Risk factors for postoperative pulmonary complication (PPC) have not been determined according to preoperative respiratory spirometry. Thus, we aimed to find contributors for PPC in patients with restrictive or normal spirometric pattern. We analyzed 654 patients . PPCs comprised respiratory failure, pleural effusion, atelectasis, respiratory infection, and bronchospasm. We analyzed the association between perioperative factors and PPC using binary logistic regression. In particular, we conducted subgroup analysis on the patients stratified according to preoperative spirometry. Of 654 patients, 27/379 patients (7.1%) with normal spirometric pattern and 33/275 patients (12.0%) with restrictive spirometric pattern developed PPCs. Multivariable analysis demonstrated that high driving pressure was the only intraoperative modifiable factor increasing PPC risk . In the subgroup of patients with restrictive spirometric pattern, intraoperative driving pressure was significantly associated with PPC , whereas driving pressure was not associated with PPC in patients with normal spirometric pattern . In patients with restrictive spirometric pattern, greater intraoperative driving pressure is significantly associated with increased PPC risk. In contrast, intraoperative driving pressure is not associated with PPC in patients with normal spirometric pattern. Thus, many previous studies gave efforts to determine modifiable contributing factors for PPC to prevent PPC1. Those efforts may be more important for patients with suboptimal lung functions. In this regard, obstructive lung disease like chronic obstructive pulmonary disease (COPD), which is known to increase the risk of PPC and postoperative mortality4, has been being widely studied with respect to the association with PPC6.The incidence of postoperative pulmonary complication (PPC) is as high as 23% and PPC is related to prolonged hospital stay and mortality7.Of importance, PPC incidence of patients without obstructive lung disease has been reported as high as PPC incidence of patients with obstructive lung disease8. Moreover, PPC incidence of patients with normal spirometric pattern was reported to reach 20% in previous studies10. Thus, it is important to find modifiable contributing factors for PPC in patients with restrictive or normal spirometric pattern, which has been not actively performed compared to patients with obstructive spirometric pattern11.A previous study demonstrated that PPC risk was 4.2 times greater in patients with moderate to severe restrictive spirometric pattern compared to patients with normal spirometric pattern12, this study aimed to determine intraoperative factors related to PPCs in patients without obstructive spirometric pattern. For this purpose, we included patients with restrictive spirometric pattern or normal spirometric pattern.We hypothesized that risk factors for PPC differ according to patients’ baseline lung physiology, demonstrated by preoperative spirometry. As an extension of our previous study of patients with COPD13. The consultation criteria were patients with underlying lung disease, past history of lung disease, such as pulmonary infection, abnormal arterial partial pressure of oxygen (PaO2) in arterial blood gas analysis, older age (> 60 years), and the attending anesthesiologist’s requirement. We excluded 1022 patients with obstructive lung disease 12 or who showed abnormal pulmonary findings in preoperative examination when abnormal pulmonary findings were defined as pulmonary radiologic findings of atelectasis or pleural effusion in preoperative examination, pulmonary infection within 1 month, and PaO2 < 60 mmHg. We further excluded 118 patients who underwent local anesthesia, 10 patients with combined regional nerve block, 36 patients who underwent heart or aorta surgery, and 34 patients with previous history of lung resection. Also, we excluded 10 patients who underwent emergent surgery, and 7 patients with incomplete intraoperative data. Finally, the remaining 654 patients of non-obstructive spirometric pattern  were included in the current study  and forced vital capacity (FVC) were obtained, and percentage of the predicted values for FEV1 and FVC were calculated using a reference equation obtained in a representative Korean sample15. Normal spirometric pattern was defined when there was no airflow obstruction (FEV1/FVC ≥ 0.7) and FVC was ≥ 80% of the predicted value. Restrictive spirometric pattern was defined when FEV1/FVC was ≥ 0.7 and FVC was < 80% of the predicted value. The severity of restrictive spirometric pattern was classified as mild  and moderate-to-severe restrictive , as described previously16.Preoperative spirometry was performed using a Vmax 22 apparatus  according to American Thoracic Society/European Respiratory Society criteria13. Two respiratory physicians (B. Shin and H. Lee) reviewed the electronic medical records, laboratory, and radiologic findings and confirmed whether the patients fulfilled the definitions of PPCs and recorded the results in the PPC database. All preoperative and postoperative data analyzed in the current study  were already collected in the PPC database irrespective of the study.PPC was evaluated daily for 7 postoperative days and categorized as respiratory failure, respiratory infection, pleural effusion, atelectasis, and bronchospasm .17. Static compliance, plateau pressure, and PEEP were not included in the multivariable analysis due to mathematical connection to driving pressure. Multi-collinearity was checked using the variance inflation factor. Low-tidal-volume ventilation was defined when tidal volume was < 8 mL per kilogram of ideal body weight based on previous research19. We modified albumin as a binary variable with the cutoff value of 3.5 g/dL, which is known to be related to surgical prognosis20. Also, we converted age and hemoglobin to binary variables with the cut-off values obtained by using ROC analysis. The subgroup analysis  was conducted by multivariable logistic regression with the three variables confirmed to be significant during the multivariable analysis for the whole cohort being included . Continuous variables such as statistic compliance, driving pressure, and plateau pressure are summarized as median  and compared using t test or Wilcoxon rank-sum test, while categorical variables are presented as frequency (%) and compared using chi-square test or Fisher’s exact test. All reported p values were two-sided and p < 0.05 was considered statistically significant. Analysis were performed using SPSS 26.0 .The primary outcome was PPC. We evaluated the association between perioperative variables and PPC using binary logistic regression in the whole cohort as well as in the subgroups stratified by preoperative spirometric pattern  and the results are described as odds ratio (OR) with 95% confidence interval. The final multivariable model for the whole cohort was generated using the entry variable selection method with independent variables with p < 0.2 during univariable analysis being includedOf the total 654 patients, 60 patients (9.2%) developed 114 PPCs while 44 patients had multiple PPCs: 27 of 379 patients (7.1%) with normal spirometric pattern and 33 of 275 patients (12.0%) with restrictive spirometric pattern developed PPCs . In particular, 11 of 164 patients (6.7%) with mild restrictive spirometric pattern and 22 of 112 patients (19.6%) with moderate-to-severe restrictive spirometric pattern developed PPCs . The proportion of each PPC among 68 PPCs in patients with restrictive spirometric pattern was as follows: respiratory failure (41.2%), atelectasis (26.5%), pleural effusion (22.1%), and respiratory infection (10.3%). The proportion of each PPC among 46 PPCs in patients with normal spirometric pattern was as follows: pleural effusion (41.3%), atelectasis (34.8%), respiratory failure (19.6%), and respiratory infection (4.3%).2O in patients without PPCs vs. 13.0 [11.0–14.5] cmH2O in patients with PPCs, p = 0.026). Multivariable analysis confirmed the significance of driving pressure . The length of postoperative hospital stay and the risk of 30-/90-day mortality were not significantly different between patients without PPCs and patients with PPCs  rather than in patients with mild restrictive pattern (6.7%). Third, the kind of PPC was more severe in patients with restrictive spirometric pattern. The most common PPC was respiratory failure in patients with restrictive spirometric pattern while it was pleural effusion in patients with normal spirometric pattern, recommending particular efforts for patients showing restrictive spirometric pattern before surgery.This study aimed to find modifiable anesthetic factors contributing to the development of PPC in patients with normal or restrictive spirometric pattern. To our knowledge, there have been no studies evaluating the impact of driving pressure according to different lung physiologies. Two pulmonary physicians independently evaluated the presence and kind of PPCs irrespective of the study and recorded the results in an institutional PPC database. First, we found that driving pressure was the only modifiable intraoperative factor affecting PPC risk. Of importance, greater driving pressure was significantly associated with increased PPC risk only in patients with restrictive spirometric pattern but not in patients with normal spirometric pattern. In combination with our previous study21. Transpulmonary pressure consists of the pressure to overcome the inward elastic recoil of the lung  and the pressure through the airway (airway opening pressure). Accordingly, when there is no airway flow (i.e. zero flow), transpulmoanry pressure approximates the lung elastic recoil components, which is the pressure to quantify lung stress and lung injury risk during mechanical ventilation22. Because, measuring the exact transpulmonary pressure requires additional equipment and training, recent studies used driving pressure to estimate transpulmonary pressure and demonstrated its association with PPC23.Driving pressure is considered as a surrogate parameter representing transpulmonary pressure24. During mechanical ventilation, driving pressure can be interpreted as the pressure applied to the entire respiratory system to achieve the tidal volume; thus, the lowest driving pressure is achieved when the ventilation is performed based on patients' functional lung size23. Functional lung size is defined as the ideal lung volume for aeration during mechanical ventilation without over-distension or under-ventilation25. Optimizing the tidal volume to the functional lung size leads to a minimum driving pressure and maximum lung compliance.In specific situations, driving pressure represents the pressure from the whole respiratory system including the pressure generated out of the lung, such as chest wall, in addition to transpulmonary pressure27. In a previous study of patients undergoing thoracic surgery, individually adjusted driving pressure-guided ventilation decreased driving pressure compared to the conventional protective ventilation , while the driving pressure-guided ventilation decreased PPC incidence by 16%28. The finding that even small decrease in driving pressure positively affects clinical outcome is in line with the current study: the median difference in driving pressure between patients without PPCs and with PPCs was 1.2 cmH2O in the subgroup of patients with restrictive spirometric pattern while PPC risk increased 19% for each 1.0 cmH2O increase in driving pressure. The previous and current studies suggest the importance to give efforts to make small differences in driving pressure for patients with restrictive spirometric pattern, the so-called driving pressure-guided ventilation.In our study, low driving pressure showed significant protective effect in patients with restrictive spirometric pattern. This finding is in consistent with previous studies of patients with acute respiratory distress syndrome (ARDS) with decreased lung size and low compliance as patients with restrictive spirometric pattern. In ARDS patients, driving pressure was significantly associated with PPC risk and mortality risk29. These underlying diseases are thought to cause restrictive spirometric pattern in the current study are shown in Table 31. Thus, transpulmonary pressure increases in patients with restrictive spirometric pattern and it may be more important to minimize the