P < 0.05). Like systolic and diastolic BP, changes in serum creatinine, 24-h urine volume and protein, fasting blood sugar (FBS), hemoglobin A1C (HbA1C), insulin, homeostasis model of assessment-insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), and PAB did not differ significantly between the two groups (P > 0.05). No serious adverse events were reported throughout the study. Propolis supplementation may improve the HRQoL of CKD patients. More studies are needed to validate the adjunct use of propolis for metabolic control of CKD patients.Chronic kidney disease (CKD) is a progressive kidney damage with an increasing prevalence. Some evidence suggests that propolis as a novel antioxidant, anti-inflammatory, and immunomodulatory agent may have beneficial effects in CKD. The aim of this study was to evaluate the efficacy of propolis on some kidney function parameters, pro-oxidant–antioxidant balance (PAB), glycemic status, quality of life, and blood pressure (BP) in patients with CKD. In this study, 44 patients with CKD were randomly assigned to receive propolis capsules at a dose of 250 mg daily or placebo for three months. Of 44 randomized individuals, 35 completed the trial. At the end of the intervention (end of month three), improvement in some dimensions of health-related quality of life (HRQoL) (derived from Kidney Disease and Quality of Life Short-Form (KDQOL- According to the current guidelines, CKD is described as a gradual and permanent loss of kidney function, an estimated glomerular filtration rate (eGFR) below 60 mL/min per 1.73 m2, or presence of markers of renal impairment, including hematuria, albuminuria, or defects detected by imaging or laboratory analysis, which last for more than three months1. The severity of CKD varies from kidney damage with normal function to kidney failure , which defines as eGFR less than 15 mL/min per 1.73 m21. Many cases of early-stage CKD are usually asymptomatic until the advanced stages with some clinical or sub-clinical manifestation 1. Chronic kidney disease poses a substantial and growing global health burden: Some forms of CKD affects about 10% of the adult population worldwide, resulting in 1.2 million deaths and 35.8 million disability-adjusted life-years (DALYs) in 20173. Premature death in People with CKD is up to ten times more likely than progression to ESRD, mainly due to cardiovascular diseases (CVD)4. Compared with the general population, people with CKD have a considerably lower health-related quality of life (HRQoL), and it falls in proportion to the decreasing rate of eGFR4. Therefore, early detection and treatment of CKD are crucial for attenuating ESRD, CVD, and total mortality5. Evidences have shown that, oxidative stress (OS), HTN, and hyperglycemia are three critical parameters for CKD pathogenesis and progression6. Although non-pharmacological interventions  along with pharmacological treatments can be used to maintain kidney function, the development of innovative approaches can be very helpful for slowing the progression of the disease, avoiding the complications, achieving greater longevity, and enhancing HRQoL, particularly in early-stage1.Chronic kidney disease (CKD) is a non-communicable progressive disease with a wide range of morbidity and mortality characterized by functional and structural changes in the kidney that occurs more often in patients with diabetes and hypertension (HTN)7. Bees use propolis to seal the holes in their honeycombs and cover surfaces to maintain inner moisture and temperature, provide an internal sterile environment and protect the entrance against invaders8. Since ancient times, propolis has been used in traditional medicine due to numerous beneficial properties8. The chemical composition of propolis is highly variable, depending on factors such as the season and vegetation at the collection site and the bees' species8. Propolis samples from different world areas have been reported to contain more than 300 active constituents, including phenolic acids and the related esters, flavonoids, terpenes, aromatic aldehydes and alcohols, stilbenes, b-steroids, and fatty acids10. Propolis and its compounds are usually well-tolerated and nontoxic when used in moderation8. Based on previous animal studies, ingestion of approximately 1.4 mg propolis/kg/day or 70 mg propolis/day is potentially nontoxic for the organism; however, exceeding the dose of 15 g/day may cause adverse effects8. The median lethal dose (LD50) of propolis extract while given to mice is higher than 7.34 g/kg, which assures human therapeutic dosage safety11. Many studies have demonstrated the beneficial effects of propolis on some chronic diseases due to its antibacterial, antiviral, antifungal, antiprotozoal, antioxidant, anti-inflammatory, immunomodulatory, antihyperglycemic, antihypertensive, antiproliferative, and hepatoprotective properties7. Recently propolis was also examined in vivo and in vitro for nephroprotective effects, with promising results13. Considering CKD pathophysiology and unique possessions of propolis, this study was aimed to assess the effects of propolis supplementation on some kidney function parameters, pro-oxidant–antioxidant balance (PAB), glycemic status, quality of life, and blood pressure (BP) in patients with CKD. Given the current evidences, experimental studies and clinical trials regarding propolis effectiveness on CKD are rare, and their results are usually controversial. Therefore, further clinical trials are required to clarify propolis efficacy in this group of patients.Propolis is a natural resinous mixture produced by honeybees (mostly Apis mellifera) by mixing exudate gathered from various plant sources with salivary enzymes and waxhttps://en.irct.ir/trial/48603. Participants were recruited from Salamat Polyclinic and Asad Abadi Academic Hospital, Tabriz, Iran, over September 2020 to October 2021. All participating patients provided written informed consent.The present study was a multi-centered, randomized, parallel double-blind, placebo-controlled, phase ΙΙI clinical trial to evaluate the effect of propolis on patients with CKD. This trial was conducted according to the latest version of the declaration of Helsinki and was approved by the Medical Ethics Committee of Tabriz University of Medical Sciences . Furthermore, it was prospectively registered at the Iranian Registry of Clinical trial (registration number: IRCT20191218045798N1) on 07/06/2020 and is available at 2 and body mass index (BMI) of 18.5–35 kg/m2 were included. The patients who underwent a kidney transplant; those who were pregnant or breastfeeding mother or professional athletes; treating with steroids or other immune system suppressors; patients with malignancy, inflammatory or infectious diseases, asthma, hepatic disorders, or severe depression; allergies to