mediators, such as TNFα, IL-β1, IL-6, or CRP , 8. FurtCirculating extracellular microvesicles (EVs) are a heterogeneous population of extracellular vesicles with a size range between 20 and 1,000 nm. Composed of a phosphatidylserine-rich phospholipid bilayer that exposes transmembrane proteins and receptors from their parental cells, EVs are released after physiological stimuli such as cell activation, damage, stress, apoptotic or necrotic damage , 15. EVsTo gain more insight in the pathophysiology of chronic HF (cHF), we aimed to investigate whether immune cells were activated in patients, even though treated according to guideline-directed medical therapy. Several studies have investigated endothelial-derived EVs in relation with cardiovascular risk factors and cardiovascular disease pathophysiology –29. HoweIn this study we have investigated whether in chronic heart failure there is innate and adaptive immune cell release of EVs and whether specific circulating EV patterns associate with the underlying cHF etiology (ischemic and non-ischemic) or the disease severity in cHF patients.A total of 119 ambulatory patients with clinical diagnosis of cHF and under guideline-directed medical therapy were prospectively recruited in the outpatient HF unit of the Hospital de la Santa Creu i Sant Pau  between September 2016 and July 2018 and were followed-up until October 2019. During follow-up patients were treated according to international guidelines and at clinician discretion in the outpatient unit of our hospital. A control group was also included, comprising 60 sex- and age-matched controls without cHF. Baseline demographic data, classical cardiovascular risk factors and background medication of the study population (cHF and control groups) are shown in The ethics committee at the Hospital de la Santa Creu i Sant Pau in Barcelona (Spain) approved the study (Ref 16/44) and it was conducted under the principles of the Declaration of Helsinki. A written informed consent was obtained from all participants prior recruitment.in vitro platelet activation. Platelet-poor plasma (PPP) was carefully aspirated, leaving about a 1 mm undisturbed layer on top of cells. A second centrifugation step was then performed at 1,500 g for 10 min at 20°C  to ensure the complete removal of cells and obtain the platelet-free plasma (PFP). PFP aliquots were stored at −80°C until flow cytometry studies.Venous blood was withdrawn from the cubital vein without tourniquet using a 20-gauge needle after 10–14 h of fasting into 3.8% sodium citrate tubes . All samples were processed identically and within the first 2 h. Blood was centrifuged at 1,560 g for 20 min at 20°C  to avoid The EVs fraction was isolated from PFP by a two-step high-speed centrifugation, according to the procedure previously described , 24, 30.2 . Afterward, combinations of CFBlue-conjugated annexin V (AV)  to detect phosphatidylserine, and two specific monoclonal antibodies (mAb)   labeled Sample acquisition was performed during 1 min per sample at “low flow” rate. Forward scatter (FSC), side scatter (SSC), and fluorescence data were obtained with the settings in the logarithmic scale. Gate limits were established as follows with the same criteria previously described , 24, 30.Data was analyzed with BD FACSDiva™ Software . The concentration (number of EVs per μl of plasma) was determined according to Nieuwland’s procedure , based op < 0.05 was considered statistically significant. Sample size was determined using the GRANMO sample size calculator . To detect mean differences in the number of EVs, a total of 126 subjects (controls and cHF) would be needed to complete the study .Statistical analysis was performed using SPSS Statistical Analysis System . Normality of variables was assessed with Saphiro–Wilks test. Descriptive analysis for qualitative variables was performed using number of cases and percentages, while for quantitative variables, mean ± SD were used except when specified. Frequencies of qualitative variables  were compared between groups using Chi-squared analysis. Median values of quantitative variables were contrasted with non-parametric tests. Statistical significances between groups were determined with U Mann–Whitney tests. Correlation analyses were pursued using the Spearman correlation test. Receiver operating characteristic (ROC) curve analyses for predicted probabilities were performed to identify threshold concentrations of EVs able to discriminate between cHF NYHA stages, or between severity in ischemic patients, and the corresponding area under the curve (AUC) with its 95% confidence interval (CI) was calculated. Binary logistic regression models were pursued to estimate predicted probabilities for NYHA severity using combinations of EVs and/or the currently used biomarker NT-proBNP. A n = 60) while 59 (49.6%) had cHF with preserved ejection fraction . Additionally, 52 (43.7%) cHF presented a less severe cHF symptomatology according to the NYHA classification (class II), while 66 (55.5%) and 1 (0.8%) were in the higher severity spectrum, being in the NYHA stages III and IV, respectively. Demographics, classical risk factors and background medication are shown in Mean age of cHF was 67 ± 12 years 68% of men). Sixty cHF patients presented reduced ejection fraction  or both (CD3+/CD45+), were significantly increased in cHF (p < 0.0001). In addition, patients had significantly higher amounts of monocyte- and neutrophil-derived AV+-EVs carrying CD16+ (receptor Fcγ III) and CD15+ , respectively. EVs shed by natural-killer cells carrying the neural cell adhesion molecule-1 (CD56+) were significantly elevated in cHF compared to controls.Total levels of EVs-AV+-EVs from activated leukocytes  and activated neutrophils  were significantly increased in patients compared to controls . Specifically, AVcontrols .+/CD45+/AV+) significantly correlated with the cardiac damage markers hsTnT and NT-proBNP, while levels of EVs from activated neutrophils (CD29+/CD15+/AV+) and hsTnT and NT-proBNP showed a trend toward significance .The activation of the immunity cells measured by EVs did not correlate with the number of blood leukocytes or CRP levels, indicating different pathophysiological pathways. Interestingly, circulating EVs from T-lymphocytes (CD3ificance . cHF and+/AV+) and specifically those shed by T-lymphocytes . In addition, a significant increase in EVs from activated leukocytes (CD11b+/AV+) and activated neutrophils (CD29+/CD15+/AV+) was observed in these patients, compared to those with NYHA II (Chronic heart failure patients with more severe form of disease (a higher score in the NYHA classification [NYHA III-IV]) had higher values of EVs derived from leukocytes (CD45 NYHA II . Interes NYHA II .p = 0.031) and NT-proBNP .Distribution of the cHF population according to disease severity (according to the NYHA classification) is given in +/AV+ and CD45+/AV+ showed an AUC  (p = 0.001), hormone secreted by cardiomyocytes in the heart ventricles in response to stretching caused by increased ventricular blood volume that signals for a different pathophysiological pathway to that mapped by EVs   similar d by EVs .p = 0.015). These results indicate that immune cell activation is a common feature in patients with both HFpEF and HFrEF. Demographic, biochemical and clinical classification of the patients according to their LVEF is shown in Interestingly, no differences in immune cell-derived EVs levels/phenotypes were detected in HFpEF and HFrEF patients. Instead, as expected, a negative correlation of NT-proBNP and LVEF was observed  or carrying the leukocyte activation marker integrin β-1  were significantly increased in HF-Isch compared to non-ischemic patients (HF-NIsch). Moreover, EVs from activated neutrophils (CD29+/CD15+/AV+) displayed higher levels in HF-Isch compared to HF-NIsch, although this trend was non-significant (p = 0.079) (Chronic heart failure patients with ischemic etiology (HF-Isch) presented increased levels of EVs. Specifically, EVs derived from T-lymphocytes (CD3= 0.079) .+/AV+; p = 0.013), T-lymphocytes , and activated leukocytes, monocytes and neutrophils  in severe disease patients. No differences associated to severity were detected in HF-NIsch (+/AV+) had the best discrimination power, with an AUC of 0.848 ± 0.058 , followed by activated neutrophils (CD29+/CD15+/AV+), with and AUC of 0.811 ± 0.065 . Again, in an order of magnitude comparable to the contribution of disease showed by NT-proBNP, with an AUC of 0.816 + 0.071 . When analyses were performed with the combined probabilities of EVs from activated leukocytes and neutrophils (CD11b+/AV+ and CD29+/CD15+/AV+) the AUC increased to 0.858 ± 0.056 , with a sensitivity and specificity of 80.0 and 83.3%, respectively. The addition of NT-proBNP to the analysis did not improve discrimination  . There were significantly higher levels of EVs derived from leukocytes (CD45HF-NIsch . C-stati< 0.001)  indicatiExtracellular vesicles shedding levels, considering elapsed time between any ischemic event and sample collection, was similar in those patients with longer elapsed time between ischemic event and blood withdrawal (more than 1 year: 7 ± 7 years) and those with lower time-periods (less than a year: 5 ± 2 months) .Heart failure is a complex syndrome where the heart is unable to maintain a correct blood supply to ensure that the metabolic needs of the body are met . This caThe immune system has an important role in the setting of structural and functional changes in many organs, especially when the immune reparative processes become impaired. The present study has aimed at obtaining information on the activation state of different immunity cells in cHF patients. We have demonstrated that cHF patients present differential EVs profiles in comparison to control subjects, observing increased levels of EVs derived from leukocytes in cHF, reflecting the chronic activation state of these cells.+/AV+ and CD45+/AV+ was able to discriminate between NYHA stages II and III-IV, with an AUC of 0.658, indicating the implication of the activation of these immunity cells in disease severity pathophysiology. We also found that EVs could discriminate the etiology of cHF, with those patients with underlying ischemic etiology presenting significantly increased immune cell shed EVs. Further, we observed that in these patients EVs from activated leukocytes in combination with those from activated neutrophils, had a high discriminative potential in assessing cHF severity similar to NT-proBNP that measures another component of disease pathophysiology. We did not observe, however, differences in immune-cell-derived EVs profiles depending on the LVEF of cHF patients, indicating that both HFpEF and HFrEF patients have activation of immunity cells.We have specifically detected EVs derived from lymphocytes, neutrophils, monocytes/macrophages and natural-killer cells in cHF compared to controls. In addition, we have shown that numbers of these EVs, except those shed by natural-killer cells and monocytes, correlated with the severity of the symptomatology. The combination of EVs CD11b+ , T-lymphocytes (CD3+/AV+ and CD3+/CD45+/AV+) or carrying the activation marker CD11b+/AV+, positively correlates with disease severity. Further, we have shown for the first time that immune-cell-derived EVs associate to NYHA disease stage, and that immune cells are similarly activated in HFpEF and HFrEF patients. A non-resolving inflammation is a reported characteristic of HF  is expressed by activated cells and indicates the type of activated cell when is associated with another CD marker. Additionally, due to methodological issues we could not analyze EVs released by B lymphocytes, nor determine cell counts of all white blood cells analyzed. However, in view of our results, further studies addressing EVs from B cells are warranted.In conclusion, HF is a complex syndrome where multiple pathophysiological pathways converge. In well treated chronic heart failure patients, immunity cells, both innate and adaptive immunity cells, are activated and release high number of circulating EVs. The released EVs may amplify the underlying inflammatory processes in these patients, affecting different tissues. Indeed, EVs are involved in crosstalk with other cells, acting as distant cell function regulators of targeted-receptor cells, even in distant organs.The data that support the findings of this study are available from the corresponding author upon reasonable request.The studies involving human participants were reviewed and approved by Ref 16/44; Ethics Committee of the Hospital de la Santa Creu i Sant Pau in Barcelona. The patients/participants provided their written informed consent to participate in this study.LB, TP, ER, and SM designed the research. AV-F performed the experiments. AV-F, TP, and LB analyzed the results. AV-F, TP, SM, and LB wrote and revised the manuscript. All authors contributed to the article and approved the submitted version.