pressure applying to the lung by adjusting tidal volume and PEEP during mechanical ventilation.Restrictive spirometric pattern may reflect a number of diseases 23, which included 17 randomized controlled studies of abdominal, thoracic, and cardiac surgery, demonstrated the significant association between driving pressure and PPC risk. However, in this meta-analysis, only 6 of 17 studies took into account preoperative spirometry and some studies did not limit the subjects to patients without lung disease or abnormal spirometry. None of the studies included in the meta-analysis analyzed as large number of subjects with normal spirometric pattern as the current study. Our study has additional strength because 258 of 379 patients (68.1%) with normal spirometric pattern underwent extra-thoracic and extra-abdominal surgery, while the meta-analysis included only 24 patients (1.1%) with extra-thoracic and extra-abdominal surgery23. When the surgical field is close to the thorax as in thoracic or abdominal surgery, the airway pressure could differ dynamically depending on the surgical procedure. Thus, it is difficult to actually estimate the pressure generated from the external lung and assume driving pressure as a surrogate parameter of transpulmonary pressure32. Because the majority surgical fields were not close to the thorax in the current study, driving pressure was thought to better represent transpulmonary pressure in a better environment to evaluate the real association between driving pressure and PPC risk.In contrast to the patients with restrictive spirometric pattern, driving pressure was not associated with PPC risk in patients with normal spirometric pattern. Previous meta-analysis12, being in contrast to patients with restrictive spirometric pattern in the current study. The difference may be attributable to their different lung physiology. The physiological basis of COPD is decreased elasticity of the lung and airflow limitation resulting in lung hyperinflation33. In the hyperinflated lung, the compliance is increased resulting in a large divergence between driving pressure and transpulmonary pressure34. However, in patients with restrictive lung disease and reduced lung compliance, the majority fraction of driving pressure is used to inflate the lung, reducing the difference between driving pressure and transpulmonary pressure. Thus, driving pressure can be a reasonable surrogate parameter representing transpulmonary pressure in patients with restrictive spirometric pattern, regardless of the abnormal lung compliance, while this approach may not be relevant for patients with obstructive lung disease. In this regard, the difference between driving pressure and transpulmonary pressure in patients with COPD may influenced the insignificance of driving pressure. Future research is needed to evaluate the association between transpulmonary pressure and PPC risk or the impact of driving pressure-guided ventilation on PPC in patients with different lung physiologies or spirometric patterns.Low driving pressure did not show protective effects in our previous study of patients with COPD2. Analyzing patients at higher risk for PPC better suits the purpose of the study. Second, as this was a retrospective study, we were unable to provide transpulmonary pressure and verify the relationship between driving pressure and transpulmonary pressure. Based on the recent data, end-expiratory esophageal balloon pressures can reliably estimate transpulmonary pressures35. Thus, future studies simultaneously measuring transpulmonary pressure  and driving pressure are warranted to confirm the relationship between driving pressure and transpulmonary pressure according to different lung physiology or spirometric pattern. Third, preoperative PPC risk evaluation was performed based on a guideline and lung volume was not measured in general because lung volume measurement is time consuming and expensive14. Accordingly, further information about the etiologies of the restrictive spirometric pattern could not be obtained. Thus, instead of the term “restrictive disease”, “restrictive spirometric pattern” is used for the appropriateness of the nomenclature36.This study has some limitations. First, there was a potential risk of selection bias because patients who did not perform preoperative pulmonary function test prior to extra-pulmonary surgery were not included in the study. In this regard, young patients without previous lung abnormalities were not included in the study. However, PPC risk is known to increase with age and the PPC incidence increases markedly at 80 years oldIn the current study, we found that driving pressure is the only modifiable intraoperative factor contributing to PPC risk in patients with restrictive spirometric pattern. PPC risk was decreased in relation to lower driving pressure. Only small decrease in driving pressure was significantly associated with lower PPC risk. In contrast, driving pressure was not significantly associated with PPC risk in patients with normal spirometric pattern. Our findings suggest that optimal intraoperative lung protective strategy may differ by baseline lung physiology or preoperative spirometric pattern.Supplementary Information.Supplementary Tables.


Furthermore, the association between a low ABI and end-stage kidney disease (ESKD) was determined.The effect of a low ankle-brachial index (ABI) in patients with advanced-stage diabetic kidney disease is not fully understood. This study investigates the prevalence of a low ABI in patients with advanced-stage diabetic kidney disease, which was defined as a urinary albumin-to-creatinine ratio (UACR) ≥300 mg/g and an estimated glomerular filtration rate (eGFR) between 15–60 mL/min/1.73 mThis single-center, retrospective, cohort study included 529 patients with advanced-stage diabetic kidney disease who were stratified into groups according to the ABI: high (>1.3), normal (0.9–1.3), and low (<0.9). The Kaplan-Meier method and Cox proportional analysis were used to examine the association between the ABI and ESKD.p = 0.001).A total of 42.5% of patients with a low ABI progressed to ESKD. A low ABI was associated with a greater risk of ESKD (hazard ratio (HR): 1.073). After adjusting for traditional chronic kidney disease risk factors, a low ABI remained associated with a greater risk of ESKD (HR: 1.758; 95% confidence interval: 1.243–2.487; These results indicate that patients with a low ABI should be monitored carefully. Furthermore, preventive therapy should be considered to improve the long-term kidney survival of patients with residual kidney function. In 2017, 451 million patients had type 2 diabetes mellitus (DM) worldwide, and this number is expected to increase to 693 million by 2045 [The ankle-brachial index (ABI) has recently become a routine screening parameter for vascular complications in patients with DM . A correTo the best of our knowledge, no study regarding the association between the ABI and the progression to end-stage kidney disease (ESKD) in patients with advanced-stage diabetic kidney disease has been reported. Therefore, this single-center, retrospective, cohort study determines the effect of the ABI on adverse kidney outcomes and progression to ESKD in patients with advanced-stage diabetic kidney disease, providing a theoretical basis for its potential clinical role.2 who did not undergo kidney replacement therapy and for whom ABI data were available were included in this study. Patients with a baseline eGFR <15 mL/min/1.73 m2 and those with acute infections  were excluded from the study. The study was approved by the investigational review board of Jiangmen Central Hospital  and was conducted in accordance with the Declaration of Helsinki.This single-center, retrospective, cohort study included 529 patients with type 2 DM and advanced-stage diabetic kidney disease. Patients ≥18 years of age with type 2 diabetic kidney disease, an UACR ≥300 mg/g, and an eGFR between 15–60 mL/min/1.73 mThe patients’ baseline characteristics, including age, sex, DM duration, height, weight, medication list, and blood pressure were obtained from the medical records.Data on the white blood cell count (WBC), hemoglobin level (HGB), platelet count, neutrophil count, 24-h proteinuria, UACR, liver function tests, kidney function tests, blood glucose level, glycated hemoglobin (HbA1C) level, serum albumin (ALB) level, blood lipid levels, eGFR, parathyroid hormone level, C-reactive protein (CRP) level, and uric acid (UA) level were also obtained.The ABI was measured with the patient in the supine position on the same day that the patient’s kidney function was evaluated. The blood pressure was measured at the bilateral brachial and ankle arteries using an arteriosclerosis Doppler monitor . According to the American Heart Association guidelines , systoli−1.154 × age−0.203 × 0.742  × 1.233.The eGFR from serum Creatinine was estimated using the modification of diet in kidney disease equation  . Differences among the three groups were assessed using one-way analysis of variance, the Kruskal–Wallis test, or the chi-squared test, as appropriate. The cumulative, event-free probabilities of ESKD were determined using the Kaplan-Meier method and compared using the log-rank test. A univariate Cox proportional analysis was conducted, and the relevant variables were included in a multivariate Cox proportional analysis using different models. The results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). SPSS statistical software, version 22 , was used to conduct the statistical analyses. Statistical significance was set at A total of 529 individuals with advanced-stage diabetic kidney disease were followed up for a duration of 63.5 months (IQR: 37.7–67.1 months). The baseline ABI showed a non-normal distribution . During p < 0.05)  .p = 0.002, log-rank test; A total of 164 patients developed ESKD events that were recorded during follow-up. The results of survival analysis for ESKD showed that ABI level <0.9 had the worst prognosis. Patients in the low ABI group had a significantly higher rate of ESKD events than patients in the other groups , diabetes duration , higher HbA1C levels , higher serum CRP levels , and lower ABI  were associated with ESKD events (p = 0.004). Furthermore, after stratifying the ABI into three groups according to the upper and lower limits of its normal range and following adjustments for the above-mentioned confounding factors in the similar model, compared with the ABI normal range group, the ABI < 0.9 group remained a greater risk of ESKD events (HR: 1.758, 95% CI: 1.243, 2.487, p = 0.001; In the univariate analyses, we found that female sex (HR: 1.360, 95% CI: 1.000, 1.850, D events  and 4. AThis study identified an association between a lower baseline ABI and ESKD in patients with advanced-stage diabetic kidney disease and found that individuals with ABI < 0.9 had a significantly higher rate of ESKD events, accounting for 42.5%. Patients with an ABI < 0.9 had an increased risk of ESKD compared to that in those with a normal ABI, indicating that patients with advanced-stage diabetic kidney disease with a lower baseline ABI, which is associated with arterial disease in the lower extremities, are at higher risk of developing ESKD. This is the first study to evaluate the precise role of a low ABI in the progression to ESKD in patients with advanced-stage diabetic kidney disease.Diabetic nephropathy is a common microvascular complication of DM and is associated with macrovascular disease . As kidnCRP, an inflammatory marker, may be independently associated with an abnormal ABI ,29. ConsAn important methodological advantage of the current study is the analyses being conducted after the adjustment for renin-angiotensin system inhibitors, which have been shown to reduce proteinuria and protect kidney function . HoweverIn conclusion, lower-extremity arterial disease, defined as a baseline ABI < 0.9, may be associated with ESKD in patients with advanced-stage diabetic kidney disease. Potential mechanisms of this association include atherosclerosis, inflammation, and subsequent deterioration of kidney function. Improving atherosclerosis and the inflammatory status in patients with advanced-stage diabetic kidney disease may provide clinical benefits in the long-term kidney survival of patients with residual kidney function. However, prospective studies with larger sample sizes are needed.