plants, especially honey bee products; those who were on any forms of herbal supplements within the past three months, and those who abused alcohol, cigarettes and drugs were excluded from the study. The sample size was calculated based on the previous clinical trial using mean ± standard deviation (SD) for eGFR14. Considering 95% confidence interval, α = 0.05, and 80% power, 17 CKD patients for each study group were estimated using PASS software, version 15. To compensate for an expected drop-out rate of 30%, we considered 22 patients for each group.Patients aged 20–80 years who had been diagnosed with CKD due to diabetes, HTN, or another underlined reasons, with an eGFR of 30–89 mL/min per 1.73 mOf 220 patients evaluated, 53 were considered eligible to be enrolled in the study. Finally, 44 patients were randomly assigned in a 1:1 ratio to either propolis or placebo group. The randomization sequence was generated using a table of random numbers in permuted blocks of two, with patients stratified according to age (20–60 and 60–80 years) and present of diabetes. The propolis and placebo capsules were provided in the same organoleptic characteristics and packaging for appropriate blinding. Drug containers were labeled with two different codes for propolis or placebo by the company that manufactured the capsules. A randomization coordinator, who was not involved in the study, created an allocation sequence and provided drug containers inside the consecutively numbered, opaque, sealed envelopes to ensure concealed allocation. All research contributors and the study subjects were blind to the intervention codes and the patients’ assignment.15, total flavones/flavonols, flavanones/dihydroflavonols, and total phenolics content in propolis sample were measured using the spectrophotometric assay based on the formation of aluminum chloride complex, the colorimetric method with DNP , and the Folin–Ciocalteu procedure, respectively. These amounts were within the recommended range: total flavones and flavonols: 8.4%, total flavanones and dihydroflavonols: 4.6%, and total phenolic compounds: 28%. Patients in the intervention group received the poplar propolis capsule twice a day  for three months before breakfast and dinner. Those in the placebo group received the placebo capsule  twice daily, based on the same protocol as the propolis group. The dosage of propolis was obtained from studies that had used similar amounts without observing side effects16. The study did not change any conventional treatment of the patients. Along with supplementation, subjects in both groups received a renal diet and were asked to follow a moderate-intensity exercise like walking. Patients' compliance to supplementation, renal-specific diet, and physical activity program was evaluated by phone calls twice a month. It was reconsidered by counting the returned capsules and assessing the 24-h dietary recall, 3-day food record (two weekdays and one weekend day), and international physical activity questionnaire-short form (IPAQ-SF) monthly. All patients were encouraged to adhere to intervention protocol regarding regular consumption of the capsules, following the physical activity program and renal diet at each phone call and interval visits. Participants taking less than 80% of supplementation dosage were excluded.Propolis and placebo capsules were prepared by Asal Shahdineh Golha Co., Isfahan, Iran. The propolis sample was collected during the fall season from beehives located in Isfahan, Iran, verified by an expert agricultural organization. Each propolis capsule contained 125 mg poplar type propolis ethanol extract, 187.5 mg bee pollen, and 187.5 mg oat, a total amount of 36 mg phenolic compounds . According to Bankova recommendation for chemical standardization of poplar type propolisThe primary outcome of the current randomized clinical trial (RCT) was the changes in some kidney function parameters, and the secondary outcomes were changes in PAB, glycemic status, quality of life, and BP from baseline to the end of the intervention.17. Insulin sensitivity and insulin resistance were calculated by quantitative insulin sensitivity check index (QUICKI) and homeostasis model of assessment-insulin resistance (HOMA-IR), respectively19:The socio-demographic questionnaire was filled by an interview in the first visit. Anthropometric parameters such as weight and height were assessed by validated tools at the baseline and the end of the trial. Weight was measured with light clothes and no shoes, close to 100 g, by a digital Seca scale . Height was measured using a portable stadiometer , in a straight standing position and without shoes, close to 0.5 cm. Then, BMI was calculated by dividing weight in kilograms by height in meters squared. At the end of each medical appointment, BP was measured using a mercury sphygmomanometer . 10 mL of blood sample was obtained from each participant after 12 h of overnight fasting at the commencement of the study and at the end of intervention. Samples were collected into the tubes containing ethylenediamine tetraacetic acid (EDTA) for Hemoglobin A1C (HbA1C) analysis and tubes without anticoagulant (for centrifugation and obtaining the serum). Separated serums were used to measure insulin (enzyme-linked immunosorbent assay (ELISA), Monobind), fasting blood sugar (FBS) , and creatinine . The other aliquoted serum samples were frozen at − 20 °C for the PAB assay analysis . Hemoglobin A1C  was determined from whole blood samples. The 24-h urine samples were collected to measure volume and the protein content  at the baseline and the end of the study. All biochemical measurements except PAB were performed immediately after sampling. Pro-oxidant–antioxidant balance assay was done as described by Faraji-Rad et al.20. This questionnaire consists of 36 questions about the general health status from both mental and physical dimensions and 43 kidney disease-specific questions. Responses were transported to an excel spreadsheet provided by the website . This worksheet recodifies data from any items of the questionnaire, resulting in a standardized scale ranging from 0  to 100 . Dimensions have been scored separately, and there is no single value as a result of overall assessment of the KDQOL-Health-related quality of life was evaluated with the Kidney Disease and Quality of Life Short-Form . The data entry was checked double times by the authors. Missing data were completed using a multiple imputation approach by chained equations with 20 iterations. The Shapiro–Wilk test was used to examine the normal distribution of variables. Results were presented as frequency (percentage) for qualitative variables, mean ± SD for normally distributed continuous data, or median (interquartile range 25–75 percentile) for values with skewed distribution. For comparing the baseline