Background: High total IgE levels are weak predictors of T2High and have been reported in nonallergic asthma. Therefore, the role of total serum IgE (IgE) in the T2High phenotype is still debated. Objective: This study investigated the reliability of stratifying asthmatics into IgEHigh and IgELow within the T2High and T2Low phenotypes. Methods: This cross-sectional single-center study investigated the association of clinical, functional, and bio-humoral parameters in a large asthmatic population stratified by IgE ≥ 100 kU/L, allergen sensitization, B-EOS ≥ 300/µL, and FENO ≥ 30 ppb. Results: Combining T2 biomarkers and IgE identifies (1) T2Low-IgELow (15.5%); (2) T2Low-IgEHigh (5.1%); (3) T2High-IgELow (33.6%); and T2High-IgEHigh (45.7%). T2Low-IgELow patients have more frequent cardiovascular and metabolic comorbidities, a higher prevalence of emphysema, and higher LAMA use than the two T2High subgroups. Higher exacerbation rates, rhinitis, and anxiety/depression syndrome characterize the T2Low-IgEHigh phenotype vs. the T2Low-IgELow phenotype. Within the T2High, low IgE was associated with female sex, obesity, and anxiety/depression. Conclusions: High IgE in T2Low patients is associated with a peculiar clinical phenotype, similar to T2High in terms of disease severity and nasal comorbidities, while retaining the T2Low features. IgE may represent an additional biomarker for clustering asthma in both T2High and T2Low phenotypes rather than a predictor of T2High asthma “per se”. Asthma is a heterogeneous disease concerning onset, natural course, and response to treatment . Demographic, clinical, pathophysiological, and genetic characteristics of asthmatics guided the identification of recognizable clusters termed «asthma phenotypes» . This apHigh) and low (T2Low) airway inflammation phenotypes [High and T2Low phenotypes [ENO) are routinely used for identifying the type-2 asthma phenotype. Generally, the assessment of FENO < 25 ppb has a higher negative predictive value for likely eosinophilic airway inflammation in asthma. However, both FENO and B-EOS often lack specificity [Asthma includes type-2 high  is a protein involved in the immune response. In particular, IgE is known for its role in allergic reactions. Allergen-specific IgE binds the high-(FcεRI) and low-affinity receptors (FcεRII) for IgE located on effector cells and antigen-presenting cells. The FcεRI is constituted by α, β, and γ subunits . FcεRIα is IgE receptor-specific, whereas FcεRIβ and FcεRIγ are also part of other Fc receptors . Allergy is per se a type-2 biomarker . AllergeTotal serum IgE levels are usually higher in allergic versus nonallergic asthma, but they may substantially overlap. One study described three clusters of allergic asthma, with cluster 1 having a higher proportion of patients with total serum IgE levels < 100 (kU/L) (31.5%) compared with the others . The SpaHigh IgE levels have, conversely, been extensively reported in nonallergic asthma. The bronchial mucosa of nonallergic asthma includes not only a similar expression of T2 cytokines and eosinophils as that of allergic asthma but also a similar number of FCεR1β positive cells . PolymorThe usefulness of total serum IgE in differentiating allergic and nonallergic asthma is not so well defined compared with other T2 diseases, such as atopic dermatitis .ENO [Therefore, it can be generally assumed that IgE is a common product of the type-2 inflammation pathway, shared by both allergic and nonallergic asthma. Unfortunately, the sensitivity and specificity of total serum IgE as a predictor of airway eosinophilia are quite weak and significantly lower than those of B-EOS and FENO .To date, total serum IgE is approved as the biomarker for determining eligibility in severe asthma patients for omalizumab  treatment . The other currently used biologics in severe asthma treatment are represented by monoclonal antibodies directed towards the main effectors of T2 inflammation: mepolizumab, an anti-IL-5 antibody; benralizumab, an anti-IL-5R antibody; and dupilumab, a fully human monoclonal antibody against the IL-4Rα subunit of the IL-4/13 receptor . The modENO (≥35 ppb) [ENO > 25 ppb [High marker, but only 52% and 12% of them had allergic rhinitis or atopic dermatitis, respectively. In a Danish study, the single elevation of total serum IgE of >150 IU/mL (reported in 18% of patients) was consistent with higher S-EOS in only 35%, being the majority associated with a neutrophilic or paucigranulocytic phenotype (T2Low) [A post-hoc analysis of moderate-to-severe asthma showed a high degree of overlap among allergen-specific IgE (positive skin prick and/or serum > 0.35 kU/L), B-EOS ≥ 300 cells/μL, and F≥35 ppb) . In this> 25 ppb . Interes (T2Low) .Low phenotype has been extensively described in asthma, often characterized as non-eosinophilic asthma, accounting for slightly more than 50% in both steroid-naïve and steroid-treated patients [The T2patients  and varipatients . Mean topatients ,26. A mopatients .High and IgELow subpopulations in terms of demographic, clinical, and functional traits and to compare them within the commonly recognized T2High and T2Low phenotypes. We also reasoned that high total serum IgE levels may be detected in non-allergic states, e.g., obesity, viral infections, air pollution, or smoking [These data suggest the limited value of total serum IgE as a biomarker predictive of allergic or type-2 inflammation in asthma. This study aimed to replicate, in a cross-sectional, single-center, large asthmatic cohort, the characteristics of IgE smoking .In the current observational cross-sectional real-life study, we consecutively enrolled adults age ≥ 18 years) as outpatients with guideline-validated asthma diagnoses 8 years a, attendiENO were collected before treatment initiation for severe asthma patients on biologics. Parasite infestation was not ruled out due to the low prevalence in Italy. Patients enrolled in the study did not have active cancer disease and had to be cancer disease-free (remission) for at least 5 years.Demographic data, clinical history, functional and humoral parameters, and medications were retrieved from chart data for each patient after at least a complete assessment, adjustment, and review management cycle, according to GINA guidelines . Data weENO was analyzed at a flow rate of 50 mL/s with FeNO+ . Asthma Control Test (ACT) questionnaire was used to evaluate asthma control perception [Pulmonary function and lung volumes were assessed by spirometry, performed before and 15 min after albuterol administration (400 µg), and plethysmography using a Vmax Encore 62 . Frception . Asthma treatment strategy and asthma severity were graded according to the GINA guidelines . We consAllergen sensitization to perennial  and seasonal inhalants was detected by positive skin prick tests and/or serum allergen-specific IgE. Patients sensitized to ≥2 allergens were considered polysensitized.High inflammation occurred whether at least one condition among B-EOS > 300 cells/µL, FENO ≥ 30 ppb or allergic sensitization existed [ENO cut-off value ≥ 30 ppb, as previously found in our population [T2 existed . We selepulation . Total spulation .High, ≥100 kU/L).Patients were stratified according to both T2 inflammatory status and total serum IgE concentrations (IgE2) tests (with Fisher’s exact post-hoc approach) [In descriptive analyses, the ROUT method detected outliers to be excluded . The devpproach)  comparedp-value less than 0.05 was set as the significant cut-off. Variables for which a statistically significant difference resulted in comparing the groups were selected for further binomial logistic regression analysis. Variables giving significant odds or odds ≥ 2 were put into the multinomial LRM. The collinearity test for continuous variables was applied through inspection of correlation coefficients and accepted for variance inflation factors (VIF) values between 1 and 10.In binomial and multinomial logistic regression models (LRMs), odds ratios (ORs) were calculated as exponentiation of the B-coefficient for each factor, which represents the odds of a unit change occurring in the independent variable in the analysis when all the others are kept constant. The 95% confidence interval (CI) was also reported. A Descriptive statistics and regression models were analyzed with IBM SPSS Statistics version 28 .Descriptive statistics for the whole cohort of 547 asthmatic patients are reported in High due to the presence of one or more markers of T2 inflammation were 434 (79.3%). In detail, 298 (68.7%) had allergic sensitization, 232 (42.4%) had FENO ≥ 30 ppb, and 227 (41.5%) reported B-EOS ≥ 300 cells/µL. The remaining 113 subjects (20.7%) were defined as T2Low (High (IgE ≥ 100 kU/L) were 278 (50.8%), while 269 (49.2%) were considered IgELow. The descriptive statistics of clinical and functional parameters among groups of patients stratified by a single biomarker were reported in Low and IgELow; (2) T2Low and IgEHigh; (3) T2High and IgELow; and (4) T2High and IgEHigh. The distribution of one to three positive T2 markers among IgELow  or without  high total serum IgE levels, is represented outside the blue line.In both diagrams, the blue line delimits the T2ENO were significantly higher in the T2High compared with the T2Low groups, irrespective of their total serum IgE values  in T2Low, 153 ± 65 cells/µL in IgELow, and 161 ± 69 cells/µL in IgEHigh, respectively; mean T2High FENO was 44.0 ± 36.9 ppb, 43.0 ± 32.4 ppb in IgELow, and 44.7 ± 40.1 ppb in IgEHigh; significantly increased compared with T2Low FENO of 14.7 ± 7.3 ppb . Mean total IgE levels were significantly higher in the whole T2High group (306.6 ± 572.3 kU/L) compared with the T2Low group (131.2 ± 337.8 kU/L) but were comparable within both the IgEHigh  and the IgELow subgroups (44.3 ± 27.6 kU/L in T2High and 30.2 ± 23.5 kU/L in T2Low). The T2High-IgEHigh subjects reported higher allergen sensitization and prevalence of perennial allergens compared with the T2High-IgELow subjects .Both B-EOS and FE values A. In detHigh and T2Low patients significantly differed in terms of age, sex, and BMI, with T2High patients being significantly younger, more frequently male, and thinner. T2High had a shorter age of onset but did not differ in asthma duration  and more frequently in the GINA Step 4 level of treatment (p < 0.01) compared with T2High patients, often needing LAMA add-on treatment, while T2High patients were more frequently in GINA Step 5 (p < 0.05) (T2duration A. Signif < 0.05) B.High compared with T2Low patients . On the other hand, T2Low patients reported higher rates of emphysema , obesity , diabetes mellitus , arterial hypertension , and acute myocardial infarction   C. High groups showed significant differences compared with the T2Low-IgELow group but less compared with the T2Low-IgEHigh one , earlier onset (p < 0.01 and p < 0.05), a higher proportion of male subjects (p < 0.001 and p < 0.05), and better lung function in terms of absolute FVC (p < 0.001 and p < 0.05) compared with the T2Low-IgELow group.Both T2High-IgEHigh had a lower BMI (p < 0.001), a higher AE rate (p < 0.05), and a significantly higher FEV1 (p < 0.001) compared with T2Low-IgELow, while T2High-IgELow had a FRC% significantly lower than T2Low-IgELow (p < 0.05). T2High-IgEHigh and T2High-IgELow were more often classified in GINA Step 5 and less in GINA Step 4 and used more frequently nasal corticosteroids (CS) compared with the T2Low-IgELow group (p < 0.001).Both T2High groups. T2High-IgELow patients were more often female with a significantly higher BMI than the T2High-IgEHigh patients (p < 0.05).Some differences are also evident between the two T2Low groups, T2Low-IgEHigh subjects showed a higher prevalence of male sex (p < 0.01), past smokers (p < 0.05), GINA Step 5 and Step 2 (p < 0.05), and lower FVC% (p < 