Intrauterine growth restriction (IUGR) has been associated with changes in kidney anatomy, nephrogenesis and the vascular system, resulting in secondary arterial hypertension and kidney damage in adulthood. Here, we compare routine clinical and metabolic parameters between IUGR and non-IUGR study participants in the neonatal and early infant period.A total of 39 IUGR and 60 non-IUGR neonates were included during an 18-month study period. We compared blood pressure, serum creatinine (SCr), urea nitrogen (BUN), urinary albumin, α-1-microglobulin, transferrin, immunoglobulin G and total protein excretion in spontaneous urine normalized by urine creatinine level during the hospital stay.There were no significant differences in mean values of blood pressure and urinary protein excretion between cases and controls. SCr and BUN levels were lower in the IUGR group compared to the non-IUGR group.The lower levels of SCr and BUN may be attributed to lower liver and muscle mass in IUGR neonates and young infants. Biomarkers currently used in routine clinical care do not allow early postnatal prediction of higher blood pressure or worse kidney function due to IUGR, so further studies are needed.A higher resolution version of the Graphical abstract is available as The online version contains supplementary material available at 10.1007/s00467-022-05713-z. Intrauterine or fetal growth restriction (IUGR/FGR) is defined by the inability of a fetus to reach its genetically predetermined growth potential due to an underlying pathology and occurs in approximately 5–10% of all pregnancies . Causes IUGR neonates admitted to our neonatal intensive care unit (NICU) or to our newborn nursery at the University Hospital Bonn between April 2019 and September 2020 were eligible for study participation if they remained hospitalised in our NICU and/or newborn nursery for at least 72 h after birth and prenatal findings met the German, Austrian and Swiss 2017 consensus guideline for IUGR  . The stuWe retrospectively extracted the following information from the mothers’ and children’s patient records: sex, birth weight, gestational age, length at birth, APGAR scores, pH from the umbilical artery, occurrence of birth complications, birth mode, maternal age, gravida, body mass index, maternal morbidity, medication during pregnancy, drug abuse in pregnancy, single or multiple pregnancy, pregnancy complications, blood pressure levels, protein intake, medication, drug levels in serum when taking nephrotoxic drugs , body weight and length on discharge and duration of hospitalisation.Both invasive and non-invasive blood pressure measurements were considered. Systolic, diastolic and mean arterial pressures were calculated as means of individual measurements during a predefined period of time . Since the time of discharge significantly differed between cases, there is a large variability in timing of BP3 (between 3rd and 161st day of life). In cases where children were discharged before the 8th day of life (10/99), no BP2 could be registered and the blood pressure before discharge was registered (BP3). In order to control for patient’s age and gestational age, we compared the blood pressure measurements to the patients’ individual reference values obtained from Pejovic et al. .Serum creatinine (SCr) and blood urea (BUN) levels were measured only in the context of medically necessary blood sampling. The first measurement was between the 30th and 120th hour of life (serum 1), mostly at the time of the newborn screening (between 36th and 72nd hour of life). Also, a second blood sample was obtained only when a blood draw was medically indicated (e.g. repeat newborn screening in preterm neonates or infants) and therefore shows a high variability with regard to timing (between 5th and 53rd day of life) (serum 2). We compared the measured SCr and BUN values with corresponding reference values , 23 to iDuring inpatient treatment, the protein intake of all patients in our department is automatically calculated by our medical documentation system. To exclude protein intake as a major confounder influence of BUN concentration, we extracted the respective intakes at both times of serum sampling from the individual patient’s medical record and compared it between cases and controls.t test and Mann–Whitney U test for normally distributed and non-normally distributed data, respectively, were applied to the collected metric data. The Shapiro–Wilk test with an error level of p = 0.01 was used to check whether there was a normal distribution for the collected data. Starting from an alpha error of 5% for a two-sided test, Bonferroni correction was applied to neutralise the accumulation of alpha errors in multiple comparisons. Due to the variable timing of the second serum analysis  and BP3 (prior to discharge), we performed an analysis of covariance (ANCOVA) to control for timing as a confounder. Percentile and z scores were adapted from Voigt et al.  g/kg body weight) compared to controls (3.3 [IQR 2.6–3.9] g/kg body weight).There was no significant difference , with higher concentrations in the control group.IUGR is under investigation as a possible risk factor for kidney damage and high blood pressure in later life. Our aim was to detect early deviations in standard clinical and laboratory parameters between IUGR and non-IUGR neonates and young infants, which are in line with the observations of associated changes in kidney anatomy, nephrogenesis and the vascular system in other studies –16. To oOur recorded lower levels of BUN might be explained by a lower liver mass in IUGR patients . This miFor further investigation based on our study results, it would be useful to expand the examined parameters, e.g., by adding cystatin C, angiotensin II, endothelin-1, fibroblast growth factor 23 and cathepsin B or even investigating the total proteome. In addition, an increase in the number of study participants, as well as long-term follow-up, would also be reasonable pursuits.There is still a lack of fundamental studies that look closely at the possible early postnatal effects of IUGR, as often only generally low birth weight (LBW), small for gestational age (SGA) or prematurity has been investigated. It is of great importance to identify a risk for kidney damage as early as possible to initiate preventive steps before kidney impairment occurs. The investigation of routine parameters as a first step seems especially essential to us, as these parameters are determined during follow-up visits of at-risk children, following current recommendations . Our stuCertainly, our study design has some limitations that cannot be ignored. The more frequent occurrence of morbidities associated with IUGR could lead to a bias in the comparability of our groups. Numerous drugs, which were used both in IUGR and control patients, are classified as nephrotoxic or potentially nephrotoxic, though the exact dose-toxicity relationship is not completely clear, especially regarding neonates and young infants , 35. It In summary, our study did not identify any differences in the clinical routine parameters investigated that indicate higher blood pressure or worse kidney function due to IUGR. Further studies including more specific parameters are needed to hopefully identify newborns and infants at-risk for future changes in kidney function.Graphical Abstract (PPTX 68 KB)Supplementary file2 (DOCX 20 KB)Supplementary file3 (DOCX 18 KB)Below is the link to the electronic supplementary material.