characteristics between the two groups, independent samples t-test was used for normally distributed continuous variables, and Chi-square test or Fisher's exact test for qualitative variables, and Mann–Whitney U test for non-normally distributed variables were used accordingly. Within-group changes were assessed by Paired samples t-test or Wilcoxon signed-rank test, as appropriate. Final differences between the two groups were identified using analysis of covariance (ANCOVA) or rank ANCOVA , adjusting for age, present of diabetes, and baseline values as covariates. A Of 44 patients who were included, 35 (17 in the propolis group and 18 in the placebo group) completed the study. As the present study was conducted at the peak of the coronavirus disease 2019 (COVID-19) pandemic in IRAN, four patients in the propolis group and two patients in the placebo group withdrew from the study due to COVID-19 lockdowns, and in both groups, one patient did not continue the study because of getting COVID-19; One of the patients in the placebo group also discontinued for the personal reasons. The trial flowchart was shown in Fig. The demographic and baseline characteristics of the patients were shown in Table entclass1pt{minimaAt the end of the intervention, systolic and diastolic BP values decreased non-significantly in both groups, while the propolis group showed higher reductions Table . No signP = 0.018), effects of kidney disease (P = 0.036), overall health (P = 0.029), physical performance (P = 0.046), and general health (P = 0.014). Notably, the control group showed no significant changes in all the dimensions of the KDQOL-P = 0.002), overall health (P = 0.038), and general health (P = 0.035) adjusting for the baseline values, present of diabetes, and age, compared to the placebo group . To the best of our knowledge, this is the first clinical trial investigating the effect of propolis supplementation on quality of life, PAB, renal function and glycemic parameters, and BP in CKD patients.17. It plays a pivotal role in the progression of CKD, directly by inducing tubular and glomerular injury or indirectly by developing HTN, inflammation, and/or endothelial dysfunction21; therefore, the effects of propolis on OS in CKD patients was evaluated in the current study. In a review by Kocot et al., it has been stated that propolis, as one of the richest sources of plant-based polyphenols, including flavonoids, can neutralize the effects of OS, which plays a detrimental role in the pathogenesis of several diseases10. In the study by Mujica et al., propolis administration for 90 days in a human population in Chile resulted in the increased serum levels of glutathione (GSH) and decreased thiobarbituric acid reactive substance (TBARS) levels, as a strong indicator for cardiovascular events22. In another study, Hesami et al. conducted a double-blind, placebo-controlled clinical trial on type 2 diabetic patients and reported that in the propolis group compared to the placebo, the level of oxidized low-density lipoprotein cholesterol (Ox-LDL-C) reduced and catalase (CAT) activity improved significantly23. In addition, in the studies by Gao et al. and Zhao et al. on type 2 diabetes mellitus (T2DM) patients, administration of propolis for 18 weeks caused a significant increase in serum GSH and total polyphenols in comparison to the control; however, the ferric-reducing ability of plasma (FRAP), superoxide dismutase (SOD), glutathione peroxidase (GPx), malondialdehyde (MDA) or Ox-LDL-C levels were not affected by propolis supplementation25. As mentioned in our systematic review, the experimental studies came to the conclusion that propolis could be effective in decreasing the MDA levels in serum, urine, as well as renal and liver tissues and it may increase antioxidant parameters such as serum SOD and glutathione-S-transferase (GST), liver GPx, and renal GSH, CAT, FRAP, paraoxonase (PON1), heme-oxygenase-1(HO-1) score, and total antioxidant status (TAS) in the rat models with CKD or acute kidney injury (AKI) caused by diabetes, HTN, ischemic-reperfusion, or partial nephrectomy11. Moreover, experimental studies showed that propolis had potential effects on reducing urinary levels of TBARS, the levels of renal tissue total oxidant status (TOS) and oxidative stress index (OSI), as well as 8-hydroxy-2′-deoxyguanosine (8-OHdG) generation, as a notable biomarker of DNA damage in the kidney tissues11. Nevertheless, propolis supplementation had no significant effect on serum GPx and liver CAT in these animal studies. The current study showed that propolis had no considerable impact on PAB in patients with CKD. As antioxidant properties of propolis strongly depend on its dosage and polyphenol contents26, various propolis obtained from different geographical areas with diverse plant species may be responsible for these controversies in outcomes across studies. Moreover, the components of the antioxidant system, such as antioxidant enzymes as well as MDA as a reactive compound and strong marker for oxidative stress, were not investigated separately in this study, and some of these components may have changed in line with the improvement of the antioxidant status, although not so much that it caused the change of PAB. Differences in the health and nutrition status of the participants at the baseline could be another reason for this inconsistency.Oxidative stress is defined as a state of imbalance between pro-oxidants and antioxidants in favor of pro-oxidants27. Hyperglycemia leads to renal lesions through multiple mechanisms, including stimulating the reactive oxygen species (ROS)-mediated pathways such as nuclear factor kappa B (NF-κB) activation, angiotensin II synthesis, protein kinase C (PKC), hexosamine pathway flux, polyol pathway flux, and advanced glycation end products (AGEs) formation11. As good glycemic control can prevent the initiation and progression of CKD, the effects of propolis on glycemic indices were addressed in this study. Obtained results showed that propolis had no significant effect on FBS, serum insulin, HbA1C, insulin sensitivity, and insulin resistance. In line with our results, two clinical trials reported that administration of propolis to patients with CKD (500 mg/day for one year)14 or nonalcoholic fatty liver disease (NAFLD) (500 mg/day for four months)28 had not any significant effects on glucose metabolism. In addition, the studies by Mujica et al., Gao et al., Zhao et al., and Fukuda et al. showed no significant effects of propolis administration on FBS, insulin, HbA1C, and HOMA-IR in patients with T2DM or cardio-metabolic abnormalities25. Conversely, Koo et al. reported that propolis supplementation (600 mg/day for four weeks) in healthy smokers reduced serum levels of FBS29. Moreover, Afsharpour et al. demonstrated that propolis supplementation