0.05) compared with the T2Low-IgELow group  and higher OCS and lower anti-leukotriene use compared with the T2High subgroups. Within the two T2ow group A,B. The High-IgEHigh from T2Low-IgELow included a higher prevalence of rhinitis (p < 0.001) and chronic rhinosinusitis with nasal polyps (CRSwNP) (p < 0.01) and a lower occurrence of emphysema (p < 0.01), cardiovascular comorbidities (p < 0.05), GERD (p < 0.05), diabetes mellitus (p < 0.05), and obesity (p < 0.05). More frequently, rhinitis (p < 0.001) and CRSwNP (p < 0.01) were observed in T2High-IgELow patients compared with T2Low-IgELow patients (Low-IgEHigh had a lower rate of rhinitis (p < 0.05) but a higher rate of OSAS (p < 0.01) and obesity (p < 0.05) compared with T2High-IgEHigh. Finally, within the T2High subgroups, the presence of low IgE was associated with higher obesity (p < 0.05), arterial hypertension (p < 0.05), and anxiety/depression syndrome (p < 0.05).The main comorbidities that differentiated T2patients C. T2Low-When applying a binomial LRM to variables to predict T2 High versus T2 Low status, significantly higher odds were obtained for rhinitis (OR: 2.0), use of nasal CS (OR: 3.3), and GINA Step 5 (OR: 4.8), while lower risks were associated with emphysema (OR: 0.3) and poor ACT (OR: 0.3) . The risLow-IgELow group, either positively  or negatively . A forest graphic representation of binomial LMR is reported in When applying a binomial LRM for variables to predict the odds for each single subgroup compared with the rest of the population , major sLow-IgELow compared with T2High-IgEHigh were high BMI (OR: 1.1), emphysema (OR: 4.0), heart failure (OR: 8.0 × 108), LAMA use (OR: 9.9), and poor control (OR: 3.6), while negative odds were found for rhinitis and nasal CS use compared with both the T2High subgroups (p < 0.05).After adjusting for colinearity, multinomial LRM was applied to predict the odds for variables associated with each subgroup compared with each of the others . The maiLow groups, factors associated with high total serum IgE were the frequent exacerbator phenotype (OR: 7.9), rhinitis (OR: 8.5), and anxiety/depression .Within the T2Low group within the T2High. A forest graphic representation of binomial LMR is reported in Finally, the presence of anxiety/depression, use of LAMA, and lower FRC% could predict entry into the IgEENO [High and type-2Low asthma phenotypes is still open.Total serum IgE levels above a predefined cut-off have been widely used as a biomarker of type-2 inflammation in asthma. This paradigm derives from the expected association between high total serum IgE and the presence of allergen-specific IgE. High total serum IgE has also been found in “intrinsic” asthma, likewise characterized by eosinophilic airway inflammation . HoweverENO , indepenENO . A systeENO . The numENO . TherefoLow and IgELow; T2Low and IgEHigh; T2High and IgELow; and T2High and IgEHigh. Their distribution revealed T2High-IgEHigh as the most prevalent (45.7%), followed by T2High-IgELow (33.6%). Overall, T2High patients accounted for 79.3% of the total population. These data were consistent with studies derived from moderate to severe unselected populations with T2High prevalence ranging from 79.1% to 45.3% depending on whether allergy, B-EOS, or FENO were chosen as selective criteria [High phenotype were younger age, male sex, lower BMI, better lung function (FVC and FEV1) control, and a more frequent GINA Step 5 level of treatment. Moreover, rhinitis and nasal polyps were significantly more prevalent in T2High patients compared with T2Low patients. On the other hand, T2Low subjects were worst controlled, more frequently in GINA Step 4 level of treatment, often needed LAMA as add-on treatment, and reported higher rates of comorbidities related to past smoking habits (emphysema), metabolic syndrome , and cardiovascular disease . We applied a binomial logistic regression analysis to identify variables as predictors of T2High versus T2Low status, finding the highest odds for rhinitis, use of nasal CS, and GINA Step 5, and the lowest risks for emphysema and poor ACT. These findings are in line with previous reports [High and T2Low groups may develop fixed airflow obstruction due to both eosinophilic and neutrophilic-derived airway remodeling [The ISAR used a predefined total serum IgE cut-off of 75 kU/L or greater as a biomarker of type-2 severe asthma , while acriteria . In our h-IgELow .6%. Overh-IgELow .6%. Overmodeling .High and T2Low phenotypes according to the serum IgE cut-off of 100 kU/L. This cut-off overlaps with the median value within our population (102 kU/L) and has been previously used both for cluster analysis in asthma populations [Low-IgELow patients have no marker of type-2 inflammation. As expected, these subjects showed B-EOS and FENO significantly lower and sharply below the thresholds of the T2High groups. These patients had different clinical presentations and comorbidities than others. First, compared with the T2High groups, they had more frequent cardiovascular comorbidities, known to be associated with an accelerated Th1/Th17 inflammatory milieu [The further sub-analysis detected two distinct populations within the T2ulations  and typeulations . The T2Ly milieu ,40. Low-IgELow group was positively identified by high BMI, low FEV1, emphysema, and LAMA use. Obesity was an important feature of this subset, composed mainly of older females with late-onset asthma, gastroesophageal reflux, and diabetes mellitus. A late-onset, obesity, high neutrophils, low eosinophils, and low IgE cluster in females was reported in a Taiwanese Adult Asthma Cohorts study [Low asthma phenotype associated with frequent bronchial hyperresponsiveness (BHR) and a lower AE risk than subjects with T2High disease [Low-IgELow patients collected both the mild and severe T2Low asthmatics described in the above-mentioned cohorts. When applying a logistic regression analysis, the T2ts study . Obesityts study . The SAR disease . In this disease . Moreove disease . Exercis disease . We can Low-IgEHigh). They differed from the T2Low-IgELow group in having a higher male and ex-smoker prevalence. Applying a logistic regression analysis model, the T2Low-IgEHigh subjects showed significant odds of being AE-prone and suffering from rhinitis and anxiety/depression. In addition, sex , past smoking habits, OSAS, and obesity were also factors associated with high but not statistically significant odds for the T2Low-IgEHigh group compared with the T2Low-IgELow group.We here describe a small group of patients characterized by low T2 biomarkers and high total serum IgE levels (T2ENO (14 ppb) and B-EOS (140 cell/µL) corresponded to those of our T2Low-IgEHigh group. However, both the clusters above mentioned did not exclude patients with allergic sensitization, as we did. Therefore, we can assume that just a proportion of those populations corresponded to ours. Accordingly, cluster analysis from the U-BIOPRED network, based on sputum omics, reported that about half of the cluster T2  were not allergic, but a sub-analysis of this group was not available [A similar phenotype has been described by a cluster analysis of inflammatory biomarker expression in the ISAR, in which cluster 4 was characterized by very high levels of total serum IgE 1932 kU/L), the longest duration of asthma, and a high BMI 32 kU/L, . What isvailable . Smokingvailable ,47. Finavailable .High subgroups, the presence of low IgE was associated with higher obesity (p < 0.05), arterial hypertension (p < 0.05), and anxiety/depression syndrome (p < 0.05). This last feature, as well as the use of LAMA and a lower FRC%, could predict the entry of the IgELow group within the T2High. A cluster analysis study applied to allergic asthmatics described a similar cluster 1, gathering a high proportion of IgE-low patients (<100 kU/L), prevalently female, with mean FENO levels of 44.3 ppb and mean B-EOS of 304.0/mL [In our study, within the T2304.0/mL . Within 304.0/mL . Among tatients <0 kU/L, p304.0/mL .We can speculate that serum total IgE is a biomarker able to identify different and specific (sub)phenotypes in both T2 high and T2 low clusters. Low, triple “negative” for the expression of T2 biomarkers [Low patients. Here, we extend the previous observation to a wider population, describing the two subgroups of T2Low-IgELow and T2Low-IgEHigh, which are different from a clinical point of view, as detailed above.In a recent study, we described among a population of 503 mild to severe asthmatics that about 19.5% of patients could be classified as T2omarkers . These pThe strength of this study was the large cohort of included patients and the vast number of evaluated parameters. However, the study had some limitations. One limitation was the cross-sectional nature, such as the variability of biological measures along the natural history of a patient. Some data reported during a 5-year retrospective observation showed that 66.7% of participants had more than 50% alternations of IgE in two different measures . In addiHigh and T2Low phenotypes according to the most routinely accepted measurable biomarkers. The role of a high level of IgE in T2Low patients seems to be associated with a peculiar clinical phenotype, distinct not only from T2High patients but also from patients with low total serum IgE and low T2 markers. Moreover, lower levels of total IgE in the context of a type-2 phenotype seem to confer a somehow different profile on the T2High subgroup. Therefore, measuring total IgE could be useful in asthma workup and management as it may suggest clinically relevant information orienting towards forms of asthma with overlapping pathogenetic mechanisms involving IgE and other inflammatory pathways. According to a recent review [low phenotype, such as in smokers or obese asthmatics, could move towards a high IgE response, generating novel phenotypes with pathognomonic clinical signs.This single-center cross-sectional study explored the role of total IgE levels among asthmatic patients classified into T2t review , this st


Conversely, in another propensity score-based study, norepinephrine administration within the first hour following shock diagnosis increased day-28 mortality. The only randomized controlled study that compared the early administration of norepinephrine alone to a placebo showed that the early continuous administration of norepinephrine at a fixed dose of 0.05 µg/kg/min, with norepinephrine added in open label, showed that shock control was achieved more often than in the placebo group. The choice of starting norepinephrine administration early should be adapted to the patient’s condition. Logically, it should first be addressed to patients with profound hypotension, when the arterial tone is very low, as suggested by a low diastolic blood pressure (e.g. ≤ 40 mmHg), or by a high diastolic shock index (heart rate/diastolic blood pressure) (e.g. ≥ 3). Early administration of norepinephrine should also be considered in patients in whom fluid accumulation is likely to occur or in whom fluid accumulation would be particularly deleterious .During septic shock, vasopressor infusion is usually started only after having corrected the hypovolaemic component of circulatory failure, even in the most severe patients. However, earlier administration of norepinephrine, simultaneously with fluid resuscitation, should be considered in some cases. Duration and depth of hypotension strongly worsen outcomes in septic shock patients. However, the response of arterial pressure to volume expansion is inconstant, delayed, and transitory. In the case of profound, life-threatening hypotension, relying only on fluids to restore blood pressure may unduly prolong hypotension and organ hypoperfusion. Conversely, norepinephrine rapidly increases and better stabilizes arterial pressure. By binding venous