Systemic lupus erythematosus (SLE) is a relatively common rheumatic disease in children. The characteristics of blood lipid metabolism in children with LN are little reported. This study aimed to explore the relationship between blood lipid profiles and the risk of lupus nephritis (LN) in children.  A total of 134 children with newly diagnosed SLE were divided into LN and non-LN groups according to pathological renal biopsy results. Clinical manifestations and blood lipid profiles were analyzed and compared between the two groups, and the relationships between blood lipid profiles and risk of LN were evaluated. α1 microglobulin, and urinary N-acetyl glucosidase, respectively. Receiver-operating characteristic curves showed that combined detection of TC, TG, LDL-C, and VLDL-C had higher discrimination capacity than that in individual measures. Additionally, increased TC was independently associated with the occurrence of LN.  The positivity rate of an anti-dsDNA antibody and an SLE disease activity index (SLEDAI) were significantly increased, and C3 and C4 levels were significantly reduced in the LN compared with the non-LN group. The overall incidence of dyslipidemia was 79.9%, with a significantly high incidence in the LN group compared with the non-LN group. Total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and very LDLC (VLDL-C) were all higher in the LN group than those in the non-LN group. However, there was no significant difference in high-density lipoprotein cholesterol (HDL-C) between the two groups. The blood lipid levels were positively correlated with 24-hour urine protein quantification, urea, creatinine, uric acid, urinary IgG, urinary microalbumin, urinary transferrin, urinary  Children with LN have significant dyslipidemia. High levels of TC, TG, LDL-C, and VLDL-C are closely related to the occurrence of pLN. Clinical attention should be paid to monitoring and managing blood lipid profiles in children with LN. Systemic lupus erythematosus (SLE) is a chronic multisystem autoimmune disease resulting in multiple organ damage . SLE is A variety of substance metabolism disorders occur in patients with SLE, mediated by the immune response and inflammation. Dyslipidemia is believed to decisively influence the long-term prognosis of SLE patients, affecting both cardiovascular events and LN . PreviouThe current study aimed to explore the characteristics of blood lipid metabolism in children with LN and to estimate the association between relatively complete lipid profiles and the risk of LN, to provide a valuable reference for the clinical management of blood lipids and the prevention of LN in children.This study was conducted at the Division of Rheumatology, Beijing Children's Hospital, Capital Medical University, China, from January 2020 to December 2021, with approval from the Beijing Children's Hospital research ethics committee. This retrospective longitudinal study included 134 patients with newly diagnosed SLE, who were aged <18 years at the time of diagnosis. All the patients met the American College of Rheumatology/European League against Rheumatism revised 2019 criteria for SLE. Sixty-five patients had renal biopsy-proven LN (LN group), and 69 had negative urinalysis results  and normal renal function (non-LN group).The exclusion criteria were as follows: age >18 years; diagnosis of Sjögren's syndrome, mixed connective tissue disease, or other autoimmune diseases; diagnosis of chronic kidney disease (CKD), diabetes mellitus, essential hypertension, liver or thyroid disease, or familial hyperlipidemia; and children receiving lipid-lowering therapy.Another 70 children who underwent a physical examination during the same period were selected as the healthy control group.The study was conducted in accordance with the principles of the Declaration of Helsinki, 1975. Signed informed consent was obtained from the children's parents and/or legal guardians.α1 microglobulin (A1M); and N-acetyl glucosidase (NAG).Baseline data, including sex, age, body mass index (BMI), clinical symptoms, were recorded retrospectively. Within two to five days of admission, before trying any treatment, cubital venous blood and urine specimens were collected from each patient for biochemical and immune parameters, and then, laboratory data were recorded and analyzed: blood lipid profiles including total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and very LDL-C (VLDL-C), nonesterified fatty acid; fasting blood glucose (FBG); erythrocyte sedimentation rate (ESR); complement C3 and C4; specific antibodies including anti-double-stranded DNA (anti-dsDNA) and anti-Smith (anti-Sm); 24 h urine protein quantification (24 h M-TP); kidney function indicators including urea, creatinine, and uric acid; early renal injury markers including urinary IgG, urinary microalbumin (mAlb), urinary transferrin (TRU), and urinary SLE severity was assessed at the enrollment visit using the SLE disease activity index (SLEDAI) as follows: (1) no activity (score: 0–4), (2) mild-to-moderate activity (score: 5–14), and (3) high activity (score: ≥15).χ2 tests, and differences between continuous variables were determined using the t-tests for independent samples and Wilcoxon's rank-sum test. Correlations between parametric variables were evaluated using Pearson's correlation curve. Receiver-operating characteristic (ROC) curves were drawn for LN predictor variables, and optimal cutoffs were confirmed by maximizing the sum of sensitivity and specificity. The coefficient of univariate logistic regression and the odds ratio (OR), including the 95% confidence interval (CI), were calculated to identify variables associated with the presence of LN. Predictors that were significantly correlated with the presence of LN in univariate logistic regression analysis were analyzed by stepwise regression in multivariate analysis, to identify independent risk factors for LN. P < 0.05 was considered significant.The data were analyzed, and images were created using SPSS 24.0 . Quantitative data were expressed as a mean ± standard deviation, and categorical variables, such as sex, SLE disease activity, and dyslipidemia, were presented as percentages. Differences between categorical variables were determined by 2) and 69 in the non-LN group  years, and BMI (20.73 ± 2.03) kg/m2). There were no significant differences in sex, age, or BMI between the two groups . There were also no significant differences in FBG, ESR, or anti-Sm antibody positivity rate in peripheral blood between the LN and non-LN groups . However, C3/C4 levels were significantly lower (both P < 0.05), and the anti-dsDNA antibody positivity rate was significantly higher  in the LN group than in the non-LN group. The SLEDAI scores were also significantly higher in the LN group than in the non-LN group (P < 0.05). The overall incidence of dyslipidemia was 79.9%, and the incidence was significantly higher in the LN than in the non-LN group   years, and BMI (21.96 ± 7.91) kg/m < 0.05) .P < 0.05 for all analyses). However, there was no significant difference in HDL-C levels between the LN and the non-LN groups (P > 0.05)  .P < 0.05)  .P > 0.05). Thus, these results suggest that TC, TG, LDL-C, and VLDL-C may be useful predictors of disease severity in children with LN. In addition, we analyzed the correlation between blood lipid profiles and immunological indicators, such as complement C3, C4, anti-dsDNA, and anti-Sm, and we found no significant difference (relevant data not shown in this article).Pearson's correlation analysis revealed significant positive correlations between TC, TG, LDL-C, and VLDL-C and kidney function indicators  but not P < 0.05). These findings demonstrated that TC was independently associated with the occurrence of LN and may thus be a better risk predictor for LN.The ORs and 95% CIs of the risk factors of pLN are shown in SLE is a chronic inflammatory autoimmune disease that affects many organs, including the skin, joints, lungs, heart, kidneys, and nervous system. LN is the most common type of organ damage in patients with SLE, occurring in up to 40% of patients , 5, 6. TSome studies have found significant changes in lipid metabolism in SLE patients, including the “lupus model” of dyslipoproteinemia , abnormaThe specific pathogenesis of LN is still unclear. Some researchers have proposed the “lipid nephrotoxicity hypothesis,” which states that lipid metabolism disorders can promote and/or aggravate glomerular and tubulointerstitial lesions by changing the lipid homeostasis of renal tissue. In addition to the deposition of immune complexes, the kidneys may also have abnormal lipid deposition in patients with LN, leading to glomerular capillary endothelial cell damage. Infiltrated monocytes and macrophages phagocytose lipids and are then transformed into foam cells . Thus, hThe current study had a number of limitations. This was a retrospective single-center study, and thus, there may have been selection bias in the baseline characteristics. Further longitudinal studies and larger samples are therefore needed to explore underlying mechanisms.Our study showed that dyslipidemia is common in children with LN and that the risk of LN is related to the blood lipid profile, which includes modifiable risk factors . Dyslipi


Nature Communications 10.1038/s41467-019-13033-4, published online 14 November 2019.Correction to: p-values to be generated by the likelihood ratio test model comparisons. This error occurred because a glmer-based script previously used by the authors to analyze data with non-normally distributed errors was run under the assumption it was equally applicable to a normally distributed error model for use in the analysis in this article. However, in such a setting glmer automatically refers the data to the lmer function. Whereas REML is disabled in glmer, it is enabled by default in lmer, which led to incorrect p-values. The error was noted after publication of the article when an unrelated analysis led to conspicuously low p values.The original version of this article contained an error in the statistical analysis of the telemetry data comparing male WT and male R180Q/+ mice, which were analyzed by mixed effect modeling using the lme4 R package and the lme4::glmer function. The approach called “restricted maximum likelihood” (REML) was inadvertently and erroneously used during the analysis, causing spuriously low p-values:P = 0.028; diastolic, 87.3 ± 0.1 mmHg versus 85.1 ± 0.1 mmHg; P = 0.041; MAP, 103.3 ± 0.1 versus 100.8 ± 0.1; P = 0.033; all mean ± SEM and Nested-model likelihood ratio comparisons). Similarly, heart rate was elevated in Clcn2R180Q/+  versus WT  .”The second and third sentences of the results section titled “Clcn2 R180Q/+ mice have slightly elevated blood pressure”, which originally read “Systolic and diastolic blood pressures as well as mean arterial pressure (MAP) of Clcn2R180Q/+ mice were slightly but significantly increased when compared to WT  of Clcn2R180Q/+ mice were slightly but significantly increased when compared to WT . Heart rate was not significantly changed in Clcn2R180Q/+  versus WT  .”