Similarly, only the Forrester classification according to systemic congestion predicted waitlist death, emergency HT, or LVAD implantation at 12 months, with p = 0.010 and p = 0.189 for systemic and pulmonary congestion, respectively. Moreover, systemic congestion was the main driver of cardiorenal events on waitlist. Conclusions: Forrester classification according to systemic congestion is associated with cardiorenal outcomes in patients listed for heart transplant and the risk of waitlist death, emergency HT, or LVAD implantation at 12 months.Introduction: Heart transplantation (HT) remains the gold-standard treatment but is conditioned by organ shortage. This study aimed to evaluate the value of Forrester classification and determine which congestion criteria had the best prognostic value to predict cardiorenal events on heart transplant waiting list. Methods and results: One hundred consecutive patients  with available right heart catheterization (RHC) listed in our center for HT between 2014 and 2019 were included. Cardiac catheterization measurements were obtained at the time of HT listing evaluation. Patients were classified according to perfusion and congestion status in four groups: “warm and dry”, “warm and wet”, “cold and dry”, and “cold and wet”. pWet was used to classify patients with pulmonary congestion and sWet for systemic congestion. The primary endpoint was the rate of a composite criteria of cardiogenic shock, acute kidney injury, and acute heart failure. Secondary endpoint was the incidence of waitlist death, emergency HT, or left ventricular assist device (LVAD) implantation at 12 months evaluated by Kaplan–Meier curves and log-rank test. Only Forrester classification according to systemic congestion was associated with the primary composite endpoint ( Heart failure (HF) is a public health problem affecting 1% to 2% of the European population, and advanced or refractory heart failure affects 1% to 10% of this population . Thus, iThis study aimed to evaluate the value of Forrester classification to predict cardio-renal events while listed for heart transplant and assess whether systemic or pulmonary congestion criteria has the best prognostic value. This study consisted of a mono-center cohort analysis using the French national CRISTAL database. All 201 new adult patients (18 years or older) registered on the French heart transplant waiting list between 1 January 2014 and 31 December 2019 at the Hospices Civiles de Lyon heart transplant center were included. In order to assess the value of right heart catheterization on events, patients without a cardiac catheterization and patients that were on the HT list for less than 30 days were excluded. Right heart catheterization measurements were obtained as part of the assessment for heart transplant listing according to the 2016 International Society for Heart Lung Transplantation guidelines . Elevated pulmonary vascular resistance (PVR) that is refractory to medical therapy may be a contraindication to HT [0.725 × Weight0.425). The PAPi (pulmonary artery pressure index) corresponded to (sPAP-dPAP)/CVP. The RVSWi (right ventricular stroke work index) corresponded to CI × mPAP × 0.0144.Hemodynamic variables obtained during cardiac catheterization included systolic blood pressure (SBP), diastolic blood pressure (DBP), cardiac output (CO), pulmonary capillary wedge pressure, systolic pulmonary arterial pressure (sPAP), diastolic pulmonary arterial pressure (dPAP), mean pulmonary arterial pressure (mPAP), and right atrial pressure (RAP) as indicator of central (CVP). Cardiac index (CI) is determined from cardiac output divided by body surface area (BSA), calculated from the Dubois formula . This registry is managed by the Agence de la biomédecine and prospectively collected data from every candidate for organ transplant in France . Data co“Acute heart failure” was defined as an unscheduled hospitalization for management of an episode of congestive heart failure with introduction of intravenous diuretics.“Cardiogenic shock” was defined by a hospitalization in intensive care unit requiring intravenous inotropic support.“Acute kidney injury” was defined according to KDIGO  −1.209 × 0.993age × 1.018  × 1.159 (for black patients), which α: −0.329  or −0.411  and k: 61.9  or 79.6 ).Estimated glomerular filtration rate was estimated using the CKDepi formula  , the study population was classified into four groups according to hemodynamic state (estimated by CI—“warm” or “cold”) and pulmonary congestion (estimated by PCWP—“dry” or “wet”) as Forrester classification. In parallel, the study population was classified according to hemodynamic state and systemic congestion (estimated by CVP).Cold patients were defined by a cardiac index ≤ 2.0 L/min/m²; pWet was used to classify patients with pulmonary congestion defined by pulmonary capillary wedge pressure ≥ 20 mmHg and sWet for systemic congestion defined by central venous pressure ≥ 10 mmHg.The primary composite endpoint was the annual rate of cardiogenic shock, acute kidney injury, and acute heart failure. Rates were the mean of the number of events during the waiting period on heart transplantation list divided by the time spent on waitlist in years in each patient and were expressed in events/patient*year.Secondary endpoint was one-year waiting list death or urgent heart transplant or LVAD implantation evaluated by Kaplan–Meier curves and log-rank test.25–75) range or mean (± standard deviation). Comparisons of continuous variables were carried out using Kruskal–Wallis test and t-test as required. Categorical variables were expressed as frequencies (percentages) and compared using chi-square test. Comparisons of events rate  were carried out using Kruskal–Wallis test. The two-tailed significance level was set at p < 0.05. Outcomes were reported according to Forrester’s groups. Secondary endpoint was assessed and illustrated by Kaplan–Meier analyses using log-rank test. After graphically verifying the assumption of proportional hazard model, multivariable Cox proportional hazard regression was used to examine the association between Forrester’s profile and outcome. A multivariable model adjusted for baseline characteristics  and eGFR was built. Correlation between PCWP and CVP was assessed using Pearson’s correlation test.Baseline continuous variables were expressed as medians with interquartile 25–75% . Among the 100 patients included, 72% were men, and the median age was 54 years. They received guideline directed treatments  inhibitors, or an angiotensin-2 receptor blockers or combination of valsartan-sacubitril and mineralocorticoid receptor antagonists). A minority of patients had comorbidities . All patients had severe left ventricular dysfunction with a median LVEF of 25% and were severely symptomatic (mean NYHA 2.76 and mean VO2max 11.2 mL/kg) a,b.p = 0.002 according to PCWP and p = 0.013 according to CVP). Loop diuretic dose was significantly different between Forrester’s profile according to PCWP (p = 0.010) but only tended to differ when CVP was considered (p = 0.058). Other treatments were comparable between groups except ARNI: Patients with systemic congestion had a lower likelihood of receiving ARNI (p = 0.019).Whether based on PCWP or CVP, eGFR was significantly different between group (p = 0.331). There was also no difference between the four groups in the rate of acute heart failure (p = 0.594), cardiogenic shock (p = 0.430), or acute kidney injury (p = 0.404)  A.p = 0.011). “Cold and sWet” (p = 0.007) and “Warm and sWet” (p = 0.009) had a higher rate of the primary criteria compared with “Warm and sDry” profiles. Other intergroup comparisons were not significant despite a trend of increased rate for “Cold and sWet” (p = 0.075) and “Warm and sWet” (p = 0.065) profiles compared with “Cold and sDry” profile. Similarly, the rate of AKI was different among groups, 0.57, 3.31, 1.92, and 2.45 for “Warm and sDry”, “Warm and sWet”, “Cold and sDry”, and “Cold and sWet” profiles, respectively (p = 0.049) (p = 0.010)  B. Interg= 0.010) B.p = 0.189)  A. Forres= 0.189) .p = 0.010) (p = 0.030)  B. The “C= 0.030) .Ten patients died on the waiting list .p = 0.01)  . CorrelaIn this retrospective study, we showed that Forrester’s classification is feasible and useful to stratify the risk of cardiorenal events for patients awaiting heart transplantation. Moreover, Forrester’s classification using systemic congestion (instead of pulmonary congestion) predicted the rate of composite criteria of acute heart failure, cardiogenic shock, acute kidney injury, and the risk of waitlist death, urgent LVAD implantation, or heart transplantation.Described in the late 1970s, Forrester’s classification was later set aside to reappear in the early 2000s. Indeed, whether by clinical  or invasSystemic congestion (assessed by RAP) was associated with poor prognosis in our study and seems to be the main driver of events on HT waiting list. It is indeed in accordance with literature, there is a growing amount of data on the role of congestion in heart failure. In the study published by Chioncel et al. , congestHowever, no association was found between cold profiles and primary cardiorenal endpoint in our population of advanced HF patients. The rate of cardiogenic shock, acute heart failure, and acute kidney injury did not differ significantly between groups. It is concordant with a recent meta-analysis , includiCongestion seems to be also the main driver of cardiorenal events. Indeed, Damman et al.  highlighWe hypothesized and showed that systemic and pulmonary congestion did not have the same prognostic value to identify cardiorenal events in patients awaiting heart transplantation. Despite a good correlation in our study and literature between RAP and PCWP ,24,25, tPCWP was neither associated with baseline GFR in patients hospitalized for acute heart failure nor worsening renal function ,21. In pSurprisingly, an association with one-year waitlist death, urgent HT, or LVAD implantation was found only with systemic congestion in our cohort. Interestingly, this is concordant with a recent study of Sokolska et al.  on patieFirst, we showed that patients listed for heart transplantation had a high rate of cardiorenal events while on the waitlist. Our data reinforced idea that congestion, especially systemic congestion, is the main driver of cardiorenal events in patients with advanced heart failure. There in emerging literature on available tools to evaluate congestion. These tools are listed in a recent expert review  and coulSecondly, acute kidney injury accounts for almost one-third of events on the waitlist, and particular attention should be paid to this complication. AKI is a life-threatening situation leading to a short-term risk of death, secondary to hyperkaliemia and hemodynamic compromise, but also long-term risk of chronic kidney disease and death ,33. AKI There are several limitations in our study. First, this study was a retrospective analysis, and some events could be missing if the patient was not hospitalized in our center. However, we are the only tertiary center in the geographic area, and patients listed for HT are systematically transferred in our institution in case of acute decompensation. Second, our study was focus on patients with advanced HF listed for heart transplantation, and results could not be generalizable to less severe, more co-morbid, or older heart failure patients. Third, hemodynamic variables were subject to measurement errors as well as intra- or interoperator variability. However, our center has a vast experience in pulmonary artery catheterization, limiting this bias. We did not systematically assess symptoms and clinical signs of congestion and could consequently not determine the agreement between clinical and hemodynamic assessment; however, prior studies suggest that these two approaches to congestion vary widely in advanced heart failure . Next, tOur study highlights the feasibility and prognostic value of Forrester classification in advanced heart failure patients for stratifying the risk of events in patients awaiting heart transplant. Moreover, Forrester classification using systemic congestion is associated with the rate of composite criteria of acute heart failure, cardiogenic shock, and acute kidney injury.