adrenergic receptors, it transforms part of the unstressed blood volume into stressed blood volume. It increases the mean systemic filling pressure and increases the fluid-induced increase in mean systemic filling pressure, as observed in septic shock patients. This may improve end-organ perfusion, as shown by some animal studies. Two observational studies comparing early The online version contains supplementary material available at 10.1186/s13054-023-04593-5. During septic shock, vasopressors, first and foremost norepinephrine, are administered to restore tissue perfusion pressure. For a long time, the rule was to start vasopressors only after correcting the hypovolaemic component of circulatory failure, even in most severe patients. However, starting norepinephrine earlier, simultaneously with fluid resuscitation, should be considered. Initiating infusion peripherally may avoid delays in norepinephrine administration, as recognized by the Surviving Sepsis Campaign guidelines. As this attitude is safe when low doses are used , the reaDuration and depth of hypotension are strongly associated with poor outcomes in septic shock. Volume expansion may induce variable responses in arterial pressure, first because its effects on stroke volume are inconstant, delayed, and transitory, second because arterial elastance, which determines the relationship between the fluid-induced changes in flow and in arterial pressure, is variable. In the case of profound, life-threatening hypotension, relying only on fluids to restore blood pressure may unduly prolong hypotension and consequently organ hypoperfusion. Conversely, norepinephrine rapidly increases arterial pressure, its dose being adjusted according to the arterial pressure target , the upstream pressure of systemic venous return. This fluid-like effect has been demonstrated in septic shock patients  or afterOnce started, norepinephrine may even increase the effectiveness of future fluid loadings. Once the venous reservoir is constricted by norepinephrine, the fluid administered increases the stressed blood volume more than if it spreads in a dilated venous system. In septic shock patients, the effect on Pmsf of a passive leg raising, mimicking the effects of a fluid challenge, was greater at a higher dose than at a lower dose of norepinephrine . These sTo this preload-related synergy between norepinephrine and fluid, an improvement in cardiac output may be added through an increased cardiac contractility , possiblThe final question is of course whether early norepinephrine improves tissue perfusion and/or organ function. Only experimental data are available. In two studies in animals with endotoxic shock, early norepinephrine administration added to fluid infusion improved cardiac output, increased gut microvascular blood flow, and blunted the increase in lactate levels , 9.vs. later administration of norepinephrine, three used propensity scores. Ospina-Tascon et al. [Among the observational studies comparing early n et al.  and Xu en et al.  showed tConversely, in another propensity score-based study, norepinephrine administration within the first hour following shock diagnosis increased day-28 mortality . The maiSo far, only one randomized controlled study has compared the early administration of norepinephrine alone to the concomitant administration of a placebo . PermpikIn the recent CLOVERS  study, pFrom the expected effects of the early administration of norepinephrine, it should first be used in patients with severe hypotension even though there is no consensual definition about the level. On the other hand, it should be used in patients with very low arterial tone, as suggested by low diastolic blood pressure (e.g. ≤ 40 mmHg), or a high diastolic shock index (heart rate/diastolic blood pressure) (e.g. ≥ 3). Computation of arterial elastance may identify patients who would necessitate norepinephrine to correct hypotension, but this approach requires cardiac output monitors which are not available at this very early stage of resuscitation.Early administration of norepinephrine should also be considered in patients in whom fluid accumulation has occurred prior to hypotension, is likely to occur , or those in whom fluid accumulation would be particularly deleterious  Fig. . AdminisEarly administration of norepinephrine during shock may be justified in patients with profound vasoplegia and/or at high risk of fluid overload, along with a personalized fluid administration strategy .Additional file 1: Figure S1 Potential benefits and risks of early administration of norepinephrine in septic shock


Preload responsive postoperative patients with signs of inadequate organ perfusion are commonly assumed to be hypovolemic and therefore treated with fluids to increase preload. However, preload is influenced not only by blood volume, but also by venous vascular tone and the contribution of these factors to preload responsiveness in this setting is unknown. Based on this, the objective of this study was to investigate blood volume status in preload-responsive postoperative patients.Data from a clinical trial including postoperative patients after major abdominal surgery were analyzed. Patients with signs of inadequate organ perfusion and with data from a passive leg raising test (PLR) were included. An increase in pulse pressure by ≥ 9% was used to identify patients likely to be preload responsive. Blood volume was calculated from plasma volume measured using radiolabelled albumin and hematocrit. Patients with a blood volume of at least 10% above or below estimated normal volume were considered hyper- and hypovolemic, respectively.A total of 63 patients were included in the study. Median (IQR) blood volume in the total was 57 (50–65) ml/kg, and change in pulse pressure after PLR was 14 (7–24)%. A total of 43 patients were preload responsive. Of these patients, 44% were hypovolemic, 28% euvolemic and 28% hypervolemic.A large fraction of postoperative patients with signs of hypoperfusion that are likely to be preload responsive, are hypervolemic. In these patients, treatments other than fluid administration may be a more rational approach to increase cardiac output.Trial registration EudraCT 2013-004446-42 Signs of inadequate organ perfusion in the postoperative phase after major surgery are often treated with fluid to increase preload. Given that overly aggressive fluid treatment is likely to have adverse effects, guidelines suggest that prediction of fluid responsiveness by dynamic indices should be used to identify patients that are likely to respond to fluid therapy with an increase in cardiac output; in other words, patients on the steep part of the Frank–Starling curve .Pmsf) which, together with right atrial pressure, are the major determinants of venous return and hence preload [The physiological rationale for treating all preload-responsive patients with signs of inadequate organ perfusion with fluids could, however, be questioned. The volume of the venous circulation can be divided into the unstressed and stressed volumes. The unstressed volume is the volume that fills the vessel without exerting pressure on the vessel walls. The stressed volume is the volume that distends the veins and creates the mean systemic filling pressure  trial. AIR was a single-center, investigator-initiated prospective parallel-group, randomized trial designed to assess if plasma volume expansion by 5% albumin is influenced by infusion rate after major abdominal surgery. The main results have been published elsewhere . BrieflyEthical approval was provided by the regional ethical vetting board in Lund, Sweden (# 2014/15) and all patients gave written consent. The AIR study was registered in the European Clinical Trials Database (EudraCT 2013-004446-42).2 < 70%,ScvOlactate > 2 mmol/l,urine output < 0.5 ml/kg in the hour prior to inclusion,systolic BP < 100 mmHg or mean arterial pressure (MAP) < 55 mmHg.One or more signs of hypoperfusion was observed within 5 h of admission to the post-anesthesia care unit. Signs of hypoperfusion were defined as:Complete data from a passive leg raise test were available.A plasma volume measurement performed within 30 min after the passive leg raise test was available.Patients in the AIR database were included in the present study if:All measurements were thereby performed within 5.5 h of admission to the post-anesthesia care unit.The passive leg raising test reversibly increases preload by transferring blood from the lower extremities to the central compartment and meta-analyses suggest good discrimination between preload responders and non-responders in a large number of studies –7. At thBlood volume (BV) was calculated from baseline plasma volume (PV) and hematocrit (Hct). The formula used was :\documen125I human serum albumin (HSA) (SERALB-125®) as described in detail previously [125I-HSA was injected intravenously and the concentration of 125I-HSA in plasma at 10 min post-injection was measured using a gamma counter. Plasma volume was then calculated by dividing the injected dose of 125I-HSA by the change in concentration of 125I-HSA in plasma at 10 min post-injection and was indexed according to patient weight. Hematocrit was measured by colorimetric analysis using a blood gas analyser . Since large vessel hematocrit is higher than the body hematocrit, the measured Hct-value was corrected by multiplying with 0.9 [125I-HSA was measured to assess vascular leak [PV was measured using eviously . Brieflywith 0.9 , 12. Prewith 0.9 . Patientlar leak .p-value < 0.05 was considered to reflect statistical significance. All analyses were performed using PRISM 9.0.0 .The analysis plan was determined prior to performing the analysis. No power calculation was carried out; number of available patients determined sample size. Continuous variables were analyzed using descriptive statistics, and categorical data using proportions. Correlation analysis was performed using Spearman’s correlation for non-parametric data. A 2 (30%). Most patients expressed more than one sign of hypoperfusion (61%). Hemodynamic data and laboratory data prior to measurement of blood volume are presented in Table A total of 63 patients of the 70 patients included in the AIR trial were included in the present analysis , P = 0.254). When analyzing each hypoperfusion criterion  separately, no correlation between blood volume and change in pulse pressure was found in any of these subgroups  plasma volume in the whole cohort was 2830 2460–3495) ml. Blood volume was 4165 3575–5153) ml, corresponding to 57 (50–65) ml/kg. Median change in pulse pressure after PLR was 14 (7–24) %; see Fig. 60–3495 m5–5153 mln = 43) of the patients and these patients were therefore deemed likely to be preload responsive. Of these patients, 44% (19/43) were hypovolemic, 28% (12/43) were euvolemic and 28% (12/43) were hypervolemic; see Fig. n = 20), 25% (5/20) were hypovolemic, 55% (11/20) were euvolemic and 20% (4/20) were hypervolemic. In a sensitivity analysis, we used a 15% deviation or more from predicted normal value to define hyper- and hypovolemia. Using these criteria in patients assumed likely to be preload responsive, 23% (10/43) were hypovolemic, 58% (25/43) were euvolemic and 19% (8/43) were hypervolemic. Among patients unlikely to be preload responders, 5% (1/20) were hypovolemic, 80% (16/20) were euvolemic and 15% (3/20) were hypervolemic.The change in pulse pressure was ≥ 9% in 68% n = 4 of the pThis study on postoperative patients with clinical signs suggesting inadequate organ perfusion shows that about two thirds are likely to be preload responsive as judged by the response to passive leg raising. Less than half of the patients that were likely to be preload responsive were hypovolemic and a fourth were hypervolemic. No correlation between blood volume and change in pulse