.The corrected version of the manuscript replaces blood pressure, heart rate, and This has been corrected in both the PDF and HTML versions of the article.Fig. 7, which originally showed loess regressions of BP/HR recorded over 7 days in 11 WT animals and 11 Clcn2 R180Q/+ animals.The correct version of Fig. 7 iswhich shows loess regressions of BP/HR recorded over 12 days in 18 WT animals and 18 Clcn2 R180Q/+ animals and replaces the previous incorrect version:This has been corrected in both the PDF and HTML versions of the article.N = 11 biologically independent animals) and Clcn2R180Q/+  animals, with gray intervals denoting 95% confidence intervals for loess regressions. Horizontal axis shows time, with black markers noting nights . Horizontal lines show mean for the entire measurement period for WT (black dash-dotted) and Clcn2R180Q/+ (red dashed) animals. Nested-model likelihood ratio comparisons reveal significantly higher BP  and HR (P = 0.002) of Clcn2R180Q/+ animals than WT. Average  systolic BP is 117.3 ± 0.1 mmHg (WT) and 120.2 ± 0.1 mmHg (Clcn2R180Q/+). Average diastolic BP is 85.1 ± 0.1 mmHg (WT) and 87.3 ± 0.1 mmHg (Clcn2R180Q/+). Average MAP is 100.8 ± 0.1 mmHg (WT) and 103.3 ± 0.1 mmHg (Clcn2R180Q/+). Average HR is 497.6 ± 0.7 BPM (WT) and 516.1 ± 0.7 BPM (Clcn2R180Q/+)”.The legend of fig. 7, which originally read “Clcn2R180Q/+ mice have higher blood pressure. Blood pressure (BP) and heart rate (HR) of animals assessed through telemetry sensors. Shown are loess regressions of BP/HR of WT  and heart rate values (means and errors). Therefore, the corrected figure legend now reads: “Clcn2R180Q/+ mice have higher blood pressure. Blood pressure (BP) and heart rate (HR) of animals assessed through telemetry sensors. Shown are loess regressions of BP/HR of WT  and Clcn2R180Q/+  animals, with gray intervals denoting 95% confidence intervals for loess regressions. Horizontal axis shows time, with black markers noting nights . Horizontal lines show mean for the entire measurement period for WT (black dash-dotted) and Clcn2R180Q/+ (red dashed) animals. Nested-model likelihood ratio comparisons reveal significantly higher BP  of Clcn2R180Q/+ animals than WT and unchanged HR (P = 0.225). Average  systolic BP is 116.4 ± 0.1 mmHg (WT) and 120.6 ± 0.1 mmHg (Clcn2R180Q/+). Average diastolic BP is 84.2 ± 0.1 mmHg (WT) and 88.0 ± 0.1 mmHg (Clcn2R180Q/+). Average MAP is 100.1 ± 0.1 mmHg (WT) and 103.8 ± 0.1 mmHg (Clcn2R180Q/+). Average HR is 495.2 ± 0.4 BPM (WT) and 507.9 ± 0.4 BPM (Clcn2R180Q/+)”.The corrected version of the manuscript replaces number of animals, This has been corrected in both the PDF and HTML versions of the article.57 R package. Blood pressure data were plotted as loess regressions using the ggplot258 R package, separately by genotype, showing 95% confidence interval of loess parameters in the figure”.The last two sentences of the methods section titled “Blood pressure measurements”, which originally read “Significance of genotype, correcting for activity, was done through likelihood ratio test comparison with a model omitting the tested predictor as implemented in the lmtest57 R package. Nights were defined as periods when light was off (6 p.m. to 6 a.m. or 7 p.m. to 7 a.m.). Blood pressure data were plotted as loess regressions using the ggplot258 R package, separately by genotype, showing 95% confidence interval of loess parameters in the figure.”.The corrected version of the manuscript omits the activity correction because, following the new analyses, median activity was found to be similar between genotypes. Additionally, the corrected version of the manuscript includes a new sentence to define nights as the periods when the light was off, which were 6 p.m. to 6 a.m. for the original group of animals and 7 p.m. to 7 a.m. for the newly recorded ones. Therefore, the corrected final sentences of the methods section titled “Blood pressure measurements” now read: “Significance of genotype was done through likelihood ratio test comparison with a model omitting the tested predictor as implemented in the lmtestThis has been corrected in both the PDF and HTML versions of the article.The tenth sentence of the “methods” section titled “Blood pressure measurements”, which originally read “After 1 week of recovery, blood pressure and heart rate were recorded for ten days .” The corrected version of the manuscript gives twelve days because a greater time span after recovery from surgery was used in the analysis. The corrected sentence now reads: “After 1 week of recovery, blood pressure and heart rate were recorded for twelve days .”This has been corrected in both the PDF and HTML versions of the article.The last four rows of the seventh and eighth columns in Table 1, in which the systolic, diastolic and mean arterial pressure values, and the heart rate values (means and errors) of male WT and male R180Q mice were reported.The correct version of Table 1 is shown here: To correct this mistake and confirm the statistical significance of the originally reported changes in blood pressure, new telemetry data have been acquired in additional animals to increase the sample size, and the data have been reanalyzed without REML transformation. Consequently, the following sections of the manuscript have been corrected to account for changes in number of animals and experimental conditions, and for changes in the obtained blood pressure values (means and errors), heart rate values (means and errors), and which replaces the previous incorrect version shown here.This has been corrected in both the PDF and HTML versions of the article.7)N = 10 per group. We added an additional animal because of potential drop-outs. Data of general phenotype were collected during breeding of experimental groups”. The corrected version accounts for the change in the number of animals and now reads “Based on power calculations, n = 18 animals per group were used for blood pressure analyses. Data of general phenotype were collected during breeding of experimental groups.”. The HTML has been updated to include a corrected version of the reporting summary.The original version of the reporting summary, in which the last sentences of the “Sample size” section read “For blood pressure analysis, we relied on experience from the group of Dominik Müller, based on which even small blood pressure changes can be detected with sample sizes of Furthermore, the HTML version of the published article incorrectly omits Supplementary Software Supplementary Software 1Updated Reporting Summary


The prevalence of CKD among hypertensives (379 from 883) was 4.49% (17/379), while 17 out of 19 subjects with CKD had HT (89.47%). After adjusting for age, sex, and diabetic status, only serum uric acid (SUR) > 6.9 mg/dL  was an independent risk factor and a predictor of CKD. Conclusions: The prevalence of CKD in hypertensive Romanian adults was more than ten times higher than in the normotensive population. Levels of SUR > 6.9 mg/dL were predictors of CKD.Background: The prevalence of chronic kidney disease (CKD) correlates with the prevalence of hypertension (HT). We studied the prevalence and predictors of CKD in a representative sample of the Romanian adult population. Methods: A sample of 1470 subjects were enrolled in the SEPHAR IV  survey. All subjects were evaluated for blood pressure (BP) and extensive evaluations of target organ damage, blood, and urine samples were undertaken. Results: A total of 883 subjects were included in the statistical analysis. Those experiencing CKD with an eGFR < 60 mL/min/1.73 m Therefore, 82% of hypertensive patients in the SEPHAR III survey were at high or very high risk. Most (72.2%) of the hypertensive subjects were treated with two or more drugs (51.9%), but only 30.8% of them had controlled BP values [The incidence and prevalence of CKD are increasing worldwide, along with the burdens of HT, diabetes mellitus (DM), hyperlipidemia, obesity, smoking, and aging. Patients with CKD are at a high risk of progressing toward end-stage renal disease (ESRD) and early morbidity and mortality due to premature cardiovascular disease (CVD). As a result, the increasing number of CKD patients is becoming a common and significant public health problem that leads to poor quality of life and demands expensive health resources ,2,3,4,5.P values ,15,16. IA detailed description of the SEPHAR IV methodology was previously published; therefore, we present below only those aspects regarding the collection of SEPHAR IV data that were the object of this study .The sample population of SEPHAR IV consisted of 1470 adult subjects (18–80 years) who provided informed consent for participation in this new survey and were enrolled from the eight territorial regions of Romania according to the National Statistics Institute, resulting in a nationally representative sample for the Romanian adult population . In each of the eight regions, the fieldwork was carried out by a study team of four members: one senior cardiologist, two young cardiologist specialists, and one study nurse. They received specific training concerning the completion of the questionnaire and the correct anthropometric, BP, and arterial rigidity measurements, including proper echocardiographic image acquisition. All the nurses received specific training regarding the collection and transportation of biological samples. The fieldwork was performed in a particular medical caravan, namely, the SEPHAR BUS, which was fully equipped with the medical and laboratory equipment required for the clinical, laboratory, and echocardiographic assessments and traveled to each of the study sites according to the regional schedule of the survey. During the two study visits, in the morning during a four-day interval (like previous SEPHAR surveys), all enrolled individuals were evaluated using the following: 51-item survey questionnaire  and anthropometric and BP measurements, together with evaluation for target organ damage, blood, and urine sample collection after a proper fasting time. The study protocol and its implementation procedures were supervised by the project reviewers and approved by the National and Local Ethics Committee .The BP measurement technique and definitions of HT were in accordance with the current European Society of Hypertension (ESH) guidelines . During The classification of optimal, normal blood pressure (NBP) and high blood pressure (HBP) was performed in accordance with the current ESH guidelines . HBP wasThe SEPHAR IV questionnaire included items dedicated to HT-related comorbidities: coronary heart disease (CHD); heart failure (HF); atrial fibrillation (AF); transient ischemic attack (TIA); stroke; peripheral artery disease (PAD); and renal failure (RF), including the need for dialysis. The 12-lead ECG recording was performed with standard electrode placement with the patient in a supine position, standing still, and breathing normally. Standard transthoracic echocardiography two-dimensional, color and spectral Doppler was performed with the patients in the left lateral decubitus position, ECG-gated, with image acquisitions over three cardiac cycles to measure the following parameters: the thickness of the interventricular septum, the thickness of the left ventricular (LV) posterior wall, the LV mass, the LV ejection fraction, the transmitral E/A ratio, the left atrial area, and the left atrial volume. Echocardiographic measurements were performed at a workstation by two independent operators. Arterial stiffness was assessed using an oscillometer (Arteriograph TensioMed) with the subject at rest in a supine position on the dominant arm. The following parameters were measured: aortic pulse wave velocity, aortic augmentation index, aortic systolic BP, aortic pulse pressure, reverse time, systolic area index, diastolic area index, ejection duration, and diastolic reflection area. The standard ankle–brachial index measurements (ABI) were assessed using a Doppler pencil and an adequately sized BP cuff. The ABI for each subject was the smallest between the right ABI and the left ABI. Blood samples were drawn by the nurse after checking the proper fasting time before the test (between 8 and 14 h). The samples were collected while the subject was sitting. The following laboratory tests were performed: total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), triglycerides (TG), apolipoprotein B (ApoB), blood glucose, glycated hemoglobin (HbA1c), serum creatinine, serum uric acid (SUR), albuminuria, urinary sodium, urinary creatinine, and urine albumin-to-creatinine ratio. 