Soluble tumor necrosis factor receptor (sTNFR)1, sTNFR2, and plasma kidney injury molecule-1 (KIM-1) are associated with kidney events in patients with and without diabetes. However, their associations with clinical outcomes when obtained pre-operatively have not been explored.The TRIBE-AKI cohort study is a prospective, multicenter, cohort study of high-risk adults undergoing cardiac surgery. We assessed the associations between pre-operative concentrations of plasma sTNFR1, sTNFR2, and KIM-1 and post-operative long-term outcomes including mortality, cardiovascular events, and chronic kidney disease (CKD) incidence or progression after discharge.Among 1378 participants included in the analysis with a median follow-up period of 6.7 (IQR 4.0–7.9) years, 434 (31%) patients died, 256 (19%) experienced cardiovascular events and out of 837 with available long-term kidney function data, 30% developed CKD. After adjustment for clinical covariates, each log increase in biomarker concentration was independently associated with mortality with 95% CI adjusted hazard ratios (aHRs) of 3.0 (2.3–4.0), 2.3 (1.8–2.9), and 2.0 (1.6–2.4) for sTNFR1, sTNFR2, and KIM-1, respectively. For cardiovascular events, the 95% CI aHRs were 2.1 (1.5–3.1), 1.9 (1.4–2.6) and 1.6 (1.2–2.1) for sTNFR1, sTNFR2 and KIM-1, respectively. For CKD events, the aHRs were 2.2 (1.5–3.1) for sTNFR1, 1.9 (1.3–2.7) for sTNFR2, and 1.7 (1.3–2.3) for KIM-1. Despite the associations, each of the biomarkers alone or in combination failed to result in robust discrimination on an absolute basis or compared to a clinical model.sTNFR1, sTNFR2, and KIM-1 were independently associated with longitudinal outcomes after discharge from a cardiac surgery hospitalization including death, cardiovascular, and CKD events when obtained pre-operatively in high-risk individuals. Pre-operative plasma biomarkers could serve to assist during the evaluation of patients in whom cardiac surgery is planned.The online version contains supplementary material available at 10.1186/s13019-022-02066-4. Pre-existing kidney disease and postoperative acute kidney injury (AKI) are associated with poor short and longitudinal adverse outcomes in patients undergoing cardiac surgery –3. AlthoThe last decade has brought advances to non-invasive systemic and organ-specific biomarkers to assess the degree of injury and disease as well as the potential risk for complications in various clinical settings . Three oHuman studies have revealed that sTNFR1, sTNFR2, and KIM-1 are strong prognostic markers for incident and progressive CKD in persons with type 1 and type 2 diabetes mellitus in the ambulatory setting. , 31–34. This is an ancillary study from TRIBE-AKI, a prospective cardiac surgery cohort of 1474 adults who underwent cardiac surgery . Participants undergoing elective or emergent surgery were recruited at six academic medical centers in North America between July 2007 and December 2010. Patients were at high risk for AKI, defined by the presence of one or more of the following characteristics: emergency surgery, preoperative serum creatinine > 2 mg/dl, ejection fraction < 35%, age > 70 years, diabetes mellitus, simultaneous CABG, and valve surgery, or repeat revascularization surgery. Participants with evidence of AKI before surgery, prior kidney transplantation, preoperative serum creatinine level > 4.5 mg/dl or end-stage kidney disease (ESKD) were excluded. Patients who died during the index admission (n = 19) were excluded from the analysis as our main goal was to incorporate survivors and estimate the cumulative long-term risk of death as our primary endpoint. The study was approved by the institutional review board of each participating site, and written informed consent was obtained from all participants. We collected preoperative characteristics, intra-operative clinical data, and post-operative complications using the definitions of the Society of Thoracic Surgeons, and laboratory measurements in blood and urine prospectively, up to 5 years after the index admission. Full study details, including protocolized participant inclusion details and prospective clinical assessment have been described elsewhere. [Details of sample collection and processing have been described rigorously in previous reports –39. ThisWe recorded pre-operative serum creatinine values obtained within 2 months before surgery which served to estimate baseline kidney function. The preoperative and post-operative serum creatinine measurements were performed in the same clinical laboratory for each patient at all sites. The pre-operative estimated GFR (eGFR) was obtained via the Chronic Kidney Disease Epidemiology Collaboration equation.   ClinicalThe primary outcome for this study was longitudinal mortality after discharge. Vital status after the hospitalization was obtained through various mechanisms (and cross-referenced when possible). We performed phone calls to patients’ homes, searched the National Death Index, reviewed hospital records, and linkages with Center for Medicare and Medicaid Services (CMS) databases for those patients living in the United States. For Canadian participants , we obtained data from the Registered Persons Database held at ICES to acquire vital status. These datasets were linked using unique, encoded identifiers and analyzed at the ICES. Death status and date of death were recorded accordingly.2 pre-operatively, CKD incidence was defined as a 25% reduction in eGFR and a fall below 60 mL/min per 1.73 m2. In those individuals with an eGFR < 60 mL/min per 1.73 m2 pre-operatively, CKD progression was defined as a 50% reduction in eGFR or a fall below 15 mL/min per 1.73 m2. These definitions were based on established cutoffs as outlined by the multi-center Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study [The secondary outcomes were (a) cardiovascular events   and (b) I) Study . Follow-e transformed biomarker levels) and categorical (tertile) variables, with the lowest tertile serving as a reference group. Multivariable analyses were adjusted for the following variables: Age (per year), gender, white race (Yes/No), non-elective surgery, surgery type, pre-op eGFR, diabetes, hypertension, CHF, history of MI, pre-op urine albumin and urine creatinine, and clinical site. Cox proportional hazards regression models were used to examine the cause-specific association between the pre-operative biomarker measurements and the primary and secondary outcomes. Correlations between biomarkers are also presented. Kolmogorov-type supremum tests were used to evaluate proportional hazards assumptions for all models. Wilcoxon signed-rank test was used to ascertain biomarker concentrations per each outcome. In sensitivity analyses, we examined whether diabetes, age, and eGFR were effect modifiers on the relationship between the preoperative plasma biomarkers and the outcomes. Additionally, we explored the prognostic value of these biomarkers when added to a clinical model; comprised of comorbidities and kidney-specific laboratory parameters except the 3 novel biomarkers tested. The change in the Harrell’s c-index was used to quantify the incremental value of the biomarkers considering the time-to event approach used in this study. Delong test was used to assess statistically significant differences in discrimination. Small cell counts are only presented for data collected by TRIBE-AKI and not from ICES data holdings. For the latter, counts of 5 or fewer participants are suppressed to minimize the risk of participant re-identification. Two-tailed P values of less than 0.05 were considered statistically significant. Analyses were performed in SAS version 9.4.  and R version to 3.1.2 .Descriptive statistics were reported as mean (SD) or median (interquartile range) for continuous variables, and as frequency (percentage) for categorical variables. When evaluating the association between biomarkers and outcomes, biomarkers were analyzed both as continuous . Biomarker correlations included: TNFR1-TNFR2: 0.9, TNFR1-KIM1: 0.5 and KIM-1-TNFR2: 0.5.A total of 1378 participants who underwent cardiac surgery and had the 3 pre-operative biomarkers measured were included in the analysis and CKD events were adjudicated in 837 participants  than those with a baseline eGFR < 60 ml/min/1.73/m2  years, 434 31%) died within the study timeframe. Pre-operatively, median levels of sTNFR1, sTNFR2 and KIM-1 were higher in participants who died during the study timeframe  years, 256 (19%) participants had a cardiovascular event. At baseline, median levels of sTNFR1, sTNFR2 and KIM-1 were higher in participants who had cardiovascular events compared to those who did not within the study timeframe  than those with a baseline eGFR < 60 ml/min/1.73/m2 , 251 (30%) of 837 participants with follow-up kidney function measurements developed incident or progressive CKD. Characteristics between those that had data on longitudinal kidney function vs. those without were largely similar  obtained prior to a clinical insult and longitudinal post-operative kidney and non-kidney related outcomes. Additionally, we ascertained mortality status in 99% of study participants. In terms of limitations, we were only able to ascertain CKD outcomes post-surgery in two of the participating institutions, and the lack of kidney events could have compromised our analyses. Furthermore, this study did not account for post-index surgery hospitalizations or visits to the emergency department after discharge which could arguably modify the risk of subsequent clinical events. It is also possible that capturing only 2 pathways of kidney disease (injury and inflammation) remains a limited approach to predict kidney disease progression. Additional biomarkers may be needed to improve discrimination for longitudinal outcomes post-surgery. Lastly, our study participants were predominantly white, and this study lacks a validation cohort, both of which could limit the generalizability of our results.In conclusion, pre-operative concentrations of plasma sTNFR1, sTNFR2, and KIM-1 were associated with longitudinal cardiovascular complications, kidney events, and death after cardiac surgery in a high-risk population for kidney disease and complications. Diabetes mellitus did not modify the association between these biomarkers and all three clinical outcomes. Future studies should examine the utility of measuring plasma sTNFR1, sTNFR2, and KIM-1 in other surgical settings.Additional file 1. Table S1. Patient characteristics stratified by sTNFR2 and KIM-1 tertile in TRIBE-AKI. Table S2. Biomarker Concentrations by all-cause mortality, cardiovascular events, chronic kidney disease events . Table S3. Assessment of the interaction by diabetes mellitus on the association between pre-operative biomarkers and long-term outcomes in TRIBE-AKI (Adjusted for recipient Age (per year), recipient gender, white race (Yes/No), non-elective surgery, surgery type, pre-op eGFR, diabetes, hypertension, CHF, history of MI, pre-op urine albumin and urine creatinine, clinical site). Table S4. Pre-operative patient characteristics stratified by availability of CKD outcome assessment in follow-up