pressure was found.To identify patients with inadequate organ perfusion, markers such as hypotension, low urine output, high lactate and low central venous oxygen saturation were used. Because these symptoms may be explained by mechanisms other than low cardiac preload, it could be argued that other methods or symptoms may have offered better discrimination in identifying patients with inadequate cardiac output. However, observational trials of fluid resuscitation practices have shown that the symptoms used in this study are common indications of fluid administration and hence identify a population in which fluid administration is likely to be considered in clinical reality .To identify likely preload responders, we used a PLR test and change in pulse pressure. Patients had an arterial line according to unit protocol and therefore, pulse pressure was used as a pragmatic surrogate measure of change in cardiac output. We chose the 9% cut-off derived in one of the few studies including only spontaneously breathing patients . In that125I-HSA to the extravascular compartment due to ongoing bleeding or an increased microvascular permeability could have caused an overestimation of blood volumes [Accurate measurement of blood volume is dependent on blood volume remaining constant during the 10-min period from injection of tracer to blood sampling. This means that loss of  volumes . Based o volumes . Taken tHow do we explain our finding that some hypervolemic patients are likely to be responders whereas some hypovolemic patients are unlikely to respond to fluid administration?Preload will dictate where on the Frank–Starling curve the heart is operating at, whereas cardiac contractility and afterload will affect the slope [msf) and the right atrial pressure, the former being dependent on the stressed volume [msf and lower preload. This would increase the likelihood of preload responsiveness at a higher-than-normal blood volume. Such venous vasodilation could be secondary to epidural analgesia or surgery-induced inflammation [Pmsf and preload.To find the answer, we must discuss the determinants of preload responsiveness—preload, cardiac contractility and afterload. he slope , 16, 17.d volume , 16, 17.ammation , 19. Secammation , 19. In ammation . Conversammation , 22. LasIn postoperative patients with signs of hypoperfusion, therapy aims at increasing tissue perfusion by increasing cardiac output. In a preload-responsive patient, this is commonly achieved by increasing preload by intravenous fluid administration. Assuming that our definition of hypervolemia is accurate, our results indicate that an increase in cardiac output by other means than fluid administration could be more physiological in a large fraction of preload-responsive postoperative patients. As suggested previously, such approaches may also have the potential to improve outcome by avoiding unnecessary fluid therapy –25.Pmsf), which is dependent on the stressed venous volume. Alpha1-receptors are abundant in venous vessels and administration of an α1-receptor agonist such as noradrenaline may increase venous return by increasing the stressed venous volume and by that Pmsf, [1-receptor stimulation can be inferred from studies in which noradrenaline was suggested to dose dependently increase the filling pressure of the heart [1-receptor stimulation increases preload by an increase in the stressed venous volume [One approach to increase preload without administration of fluid is to increase the stressed volume by administration of a venoconstrictor. As mentioned in the introduction and above, preload at a given right atrial pressure is determined by mean systemic filling pressure (at Pmsf, –27. The he heart , 28. Mors volume , 29. DepAt present, measurement of blood volume using our methodology is technically challenging, costly and time consuming. Based on this, we believe that novel methods for bedside determination of blood volume and/or stressed venous volume are required before the clinical benefit of interventions other than fluid administration to hypervolemic fluid responsive patients can be assessed.The data for this post hoc analysis were derived from a randomized trial monitored by external monitors and plasma volume was measured using an established method by an investigator blinded to the response to the passive leg raise.As mentioned above, the fact that we assessed fluid responsiveness by measuring change of pulse pressure after passive leg raise test is a limitation and direct measurement of change in cardiac output after a PLR, using for example transpulmonary thermodilution, may have resulted in better discrimination between preload responders and non-responders.Although all efforts were made to minimize bias when designing the AIR trial and the present study, we acknowledge that this is a post hoc analysis of data with an inherent risk of bias. No power analysis was performed, and we cannot exclude that an association between blood volume and response to passive leg raising had been detected if sample size would have been larger. It should also be mentioned that blood volume can be regarded as a controlled parameter. That is, the clinician adjusts treatment to maintain what is perceived to be euvolemia and this probably limits the range of blood volumes observed in clinical practice. Moreover, it is possible that the fraction of preload-responsive patients with hypervolemia is dependent on intra- and post-operative care of the included patients and the results may not be applicable to patients with circulatory impairment in other settings, such as in the intensive care unit.A large fraction of postoperative patients with signs of hypoperfusion that are likely to be preload responsive are hypervolemic. In these patients, other therapeutic approaches than intravenous fluid administration may be a more rational approach to increase cardiac output.


Although great progress has been made in caring for patients with acute coronary syndrome (ACS), the incidence of heart failure (HF) after discharge remains high after ACS.We aimed to investigate the risk predictors for new-onset HF and build a simple nomogram to optimize the clinical management of female patients.The clinical data of 319 female patients with ACS between January 1, 2021 and January 1, 2022, were obtained from the Zhejiang Provincial People’s Hospital. Multivariate logistic regression analysis was carried out to build the prediction model among all participants and then verified by 10-fold cross-validation. The discrimination, calibration, and clinical usefulness of the prediction model were assessed using receiver operating characteristic curve, calibration curve, and decision curve analyses.This study analyzed 15 potential independent risk predictors of new-onset HF in 319 female patients with ACS. The incidence of HF onset was 23.2%. The following 5 independent risk predictors were filtered out as most relevant for predicting 12-month HF onset: left ventricular ejection fraction ≤ 60.5%, high-density lipoprotein ≤ 1.055 mmol/L, human epididymal protein 4 > 69.6 pmol/L, creatinine > 71.95 µmol/L, and diagnosis of myocardial infarction (MI).Our nomogram, which used five easily obtained clinical variables, could be a useful tool to help identify female individuals with ACS who are at high risk of developing HF after discharge and facilitate communication between female patients and physicians. Although great progress has been made in the care of patients with acute coronary syndrome (ACS), the incidence of heart failure (HF) at discharge remains high after ACS . In addiWe conducted a single-center, retrospective, observational cohort study in Zhejiang Provincial People's Hospital . A total of 319 female patients who experienced ACS and underwent CAG from January 1, 2021 to January 1, 2022, were included in this study and followed up for one year by telephone, and all subsequent hospitalizations, emergency admissions, and outpatient visits were reviewed. The development of a new-onset HF event (New York Heart Association heart failure classes ranging from II to IV) was our main observation outcome. When screening the included population, we excluded patients with heart failure or those who had previously underwent percutaneous coronary intervention (PCI) or coronary artery bypass grafting. In addition, patients were diagnosed with ACS, including myocardial infarction (MI) and unstable angina (UA), according to the criteria of the 2020 ESC Guidelines , while HClinical information was obtained from the patients' medical records, including demographics ; history of hypertension and type 2 diabetes; vital signs at admission , and diastolic blood pressure (DBP)); left ventricular ejection fraction (LVEF); administration of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (ACEI or ARB), calcium channel blocker (CCB), oral antidiabetics, and insulin 3 months before admission; results of coronary angiography; whether PCI was performed; and medications at discharge . In addition, we collected the results of patients' laboratory findings during hospitalization, including hemoglobin, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glycosylated hemoglobin, type A1C (HbA1c), estimated glomerular filtration rate (eGFR), serum creatinine, human epididymis protein 4 (HE4), and troponin I peak levels. In addition, the Global Registry of Acute Coronary Events (GRACE) score at discharge was calculated  for subsP < 0.05) were further analyzed using multicollinearity analysis. If there were variables with a tolerance lower than 0.2 or a variance inflation factor (VIF) higher than 5, we incorporated more meaningful variables based on the empirical screening into the multivariable analysis. Receiver operating characteristic curves were used to detect the relevant cutoff values for selected continuous potential risk predictor variables. To facilitate clinical use, we converted these meaningful continuous variables into binary variables according to the cutoff points. Multivariate logistic regression was performed to build a stepwise nomogram model for new-onset HF within 12 months after ACS. The nomogram was built using forward stepwise method with a threshold of P < 0.1 based on the Akaike information criterion. We evaluated the discriminative ability of the model using receiver operating characteristic (ROC) curve analysis and detected the calibration capability between the model probability curve and the ideal calibration curve using a calibration curve. Later, we verified the accuracy of the model by 10-fold cross-validation and evaluated the clinical usefulness of the prognostic nomogram model using decision curve analysis (DCA).We used univariate and multivariate logistic regression models to identify the relationship between the variables and new-onset HF. We preliminarily detected potential risk factors using univariate analysis. Potential risk factors . Statistical significance was set at P < 0.05. Statistical analyses were performed using IBM SPSS Statistics  and STATA version 15.0 .Means (standard deviation) or medians (interquartile range) were used for continuous variables, and numbers and percentages were used for categorical variables. The associations between HF and variables were tested using Student's t-test, Mann–Whitney U test, We retrospectively analyzed the clinical data collected from Zhejiang Provincial People's Hospital, summarized in P < 0.001), use of oral antidiabetics or insulin , morbidity of MI , and proportion of performed PCI . In addition, the risk of HF among patients with HbA1c > 6.0% was higher than that of those with HbA1c ≤ 6.0% . Moreover, the continuous variables of LVEF, HDL-C, creatinine, and HE4 levels were potential predictors of 12-month incidence of HF in the univariate analysis (P < 0.001); the categorical variables of