reduced FBS, 2-h postprandial glucose (2hpp Glc), insulin, insulin resistance, and HbA1C at a daily dose of 1500 mg after eight weeks of intervention in T2DM patients30. Also, the study of Zakerkish et al. revealed that 1000 mg/day of propolis for 90 days could significantly decrease HbA1C, insulin, and 2hpp Glc levels and increase insulin sensitivity in T2DM patients, while it did not affect FBS31. A recent meta-analysis evaluated the effectiveness of propolis supplementation on markers of glycemic status in patients with T2DM32. It illustrated that propolis supplementation could significantly lower FBS and HbA1C levels; However, it had no effects on serum insulin concentrations and insulin resistance. Another meta-analysis conducted on different health populations revealed that propolis consumption decreased FBS, HbA1C, and insulin levels while not improving HOMA-IR33. Patients with CKD usually have T2DM as a concomitant and/or underlying disease, but few participants were diabetic in this trial. It seems that, existing inconsistency may be due to differences in the study population. The possible properties of propolis to exert glucose-lowering effects is suggested to be due to the increased insulin production and/or sensitivity, inhibitory effects on α-glycosidase and intestinal sucrose, increased glucose uptake as well as translocation of insulin-sensitive glucose transporter (GLUT) 4 via inducing phosphorylation of both phosphatidylinositol 3-kinase (PI3K) and 5’-adenosine monophosphate-activated protein kinase (AMPK), down-regulation of gluconeogenic genes in the liver, and increased glucose utilization and glycolysis in the hepatocellular cells11.Chronic hyperglycemia is the major cause of micro-and macro-vascular complications associated with diabetes mellitus and the leading reason for CKD14. Contrary to our findings, experimental studies have shown the antihypertensive effects of propolis. Angiotensin-converting enzyme inhibitor-like effect, diuretic effects, activation of nitric oxide pathway, acetylcholine-induced vasodilation, and the antioxidant and anti-inflammatory properties of propolis are among the suggested mechanisms40. The vasorelaxant effects of propolis also occur as a result of inhibitory action on calcium movements through smooth muscle cells membrane40. Different results obtained may be due to the fact that the hypertensive patients of the current study were taking antihypertensive medications, as seen in the study of Silveira et al.14This study also showed that propolis had no significant effects on BP as one of the leading causes of CKD. In accordance with our findings, Silveira et al. reported that propolis supplements at a daily dose of 500 mg for one year had no effect on BP in CKD patients14. Likewise, in another study, 1000 mg/day of propolis administration for 90 days in T2DM patients had no effects on serum creatinine31. Teles et al. have reported renal protective properties of the alcoholic extract of red propolis (150 mg/kg/day for two months) in hypertensive rats with CKD and proteinuria , manifested by reducing proteinuria, serum concentrations of creatinine, glomerulosclerosis, and renal macrophage infiltration12. Moreover, it has been demonstrated that oral administration of chrysin , one of the flavonoid compounds found in propolis, can attenuate proteinuria, glomerular injury, and podocyte apoptotic loss due to exposure to high levels of glucose in diabetic rats13. In the present clinical trial, we did not find a statistically significant effect of propolis on serum levels of creatinine, proteinuria, and urine volume, although 3-month intervention period may be too short to assess CKD progression through the measurement of serum creatinine concentrations and proteinuria, which has well-known limitations.Only few studies have been carried out using CKD models. Silveira et al. reported that daily consumption of 500 mg propolis extract for one year in patients with CKD significantly reduced proteinuria but not plasma creatinine41. Nonetheless, in the study by Matsumoto et al., after 24 weeks of intervention with propolis at a daily dose of 508.5 mg in women with rheumatoid arthritis, no significant difference was observed in the quality of life42. As there are no consistent results in this regard, without any clear underlying mechanisms, more clinical trials are needed to draw robust conclusions about propolis efficacy in HRQoL for patients with CKD.The present study showed that propolis supplementation at a dose of 250 mg daily for three months improved some dimensions of HRQoL in patients with CKD . Davoodi et al. also found that propolis administration (250 mg/day for three months) in patients diagnosed with breast cancer receiving chemotherapy improved the quality of life, particularly with regards to global quality of life as well as emotional functioning and financial difficulties compared to the placebo groupThere were some limitations in this research. The chemical profile of the propolis extract was determined by the manufacturer laboratory in Isfahan, Iran, including general information, and the effective components and their exact values within each capsule were not identified. Additionally, serum levels of propolis bioactive compounds were not measured for assessing the patients’ compliance to propolis supplementation; however, compliance rate was measured by counting the returned capsules by the participants during the pre-arranged appointments. Notably, one of the challenges we faced was that the study was conducted at the peak of the COVID-19 pandemic in IRAN, so some participants may had asymptomatic COVID-19 infection, that affected the results. The relatively low dose of porpolis—due to safety considerations for CKD patients—and the short duration of intervention, which may be the reasons for the lack of significant changes in serum creatinine and proteinuria, are other limitations of this study. All patients had stages 2 and 3 of CKD, so the generalizability of these results in patients with more advanced stages of CKD needs to be confirmed in future clinical trials. However, attempts were made to overcome the impact of limitations by stratified block randomization with a block size of two (based on age and present of diabetes), adjusting the results for confounding factors, and involvement of patients with CKD of different causes.In conclusion, treatment with Iranian poplar type propolis may improve HRQoL in patients with CKD of any causes and moderate renal dysfunction. These results indicated the therapeutic effects of Iranian poplar type propolis in this group of patients, and opened a window for further researches on this compound with larger sample size, longer duration, and higher dosage, considering propolis as an adjuvant therapy in CKD patients.Supplementary Information.