Clinical data for 97,443 surgery patients were abstracted from the DATADRYAD website; approximately 75% of these patients were enrolled in the derivation cohort, while approximately 25% were enrolled in the validation cohort. Multivariate logical regression was used to identify predictive factors for transfusion. Receiver operating characteristic (ROC) curves, calibration plots, and decision curves were used to assess the model performance. In total, 5888 patients received > 1 unit of red blood cells; the total transfusion rate was 6.04%. Eight variables including age, race, American Society of Anesthesiologists' Physical Status Classification (ASA-PS), grade of kidney disease, type of anaesthesia, priority of surgery, surgery risk, and an 18-level variable were included. The nomogram achieved good concordance indices of 0.870 and 0.865 in the derivation and validation cohorts, respectively. The Youden index identified an optimal cut-off predicted probability of 0.163 with a sensitivity of 0.821 and a specificity of 0.744. Decision curve (DCA) showed patients had a standardized net benefit in the range of a 5–60% likelihood of transfusion risk. In conclusion, a nomogram model was established to be used for risk stratification of patients undergoing surgery at risk for blood transfusion. The URLs of web calculators for our model are as follows:  The selection of blood donors is becoming stricter, and people are simultaneously less interested in voluntary blood donation2; the combination of these factors has led to substantial blood shortages. Especially due to the global COVID-19 outbreak, blood shortage caused by a decrease in blood donations has become an important global public health concern that needs to be solved urgently3. Additionally, preoperative testing for surgeries often includes blood typing and antibody screening (T&S). However, most patients are not transfused after all4. Transfusion improves patients' ischemia and hypoxia symptoms and reduces mortality, though it may also cause adverse transfusion reactions 5. In the current study, we aimed to develop a blood transfusion prediction model for identifying high risk patients prior to surgery. Preoperative knowledge regarding high-risk transfusion patients might improve blood resource management, inform clinicians of the likelihood of postoperative morbidities, reduce costs by avoiding unnecessary preoperative typing expenditures, and more effectively triage patients unlikely to need a transfusion.Blood transfusion is an essential life-saving treatment that is particularly suitable for surgical patients. In the United States, more than 13 million units of red blood cells (RBCs) are transfused clinically each year; the majority of these are used for surgery6, liver surgery7, or spine fusion surgery8. There is little study on the model forecasting red blood cell transfusion for normal perioperative patients. According to current medical guidelines9, surgical risk is divided into low risk, moderate risk, and high risk categories according to the difficulty and complexity of the operation. In addition, surgical patients have been divided into six grades within the American Society of Anesthesiologists scoring system according to patients' physical status prior to anaesthesia10. This combination of surgical risk grades and The American Society of Anesthesiologists' Physical Status Classification (ASA-PS) scores can provide reasonable classifications informing medical decision-making within operations.In prior research, most models have been developed in order to predict patients’ surgical transfusion needs for a single type of operation, such as for gynecologic surgeryIn this study, we combined blood routine and clinical parameters, including surgical risk grades and ASA-PS scores, in order to construct a predictive model for blood transfusion in perioperative patients via secondary analysis of a large dataset. We hypothesized that this model would predict the necessity for RBCs transfusion reliably and effectively and thus help identify patients that might benefit most from a patient blood management (PBM) program.www.datadryad.org). This website allows users to freely download raw data. According to the Dryad Terms of Service, we cited the Dryad data package in the present study . Variables included in the database file were as follows: age, gender, race, ASA-PS, cerebrovascular accidents, ischemic heart disease, congestive heart failure, diabetes mellitus, grade of kidney disease, type of anaesthesia, priority of surgery, surgery risk, anemia, mean corpuscular volume (MCV), red cell distribution width (RDW) and transfusion intra- and post-operation. 97,443 patients aged ≥ 18 years who underwent cardiac and non-cardiac surgeries  under anesthesia between 1 January 2012 and 31 October 2016 at Singapore General Hospital. Institutional review board approval was obtained  and all participants provided their written informed consent. In addition, we retrospective collected clinical data among 400 patients aged ≥ 18 years who underwent surgeries  using systematic random sampling11 between 1 March 2021 and 30 June 2021 at Xiangya Hospital of Central South University. Surgical disciplines that were included cardiothoracic, orthopaedics, obstetrics and gynaecology, general surgery, otolaryngorhinology, hand surgery, neurosurgery, colorectal surgery, urology, plastic surgery, and oromaxillofacial surgery. Clinical data of these patients included age, race, ASA-PS, grade of kidney disease, type of anaesthesia, priority of surgery, surgery risk, anemia, MCV, RDW and transfusion outcomes intra- and post-operation. These surgical patients completed pre-transfusion testing, including T&S, cross-matching (anti-human globulin microcolumn method) and transfusion infectious disease testing, prior to surgery. For elective surgical patient, after evaluating preoperative anemia, coagulation function and the amount of intraoperative blood loss by surgeon, blood bank determined eligibility for T&S and cross-matching based on the composition and quantity of blood requested by surgeon, then prepared the blood and preserved for 72 h. The Medical Ethics Committee of Xiangya Hospital of Central South University approved the sub-study  and waived the informed consent for individual patients sowing to the retrospective nature of the study. This study was conducted in accordance with the principles of the Declaration of Helsinki and its later amendments.Data were obtained from the 'DATADRYAD' database 13 and expert consensus14. Transfusion was implemented when the haemoglobin level was lower than 70 g/L. For patients with haemoglobin between 70 and 100 g/L, decisions regarding RBCs transfusion should be based on their age, clinical signs, cardiac and pulmonary compensation, metabolic rate, and the presence of active bleeding, etc. For non-urgent surgery patients with moderate/severe anemia, surgery needs to be postponed until anemia is corrected.All the included patients underwent a thorough physical examination and routine preoperative laboratory measurements at our institute. Following procedures reported in the previous literature15, anemia was classified as mild , moderate (Hb: 8–10.9 g/dL for both genders), or severe anemia (Hb: < 8.0 g/dL for both genders). According to the relevant literature16, RDW > 15.7% was defined as high RDW, whereas values ranging between 10.9 and 15.7% were defined as normal RDW. MCV > 100 fl was defined as high MCV, while 80–100 fl was defined as normal MCV and < 80 fl was defined as low MCV. Surgical risk and surgery priority classifications were based on the 2014 European Society of Cardiology and European Society of Anesthesiology guidelines9. The surgical risk was divided into low risk, moderate risk and high risk according to the difficulty and complexity of the operation and listed in Table 10. Specifically, the ASA score was divided into six grades and listed in Table 17 and the glomerular filtration rate.Based on WHO standards16.Perioperative blood transfusion, defined as ≥ 1 unit of red blood cells were accepted during surgery and within 30 days after surgery, was regarded as the outcome variable. Data regarding the transfusion of other blood components was not available in this study18. The points of each independent variable were summed, and the predicted probabilities were derived from the total points. Fourthly, model performance was assessed via discrimination and calibration in the derivation and validation cohorts. A receiver operating characteristics (ROC) curve was used to evaluate the distinguishing ability of the model. Youden index (sensitivity + specificity − 1) was applied to determine optimal cutoff values. A calibration curve was used to evaluate the relationship between the predicted and actual probability. A decision curve was used to evaluate the clinical application value of the model. Briefly, the decision curve is a form of decision analysis that informs clinicians regarding the threshold probability range for predicting the clinical value of a model. Decision curve analysis determines the association between the selected prediction probability threshold and the relative value of false-positive and false-negative results in order to obtain the net benefit of using the model under this threshold. Then we validated the clinical application of this model at our institution. According to the formula for calculating the sample size: n ≥ (k/β)2P(1 − P). The estimated objective difference does not exceed 2% (β = 0.02), α = 0.05, one-side: k0.05 = 1.645, the probability of blood transfusion in the model is 6.04% (P = 6.04%). And we calculated sample size n ≥ 384. We collected variables according to predictors of model. Each variable corresponded a point which accorded to nomogram. Assigned points for all variables are then summed and can be