age, eGFR, troponin I peak, hemoglobin, and the number of coronary arteries showed a statistically significant difference between the without-HF and with-HF groups (P < 0.05). Multicollinearity analyses revealed that the tolerances of the history of diabetes and antidiabetic drugs or insulin prescriptions were smaller than 0.2, and the VIFs were larger than 5, confirming that the regression models were affected by the presence of multicollinearity. Considering that diabetes mellitus may be regarded as equivalent to coronary heart disease . And its performance of prediction ability was better than that of GRACE score  , HDL-C , creatinine , and HE4 . Fifteen clinical factors were analyzed to test their relationships with HF using a multivariate logistic model following forward stepwise process and five variables were filtered out from the initial 15: LVEF, HE4, creatinine, HDL-C, and MI . The inc= 74.7%) .P = 0.778). Additionally, decision curve analysis showed great positive net benefits in the model under a threshold probability in the primary cohort  and was validated internally using 10-fold cross-validation with AUC = 0.907. Moreover, the developed model presented a superior performance in clinical settings, as shown in the results of the calibration curve and decision curve analysis.Compared with other models, our model showed some specific strengths. Three risk stratification tools are available. The GRACE score  was builAlthough these risk scores were validated, they did not include important factors. For example, as we emphasized, sex differences cannot be ignored in the prognosis of ACS, especially for HF onset; however, women are often under-represented in large clinical trials . Some neConsidering these shortcomings, our model has attempted to address these issues. First, specifically targeting female patients with ACS is one of the most significant strengths of our nomogram model, which can more accurately assess the incidence of HF in women. The main endpoint of the present study was new-onset HF, which might further refine the management and prognosis of female patients after ACS. Furthermore, all analyses were conducted after adjusting for confounding factors, which were selected from the variables considered reasonable confounders. In our study, we considered all the general cardiovascular risk factors mentioned in the Framingham prediction model , includiHowever, our study was subject to inherent limitations associated with retrospective analysis. First, selection or recall bias was possible in our study. Second, although our model adjusted for confounders and mediators, several confounders that were not included might play an important role, such as other undetected cardiac biomarkers. Third, to clarify the good discrimination and calibration of the model through internal verification, the model requires external validation before we can determine its applicability to other patient populations. Finally, the results of our study might be weakened because of the small sample size. Therefore, we will address these limitations in subsequent prospective studies.In conclusion, HF is a common complication in patients with ACS, particularly in women. LVEF, HE4, HDL-C, creatinine, and MI were independent risk factors for new-onset HF after ACS among female patients. The novel nomogram that we developed can identify genuine high-risk patients and facilitate communication between female patients and physicians. The prediction model may provide an important early warning for high risk of HF in female patients with ACS.


To estimate the usefulness of data mining algorithms for extracting risk predictors of diabetic vascular complications in proper order in the future, we tried applying the Classification and Regression Trees (CART) method to the prevalence data of 165 type 2 diabetic outpatients and already known risk factors. Among the 6 categorical and 15 continuous risk factors, age (cutoff: 65.4) was the best predictor for classifying patients into groups with and without macroangiopathy (p=0.000). Body weight (cutoff: 53.9) was the best predictor (p=0.006) in the older group (age >65.4), whereas systolic blood pressure (cutoff: 144.5) was the best predictor in the remaining group (p=0.002). Age (cutoff: 64.8) was also the best predictor for categorizing them into groups with and without microangiopathy (p=0.000). In the older group (age >64.8), BMI (cutoff: 21.5) was the best predictor (p=0.001), whereas morbidity term (cutoff: 15.5) was the best predictor in the other group (p=0.010). Because the orders and values of all risk factors and cutoff points mined were reasonable clinically, this method may have the potential to highlight predictors in order of importance to apply tailor-made prevention of diabetic vascular complications. Age, high blood pressure, long morbidity term, poor glycemic control, high triglyceride, high total cholesterol, high low density lipoprotein, low high density lipoprotein, high BMI, smoking, and male sex are known to be the risk factors for macroangiopathy. Age, poor glycemic control, high blood pressure, high BMI, smoking and male sex are known to be the risk factors for microangiopathy.Type 2 diabetes mellitus is one of the most common diseases in developed countries. Since its morbidity term is quite long and it greatly affects patient’s quality of life, the prevention of its complications is very important. Various risk factors for diabetic complications have been revealed in previous studies5-7). It has yielded odds ratios with 95% confidence intervals for each factor, and the bigger the odds ratio is, the larger its influence would be expected to be. However, since the regression model is optimized for fitting to whole samples, this reasoning is valid only in whole populations, not in the latent subgroups within it. Usual statistical methods are not effective enough to indicate the priority of risk factors in individual patients.Logistic regression analysis in large scale randomized controlled trials has made great contributions to identifying multiple risk factorsOur final goal is to determine which factors contribute most to the occurrence of complications in each latent subgroup by applying the Classification and Regression Trees (CART) method. As a preliminary step, we estimate here the usefulness of this data mining algorithm using the prevalence of diabetic complications and its risk factors which are already known.8,9).We enrolled 165 type 2 diabetic patients from the Keio University Hospital outpatient clinic in the first half of 2000. We explained the study purpose and plan, and got consents by all of them before the enrollment. To protect the privacies, we omitted patient’s name and patient’s number from the data before the analysis in case of data leakage. We took the patients’ past histories of macroangiopathy and microangiopathy as the endpoints of this analysis. Macroangiopathy here means myocardial infarction, coronary stenosis of more than 75% in diameter as confirmed by coronary angiography, cerebral infarction confirmed by either brain magnetic resonance imaging or computed tomography, or a history of amputation. Microangiopathy here means stage progressions of diabetic retinopathy and nephropathy. The stage of retinopathy was diagnosed by ophthalmologists, and patients diagnosed as having diabetic retinopathy were counted. The stage of nephropathy was decided by the urine albumin-creatinine ratio (Alb/Cr) value, and a value of more than 30 mg/gCr was counted, since the Alb/Cr value have been identified as reliable and valid means of estimating albumin excretion rate in clinical settingsWe collected the patients’ clinical and biochemical data and medical histories related to type 2 diabetes, obtaining data on 6 categorical variables , and 15 continuous ones , maximum weight, hemoglobin A1c, fast blood sugar, total cholesterol, triglyceride, free fatty acid, low density lipoprotein, and high density lipoprotein) as predictor variables for the analysis. All of the measurements were done in the same laboratory at Keio University Hospital. The characteristics of the patients recruited are shown in 10) in 1980 to predict and classify data that were related in complex ways. While this method  handles categorical dependent variables, the CART developed by Breiman et al.11) in 1984 can also handle continuous variables. CART is a non-parametric method that splits a node based on a data-defined impurity function . More recently, in 1997, Loh and Shih developed the Quick Unbiased Efficient Statistical Tree (QUEST)12), which is intended to be faster computationally and less biased in variable selection than either CHAID or CART. However, QUEST currently handles only categorical dependent variables, and empirical studies indicate that QUEST is not always faster than CART.13) Thus, CART remains a popular classification tree method, and that is why we adopted it in this study. The CART method generates binary decision trees. The trees are constructed by splitting subsets of the data set, using all predictor variables to create two child nodes repeatedly, beginning with the entire data set. The best predictor is chosen using impurity measurement (see Appendix). The goal is to produce subsets of data that are as homogeneous as possible with respect to the target variable . Each predictor is evaluated for each split to identify the best cut point (continuous predictors) or groupings of categories  based on the improvement score  shown at each branch point of the trees (12). The nearer to the root node, the larger the influence is in regard to classifying patients with or without macroangiopathy and microangiopathy.We used the Answer Tree 2.1 software produced by the SPSS Company on a Windows NT workstation, and classified the 165 patients by one of the classification tree algorithms called CART. A classification tree is an empirical rule for predicting the class of an object from the values of predictor variables. The concept of classification trees is statistically mature. The first classification tree method was developed by Kasshe trees  and 2. TAt each branch point, we additionally showed the Chi-square value and p value in order to evaluate the dependency of the classification conventionally. In the branch points in which the expected value of at least one cell of 2x2 cells was less than 5, Fisher’s exact test was performed .The root node of From the standpoint of macroangiopathy, age (cutoff: 65.4) was the best predictor for classifying patients with or without macroangiopathy (p=0.000). In this old (age >65.4) group, body weight (cutoff: 53.9) was the best predictor (p<0.006), whereas systolic blood pressure (cutoff: 144.5) was the best predictor in the other (age <=65.4) group (p<0.002).The root node of In the same way, each node of the tree diagram is interpreted, and represents the patient categories. Due to the recursiveness of the CART algorithm, we can find which factors principally contribute to the occurrence of the complications in each category. For example, from the standpoint of macroangiopathy risk, patients who belong to the old (age >65.4) and heavy (weight >53.9) group in All the classifications were statistically significant at the 0.02 criterion level (two-sided) by the Chi-square test or Fisher’s exact test.The cutoff values derived from the CART algorithm seem consistent with clinical experiences. The cutoff value for systolic blood pressure (144.5) is very close to the WHO’s criterion for hypertension . The cutoff value for fast blood sugar (140.5) is about the same as the old criterion for the diagnosis of diabetes of the American Diabetes Association . All the other cutoff values were also reasonable clinically. Age, blood pressure, weight control, glycemic control and morbidity term are the dominant risk