The cellular inflammatory pattern of nonsteroidal anti‐inflammatory drug–exacerbated respiratory disease (N‐ERD) is heterogeneous. However, data on the heterogeneity of non‐eosinophilic asthma (NEA) with aspirin hypersensitivity are scanty. By examination of N‐ERD patients based on clinical data and eicosanoid biomarkers we aimed to identify NEA endotypes potentially guiding clinical management.Induced sputum was collected from patients with N‐ERD. Sixty six patients (49.6% of 133 N‐ERD) with NEA were included in the hierarchical cluster analysis based on clinical and laboratory data. The quality of clustering was evaluated using internal cluster validation with different indices and a practical decision tree was proposed to simplify stratification of patients.2 levels distinguished cluster #1 from the remaining clusters with an area under the curve of 0.94.The most frequent NEA pattern was paucigranulocytic , remaining was neutrophilic asthma . Four clusters were identified. Cluster #3 included the highest number of NEA patients (37.9%) with severe asthma and PGA pattern (96.0%). Cluster #1 (24.2%) included severe only asthma, with a higher prevalence of NA (50%). Cluster #2 (25.8%) comprised well‐controlled mild or severe asthma . Cluster #4 contained only 12.1% patients with well‐controlled moderate asthma . Sputum prostaglandin D2 levels, asthma severity, and age of patients. The heterogeneity of non‐eosinophilic N‐ERD suggests a need for novel targeted interventions.Among identified four NEA subtypes, clusters #3 and #1 represented N‐ERD patients with severe asthma but a different inflammatory signatures. All the clusters were discriminated by sputum PGD Within 1 year prior enrollment only 5 patients received oral corticosteroids due to exacerbation of asthma but not longer than for 3 weeks. None of the patients used anti‐leukotrienes or biological drugs and no participant had respiratory tract infection or asthma exacerbation during 6 weeks prior sputum sampling. On the day of sputum induction, each participant had a pulmonary function test and forced expiratory volume in the ﬁrst second was always 70% or higher. The characteristics of the study group is presented in Table Participants of this study were enrolled from a prospective database of N‐ERD patients diagnosed and treated at the Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland. The analysis included 66 nonsmoking patients with N‐ERD and a non‐eosinophilic inflammatory pattern  based on the induced sputum cytology. The diagnosis of aspirin hypersensitivity was confirmed by bronchial or oral aspirin challenge test. Patients were treated with nasal and inhaled corticosteroids (ICSs) and long‐acting β2.24 (LTE4) excretion. Following sputum induction and determination of cellular inflammatory pattern based on sputum cells differential count, NEA was defined as neutrophilic or paucigranulocytic.2 (PGD2), leukotriene E4 (LTE4) and anti‐inflammatory prostaglandin E2 (PGE2). CRS comorbidity was scored by computed tomography of the paranasal sinuses using Lund‐Mackay scale by two experienced radiologists.Patients with N‐ERD subjected to sputum induction were recruited during two prospective studies, the results of the former were already published.2.32‐agonists, or asthma that required high‐dose ICS and long‐acting β2‐agonists to prevent becoming uncontrolled. Moderate asthma was defined as well controlled asthma using Step 3 treatment.Exacerbations during the year preceding the study were defined as asthma symptoms requiring hospitalization or systemic steroid therapy. Asthma severity was assessed retrospectively based on the level of treatment required to control the patient's symptoms and exacerbations. Severe asthma was defined as uncontrolled asthma despite optimized treatment with high‐dose ICSs and long‐acting β2.4Induced sputum was collected according to the European Respiratory Society recommendations.2.52 and PGE2 or by high‐performance liquid chromatography and tandem mass spectrometry for LTE4. Results were expressed in picograms per milliliter. Analytical details were described elsewhere.Sputum eicosanoid levels were measured by gas chromatography and tandem mass spectrometry for PGD2.64 excretion. Results were presented in picograms per milligram of urinary creatinine.Urinary samples were collected in the morning, and an enzyme‐linked immunosorbent assay  was used to assess urinary LTE2.7p value of less than 0.05 was considered significant.Data were analyzed using Statistica 13.1 software package  or R package v.4.1 . Descriptive statistics for demographic, clinical, and laboratory characteristics were presented as median with 25th and 75th percentiles for continuous variables or as a frequency for categorical variables. The analysis of variance with post‐hoc Tukey procedure was used to compare continuous variables between clusters. The Spearman rank correlation coefficients were used to assess the associations between quantitative variables. A 2.8Detailed information about cluster analysis is described in an Online Supplement.1, urinary LTE4, sputum LTE4, PGD2, and PGE2. Internal cluster validation using different indices was performed to evaluate clustering propensity. One of these indices, the silhouette width (Si), quantified each point in one cluster by a distance to the points in the neighboring clusters. By this definition, Si close to 1 was expected for very well clustered data, whereas Si close to 0 characterized a case located between two clusters. In a hierarchical cluster tree, the height axis indicated dissimilarity between 2 clusters, with increasing height for the less similar the objects. Because this height represented a cophenetic distance between two objects, to assess how the cophenetic distances reflected the original distances, a correlation coefficient was calculated between the two distances.A hierarchical cluster analysis based on 16 variables covering 5 qualitative ones: sex, skin tests, asthma control, asthma severity, inflammatory phenotype, and 11 quantitative variables: body mass index, age, age at asthma onset, exacerbations, blood eosinophil counts, total serum IgE, FEV3p = 0.006). Following the clustering analysis, four candidate clusters of NEA were distinguished among N‐ERD patients with non‐eosinophilic asthma. Cophenetic correlation for the final cluster tree was r = 0.47. The demographics and clinical characteristics of patients in each cluster are shown in Table The distribution of patients according to cellular inflammatory patterns on all N‐ERD and NEA subgroup is presented in Figure 1AB. NEA was present in 66 N‐ERD patients (49.6%) and was stratified into neutrophilic or paucigranulocytic inflammatory pattern.3.1p = 0.003). Induced sputum level of PGD2 was a biomarker of cluster #3 because it was the lowest. Cluster #2 comprised 