got total points. ROC curve and cut-off value were used to assess the ability of total points in predicting transfusion risk of our institution. In addition, we obtained the percentage of benefited population by comparing the coincidence of predicted transfusion events with actual transfusion events.Patients who received 0 units RBCs transfusion were divided into the non-transfusion group, received 1 unit and 2 or more than 2 units of RBCs transfusion were divided into the transfusion group. We first compared the baseline data for patients in the transfusion and non-transfusion groups. Categorical variables were described as counts (percentages) and differences were assessed using Fisher's test. Secondly, the dataset was randomly divided in a 3:1 ratio, with the former participants used as the derivation cohort and the latter participants used as the validation cohort. To assess for interaction between Anemia and MCV/RDW, we calculated ORs comparing patients with none, mild, moderate/severe anemia after stratifying by MCV/RDW status. We did further subgroup analysis of the anemia categories stratified by MCV and RDW. We compared differences between the derivation and validation cohorts. Thirdly, univariate and multivariate logistic regression analyses were performed to explore associations between perioperative variables and blood transfusion risk in the derivation cohorts. According to the results of multivariate logistic regression analysis, we used Empower Stats software to formulate a nomogram. The nomogram can proportionally convert each regression coefficient in the logistic regression to a scale of 0 to 100 pointshttp://www.R-project.org; Vienna, Austria) and Empower Stats software .All statistical analyses were performed using R software  and a validation cohort  in a 3:1 ratio via a simple random sampling method. The baseline data for the derivation and validation cohorts are presented in Supplementary Table The following demographic and clinically relevant preoperative predictors were selected as candidate variables for the prediction model based on statistically significant associations observed in the univariate analysis within the derivation cohort Table . SpecifiAccording to the multivariate logistic regression analysis, eight predictors, including age, race, ASA-PS, grade of kidney disease, type of anesthesia, priority of surgery, surgery risk, and the 18-level variable specified above were included in the model establishment. A nomogram was generated to predict the risk of blood transfusion among patients undergoing surgeries Fig. . The dis19. As a result, patient 1 is considered a high risk transfusion population. The decision curve  and linked with electronic medical record systems and/or surgical planning systems. When we input the preoperative information of the surgical patient into this calculator, transfusion probability of patient was obtained. Thereafter, the sensitivity and specificity of this nomogram for estimating the transfusion risk were summarized, and the cut-off value of 0.163  was identified in the derivation cohort according to the maximum Youden index. Patients with a transfusion probability > 0.163 were considered as high-risk transfusion recipients, and their blood typing and cross-matching should be performed prior to surgery, and PBM should be arranged as soon as possible. Patients with a transfusion probability < 0.163 were considered as low-risk transfusion recipients, they could eliminate the need to complete pretransfusion testing and preoperative blood preparation. In addition, in Wiebe J. de Boer et al.’s study, they found the cut-off value of transfusion risk was 0.168, which was consistent with our findings20.In this study, we constructed a nomogram model to predict transfusion risk by incorporating the eight independent predictors  in surgical patients. The performance of the nomogram was validated internally, and demonstrated good discrimination and calibration, implying that the nomogram had accuracy for predicting transfusion. To confirm the clinical utility, we determined the decisions assisted by this nomogram whether improved patients’ consequences by using DCA. The clinical outcomes based on the threshold probability can be known from this new method. False positive proportion was subtracted from the true positives proportion, and then the relative risk of false positive and false negative results was weighted to obtain the net benefit. It can be gotten from the decision curve that if the threshold probability of a patient was 5–60%, the net benefit was got by using our nomogram to predict transfusion. This nomogram was based on routinely collected preoperative data so as to maximize its clinical applicability, as well as to ensure that it was generalizable and easy to use. According to the weight coefficient score of each predictor in the prediction model, nomogram could be developed into a web-based calculator  was similar to that of anemia (AUC: 0.748)  than patients with microcytic anemia, yet they were the most likely to receive RBC transfusions34. Anemia is one of the most important variables for predicting transfusion risk, but RDW and MCV provide additional prognostic value. All these measures should be considered when estimating transfusion risk in patients undergoing surgery33. Beyond hemoglobin level, anemia can be classified via RDW and MCV, and there is an established association between RDW, MCV and transfusion risk. In our study, we included preoperative anemia, MCV, and RDW as predictive factors and found interactions between RDW, MCV, and anemia that were associated with transfusion risk. Many studies have reported interactions between MCV, RDW, and anemia. Anthony et al. showed multiplicative interactions between MCV and RDW in predicting mortality among patients with or without anemia35. Kor et al.36 reported an interaction between MCV and RDW in predicting mortality within chronic kidney disease. Therefore, we performed further subgroup analyses based on an 18-level variable stratified by RDW, MCV, and anemia categories.Not surprisingly, 18-level variable was a strong predictor of transfusion in our current model. The AUC of the 18-level variable was 0.762, which was higher than other variables  for identifying patients at high risk of needing blood transfusions. In addition to these substantial strengths, this study has some limitations. Firstly, although we internally validated our model in order to assess model performance, it is necessary to externally validate the model further by using a multi-centre large samples cohort. Secondly, MCH is possible a better marker of anaemiaIn conclusion, in the current study, we established a new nomogram model predicting patients’ perioperative transfusion needs over a wide spectrum of operations.Supplementary Figure S1.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


Inflammation has been considered to play an important role in the pathogenesis of the thoracic ossification of the ligamentum flavum (OLF). However, the inflammation-related risk factors of thoracic OLF have not been fully investigated to date.  A total of 95 patients (48 in the OLF group and 47 in the control group) were included in this retrospective study to explore the independent risk factors of thoracic OLF. The following demographic and clinical variables were compared between the two groups: gender, age, body mass index (BMI), coexistence of hypertension or diabetes, and inflammation-related variables. Multivariate logistic regression analysis was utilized to determine the independent risk factors. p < 0.01) and BMI (≥25 kg/m2)  were independent risk factors of thoracic OLF. SII  and BMI  were positively associated with OLF score.  High systemic immune-inflammation index (SII) (≥621) (odds ratio [OR] = 12.16, 95% confidence interval [CI] = 2.95–50.17,  High SII and BMI were the independent risk factors of thoracic OLF. Multicenter prospective studies with a large population should be conducted in the future to verify our findings. The thoracic ossification of the ligamentum flavum (OLF) has become the leading cause of thoracic spinal stenosis, which can compress the spinal cord and lead to thoracic myelopathy , 2. Patiα, promote the onset and development of OLF [Thoracic OLF has been considered as a multifactorial disease, and several pathogenic factors, including genetic inheritance, metabolic disorders, inflammation, and mechanical stress, contribute to its onset and development –7. Severt of OLF , 12. HowSystemic inflammatory responses have been demonstrated to be associated with several nonimmunological diseases, including cancer , 14, intIn this study, we compared the demographics and clinical variables of patients with thoracic OLF with those of patients without thoracic OLF to determine the inflammation-related risk factors of OLF.This study was approved by the Ethics Committee of Peking University Third Hospital. Informed consent was not needed given the retrospective design of this study. Patients receiving posterior laminectomy for thoracic OLF from March 2021 to October 2021 at our hospital were included in the OLF group. Inflammation and abnormal immunity have been demonstrated to play important roles in the pathogenesis of degenerative musculoskeletal diseases, such as intervertebral disc degeneration and osteoarthritis , 25. Fur2 to distinguish between overweight patients and patients with normal weight.The following variables were collected for each included patient: gender; age; BMI; coexistence of hypertension or diabetes; and preoperative peripheral blood cells, including neutrophils, platelets, monocytes, and lymphocytes. The indexes of interest were calculated as follows: NLR = neutrophil counts/lymphocyte counts, PLR = platelet counts/lymphocyte counts, MLR = monocyte count/lymphocyte counts, and SII = platelet counts × neutrophil counts/lymphocyte counts. The OLF score of patients with thoracic OLF referred to the sum of segments affected by the ossified ligamentum flavum as previously reported . The cutt-test. Dichotomous variables, including gender and the coexistence of hypertension or diabetes, were compared between two groups by using the χ2 test. Variables with p < 0.10 in univariate analysis were included in the multivariate logistic regression analysis for further analysis. Variables with p < 0.05 in multivariate logistic regression analysis were considered as the independent risk factors of thoracic OLF and shown in the form of odds ratio (OR) with the corresponding 95% confidence interval (CI). The correlation between OLF score and BMI or SII was analyzed by using Spearman's correlation test. Here, p < 0.05 indicated a statistically significant result.All analyses in this study were conducted by using the SPSS 22.0 software package . Continuous variables, including age, BMI, NLR, PLR, MLR, and SII, were compared between two groups by using the independent samples 2, respectively. Moreover, 24 patients had coexisting hypertension or diabetes. The OLF and control groups comprised 48 and 47 patients, respectively.The demographics of