factors clinicians of the authors predicted before the analysis. The mined cutoff value of age (about 65 in both tree models) is not only near to the predicted value, but also to the administrative senescent cutoff (65 in Japan). Additional Chi-square tests and Fisher’s exact tests showed that all of the classifications were statistically significant. These results indicate that this classification method efficiently categorizes diabetic patients with or without diabetic complications to some extent.Examination of the nodes one by one shows that the most dominant factor differs with the patient category. The results indicate that the risk factors which type 2 diabetic patients must focus on have an order of priority, and that they differ with patient category. For example, from the standpoint of macroangiopathy risk, patients who belong to the middle-aged (age <=65.4) and hypertensive (systolic blood pressure >144.5) group in Patients at diabetes outpatient clinics often receive many instructions from their doctors on their lifestyles and day-to-day treatment. It can be difficult for the patients to sort out and follow all of their doctors’ directions at the same time. It would be less confusing for the patients if these directions were given in order of priority, preferably in written form. Educational materials for diabetic patients illustrating the order of priority of treatment would help patients to more easily follow their doctors’ advices. This study presents the possibility that the classification tree method could present the evidence needed to determine an order of priority of clinical treatments and directions concerning risk factors, which could ultimately reduce the complications associated with this disease.Since most of the predictive variables of patients in this study were collected cross-sectionally after the occurrence of the complications, we cannot insist upon the causal relations between the predictors and the outcomes. This study only describes the prevalence of macroangiopathy or microangiopathy as varying with patient’s characteristics, and that the factors have an order in terms of influence.Because the CART algorithm was originally designed for large-scale samples, the greater the enrolled number, the more the classification tree will grow, and the more useful the knowledge it produces will be.We next plan to increase the number of cases and change the study design. We are now engaged in a retrospective cohort study with more than four times of the cases to estimate the occurrence of macroangiopathy or microangiopathy from predictive variables.If used properly with an appropriate study design, this data mining method may have the potential to highlight predictors in order of importance, thus leading to tailor-made prevention plans for individual patients.


HOMA and endothelial dysfunction interact between them in a competitive manner , also showing a mutual effect modification. Our findings demonstrate that both endothelial dysfunction and HOMA are independent and strong predictors of incident HF in hypertensives, these two risk factors interact between them with a competitive mechanism. Insulin resistance and endothelial dysfunction are associated with heart failure (HF). Our objective was to investigate whether endothelial dysfunction and insulin resistance are independent predictors of incident HF and if a possible interaction exists between them. We enrolled 705 white never-treated hypertensives. Endothelium-dependent vasodilation was investigated by intra-arterial infusion of acetylcholine. During the follow-up [median: 117 months (range: 31–211)], we documented 223 new cases of HF (3.3 events/100 patient-years). We stratified the study population into progressors and non-progressors; progressors showed an older age and a higher prevalence of females, as well as higher mean values of baseline glucose, insulin, homeostasis model assessment (HOMA), creatinine, and high-sensitivity C-reactive protein (hs-CRP), whereas the estimated glomerular filtration rate (e-GFR) and endothelium-dependent vasodilation were lower. In the multiple Cox regression analysis, serum hs-CRP , HOMA , maximal acetylcholine (Ach)-stimulated forearm blood flow (FBF) , and e-GFR (10 mL/min/1.73 m Heart failure (HF), due to both structural and functional cardiac alterations with associated neurohormonal activation, is a complex clinical syndrome characterized by diminished quality of life, high mortality, and recurrent and expensive hospital admission . For theVascular endothelium is essential for the regulation of vascular tone and the maintenance of vascular homoeostasis, and is an active autocrine, paracrine, and endocrine organ . DysfuncThus, the aim of this study was to investigate the possible interaction between endothelium-dependent vasodilation (tested by pharmacologic stimulation of muscarinic receptor) and insulin-resistance state  in predicting incident HF in a very large and well-characterized group of hypertensive patients. In this paper, contrary to what is already known, we have chosen insulin resistance instead of overt type-2 diabetes mellitus because this condition occurs many years earlier.2, malignant diseases, liver and peripheral vascular disease, thyroid disorders or those taking drugs affecting thyroid function, and HF defined according to both clinical and echocardiographic findings. From a large cohort of hypertensive patients participating in the CATAnzaro MEtabolic RIsk factors (CATAMERI) study, we recruited a total of 705 Caucasian patients [358 men and 347 women aged 22–73 years (mean age 48.4 ± 10.5 years)], with systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg. Individuals came to our tertiary University Center directly or were referred by general practitioners for detection or investigation of cardiovascular risk factors. All patients underwent physical examination, review of their medical history, and anthropometrical evaluation: weight, height, and body mass index (BMI). We excluded patients with secondary forms of hypertension, previous cardiovascular events, rheumatic and non-rheumatic valvular heart disease, prosthetic valves, cardiomyopathies, type-2 diabetes mellitus defined as HbA1c ≥ 6.5% or fasting plasma glucose ≥ 126 mg/dL, chronic kidney disease defined as an estimated glomerular filtration rate (e-GFR) < 60 mL/min/1.73 mThe CATAMERI study was submitted and approved on 17 October 2012  by the Ethics Committee of the Azienda Ospedaliero-Universitaria Mater Domini of Catanzaro. The investigation conforms with the principles outlined in the Declaration of Helsinki. All the participants gave their informed written consent to study participation.After a preliminary blood pressure (BP) measurement in both arms to exclude a possible difference between them, the readings of clinic BP were obtained  after 5 min of quiet rest. A minimum of three BP readings were taken on three separate occasions at least two weeks apart. Systolic and diastolic BP were measured by a standard sphygmomanometer at the first appearance (Phase I) and the disappearance (Phase V) of Korotkoff sounds. Baseline BP values represent the average of the last two of the three consecutive measurements obtained at an interval of 3 min. The diagnosis of hypertension was obtained based on the values of clinic systolic BP ≥ 140 and/or diastolic ≥ 90 mm Hg, respectively. Secondary forms of hypertension were excluded by systematic testing according to a standard clinical protocol, which included laboratory measurements of aldosterone, plasma renin activity, Doppler studies of the renal arteries, and/or renal scintigraphy or renal angiography.At the first eligibility visit, all laboratory measurements were performed after a fasting period of at least 12 h. Total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglyceride, and fasting glucose were measured using the standard methods . Serum creatinine was measured by an automated technique based on the measurement of Jaffe chromogen and the URICASE/POD method  implemented in an auto-analyzer. Values of e-GFR were calculated using the equation proposed by investigators in Chronic Kidney Disease Epidemiology (CKD-EPI). High-sensitivity C-reactive protein (hs-CRP) was measured by a turbidimetric immunoassay .Insulin resistance was estimated by the homeostasis model assessment (HOMA) from the fasting glucose and insulin concentrations according to the equation: HOMA = [insulin (μU/mL × glucose (mmol/L)]/22.5 [Plasma insulin was determined in duplicate by a highly specific radioimmunoassay. L)]/22.5 .Vascular function assessments were obtained at the time of the first observation by the examiners that were unaware of the patient’s clinical and laboratory parameters. After overnight fasting, all studies started at 09:00 A.M. in a quiet and comfortable air-conditioned room, with the patients in the supine position. The patients were invited to continue their regular diet, whereas caffeine and alcohol were stopped 24 h before the study. Vascular wall reactivity was evaluated using a plethysmography method according to the protocol initially described by Panza  and subsAccording to current guidelines, all patients were treated to reduce clinic BP < 140/90 mmHg using the standard lifestyle and pharmacological treatment. Angiotensin-converting enzyme (ACE)-inhibitors, angiotensin II receptor antagonists, calcium channel blockers, diuretics, β-blockers, and α-blockers were used alone or in various combinations. To improve the long-term follow-up, we have planned periodic control visits in the outpatient clinic at least every six months. In addition, a questionnaire was also mailed to family physicians, and the patients who had missed the clinical evaluation were contacted periodically by phone. Incident HF cases were confirmed according to the following criteria: signs and symptoms of dyspnea on exertion, difficulty exercising, and edema; echocardiographic or other imaging tests to evaluate cardiac structure and function, particularly the ejection fraction to define HF with reduced (HF-rEF) or preserved (HF-pEF) left ventricular function; and natriuretic peptides. We stratified the study population into two groups (progressors and non-progressors) according to the development of HF or not, respectively.t test for continuous variables, and the X2 test for categorical variables. Dose responses to ACh and SNP infusion were compared by ANOVA for repeated measurements and, when the analysis was significant, Tukey’s test was applied.The differences between clinical and biological data were tested by one-way ANOVA and unpaired Student’s We reported the event rate as the number of events per 100 patient-years based on the ratio of the number of events observed to the total number of patient-years of exposure up to the terminating event or censor. For non-progressor patients, the date of censor was that of the last contact. The Kaplan-Meier product-limit method was used to estimate survival curves, which were then compared using the Mantel (logistic-rank) test. p value. The maximal ACh-stimulated FBF and HOMA were tested either as continuous variables or by tertiles. The prognostic effect on the incident HF of endothelial function  and other risk factors was analyzed by univariate and multiple Cox regression analyses. The tested covariates included age, gender, smoking, serum cholesterol, SBP, BMI, HOMA, hs-CRP, and e-GFR. In this analysis, we included only the HOMA index to avoid a possible colinearity with both fasting glucose and insulin. Proportional hazards were assessed both by visual inspection and by the log-log method for categorical variables, whereas for continuous variables, the proportional risk assumption was tested by relating the Schoenfeld residuals of the Cox analysis with the survival time. The multiple Cox regression model