17 patients (25.8%) with mild (52.9%) or severe (47.1%) non‐eosinophilic inflammatory pattern, mainly PGA (76.5%). A common clinical feature of these N‐ERD patients was well‐controlled asthma reported in 94.1%. No asthma exacerbation in the previous year was noted for 70.6% of patients in this cluster versus 25% of patients in cluster #1 (p = 0.015). Moreover, these patients had a significantly lower age of asthma onset versus patients in cluster #3. There were no significant differences in the percentage distribution of PGA and NA phenotypes between cluster #2 and the remaining clusters. Cluster #1 included 16 N‐ERD patients (24.2%) with NEA. They had equal distribution of PGA and NA. All patients had severe asthma, and their level of asthma severity was significantly higher than in clusters #2 and #4. Asthma exacerbations in the year preceding the study were noted in 75% of patients in this cluster. Patients in cluster #1 had significantly lower blood eosinophil count than patients in clusters #2 and #4, they had also a significantly lower total serum IgE level than patients in cluster #4. Induced sputum level of PGD2 was the highest by comparison with other clusters. Elevated PGE2 sputum levels were also observed, when compared to patients in clusters #2 and #3. However, sputum prostaglandins did not correlate with cellular inflammatory pattern of the sputum in neither cluster. The smallest cluster #4 included 8 N‐ERD patients (12.1%) with moderate and well‐controlled NEA. There was insignificant predominance of PGA (65.5%). The average level of asthma severity was significantly lower in cluster #4 and asthma control was significantly better than in clusters #1 and #3. However, in half of cluster #4 patients exacerbations were noted in the previous year. Moreover, patients in this cluster had significantly higher blood eosinophil count and serum total IgE levels than these in cluster #1. They had also significantly lower sputum PGD2 levels than these in cluster #1. Therefore, some significant differences in the distribution of cellular inflammatory patterns were noted only between cluster #3 (PGA) and clusters #1 or #4.The largest cluster #3 included 25 N‐ERD patients (37.9%), the most of whom had PGA (96%). Clinically, patients within this cluster had severe asthma (96%) and were older than those in clusters 2 and 1. Moreover, their age at the onset of asthma was higher and body mass index was greater than in the cluster #2  of 0.94 . Using the Youden cut‐off value of PGD2 = 45.07 pg/ml, accuracy of the discrimination of cluster 1 member was 92.4% , sensitivity = 87.5%  and specificity = 94.0% . In the studied NEA group of N‐ERD patients, sputum PGD2 level had positive predictive value for cluster #1 membership PPV = 82.45% , whereas negative predictive value was NPV = 92%  between the decision tree rules and cluster analysis. Therefore, the list of predictors of non‐eosinophilic N‐ERD variants could be limited to the age of patients, their severity of asthma and to the sputum level of PGD2  and chemoattractant receptor‐homologous molecule expressed on Th2 cells (CRTH2/DP2) antagonists, promising in animal models of inflammatory diseases, decreased macrophage activation and neutrophil accumulation.2 was shown to significantly augment inflammation by its ability to enhance vascular permeability, the proinflammatory action of macrophages and subsequent neutrophil migration and activation.1 along with DP2 would be sufficient to abolish PGD2‐induced activation of macrophages, since a downstream metabolite of PGD2 activates also proinflammatory signaling by the receptor for thromboxane (TP).2 drugs have been approved for clinical use yet. Recently Kermani et al.In the current study, the highest number of N‐ERD patients without eosinophilic inflammatory pattern had severe asthma and were characterized by the paucicellular sputum cytology (cluster #3). Our results indicate that macrophages are predominant in the sputum of patients with PGA phenotype, in line with a study by Olgac et al.2 level). Discrimination power of sputum PGD2 levels seems critical for a correct stratification of non‐eosinophilic N‐ERD. Because of complex methodology for sputum PGD2 measurements, a more readily available assays of PGD2 metabolites in urine have to be validated for this purpose in a future research. Acknowledging expansion of biologics trials in asthma and their limited efficacy in N‐ERD patients, we summarize potential interventions in severe non‐eosinophilic N‐ERD using mechanistic premises (Figure In this study we identified four subtypes of N‐ERD which departed from a classical eosinophilic asthma characteristics. Distinction of this subtypes was made using basic clinical data and lipid inflammatory mediators in sputum. We propose a decision tree stratification of non‐eosinophilic N‐ERD, assigning patients into clusters using only 3 variables (age of a patient, asthma severity and induced sputum PGD2 agonists for at least 6 weeks prior induction of the sputum. By this approach, complying with the study ethics, no stability of cellular inflammatory pattern in the sputum was ensured. Especially patients in cluster #4 could represent a beneficial effect of ICS, whilst their initial cellular pattern of inflammation could be eosinophilic. Third, this was a single center study conducted in a tertiary reference clinics, thus the study group was not representative for a real‐life N‐NERD patients.Several limitations of the current study have to be listed. First, some bias due to a small study group was inevitable despite a rigorous statistics employed. Second, all the patients of the study received anti‐asthmatic treatment for the same period of 52 weeks, which was topical corticosteroids and long‐acting β2 level. Results of this study confirms involvement of inflammatory cells other than eosinophils, namely neutrophils and macrophages in the pathogenesis of N‐ERD. Because successful trials of novel therapies for N‐ERD seem dependent on a definition of a precise endotype of the disease,In conclusion, asthma patients with N‐ERD have extensive inflammation of airways, which is either eosinophilic or non‐eosinophilic. We focused on non‐eosinophilic inflammatory pattern identified by the induced sputum cytology of N‐ERD to describe its heterogeneity. We identified subtypes of non‐eosinophilic N‐ERD as distinguished by clinical features and a single biomarker, that is, sputum PGDL. M. and M. S. involved in conception or design of the work. L.M., N.C.W., J.S., K.H.‐S. and J.T. collected the data. L.M., A.C., KW., and M.S. were involved in data analysis and interpretation. L.M. drafted the article. M.S. critically revised the final version of the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript.None declared.Supplementary MaterialClick here for additional data file.