the included patients are listed in p = 0.03) and BMI  than the control group. For inflammation-related variables, the OLF group had an obviously higher NLR  and SII  than the control group. The two groups showed no significant differences in terms of gender (p = 0.26), the coexistence of hypertension or diabetes (p = 0.38), PLR (p = 0.39), or MLR (p = 0.11).As listed in p < 0.10 in univariate analysis were further included in multivariate logistic regression analysis to determine the independent risk factors of thoracic OLF. As listed in 2)  and SII (≥621)  were independent risk factors of thoracic OLF. However, age (p = 0.20) and NLR (p = 0.48) were not significantly related to thoracic OLF after adjusting for all variables.Variables with R = 0.38, p < 0.01)    and SII  < 0.01)  were sigα [2) are independent risk factors of OLF. To the best of our knowledge, we are the first to demonstrate that high SII (≥621) is an independent risk factor of OLF.OLF has become the leading cause of thoracic spinal stenosis , 2. Inflα , 29. Howα , 22, 23.SII was introduced as an indicator of the systemic inflammatory status in the body and has attracted increasing attention from researchers . SII can2) is another independent risk factor of OLF. Obesity or overweight is involved in the development of several human diseases, including OLF [p < 0.01) [p < 0.01) and detected a significantly positive relationship between BMI and OLF score [p < 0.01) [High BMI  . In thisSome limitations should be considered when interpreting our findings. First, this study had a retrospective design and small sample size that might reduce the accuracy of its results. Second, we selected patients with reducible hernia as the control individuals. However, patients with reducible hernia are not same as the healthy population. This situation might affect the generalizability of our findings. Third, inflammatory cells in peripheral blood are easily influenced by the external environment, such as bacterial or viral infections. Fourth, blood samples were collected preoperatively, and the change in SII during the pathogenesis of OLF was not confirmed given the retrospective design of this study. Fifth, some of the included patients were diagnosed with hypertension or diabetes, which might affect the SII results , 49. MulHigh SII and BMI are independent risk factors of OLF. SII and BMI are positively associated with OLF score. Inflammation and obesity might contribute to the pathogenesis of OLF.


Studies of the clinical application of dynamic cerebral autoregulation show considerable variations, and differences in blood pressure devices may be one of the reasons for this variation. Few studies have examined the consistency of invasive and non-invasive arterial blood pressure for evaluating cerebral autoregulation. We attempted to investigate the agreement between invasive and non-invasive blood pressure methods in the assessment of dynamic cerebral autoregulation with transfer function analysis.Continuous cerebral blood flow velocity and continuous invasive and non-invasive arterial blood pressure were simultaneously recorded for 15 min. Transfer function analysis was applied to derive the phase shift, gain and coherence function at all frequency bands from the first 5, 10, and 15 min of the 15-min recordings. The consistency was assessed with Bland–Altman analysis and intraclass correlation coefficient.The consistency of invasive and noninvasive blood pressure methods for the assessment of dynamic cerebral autoregulation was poor at 5 min, slightly improved at 10 min, and good at 15 min. The values of the phase shift at the low-frequency band measured by the non-invasive device were higher than those measured with invasive equipment. The coherence function values measured by the invasive technique were higher than the values derived from the non-invasive method.Both invasive and non-invasive arterial blood pressure methods have good agreement in evaluating dynamic cerebral autoregulation when the recording duration reaches 15 min. The phase shift values measured with non-invasive techniques are higher than those measured with invasive devices. We recommend selecting the most appropriate blood pressure device to measure cerebral autoregulation based on the disease, purpose, and design. The concept and term of cerebral autoregulation (CA) were first proposed by Lassen  and refewww.car-net.org) improving the standardization of parameters and settings adopted for TFA applications in studies of dCA  , subaracs of dCA , differeFour of the most common methods for ABP measurements include arterial line measurement, finger photoplethysmography, sphygmomanometry, and tonometry . The firThe study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangzhou University of Chinese Medicine (ZE2019-247-01). Patients admitted to the neurological intensive care unit (NICU) at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine from October 2020 to December 2020 were eligible for the study. A radial artery catheter was placed in all subjects to monitor ABP for clinical purposes. Patients with bilateral poor temporal windows found in TCD, middle cerebral artery (MCA) peak flow velocity >300 or < 40 cm/s, atrial fibrillation found in electrocardiography, poor non-invasive ABP signal or poor cooperation were excluded at initial screening.Measurements were performed in the NICU room by the same professional technician in the morning. The room was a temperature-controlled environment of 22–24°C. All subjects received breathing assistance from a ventilator  and were fed liquid nutrition through a small, lightweight, portable, and accurate enteral feeding pump . Data were collected when patients were in a supine position. CBFV was assessed using TCD . Bilateral MCAs were monitored at a depth of 45–60 mm through temporal windows with 2-MHz probes attached to a head frame. BP was measured simultaneously by the bedside monitor  from the intravascular catheter inserted into the left radial artery and by servo-controlled finger photoplethysmography . A finger cuff of appropriate size was placed on the left middle finger . After tThe data were processed using MATLAB . The raw data, which were not removed or interpolated, were used for analysis. The invasive ABP and non-invasive ABP were synchronized with CBFV on the signal to eliminate the time delay. The dynamic relationship between ABP and CBFV was analyzed by TFA based on the algorithm provided by CARNet. It can be calculated as follows:f), |H(f)|, and γ2(f) represent the phase shift, the gain, and the magnitude-squared coherence function, respectively; H(f), HI(f), and HR(f) represent the transfer function, the imaginary component of the transfer function, and the real component of the transfer function, respectively; and Sxx(f), Syy(f), and Sxy(f) denote the averaged autospectra of ABP, the averaged autospectra of CBFV and the cross-spectra of ABP and CBFV, respectively. In this algorithm, the anti-leakage window was a Hanning window, whose length was 90 s with 50% superposition. The 5-, 10-, and 15-min recordings comprised 5, 12, and 19 windows of data segments, respectively. Then, the TFA calculated the phase shift, gain, and coherence function between the ABP and CBFV at the very low frequency , low frequency , and high frequency  bands. The phase shift in the LF band or gain in the VLF band reflects the CA level, and the coherence function reflects the linear correlation between CBFV and BP.where Φ were finally included in the study. Admission diagnoses included acute intracerebral hemorrhage, acute ischemic stroke, sequelae of cerebral infarction, endovascular therapy for basilar artery occlusion, and endovascular therapy for right middle cerebral artery stenosis. The characteristics of the patients are listed in P = 0.148), 0.750 (P < 0.001), and 0.909 (P < 0.001) (< 0.001) . The pha< 0.001) .P = 0.280), 0.281 (P = 0.153), and 0.613 (P = 0.010) (= 0.010) . The gai= 0.010) .P = 0.061), 0.492 (P = 0.022), and 0.492 (P = 0.036) (= 0.036) . The Mx_= 0.036) .P = 0.020), 0.543 (P = 0.032), and 0.350 (P = 0.130) (= 0.130) . The Sx_= 0.130) .In the current study, the consistency of IBP and NIBP for the assessment of dCA was poor at 5 min, slightly improved at 10 min, and good at 15 min. When the recording duration reached 15 min, the LF phase shift values measured by NIBP were higher than values derived from IBP, while VLF gain values did not significantly differ between the two arterial BP methods. In addition, the coherence function values measured by IBP were higher than those measured by NIBP.Very little is known about whether different measurements of ABP may affect the calculation of dCA parameters. Lavinio et al.  investigAlthough the two methods were consistent, the intermethod differences in values warrants examination. For example, our results show that the differences in phase shift were on average 5.19 degrees lower with IBP than with NIBP. Although the details of the waveform are scarce, the location of acquisition for the BP signal may have affected the phase shift, as distal locations have a greater influence on the rhythmic changes in arteriolar tone, resulting in an increased phase shift. Alternatively, NIBP may have significantly overestimated intra-arterial systolic MF (0.07–0.14 Hz) and LF (0.025–0.07 Hz) powers , which mExcept for the possibility of overestimating the phase shift, NIBP has lower coherence function values than IBP, perhaps because more extraneous noise is presented in the NIBP, leading to a poor signal-to-noise ratio . AccordiOf course, the aforementioned intermethod bias does not mean that we should preferentially use IBP to measure dCA. The advantages and disadvantages of both methods should be known. For critically ill patients, IBP should be the preferred method for dCA assessment . HoweverIt is well-known that the first stage of TFA is to obtain mean values of BP and CBFV for each cardiac cycle in the time-domain. Thus, it is necessary to discuss the correlation of BP readings between the two modalities. Tanioku et al. compared BP measurements during induction of anesthesia for cardiovascular surgery, and their results showed that non-invasive mean arterial pressure (MAP) could be considered as an alternative for radial artery blood pressure . The agrThis study has several limitations. First, few suitable participants underwent IBP monitoring for clinical purposes, and the size of the sample was consequently small. Second, we only focused on NICU patients suffering from neurological diseases, and the applicability of these results to other disease settings is unclear. Third, whether a recording duration longer than 15 min would lead to a different result is also unclear.In summary, we found that IBP and NIBP measurements in the low frequency band were consistent for dCA assessment as the recording duration reached 15 min. The difference in TFA parameter values between methods should be considered when using different BP devices for dCA assessment. Selecting the optimal BP method for each patient to evaluate dCA may be helpful for the clinical promotion and application of dCA assessment.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Guangzhou University of Chinese Medicine. The patients/participants provided their written informed consent to participate in this study.Original draft preparation and data collection: WZ. Writing-review and editing and investigation: HL. Methodology and validation: JL. Statistical analysis: AO. Data analysis: PZ. Conceptualization: JZ. All authors contributed to the article and approved the submitted version.