2 (GFR categories G3a–G5) and one or more markers of kidney damage: albuminuria  > 30 mg/24 h, ACR > 30 mg/g (>3 mg/mmol)), urine sediment abnormalities, electrolytes, other abnormalities due to tubular disorders, abnormalities detected using histology, structural abnormalities detected using imaging, and a history of kidney transplantation [cr = 142 × minα × max−1.200 × 0.9938Age × 1.012  where: Scr is the standardized serum creatinine in mg/dL, κ = 0.7  or 0.9 , α = −0.241  or −0.302 , min is the minimum of Scr/κ or 1.0, max is the maximum of Scr/κ or 1.0, and age is given in years [2, overweight is considered a BMI of 25–29.9 kg/m2, and obese is considered a BMI of ≥30 kg/m2 [We considered a patient as having CKD if they were diagnosed and documented in their medical history as having CKD or if they fulfilled the definition criteria at the time of the survey. CKD is defined as an eGFR < 60 mL/min/1.73 mantation . The 202in years ,20. Prot30 kg/m2 .p ≤ 0.05 was considered. Descriptive analyses ) were performed for all the target variables. Student’s t-test was used to compare the means, and the chi-squared test or Fisher’s exact test was used to compare proportions where appropriate. We conducted a logistic regression analysis to study the predictors of CKD after adjusting for age, sex, and DM for statistically significant variables after a univariate analysis. We used a k × k confusion matrix to identify and describe the performance of the regression algorithm.Statistical analysis was performed using the SciPy.Stats and Statsmodel Python packages. A significant level of A total of 1470 adults were enrolled in the study with their signed, written, informed consent. Among the total participants, 883 had completed renal laboratory tests and were included in the statistical analysis for the CKD study. 2 (71.94 ± 7.4 years) and those with an estimated GFR ≥ 60 mL/min/1.73 m2 (50.3 ± 16.21 years) (p < 0.0001). Of the participants, 341 (38.5%) were rural residents, and more than half  had low or medium levels of education. Those with high levels of education were predominantly present in the group with an estimated GFR ≥ 60 mL/min/1.73 m2 , while those with low or medium levels of education were more frequent in the group with CKD . All the results are shown in Out of the total subjects included in the analysis (883), more than half  were female. The mean age was 60.9 ± 10.12 years, with a significant difference between those with CKD with an estimated GFR < 60 mL/min/1.73 m2 was reported among 864 (97.85%) subjects, while an estimated GFR < 60 mL/min/1.73 m2, which characterized the CKD pattern, was reported in 19 (2.15%)  (p < 0.0001). The new hypertensives  were found exclusively in the group of subjects with an estimated GFR ≥ 60 mL/min/1.73 m2 (refer to 2 compared with the 5 (29.41%) in the CKD group (p = 0.04)  were hypertensives, with a greater HT prevalence among subjects with CKD  refer . Those e4) refer .2 compared with 139.32 ± 19.45 mmHg in the subjects with CKD (p = 0.003). The inhibitors of the renin–angiotensin–aldosterone system (RASi), diuretics, calcium channel blockers (CCB), beta blockers (βB), aldosterone antagonists (AA), tritherapy, and more than three antihypertensive drugs were significantly more commonly used among subjects with CKD. The lipid-lowering drugs and antiplatelets were also significantly more commonly used among these subjects. The treatment and control of hypertension are exhibited in p = 0.07), female sex , SUR > 6.9 mg/dL , ACR > 30 mg/g , DM , HbA1c % > 7.5 , LV mass > 140 g , presence of carotid plaques , obesity , AF , and HF  were associated with the risk of developing CKD. After the multivariable analysis , only SUR > 6.9 mg/dL  and the presence of carotid plaques as a marker surrogate of atherosclerosis  were found to be independent risk factors of CKD. Hyperuricemia patients with SUR > 6.9 mg/dL had a significantly lower eGFR by an average of 12.18 mL/min/m2 compared with normal uricemia patients . One could observe that the regression and confusion matrix algorithm using SUR > 6.9 mg/dL as an independent variable correctly predicted 88.72% of patients with eGFR ≥ 60 and 88.89% of those with eGFR < 60 mL/min/1.73 m2. In the ROC curve that plots the trade-off between the sensitivity (=0.99) and specificity (=0.17) of the model . It should be noted that the prevalence of HT was lower compared with the SEPHAR III survey , and the proportion of hypertensive individuals achieving BP control was higher: 46.7% versus 30.8% [p = 0.004) and the presence of carotid plaques as a marker surrogate of atherosclerosis  were independent risk factors of CKD. In our study, those with CKD had higher levels of SUR (7.44 ± 1.51 mg/dL) compared with those without renal damage , and a significantly lower eGFR by an average of 12.18 mL/min/m2 compared with normal uricemia patients, that is, 59.15 versus 71.33 mL/min/m2, p < 0.001. This observation concorded with the already published data from the SEPHAR III survey and other studies, wherein the results revealed the association between hyperuricemia and incident chronic kidney disease [2; however, the validity precludes due to the limited number of sample size and absence of follow-up. An important clinical and prognostic question in this context is whether there is a recommendation for prescribing a urate-lowering drug (ULD) for CKD individuals. None of the subjects had a ULD, such as the xanthine oxidase inhibitor allopurinol, prescribed for them during the study. Our subjects were asymptomatic, while gout is present in 25% and hyperuricemia in 60% of patients with CKD. Controversy persists over whether prescribing a ULD is warranted in CKD patients, especially in those with asymptomatic hyperuricemia. In 2020, two large randomized clinical trials (RCTs) found no benefit of allopurinol in slowing renal progression [To the best of our knowledge, our research is among the few epidemiological studies on CKD prevalence in the general Romanian population ,23. It sus 30.8% . We founus 30.8% ,32,33. Fus 30.8% ,15,16. Tus 30.8% ,35. In ous 30.8% . In a reus 30.8% . MS , 883 had completed renal laboratory tests and were included in the statistical analysis. However, the population studied was sufficiently represented and the statistical analysis was adequate. The major limitations of the study were the single measurement of corresponding variables (which could impact the SUR and CKD definitions), the limited sample of those with complete renal data, and the cross-sectional design that limited the interpretation of determinants and causal relationships.In Romanian adults, there was a decline in the prevalence of CKD that was attributable to the decline in the prevalence of HT, which was related to better control of known hypertensives. The prevalence of CKD in Romanian adults with hypertension was more than ten times higher than in the normotensive population, while 89.47% of subjects with CKD experienced HT. Levels of SUR > 6.9 mg/dL were predictors of CKD. Screening for hyperuricemia in hypertensives may play a role in identifying patients at risk of developing CKD. Our data provide important information regarding the trend in the prevalence of hypertension-related CKD and may guide the further implementation of specific preventive strategies in a country with a high cardiovascular risk.


Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should not stop clinicians from achieving SBP targets; however, registries and analyses of randomized trials present conflicting results. The purpose of the study was to apply machine learning (ML) algorithms to determine, whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary outcome in the SPRINT trial database. Methods: ML experiments were performed using decision tree, random forest, k-nearest neighbor, naive Bayesian, multi-layer perceptron, and logistic regression algorithms, including and excluding DBP as the risk factor in an unselected and selected (DBP < 70 mmHg) study population. Results: Including DBP as the risk factor did not change the performance of the machine learning models evaluated using accuracy, AUC, mean, and weighted F-measure, and was not required to make proper predictions of stroke, MI, HF, and primary outcome. Conclusions: Analyses of the SPRINT trial data using ML algorithms imply that DBP should not be treated as an independent risk factor when intensifying blood pressure control. Diastolic blood pressure (DBP) is often a neglected parameter when making decisions concerning the optimization of blood pressure (BP) control aimed at reducing the risk of cardiovascular (CV) events. Most of the trials and recommendations focus on systolic blood pressure (SBP), leaving DBP values as a subject for discussion ,2. HowevAs the problem of DBP relevance continues to remain a hot topic in hypertension, an alternative methodological approach incorporating artificial intelligence methods was used to strengthen the data that low DBP should not be the reason to abandon low SBP targets during treatment of hypertension. Machine learning (ML) methods focus on the prediction of an outcome based on variables, while statistical methods focus on the inference between variables and outcomes. Therefore, machine learning methods have different goals than conventional statistics, and might be more suitable to answer the question posed above.The purpose of the current study was to apply machine learning algorithms to determine whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary cardiovascular outcome in the Systolic Blood Pressure Intervention Trial (SPRINT) database.−1 1.73 m−2); 10-year Framingham CVD risk of 15% or higher; or if they were 75 or older. Patients were excluded from the SPRINT if they had diabetes mellitus, heart failure, or a previous stroke. The SPRINT showed that achieving SBP of less than 120 mmHg, rather than 140 mmHg, contributed to a reduced risk of fatal and nonfatal major cardiovascular events and overall mortality. Primary outcome, myocardial infarction, acute coronary syndrome, stroke, congestive heart failure, and cardiovascular death were significantly reduced in the intensive BP (SBP < 120 mmHg) management arm compared with the standard management arm (SBP < 150 mmHg) . The rationale, protocol, and results of the trial were published and widely discussed elsewhere [The SPRINT was a randomized, multi-center open-label trial aimed to verify the hypothesis that a lower SBP target would reduce clinical events . The SPRWe used the SPRINT trial data obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (accession number HLB02021921a). The data are available upon reasonable request. The authors of this manuscript have no right to share the data. This manuscript does not necessarily reflect the opinions or views of the SPRINT Research Group or the NHLBI. The analysis was approved by the Ethics Committee at the Medical University of Warsaw.The study rationale was based on the assumption that adding DBP to a ML model will not change model performance in classifying whether the outcome  is present or absent. Consequently, it would imply that the role of DBP is not relevant in predicting the events. Machine learning (ML) experiments were performed using the RapidMiner data science software platform . The varIndividual phases of the ML experiments were performed as follows . Firstlyt-test was used to compare the measures of the classifier performance with and without DBP as the predictor. The