Specificity and positive predictive values of DR for DN were 0.83 and 0.81, respectively . The Association of the duration of diabetes and the level of proteinuria with DN was statistically not significant (p > 0.05). Idiopathic MN (6) and Amyloidosis (2) were the most common isolated NDKD; whereas diffuse proliferative glomerulonephritis (DPGN) (7) was the commonest NDKD in mixed disease. Another common form of NDKD in mixed disease was Thrombotic Microangiopathy (2) and IgA nephropathy (2). NDKD was observed in 5(18.5%) cases in presence of DR. We noted biopsy-proven DN even in 14(35.9%) cases without DR, in 4(50%) cases with microalbuminuria and 14(38.9%) cases with a short duration of diabetes. Conclusion: Almost half (45%) of cases with atypical presentation have non-diabetic kidney disease (NDKD), though even among these cases with atypical presentation diabetic nephropathy  is commonly seen in 74.2% of cases. DN has been seen in a subset of cases without DR, with microalbuminuria, and with a short duration of diabetes. Clinical indicators were insensitive in distinguishing DN Vs NDKD. Hence, a kidney biopsy may be a potential tool for the accurate diagnosis of kidney disease.The heterogeneous spectrum of kidney disease in diabetes ranges from albuminuric or non-albuminuric diabetic kidney disease to non-diabetic kidney diseases. Presumptive clinical diagnosis of diabetic kidney disease may lead to an erroneous diagnosis. Material and Method: We analyzed the clinical profile and kidney biopsy of a total of 66 type 2 diabetes patients. Based on kidney histology, they were divided into—Class I (Diabetic Nephropathy), Class II (Non-diabetic kidney disease), and Class III (Mixed lesion). Demographic data, clinical presentation, and laboratory values were collected and analyzed. This study tried to examine the heterogeneity in kidney disease, its clinical indicator, and the role of kidney biopsy in the diagnosis of kidney disease in diabetes. Results: Class I consisted of 36(54.5%), class II 17(25.8%), and class III 13(19.7%) patients. The commonest clinical presentation was nephrotic syndrome 33(50%) followed by chronic kidney disease 16(24.4%) and asymptomatic urinary abnormality 8(12.1%). Diabetic retinopathy (DR) was present in 27(41%) cases. DR was significantly higher in the class I patients ( Traditionally, diabetic nephropathy (DN) is diagnosed clinically with evidence of proteinuria and diabetic retinopathy ,2,3,4. HThis retrospective cohort study included type 2 diabetes mellitus patients, who underwent kidney biopsies between October 2016 to October 2022. The study included a total of 66 cases, whose complete clinical data were available for analysis. Patient of age > 18 years and both gender  was included in the analysis. Patients of age < 18 years and with proteinuria < 30 mg per day were excluded from the analysis. All patients’ demographic data and clinical presentations were reviewed and recorded from hospital records. Fundoscopic evidence of diabetic retinopathy was noted for all patients. Laboratory value of urine analysis, 24 h urinary protein, Complete Blood Count, renal function test, liver function test, lipid profile, immunological marker , HBsAg, HCV, and HIV were obtained from records. Findings of light microscopy, immunofluorescence, and electron microscopy examination of kidney biopsy were reviewed and noted in detail for all patients. We analyzed the demographic data, clinical presentation, and laboratory value for the clinical syndrome, the indication of biopsy, and the type of nephropathy. Standard guidelines were used to define acute kidney injury (AKI), chronic kidney disease (CKD), and nephrotic syndrome ,12. Proteinuria was categorized into three categories: microalbuminuria (30–300 mg/day), Sub nephrotic (>300–3500 mg/day), and nephrotic range (>3500 mg/day) proteinuria. Based on kidney histology, patients were divided into three classes—Class I (Diabetic Nephropathy), Class II (Non-diabetic kidney disease), and Class III (Mixed lesion). Further analysis was done to find out the heterogeneity of kidney diseases, their clinical indicators, and the relevance of clinical indicators in the diagnosis of kidney disease in type 2 diabetes mellitus patients.p values to know the association between the clinical indicators and kidney histology. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value of the clinical indicators for DN. Observation considered statistically significant for p-value less than 0.05.Statistical analysis was performed using PSPP version 1.4.1 . Descriptive statistics were presented as the mean and SD for continuous, and number and percent for categorical variables. A one-way ANOVA variance analysis test was used to examine the significance of the difference between the three classes. Pearson’s chi-square test was used to compare the three classes for categorical variables. We used multinomial logistic regression to estimate odds ratios (ORs), associated 95% confidence intervals (95% CIs), and p > 0.05) (p < 0.05). Isolated DN was seen in the majority of 22(81.5%) patients with diabetic retinopathy. However, NDKD either alone or in mixed form was noted in the remaining 5(18.5%) cases even in presence of DR. Isolated DN was noted in 14(35.9%) patients without DR. DN was the predominant lesion in presence of DR (81.5%), while NDKD either alone or in mixed form was a predominant lesion in the majority (64%) in the absence of DR. Sensitivity, specificity, negative and positive predictive value of DR for diabetic nephropathy are mentioned in the A total of 66 patients  with male to female ratio of 3.7:1 was included. The mean age of patients was 51.1 ± 10.5 years. The mean serum creatinine was 2.7 ± 1.8 mg/dL. The average proteinuria of 63 patients was 3.8 ± 2.8 gm per day. Three patients were anuric. The mean duration of diabetes was 7.1 ± 3.9 years . Based o > 0.05) . The pre > 0.05) . Diabeti > 0.05) . Idiopat > 0.05) . Of 8 cap > 0.05). Our findings regarding mean age, duration of diabetes, and 24 h urine protein excretions were like other studies [p = 0.175) and microalbuminuria  had a high risk of NDKD, but it was not statistically significant. Hence, recent data do not support the classical paradigm of diabetic kidney disease. Thus, our observation reasonably suggests that the level of proteinuria does not discriminate between DN and NDKD, and proteinuria is a poor predictor of the type of nephropathy in type 2 diabetes. The prevalence of diabetic retinopathy vary widely and ranges from 30–60% [p < 0.05). Isolated DN was seen in the majority of 22(81.5%) patients with diabetic retinopathy. Although, evidence of DR strongly suggest diagnosis of DKD but does not exclude the possibility of NDKD. Our findings also support the afore statement, because we noted biopsy proven NDKD  in the remaining 5(18.5%) cases even in presence of DR. Isolated DN was noted in 14(35.9%) patients in absence of DR, while the remaining 25 patients had isolated NDKD in 15(38.5%) and NDKD mixed with DN in 10(25.6%). DN was the predominant lesion in presence of DR (81.5%), while NDKD either alone or in mixed form was a predominant lesion in the majority (64%) in the absence of DR. Although, specificity and positive predictive values of DR for DN were high ; it had low sensitivity (0.61) and negative predictive values (0.64). We demonstrated that the absence of diabetic retinopathy was strongly associated with the presence of NDKD . Our finding also backs the observation of other published studies [In the present study, we retrospectively reviewed the demographic data, clinical features, and kidney biopsy of a total of 66 type 2 diabetes patients. The number of participants in the present analysis is low, which could be a reflection of a lack of awareness, resource limitation, limited access to a tertiary health care facility, and a reluctant strategy for a kidney biopsy at this center. This limits the power of the study and restricts the generalization of results. We found the mean age of the patient was 51.1 ± 10.5 years, and the female patient was 1421%). The mean duration of diabetes in our cohort was 7.1 ± 3.9 years. The average amount of proteinuria was 3.8 ± 2.8 gm per day. The mean serum creatinine was 2.7 ± 1.8 mg/dL. The mean age and mean duration of diabetes in Class II (isolated NDKD) were comparatively lower than Class I (DN) and Class III (DN + NDKD). However, the level of 24-h proteinuria in Class II (4.2 ± 3.3 gm) was comparatively higher than in Class I (4.0 ± 2.3 gm) and Class III (2.8 ± 3.2 gm). The mean serum creatinine in Class III was higher than in class I and Class II (3.8 vs. 2.4 vs. 2.6 mg/dL), reflecting either advanced or acute worsening of the disease. The three classes did not have statistically significant differences concerning age, sex, serum creatinine, proteinuria, and duration of diabetes  of cases with atypical presentation have non-diabetic kidney disease (NDKD), though even among these cases with atypical presentation diabetic nephropathy  is commonly seen in 74.2% of cases. About one-third (36%) of cases without DR have diabetic nephropathy, and about 20% of cases with DR have non-diabetic kidney disease on kidney biopsy. An almost equal number of cases have Diabetic nephropathy and NDKD in the presence of short diabetes duration. Factors like duration of diabetes, level of proteinuria, and presence of DR are not sensitive indicators for distinguishing DN vs. NDKD. Thus, clinical diagnosis alone may give an erroneous diagnosis. Hence, a kidney biopsy may be a potential tool for the early and accurate diagnosis of heterogeneous kidney disease in diabetes mellitus.The major limitation of this study was the small number of participants, participants with symptomatic disease, and Kidney biopsy in advanced disease.