was constructed by including all variables reaching the statistical significance at univariate Cox regression analysis, allowing us to construct a Cox model of adequate statistical power . Data were expressed as hazard ratio (HR), 95% confidence interval (CI), and p values.The mutual effect modifications by altered maximal ACh-stimulated FBF [under or above the median (252%)] and HOMA [above or under the median (3.1)] on the HF incidence was investigated by simultaneously including the following variables into the same multiple Cox model (Model 1): FBF , HOMA , and their interaction term (FBF × HOMA). These mutual effect modifications were also tested in a Cox model by adjusting for age, gender, smoking, SBP, BMI, cholesterol, and antihypertensive drugs (Model 2). The risks for incident HF were calculated by the standard linear combination method expressed as HR (95% CI) with p value ≤ 0.05. Results are reported as means ± SD or as percentage frequency, and the significance was assumed for p < 0.0001), whereas no significant differences were observed in the maximal vasodilation induced by SNP . At enrollment, none of the patients had been treated with antihypertensive drugs. The baseline BP mean values in the study population were 149.2/91.0 + 16.9/11.6 mmHg, with a little but not significant difference in SBP between the two groups (150.3 + 16.1 vs. 148.7 + 17.3 mmHg). As recommended by the current guidelines, we treated all patients to reduce clinical BP < 140/90 mmHg using both lifestyle modifications and pharmacological treatment. ACE-inhibitors, angiotensin II receptor antagonists, calcium channel blockers, diuretics, β-blockers, and α-blockers were used alone or in different associations without significant differences between the groups (In e groups . The per−1 × min−1) at the three incremental doses were 1.94 ± 1.22 (+57.7%), 5.46 ± 3.56 (+162.5%), and 10.43 ± 6.31 mL × 100 mL tissue−1 × min−1 (+310.4%), respectively. At the highest dose of ACh (30 μg/min), FBF increased to 13.79 ± 3.97 mL × 100 mL tissue−1 × min−1 and VR decreased to 10.16 ± 4.94 U. Remarkably, a significant difference in the maximal ACh-stimulated FBF (221 ± 128 vs. 328 ± 189 mL × 100 mL tissue−1 × min−1) was observed by stratifying the study population into progressors and non-progressors  and a decrease in VR (−71%) after SNP infusion, without significant differences between the groups . Given tp < 0.0001) with a lower percentage of females (41.0 vs. 58.9%). We reported event-free survival curves according to the tertiles of the maximal ACh-stimulated FBF mean value, as shown in The patients have been followed up for a median period of 117 months (range: 31–211), during which we recorded 223 new cases of HF . Considering the HF classification based on ejection fraction, we documented 145 cases of HF with preserved ejection fraction (HF-pEF) and 78 cases of HF with reduced EF (HF-rEF); the patients with HF-rEF were older . 2 increase, HR = 0.538, 95% CI 0.484–0.599)]. The female gender increases the risk of incident HF more than doubled . No association was found between the occurrence of HF and SBP, BMI, smoking, and total cholesterol.In univariate analysis , the inc2 increase, HR = 0.552, 95% CI = 0.483–0.603) maintained an independent association with incident HF  and Model 2 . The results of both crude and adjusted models demonstrated that HOMA significantly modified the effect of ACh-stimulated FBF on incident HF and, given the mutualistic nature of the effect modification, ACh-stimulated FBF also significantly changed the effect of HOMA on HF development  highlighted by the results of the present study. Firstly, the evidence that endothelial dysfunction can predict HF appearance is another important finding that allows us to expand previously published data that demonstrated that endothelial dysfunction is a consequence of HF ,14,15. TOf note, it is important to underline that insulin resistance is a metabolic alteration that precedes many years before the appearance of overt diabetes, thus conferring to our evidence of an important clinical and prognostic significance because it allows us to identify subjects at risk in a very early step of the disease natural history. Furthermore, it is mandatory to remark on the strict bidirectional relationship between insulin resistance and endothelial dysfunction, since they mutually amplify their negative effects in a reverberant vicious circle ,22. WithFinally, an excess of oxidative stress that reduces NO bioavailability, as well as the following neurohormonal activation and release of inflammatory mediators, may be hypothesized as other possible pathogenetic mechanisms involved in the appearance and progression of HF . FurtherIn conclusion, it is possible to affirm that both endothelial dysfunction  and insulin resistance (measured by HOMA) are independent and strong predictors of incident HF in never treated hypertensive patients. In addition, our data demonstrate that these two conditions interact with each other with a competitive mechanism. Moreover, given the bidirectional mechanism between endothelial dysfunction and insulin resistance ,11 and tThe strengths of this study are (1) that we directly tested endothelial function by stimulating muscarinic cholinergic receptors by intra-arterial infusion of vasoactive agonist in a very large and well-characterized population, and (2) the follow-up duration. On the contrary, the major limitation of the study is the invasiveness  of the strain-gauge plethysmography.


Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.We included 8,073 people from NHANES (2017—March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.P < 0.01 and P for trend <0.001.The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176–1.658), 1.499 (1.254–1.791), and 1.645 (1.360–1.989), respectively, with The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension. StudiesThe diagnosis of hypertension mainly includes (1) office blood pressure measurement (OBPM): a simple procedure but with a poor diagnosis effect for masked hypertension and white coat hypertension ; (2) 24 Artificial intelligence has gained popularity recently, and machine learning techniques, which are an important part of artificial intelligence, are extremely effective in identifying hypertension risk factors and predicting the risk of the disease. By integrating big data resources and using machine learning methods to construct morbidity risk prediction models, early identification of people at high risk of hypertension is possible. Through early intervention, the development of hypertension can be prevented and delayed, and thus, the risk of cardiovascular diseases can be significantly decreased –13. New Several studies around the world have constructed hypertension risk prediction models. These studies used different machine learning methods and incorporated different factors for prediction, and all showed high performance of the models –6. HowevIn this study, based on literature research and expert guidance, we incorporated more comprehensive information and extracted eligible subjects and their information from NHANES to develop a high-performance fusion model based on stacking to predict the risk of hypertension. In addition, serum ferritin (SF) was included in the best feature subset for the first time among many studies that predicted the risk of hypertension by machine learning methods; therefore, we further analyzed its association with hypertension and obtained a trend correlation between the risk of hypertension and SF levels.2.2.1.http://www.CDC.gov/nchs/nhanes/.The National Health and Nutrition Examination Survey (NHANES) is a population-based cross-sectional survey conducted by the National Center for Health Statistics (NCHS) to assess the health and nutrition status of adults and children in America –22. The n = 1,618) were excluded from the original dataset containing 15,561 participants. Then, the participants with a lot of missing information, such as missing more than 80% of any dimension of demographic, body measurement, laboratory test, or questionnaire were excluded . Finally, data on 8,073 participants were included in the study. A flowchart concerning participant selection is shown in Based on literature review and the guidance of experts from the Department of Cardiovascular Medicine, we extracted information on 120 items from 37 original datasets from the four dimensions of demographics, body measurements, laboratory tests, and questionnaires contained in the NHANES database , 24. Dat2.2.We classify patients with hypertension as hypertensive (0) if they have a history of taking medication for hypertension or a history of diagnosis. If there is no such condition, it can be divided by SBP/DBP .2.3.K nearest neighbors filling were used for categorical variables and continuous variables, respectively.After obtaining the original dataset, mode filling and Mode filling was achieved by replacing missing values with the plural  of the feature. We used it for filling the categorical variables because it is easy to implement and provides satisfactory results in most cases.K nearest neighbors filling is a similarity-based padding method. It fills the missing values by finding the nearest neighbors around missing values and using their average or weighted average. We used it to fill continuous variables because it accounts for the similarity between data points and can partly reduce bias.2.4.In this study, we first removed redundant features through LASSO regression and correlation analysis, which included features that were not significantly associated with the disease and one of the two features that were highly correlated (correlation coefficient ≥0.7), and then used recursive feature elimination (RFE) to determine the optimal feature subset . Since R(1)All features were used to construct the model;(2)The importance of each feature in the model was calculated;(3)The least important features were removed;(4)Steps 2 and 3 were repeated until the required number of features reached a predetermined value.Recursive Feature Elimination (RFE) is a commonly used feature selection method that selects features by recursively constructing models and gradually reducing the number of features , 25, 30.2.5.(1)A sample of a minority category was randomly selected.(2)K nearest neighbors of that sample were found.The (3)The nearest neighbor was randomly selected, and interpolation was performed between the neighbor and the sample to generate a new sample.