Welch t-test was also used for the comparison of classifiers’ performance in the selected and unselected population. The differences were considered significant at p < 0.05. All statistical computations were performed in an R 4.1.0 environment [This is a retrospective analysis of the SPRINT data. The performance of each machine learning algorithm for each of the outcomes , expressed by accuracy, AUC, mF1, and wF1 measures, was compared when taking into account the presence or absence of the DBP variable as the predictor. The paired two-sided ironment . Standarironment ,17.The baseline characteristic of the study population was presented in detail in the original SPRINT report . Tables The results of the ML experiments performed show that variables were properly selected for the analysis with good goals of the prediction measures . Models The main finding of the study is that adding DBP as a factor does not affect the classifier performance in the prediction of stroke, MI, HF, and primary CV outcomes in the high-CV-risk populations of the SPRINT. DBP as a factor also does not improve the performance of the ML models. Even in the subpopulation of patients with DBP < 70 mmHg, adding DBP to the ML models did not improve the classifier performance. The foregoing is indirect evidence that DBP is not an important risk factor to predict CV outcomes. The question remains whether such a generalization might be made. Observational studies show the J-curve relation of DBP and cardiovascular events, especially in respect of the population with coronary artery disease ,19,20. AA different methodological approach is applied in machine learning analysis in contrast to statistical analysis. ML prediction results are now widely used in consumer and medical decision-making algorithms, showing high efficiency. In the field of cardiology and imaging medicine, practitioners use ML algorithms ,32,33,34Analysis performed previously by our team concerning the relationship between low DBP and the risk of cardiovascular events using a classic statistical approach ,6,38 proIn the current study, experiments were performed using the chosen ML classifiers of various types. Other ML methods such as support vector machine (SVM) methods did not perform better than those chosen, but the future use of alternative classifiers such as the  or the XBoost regressor does not exclude obtaining different results. However, it is worth mentioning that the study did not focus on finding the best classifier to detect CV outcomes. The objective of the experiments was to explore the role of DBP as a variable, and it was found that it is not a significant factor. Interestingly, adding DBP as a factor did not change the effect of predictions using both the best- and the worst-performing classifiers.The current study has some limitations. Firstly, it was based on a single randomized study. Although it included 9361 patients, the study population did not cover the full spectrum of CVD risks, as patients with diabetes were excluded. Research using more complex databases and different variables is needed to fully establish the role of DBP as a CVD risk factor. As mentioned in the Methods section, variables selected for the current experiments were the same as those used previously in the multivariate Cox model ,6. If diIn the original database, a significant imbalance in the count of positive and negative outcomes was noted. Therefore, automatically performed up-sampling (SMOTE) was applied. Consequently, ML experiments were performed on the up-sampled SPRINT database which—even though the SMOTE procedure is designed for such purposes—may affect study results.In conclusion, the study shows that results of the randomized clinical trials may be analyzed using ML methods and such analysis may have clinical implications. Our analysis using various types of ML models shows that adding DBP to the model does not improve model performance. This may imply that DBP should not be treated as an independent risk factor in predicting CV outcomes. The methodological approach which was used in the current study may be implemented in other cases.


To explore the prevalence and clinical significance of newly diagnosed diabetes mellitus (DM) in patients with primary aldosteronism (PA). Investigating the risk factors for cardiocerebrovascular disease (CCVD) will guide strategies for reducing CCVD in patients with PA.We retrospectively included 729 PA patients without DM and conducted oral glucose tolerance tests.P value < 0.001]. The rate of CCVD in newly diagnosed diabetic PA patients was higher than that in nondiabetic PA patients at diagnosis . Furthermore, multivariate logistic analysis revealed that HT duration  and newly diagnosed DM  were significantly associated with CCVD in PA patients.We found that 15.0% of PA patients had newly diagnosed DM. The DM prevalence increased with elevated aldosterone levels [OR = 3.20 , The prevalence of newly diagnosed DM in PA patients was higher than that in the general population. Aldosterone level was an independent risk factor for DM not for CCVD. CCVD was correlated with longer HT duration and newly diagnosed DM. Therefore, it is crucial to screen DM at the diagnosis in PA patients. Primary aldosteronism (PA) is a common secondary hypertension that accounts for 7.1% of resistant hypertension (HT) in China . The preSome studies have indicated that excess plasma aldosterone has a direct influence on glucose metabolism and lipid metabolism, resulting in impaired glucose metabolism and dyslipidemia –8. HowevThe purpose of this retrospective study was to explore the prevalence of newly diagnosed DM in PA. We also compared the clinical features of CCVD-positive and CCVD-negative PA patients and investigated the risk factors for CCVD.This retrospective study enrolled patients diagnosed with PA at the Shanghai Clinical Center for Endocrine and Metabolic Diseases in Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2008 to July 2018. PA was diagnosed according to the 2008 and 2016 PA guideline . PatientAll blood tests were performed after overnight fasting for at least 10 h in the hospital. Plasma samples for glucose and insulin detection were obtained at 0, 30, 60, 120 and 180 min after glucose loading, consistence with the 75-gram oral glucose tolerance test (OGTT). Plasma glucose concentrations and serum concentrations of triglycerides, total cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol were measured using an autoanalyzer . Serum insulin was measured using an electrochemiluminescence assay . The level of hemoglobin A1c (HbA1c) was determined using the high-performance liquid chromatography method . Serum aldosterone and plasma renin activity were measured by radioimmunoassay (RIA) following the manufacturer’s instructions (A Beckman Coulter Corp). Serum cortisol and serum ACTH were measured by immunoluminescence and RIA following the manufacturer’s instructions (A Beckman Coulter Corp.). All tests were performed in the College of American Pathologists (CAP)-accredited laboratory (No. 7217913).P value < 0.05 was considered to be statistically significant. SPSS (22.0) was used for all of the other analyses. The graphs were constructed with GraphPad (8.0).Continuous variables are expressed as medians (interquartile ranges 25–75); categorical variables are expressed as frequencies and percentages. The study population was divided into tertiles on the basis of serum aldosterone levels to explore any trends in metabolic variables, and linear regression was performed among the tertiles. The chi-square test was adopted to compare categorical variables. Logistic regression was used to evaluate the association between aldosterone and diabetes with three different models. The Mann-Whitney U test was used to compare the parameters of diabetic PA and nondiabetic PA. Multivariate logistic regression analyses were used to identify predictors associated with CCVD. A p > 0.05). BHA patients had a higher BMI than UHA patients (p < 0.001). While cortisol and ACTH were similar between two groups (p > 0.05). FBG and PBG were slightly elevated in BHA without significance (p > 0.05). Insulin at 0 and 120 min were significantly higher in BHA than in UHA (p < 0.05). The prevalence of DM and abnormal glucose metabolism did not differ between the two groups.A total of 729 patients diagnosed with PA were finally included in this study. The laboratory characteristics are listed in Table +  decreased. Glucose metabolism was also influenced by aldosterone level. Both fasting blood glucose (FBG) and postprandial blood glucose (PBG) elevated with increasing aldosterone levels . We also analyzed the AUCs for glucose and insulin according to the OGTT test in the three tertiles and found that with increasing aldosterone levels, the AUC for glucose increased and the AUC for insulin level decreased significantly . With elevated aldosterone levels, BMI 24.8 ± 3.7 vs. 24.6 ± 3.5 vs. 24.0 ± 3.3, P for trend=0.012 and serum KP for trend < 0.001), which means that newly diagnosed DM and abnormal glucose metabolism rate increased significantly with increasing aldosterone tertiles. As shown in Table The diagnosis rates of DM were 8.6%, 16.9%, and 19.3%, respectively, which significantly elevated with increasing serum aldosterone levels (P for trend = 0.001). The diagnosis rates of abnormal glucose metabolism (DM and pre-DM) were 37.0%, 49.0%, and 53.5%, respectively (P < 0.001), while blood pressure values were similar between the two groups. The aldosterone level was significantly higher in PA patients with DM; accordingly, serum sodium was higher and serum potassium was lower in these patients. However, plasma renin activity was significantly higher in patients with newly diagnosed DM than those without DM. We also compared the prevalence of CCV events in PA patients with newly diagnosed DM and without DM. The prevalence of CCVD was significantly higher in PA patients with DM than in those without DM . The prevalence of heart failure among PA patients with DM was much higher than that among PA patients without DM. We compared CCVD-positive and CCVD-negative PA patients (Supplementary Table S2) and found that CCVD-positive patients tended to be older (P = 0.001) and male predominated (P < 0.05), had a longer hypertension duration (P < 0.001), a more family history of HT (P < 0.05) and a higher DM prevalence (P = 0.005) than CCVD-negative patients. Furthermore, logistic regression analysis was performed to determine the factors affecting the presence of CCVD in PA patients , p = 0.034] were factors significantly associated with CCVD.We compared parameters between PA patients with newly diagnosed DM and without DM Table . We founIn this study, we are the first to demonstrate the prevalence of newly diagnosed DM in PA. We found that 15.0% of PA patients had newly diagnosed diabetes, which was higher than the prevalence among those in the general population . Previou+ may partially result from decreased insulin. In our study, we evaluated glucose and cortisol co-secretion (Supplementary Table S3), and we did not find a relationship, as reported previously [In our study, we found that the aldosterone level significantly influenced the glucose level. This result was in accordance with those of previous studies , 23–25. eviously , 26. We eviously . This reNewly diagnosed DM means that patients have a shorter duration of high blood glucose and a higher chance of maintaining euglycemia and normotension with treatment. In general, high blood glucose levels were detected in 21% of all patients who died from ischemic heart disease and 13% of patients who died from stroke worldwide, with 84% of these deaths from CCVD . In PA pThere are some limitations of this study. This was a retrospective study, and a longer follow-up period may be needed to evaluate the relationship of glucose metabolism with CCVD in PA patients. In conclusion, the prevalence of newly diagnosed DM in PA patients was 15.0%, which was higher than that in the general population. Aldosterone was an independent risk factor for DM. PA patients with newly diagnosed DM had more CCV events, and CCVD positivity in PA patients correlated with DM and the duration of HT. Therefore, it is crucial to screen for DM in PA patients from the beginning.Supplementary information