Only 7% of patients with CHF had weak inspiratory muscle strength (<60% of that predicted) and 40% terminated the 6MWT due to dyspnea. The 6MWT was associated with mMRC  but not with respiratory muscle strength (p > 0.05). Conclusions: Exercise intolerance in patients with CHF was associated with dyspnea and was independent of respiratory muscle strength.Background: Exercise intolerance in patients with chronic heart failure (CHF) is associated with a number of factors, including breathlessness and respiratory muscle weakness. However, many studies reported controversial results, and as yet there is no study on Arabic patients with CHF. This study aimed to examine the impact of breathlessness and respiratory muscle strength on exercise capacity in Arabic patients with CHF. Methods: This was a cross-sectional study, involving 42 stable adult male patients with CHF with a reduced ejection fraction and 42 controls who were free from cardiorespiratory and neuromuscular diseases. Patients with CHF and the controls underwent respiratory muscle strength tests and a six-minute walk test (6MWT), and the measurements were taken. Dyspnea was recorded using the modified Medical Research Council (mMRC) scale, along with the number of comorbidities. Results: Patients with CHF and controls were similar in age and sex. Patients with CHF had a greater number of comorbidities, a higher dyspnea score, a lower 6MWT score, and lower respiratory muscle strength ( Chronic heart failure (CHF) is a multifaceted disease that is associated with several manifestations beyond the heart, including exercise intolerance, breathlessness, and reduced respiratory muscle strength. Exercise intolerance is the hallmark symptom of CHF, and patients with CHF experience a progressive decline in exercise capacity, yet cardiac physiological indices are not severely impaired ,2.Exercise capacity is an important predictor of readmissions, morbidity, and mortality among patients with CHF. The mechanism of exercise intolerance is yet to be revealed; however, a number of contributing factors play a role, such as breathlessness and reduced respiratory muscle strength.Breathlessness is a term describing a patient’s experience and sensations, which is usually different from the biomedical term of dyspnea ,4. The pSeveral studies have examined the link between respiratory muscle strength and dyspnea and their effect on exercise intolerance among patients with CHF ,7,8,9,10This was a cross-sectional study involving stable adult male patients with CHF who presented with reduced ejection fraction. Patients with CHF who were suitable for the study were identified via the King Salman Heart Center’s outpatient clinic. Patients with CHF were included in the study if they were clinically stable for more than 3 months and had not received any adjustment therapy one month prior to recruitment. Patients were excluded if they had unstable angina, myocardial infarction within the previous month, cardiac surgery in the past three months, chronic respiratory disease, pulmonary artery hypertension, chronic metabolic disorders, severe musculoskeletal or neurological disorders, or infectious disease. Patients were also excluded if they were treated with steroids, hormones, or cancer chemotherapy, as this can affect or alter the patient’s central and peripheral muscle strength and exercise performance. Controls without CHF were recruited from community centers, the patients’ relatives, and smoking cessation clinic databases. Controls were included if they had no cardiorespiratory disease and similar exclusion criteria were applied to the patients with CHF. The study was approved by the King Saud University ethics committee (IRB number: E-19-4472) and the King Fahad Medical City ethics committee (IRB number: 20-104E); participants signed a consent form before enrolment. The study was conducted during the period from March 2020 to October 2020 and was performed in accordance with the principles of the Declaration of Helsinki.Sample size estimation was calculated using the G*power program, version 3.0.10 , based o2. All patients with CHF were interviewed to record their medical history and medications and had their vital signs taken using a Welch Allyn 52000 monitor . The left ventricle ejection fraction (LVEF) value over four weeks was recorded from the patient’s electronic file.All participants had their age, height, and weight details taken, and their body mass index (BMI) was calculated and recorded in kg/mAll participants underwent respiratory muscle strength measurements, using a reliable and valid device (MicroRPM CareFusion 232 (UK) Ltd., UK) ,18. All Respiratory muscle strength was assessed while the participant was sitting on a chair with their feet flat on the floor and their back against the back of the chair, with their hands resting on their thighs and their shoulders back. The procedures were thoroughly explained to the participants, and they were given time to practice before the test. During these tests, the participants were encouraged verbally to perform to the best of their ability.The participant was instructed to insert the mouthpiece into the mouth, ensuring that the flange was positioned over the gums and inside the lips. While the ‘bite block’ was held between the teeth, the participant was asked to exhale to their residual volume (RV), lungs empty, then to perform a ‘Mueller’ maneuver, a forced inhalation using as much effort as possible for as long as possible, for a minimum of 2 s. Each participant repeated the test three times, with a resting period of 30 s between each attempt, and the highest value was recorded.The participant was instructed to insert the mouthpiece into the mouth, ensuring that the flange was positioned over the gums and inside the lips, while the ‘bite blocks’ were held between the teeth, then the participant was asked to inhale to their total lung capacity (TLC), lungs full, then to perform a ‘Valsalva’ maneuver, a forced exhalation using as much effort as possible for as long as possible, for a minimum 2 s. The test was repeated three times, with a resting period of 30 s between each attempt, and the highest value was recorded.After choosing and placing an appropriate nostril probe inside the participant’s nostril, the probe was connected to the MicroRPM device. The participant was then asked to maintain normal breathing, with the mouth closed. At the end of a relaxed expiration, showing the functional residual capacity (FRC), the participant was asked to take a sharp and quick inhalation of maximum inspiratory effort. The test was repeated at least six times, with a resting period of 10 s between each attempt, and the highest value was recorded. Heritier et al.  describeThe 6MWT was carried out in accordance with a protocol adapted from the ATS guidelines, using a 30-m straight-level course in an enclosed corridor . The disDyspnea in daily life was evaluated using the modifiable Medical Research Council  . The part-test was used to compare the groups for parametric data, and the Mann–Whitney U-test was used for nonparametric variable comparisons. An analysis of covariance (ANCOVA) was used to examine the differences between subgroups, after controlling for possible confounding factors. The correlation between variables was examined using a Spearman correlation for nonparametric data or a Pearson correlation for parametric data in each group. Multiple linear regression was conducted to determine the predictive factors of exercise capacity in the patient group. The MIP reference values were calculated using the Evans and Whitelaw algorithm [Data analysis was performed using IBM SPSS Statistics for Windows, Version 27.0 . Data normality was examined prior to data analysis. For descriptive statistics, parametric data were presented as a mean and SD, and nonparametric data as the median and interquartile range (IQR). The independent lgorithm : male MIlgorithm . Patient2 saturation, but patients with CHF had greater mMRC score and a greater number of comorbidities, compared with controls   compared with controls, even after adjusting for the mMRC score and the number of comorbidities ( < 0.05) . in patients with CHF, but this was not evident in the controls. There was no correlation between 6MWT and the number of comorbidities, neither in patients with CHF , nor in controls . Neither exercise capacity nor resting or exertional dyspnea correlated with the left ventricle function ejection fraction (p > 0.05). The relationship between the 6MWT and mMRC remained significant, even after adjusting for the number of comorbidities and the left ventricle function ejection fraction . The 6MWT was inversely correlated with mMRC rs = 0.26), or SNIP (rs = 0.103), nor expiratory muscle strength or MEP (rs = 0.04) correlated with exercise capacity in the 6MWT. This finding was similar to those in the controls. The number of patients with CHF who terminated the 6MWT due to breathlessness was 17 (40%). Dyspneic patients with CHF who terminated the 6MWT prematurely had a lower exercise capacity compared to those non-dyspneic patients with CHF who did complete the test , SNIP  or expiratory muscle strength, MEP . This did not change, even after controlling for the number of comorbidities and LVEF . Similarly, the control group showed no correlation between respiratory muscle strength and mMRC (p > 0.05). Dyspneic patients with CHF showed no significant differences in inspiratory and expiratory muscle strength, compared to non-dyspneic patients with CHF .In the patients with CHF, stepwise multivariate regression analysis, after controlling for age, LVEF, and the number of comorbidities, showed that mMRC explained 38% of the variability in the 6MWT  of the patients who were recruited in this study were in stage III_IV of the NYHA Functional Classification. Although the NYHA Functional Classification is frequently used among cardiologists in cases of CHF, it is a subjective and weak outcome measure by which to discriminate between functional impairment in patients with CHF. Finally, we did not examine the effects of medications on the reported outcome measures. However, heart failure medications  have minimal effects on exercise capacity and exertional dyspnea.This study found that exercise intolerance in patients with CHF was related to a higher level of dyspnea, independent of respiratory muscle strength. The level of dyspnea was not associated with respiratory muscle strength, although the patients with CHF had lower respiratory muscle strength but were not weak compared with the controls.


To develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.A total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.A total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores . According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes.Presurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT. Cerebral infarction induced by acute large-vessel occlusion (LVO) in middle cerebral artery or vertebrobasilar artery has a high rate of disability and mortality   is widely applied in calculating disease-associated risk predictors, it still has limitations: it cannot provide satisfying accuracy, and it struggles with large numbers of variables  age ≥18 years old; (2) within 24 h of onset; (3) neuroimaging-confirmed intracranial LVO induced AIS, including anterior cerebral artery (A1/A2), middle cerebral artery (M1/M2), basilar artery, intracranial internal carotid artery (T/L), vertebral artery (V4), and posterior cerebral artery (P1); (4) received EVT, including mechanical thrombectomy, angioplasty, intra-arterial thrombolysis, and stenting; and (5) with complete follow-up data with 90-day modified Rankin scale (mRS). The exclusion criteria were (1) malignant tumor; (2) incomplete data; (3) lost to follow-up; (4) severe heart, lung, and renal disease; and (5) pre-stroke mRS > 2 scores.The external validation data, including 169 patients, was obtained from two of four hospitals from January to July 2021. The inclusion and exclusion criteria followed the above criteria as well.Baseline data, including demographic characteristics, medical history, laboratory tests, clinical characteristics, occlusion locations, treatment details, and treatment outcomes, were collected for further analysis.The 973 patients from internal data were divided into favorable group (mRS ≤ 2 scores) and unfavorable group (mRS > 2 scores) based on 90-day mRS. A total of 20 important variables before EVT were selected to construct predictive model. These variables include general parameters, medical history, stroke etiology, treatments and key time points, laboratory test, and occlusion sites.We used a traditional statistical method and machine learning algorithms, namely, LR and LightGBM. LR is a classification model. It is widely applied in industrial issues and is simple to implement. However, it has limitations in accuracy because of overfitting and poor ability to handle too many variables. However, LightGBM can be trained quickly, costs low memory consumption, has high accuracy, and can support distributed and fast processing of massive data. More importantly, LightGBM was suitable for handling the structured data used in this study, which are of various types and some are missing. Other models need to perform null value processing, normalization, and other operations.The LightGBM method involves the following steps: choice of a suitable dataset, selection of meaningful features, undersampling and splitting of dataset, training classification models, evaluation of classifiers' performance, and ranking of the weights of the influence factors.Machine learning algorithm LightGBM was trained on the dataset consisting of 973 patients. The undersampling method was used to get balanced subsets. Then, the dataset was randomly split into two subsets, namely, training subset (80%) and test subset (20%). The training subset was used for establishing the model and the test subset for evaluating the model generalization on new data. This process was repeated 100 times to obtain average values. In this model, 20 important variables were trained as inputs to classify patients into favorable and unfavorable outcome groups. For the LightGBM model, parameters were set as follows, namely, number of estimators as 65, max depth of the tree as 6, and learning rate as 0.14. We tuned each parameter of the machine learning model and finally selected the optimal parameters. What needs illustration is that 20 presurgical variables are brought into our study based on their importance of influencing prognosis, including admitting blood glucose, age, time from onset to EVT, time from onset to hospital, NIHSS score, intravenous thrombolysis, hospital, stroke etiology, gender, transient ischemic attack (TIA), hypertension, atrial fibrillation, drinking, diabetes mellitus, smoking, time from hospital to puncture, occlusion site, coronary heart disease, hyperlipidemia, and stroke.U-test. LR with statistical analyses was performed with R .If baseline characteristics were in accordance with normal distribution, they were analyzed by one-way ANOVA. If not, the data were analyzed by Mann-Whitney From January 2018 to December 2020, a total of 1,090 patients who underwent EVT for AIS were included in this study, and 117 patients were excluded due to certain reasons .n (percent) or median [interquartile range (IQR)]. Patients in favorable outcome group were generally younger (65 vs. 73 years), consisted of more men (68.1 vs. 55.6%), had lower baseline NIHSS score (12 vs. 17), and had lower incidence of hypertension (63.2 vs. 70.4%), diabetes (16.8 vs. 23.2%), atrial fibrillation (26.8 vs. 35.5%), ischemic stroke (12.5 vs. 20.6%), and coronary heart disease (9.4 vs. 14.8%).The baseline characteristics of favorable outcome group and unfavorable outcome group are summarized in In addition, as shown in The primary outcome assessed by mRS is shown in In contrast, the external data shown in To establish an accurate presurgical prediction model for EVT, we collected as many variables as possible to increase the dimensions so as to avoid overfitting. Altogether 20 closely related parameters before EVT were selected. After training and testing the datasets 100 times, the LightGBM algorithm assigned different weightings to variables based on their significance. The top 5 important parameters for predicting outcomes of EVT for AIS were admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and NIHSS scores  .p < 0.001), high baseline NIHSS score , and high levels of admitting blood glucose   .Furthermore, we compared the two prediction models and found that LightGBM model performed better than LR model (0.738 vs. 0.701 in accuracy and 0.824 vs. 0.795 in area under the curve) . LightGBTo further clarify the validity of important parameters found in LightGBM model in predicting outcomes, we first confirmed critical values and cutoff points using receiver operating characteristic (ROC) curve. The cutoff points of top 5 predictors for the prognosis were as follows: age was 67 years, NIHSS scores were 14, admitting blood glucose was 6.47 mmol/L, onset to EVT time was 380 min, and onset to hospital time was 178 min. In terms of age, we classified patients into two different groups and found that 51.5 and 25.6% patients achieved favorable outcome in ≤67 years age group and >67 years age group , respectively . In contTherefore, we further established a prediction scale based on both the cutoff points of these predictors and their weighting values . The comIn this study, we used machine learning LightGBM model to analyze 20 important presurgical variables and constructed a prognostic model of EVT for LVO in real world for the first time and identified several key predictors based on the weightings of multidimensional features, including age, NIHSS scores, admitting blood glucose, onset to hospital time, and onset to EVT time. On this basis, we further established a prediction scale to assess the proportion of patients with unfavorable outcomes and confirmed that score of 3 was the key cutoff point that can distinguish unfavorable outcomes from favorable outcomes.In recent years, several studies have revealed that brain edema, high mean blood pressure, greater blood pressure variability, and high NIHSS scores were associated with adverse outcome of successful embolectomy of AIS , and treatment method  are vital prognostic markers  and the National Natural Science Foundation of China (Nos. 82071304 and 81671149 to QZ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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