(4)Steps 2 and 3 were repeated until the number of samples in the minority category reached a predetermined value.For processing data balance, we selected the most commonly used SMOTE (Synthetic Minority Over-sampling Technique), which increases the number of samples in a minority category by synthesizing new samples –33. The The SMOTE can be easily implemented and can effectively balance datasets with unbalanced categories, but it creates some synthetic samples that might not exist in the original dataset. Therefore, to avoid producing more synthetic samples and ensure that our study was conducted efficiently, we performed the data balancing step after determining the best feature subset.2.6.The prediction model constructed in this study is a multi-classification prediction, and to evaluate our model, five metrics were used: ACC (accuracy), precision, recall, F1 value, and AUC.Since this study is a triple classification task and considering the unbalance samples, the Macro Average rule was used to weight these evaluation criteria. If the patients with hypertension are recorded as True (1), the rest are False (0) and ACC1, P1, and R1 are calculated; the people with high blood pressure are recorded as True (1), the rest are False (0) and then calculate ACC2, P2, and R2; if normal people are recorded as True (1), the rest are False (0) and ACC3, P3, and R3 are calculated. From this, the corresponding F1 values can also be calculated separately, a final value can be obtained by the weighted average of the corresponding indexes of each group separately, which can be used as the evaluation index of this research model.In order to evaluate the model as more objective and accurate, 10-fold cross-validation was carried out for the final prediction model, that is, all samples in the data set were randomly divided into 10 mutually exclusive subsets with similar sizes and approximately the same number of events, and during each training round, 9 subsets were selected in turn to form the training set and the remaining subsets formed the test set. The model needs to run 10 times with different training and test sets, respectively, and the final result is the average of the 10 test results.2.7.After determining the optimal feature subset, to avoid the noise caused by filling in the missing values, we only retain the complete part of the optimal feature subset in the original data for the construction of the prediction model . We used2.8.P < 0.05 was considered statistically significant). Processes such as feature engineering, data balancing and prediction model construction were done in python , while TensorFlow 2.11.0; scikit-learn 1.1.3; keras 2.11.0; pandas1.5.2; numpy, etc. Package installation is complete.Statistical analysis used in this study was completed through SPSS 23 and R 4.1.3. For continuous variables, normality was first tested through the Kolmogorov-Smirnov test, means and standard deviations were used if they followed a normal distribution (ANOVA was used to compare continuous variables between groups); if they did not follow a normal distribution, the median (quartiles) were used (non-parametric tests were used to compare continuous variables between groups). For categorical variables, counts and percentages were used , and all statistical tests were two-sided  years old.According to the classification criteria of this study: 3,527 (43.7%) were hypertensive, 892 (11.0%) were pre-hypertensive; 3,654 (45.3%) were normal blood pressure. The mean blood pressure of hypertensive patients was 134 (121–147) mmHg/82 (80–88) mmHg; the mean blood pressure of pre-hypertensives was 129 (122–133) mmHg/79 (71–85) mmHg; the mean blood pressure of normotensives was 113 (106–121) mmHg/70 (65–75) mmHg. More basic information such as BMI, waist circumference, educational status, marital status, physical activity, and smoking status is shown in 3.2.After LASSO regression analysis, we excluded 12 redundant characteristics: platelet count, total bilirubin, Cotinine, serum cotinine, number of ready-to-eat foods in the last month, number of frozen foods/pizza in the last month, whether or not had asthma, whether or not exercising on the advice of their doctor, whether or not had seen a psychiatrist in the last year, dairy consumption in the last 30 days, weight loss through diet pills/surgery, whether or not have hepatitis B, whether or not had tried to quit smoking. After correlation analysis, we excluded 8 more characteristics: hip circumference, Red cell distribution width, Aspartate Aminotransferase (AST), glucose (frozen serum), Osmotic pressure, total cholesterol (frozen serum), triglycerides (frozen serum), and urinary albumin. After a preliminary model trial, we found that among these thirteen ways the four tree-based classification models, i.e., XGBoost, Extra Tree, Random Forest, and LightGBM, performed better . TherefoEach of these four models has its corresponding optimal feature subset . Therefo3.3.n = 5973) with all 10 variables complete in the original data for model prediction and evaluation. Then, we selected RF, XGB, LGBM, and ET with better performance to construct the prediction models. We plotted the ROC curves with "1-specificity" as horizontal coordinate and "sensitivity" as vertical coordinate, and all four classifiers showed good performance with AUC above 0.90 , and the mean levels of the three groups are shown in Since data of SF did not obey normal distribution, we performed the Kruskal-Wallis test and found that the difference in serum ferritin between the three groups was statistically significant  was taken as reference, and model 0 was crude. Model 1 controlled for demographic information ; model 2 controlled for anemia and inflammation-related indicators ; model 3 controlled for BMI, waist, some potentially confounding medical history  and related metabolic indicators ; model 4 controlled for smoking, alcohol consumption, diet, sleep, exercise and other lifestyle habits and depression scores; model 5 controlled for factors with significant differences above-mentioned.P < 0.01. Therefore, we carried out trend analysis for the five models with the help of clm function in R, and the P for trend value was <0.001, indicating a trend correlation between the risk of hypertension prevalence and SF levels. The results of the above five models are shown in Since all models above showed that compared with the lowest level group (Q1), the other three groups  had higher risk, and the OR values with their 95% CI were greater than 1, and the OR values were increasing sequentially with 4.Our study proposes a high-performance and convenient model for predicting the risk of hypertension, which can be used as an automated diagnostic aid in a hospital environment in conjunction with EMR data for real-time prediction.In the preliminary screening stage of the model, we used 13 methods to construct the prediction model in parallel and used ACC, AUC and F1 as the measures, and found that four decision tree type methods, RF, XGB, LGBM and ET, performed better. We consider that it may be because the decision tree type methods have advantages in dealing with high-dimensional data and nonlinear relationships, and can better capture patterns in data features, which are more suitable for the data features and prediction task requirements of this study. In addition, the fusion model constructed based on the stacking strategy also has better stability and can be more reliably applied in clinical practice.The single measurement of SBP and DBP fluctuates rapidly under the influence of various factors such as mood, sleep, exercise and time. However, our proposed prediction model incorporates laboratory tests with relatively stable changes. For example, HbA1C is used to assess the average level of blood glucose levels over the past 2 to 3 months; creatinine ratio is used to reflect changes in kidney function and muscle metabolism, it always changes little in people with normal kidney function, but changes within a month in people with kidney disease; serum creatinine is used to reflect changes in filtration function of the kidney, it only changes dramatically in days in patients with acute kidney injury and significantly in months or years in patients with chronic kidney disease; fluctuations in SF are generally not significant, usually taking weeks or months, and for the treatment of anemic patients, changes in this index also take 1 to 3 months, unless the patient experiences acute inflammation, which can change dramatically in a short period of time. In a short, such a predictive model is more stable compared to office blood pressure measurements. The included laboratory data are valid for at least 1 month. Also, multiple inputs can be obtained from HIS , it is more cost-effective than ambulatory blood pressure. We considered that it could be used as an early warning signal to reduce admission rate, readmission rate and their expenses, avoiding progression to more serious disease , 38. It In addition, hypertension is a multi-factorial disease, and many factors such as age, BMI, smoking, alcohol consumption, and excessive salt consumption are widely recognized by clinical experts as major influencing factors. Therefore, most studies on hypertension risk prediction have focused on these factors. Our study, based on data from NHANES 2017 to March 2020, incorporated more comprehensive information. We considered the optimal subset of features selected, and with the exception of serum ferritin, the other 9 features have been extensively documented to correlate with hypertension, especially factors such as age and BMI have been widely accepted as influencing factors in the clinic. SF has been less studied in the field of hypertension, so we decided to further explore the relationship between SF and hypertension.SF is a recognized biomarker used to evaluate the status of iron (iron deficiency or iron loading) , 44 and The SF levels can increase significantly due to inflammation or the occurrence of certain diseases  and play an important role in energy metabolism disorders . For exaBy searching several databases, we found that very few studies investigated the relationship between SF and hypertension. Considering that high levels of SF are a characteristic of insulin resistance-associated hepatic iron overload syndrome (IRHIO), Piperno et al.  divided These four studies either confirmed significant differences in SF between hypertensive and control groups with only a small number of participants, or found that baseline SF levels were significantly higher in hypertensive patients than in normal participants through follow-up, but did not fully account for more relevant confounding factors. We comprehensively considered all the factors that may affect hypertension and SF, and included them as co-variables, and objectively investigated the correlation between SF and hypertension.Although SF is an inexpensive and widely used biomarker, it has not been used to extensively study its association with hypertension or its complications related to cardiovascular disease and is less commonly included in routine physical examinations.Of course, our study has several shortcomings. (1) This was a cross-sectional study that did not take into account the long-term development and progression of the disease. (2) Our study is only based on NHANES database, and its representation is limited to the US population. (3) The prediction model constructed in this study only predicted the end event, without further study on disease progression, such as how long it will take to develop the disease. (4) In this study, the relationship between hypertension and SF needs to be further explored on the temporal relationship.Although the results of this study are limited to the US population, we believe that the methodology proposed in this study is transferable, and we are currently conducting similar studies on Chinese populations to further validate the findings of this paper. In future studies, we will verify the usability of the prediction model constructed in this study on a larger data set based on real hospital data or in cooperation with multiple medical institutions by means of multi-center cooperation, and in this process, we will also pay more attention to sample balance. At the same time, we hope to further confirm the relationship between SF and hypertension using multiple datasets from different sources as well as explore the causal relationship between the two through cohort studies and other means.5.In this study, we propose a cost-effective hypertension risk prediction model based on the stacking strategy that integrates demographic, physical measurements, and physiological and biochemical indicators, etc. to incorporate more comprehensive information. Further statistical analysis of the SF suggested by the optimal subset of characteristics was also performed, and showed that serum ferritin level was trending correlated with the risk of hypertension. In the future, we aim to collect more targeted data and construct more robust predictive models to assist clinicians in diagnosis and increase patient awareness of the disease.