There was good agreement between the methods, with a mean bias (95% CI) of −0.4  mmHg and a standard deviation of 1.3 mmHg, which was statistically significant but of no clinical importance. The limits of agreement  were −2.9 and 2.2 mmHg. The proportional error was statistically insignificant (p = 0.85), suggesting a constant agreement between the methods across the range of values tested. The percentage error was 10.7%. Conclusions: Continuous IAP measurements using the novel monitor performed well in the clinical setup of controlled intra-abdominal hypertension across the evaluated range of pressures. Further studies should expand the range to more pathological values.Introduction: Intra-abdominal hypertension and the resulting abdominal compartment syndrome are serious complications of severely ill patients. Diagnosis requires an intra-abdominal pressure (IAP) measurement, which is currently cumbersome and underused. We aimed to test the accuracy of a novel continuous IAP monitor. Methods: Adults having laparoscopic surgery and requiring urinary catheter intra-operatively were recruited to this single-arm validation study. IAP measurements using the novel monitor and a gold-standard foley manometer were compared. After anesthesia induction, a pneumoperitoneum was induced through a laparoscopic insufflator, and five randomly pre-defined pressures (between 5 and 25 mmHg) were achieved and simultaneously measured via both methods in each participant. Measurements were compared using Bland–Altman analysis. Results: In total, 29 participants completed the study and provided 144 distinct pairs of pressure measurements that were analyzed. A positive correlation between the two methods was found (R Intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS) are serious, life-threatening conditions ,2,3. TheIAP values up to 5–7 mmHg are considered normal, but higher values are observed in ICU patients ,7. The dThe clinical assessment of IAP is unreliable with sensitivity of 40–60% ,4,7. IAPA novel urinary flow-meter  is able to continuously estimate urinary bladder pressure without the interruption of urinary flow. The technology is based on a sensor serially connected between the urinary catheter and the collection bag. Repeated aspirations of small, constant volumes of fluid allow the measurement of flow and an estimation of pressure while urine flow remains uninterrupted. The system can therefore potentially detect IAH earlier, while avoiding disconnections and reducing infection risk. However, the system’s accuracy, although tested in laboratory settings, has not been properly validated in clinical settings to date .We therefore aimed to evaluate the accuracy of the novel flow-meter in the assessment of bladder pressure and compare it to the gold-standard fluid-column foley manometer. We hypothesized that the bias, calculated as the mean difference between the study device and the foley manometer, would not exceed 1 mmHg as recommended by the Abdominal Compartment Society  guidelines .clinicaltrials.gov NCT05121454). Eligible patients were approached for potential participation and were enrolled after providing written informed consent.This single-center validation observational study was approved by the Tel-Aviv Medical Center institutional review board  and was electronically registered in the American National Institution of Health registry prior to patient enrollment . Patients with structural urologic or renal disturbances, pregnant women, and patients unable to provide informed consent were excluded.A urinary catheter was inserted after the induction of general anesthesia. The catheter was then serially connected to the study device, followed by a classic fluid–column manometer and a urine collection bag. If the initial urine volume was not sufficient to fill the fluid manometer, up to 25 mL of saline was added to the system in a sterile fashion. Once the peritoneal cavity was insufflated with carbon dioxide, the study device started to continuously record bladder pressures and a concealed envelope containing 5 random target pressures between 5 and 25 mmHg was opened. The laparoscopic insufflator was set to reach and hold each pre-defined pressure, and bladder pressures were recorded simultaneously from the two systems (namely the study device and the fluid manometer). Each target pressure was kept for 30 s prior to recording to allow pressure stabilization. Once all 5 measurements were completed, the study device and fluid manometer were disconnected, the bladder catheter was directly connected to the urine collection bag, and the surgical procedure was started. The study setup is illustrated in Baseline demographic, anthropomorphic, and medical variables, as well as details of the surgical and anesthetic procedures were summarized with descriptive statistics.Each pair of measurements (study device and fluid column manometer) was first plotted on a scatter-plot and analyzed using the Pearson correlation. We then used the Bland–Altman analysis to test the level of agreement between the bladder pressures recorded by the study device and those recorded by the fluid manometer. We pre-defined the maximal allowed difference (delta) to 3 mmHg, which is in line with the standard values limits of 4 mmHg, as recommended by the Abdominal compartment society (formerly known as the WSACS) guidelines . In ordeIn total, 41 patients consented to participate. After excluding cases that were canceled or did not require the insertion of a urinary catheter and removing cases with fundamental data missing due to technical problems , 29 patients remained in the analysis, providing 144 distinct pairs of pressure measurements . Most pap < 0.01). R2 was 0.93. The correlation formula is IBPFoley = 0.14 + 0.96 × IBPstudy device  and 95% limits of agreement (2 standard deviations around the mean difference) of −2.9 and 2.2 mmHg (p = 0.85). The percentage error was 10.7%.Bland–Altman analysis revealed good agreement between the readings from the two measurement devices, with a bias (95% CI) of −0.4  mmHg  between the methods was 0.4 mmHg, which probably carries no clinical importance. Importantly, both the bias and the confidence interval around it fall well within the Abdominal Compartment Society recommendations for validation of new IAP monitoring devices .High IAP may go unrecognized as it primarily affects patients who are already severely ill and whose organ dysfunction may be incorrectly attributed to the progression of their primary condition ,12,13. SThe Abdominal Compartment Society recommends the use of Foley manometers as the gold-standard for IAP measurements. Nonetheless, available methods are cumbersome and are therefore of limited use. The main disadvantages of the common commercial solutions are the need to perform the measurements manually, the repeated interruptions to urine flow, the repeated introduction of saline or urine back to the bladder, and sometimes the need to repeatedly disconnect the urine collection system, potentially disrupting system sterility.The novel system we tested  aimed at accurately and continuously measuring urine output. Yet another capability of the system is to continuously measure bladder pressure without interrupting urine flow. The system therefore introduces several potential advantages over existing IAP monitoring systems, including less user-dependence, a decrease in the nursing workload, potentially improved accuracy, continuous (rather than intermittent) monitoring with no interruption to urine flow, and the lack of sterility disruption.To the best of our knowledge, this is the first study that reports on continuous intra-abdominal pressure monitoring in a clinical setting. As discussed previously, the implementation of continuous IAP devices may lead to the earlier detection of high IAP and ACS and timely intervention in high-risk ICU patients, thus improving medical care. Early detection of high IAP and ACS may also lead to improved economical outcome by reducing medical costs of a delayed diagnosis and its complications .2 insufflator and to compare the pressures recorded by the study device to those captured by a commercial Foley manometer. Since all measurements were performed shortly after the induction of general anesthesia but before the actual surgery began, patients were well-paralyzed and ventilation was mechanically-controlled; thus, diaphragmatic activity and abdominal muscle tension did not interfere with measurements. Another strength of our study is that researchers were blinded to the device recordings, as it was set not to display any values in real time. Researchers only recorded values from the control measurements, and all comparisons were performed during the statistical analysis. We believe that this study design reduced potential measurement bias.We chose to test the accuracy of the system in a controlled clinical setup of increased intra-abdominal pressure—adults undergoing laparoscopic surgery. We were therefore able to control and modify intra-abdominal pressures produced by the laparoscopic COIn seven cases, no interpretable data were available due to technical issues . These issues need to be addressed by the manufacturer before the system becomes completely commercial. However, since these events occurred at random, we believe that they did not present any bias to the study results.The main limitation of our study is that the range of pressures used to test the system corresponds with normal IAP values, as well as pressures found in patients with IAH, but below the threshold of severe IAH—presumably the most important clinical application. We were limited both by the ethical concerns of exposing surgical patients to high pressures, even if for a short period, and by the technical limitations of the commercial insufflator. Our conclusions are therefore primarily valid for the pressure range tested in the study, and one could argue that at higher pressures, the system might perform differently. Nonetheless, we did not find any signal of proportional bias, meaning that within the range tested, the system performed equally well across the various pressures. It is therefore reasonable to assume that the correlation extends to somewhat higher pressures.2 volume of insufflation. Such data may allow an exploration of compliance in the abdominal compartment and may be interesting to explore in future studies of IAH and ACS.Other than sex, weight, height, and BMI, no other anthropomorphic data were collected in this study. In addition, we are unable to calculate the COIn conclusion, in the IAP range of 5 to 25 mmHg, we found that the Serenno Medical continuous bladder pressure monitor correlates well with the gold-standard method for IAP measurements. This might allow its use as a continuous intra-bladder pressure monitor in patients at risk of intra-abdominal hypertension. Furthermore, we believe that this study may pave the way for future research and an understanding of the implications of continuous intra-abdominal pressure measurements. Future studies should expand the range to more pathological values.