Most are small (<10 mm), remain stable or decrease in size over time and, when asymptomatic, simply require patients to be reassured. In contrast, large PCs may compress the tectum and aqueduct of Sylvius resulting in symptomatic hydrocephalus and/or Parinaud’s syndrome. The management of symptomatic PCs with hydrocephalus is well established—endoscopic third ventriculostomy or shunt to decompress the hydrocephalus and endoscopic biopsy/fenestration to confirm its benign nature and decompress the cyst.However, considerable controversy surrounds the diagnosis, pathophysiology and management of non-hydrocephalic PCs that are symptomatic (nhSPCs). Symptoms may appear to be rather non-specific and the demonstration of a PC on CT/MRI dismissed as incidental. Symptoms may include paroxysmal and chronic headaches, visual disturbances/gaze paresis, nausea, sleep disorders, brain fog, fatigue, dizziness, gait instability, depression, cognitive impairment, transient neurological deficits, episodic unconsciousness and seizures in the presence of a PC and absence of ventriculomegaly. Patients may be profoundly miserable with greatly impaired quality of life.2 A plea was made for prospective studies with patient-reported and objective outcome assessment to provide a higher level of evidence.A recent systematic review of surgical intervention found that, in highly selected cases with severe symptoms impacting on quality of life, improvement rate was 93%. Although surgery in the pineal region is technically challenging, in skilled hands, the overall complication rate was 17% with long-term consequences in 3.6%.1 When present, episodic positional headaches, paroxysmal headaches associated with bilateral visual blurring or diplopia, and unexplained episodic loss of consciousness are suggestive of intermittent CSF obstruction and/or pressure on the posterior midbrain even if ventriculomegaly is not present.3What mechanisms might underlie the effects of pineal cysts both locally and on the brain as a whole? Pineal cysts are in close proximity to and may compress key structures affecting vision , hearing and balance , and concentration, memory and emotion  as well as more widely distributed functional networks drained by the deep venous system .et al.5 have previously proposed the intriguing hypothesis that, in some nhSPCs, crowding of the pineal recess may impair the venous runoff from the internal cerebral veins (ICVs) resulting in deep cerebral venous hypertension thereby, they postulate, increasing interstitial water content within adjacent structures including the thalamus.Eide 6Their hypothesis comes at a time of growing awareness of the potential role of cerebral venous congestion in other conditions, a concept that has led to new therapeutic opportunities. Many symptoms that patients with nhSPCs complain of overlap with those of cerebral venous thrombosis, idiopathic intracranial hypertension (IIH) and markedly asymmetrical cerebral venous outflow, all conditions with indisputable venous components. However, there are few detailed studies of the headache, cognitive, visual, hearing, balance and gait aspects in relation to the structures and systems that may be involved.4The Oslo group found that the anteroposterior diameter of the cyst or tectal compression and stenosis of the Sylvian aqueduct were not associated with symptom severity. However, MRI-based biomarkers of a crowded pineal recess [thalamic–splenium–cyst (TSC) ratio and thalamic apparent diffusion coefficient (ADC) ratio—see below] were associated with severity of symptoms, intracranial pressure (ICP) scores and outcome after surgery. 5Ideally, direct evidence for venous engorgement would be based on quantitative CT/MR venography, regional cerebral blood volume estimation  and, although probably not feasible, retrograde cerebral venography with pressure gradient measurements. What were the dimensions of the postulated ICV narrowing around the PC? Were there any congenital variations or collateral changes in central venous drainage or areas of stenosis remote from the pineal cyst region? Unfortunately, such direct evidence has not yet been reported. Instead, blood flow velocity (FV) in the ICVs, CSF FV in the aqueduct and intrathecal gadolinium distribution (‘glymphatic function’) have been assessed in relation to pineal recess crowding in 35 patients with nhSPCs.4 Wave amplitude was greater by 1.3 mmHg which suggests that any venous congestion is regional only and/or venous collaterals have opened up.Venous congestion might be expected to raise ICP. However, mean ICP was not raised in either group.Obstruction of venous vessels increases venous pressure, reduces capillary perfusion and increases local cerebral blood volume, compensated for by recruitment of collateral vessels. Where compensation fails, both local cytotoxic and vasogenic oedema may occur.However, the Oslo group propose that the hydrostatic pressure gradient created by venous back pressure induces interstitial oedema. Conventionally, interstitial cerebral oedema refers to transependymal oedema in the periventricular white matter (WM), not grey matter (GM), caused by hydrocephalus. FLAIR is the most sensitive MRI sequence for detection of interstitial oedema but has not been reported to show such changes in the WM or GM around the ventricles or pineal recess in nhSPCs (or in IIH).For logistical reasons, the authors measured ADCs in different regions of interest, not in absolute terms but as ratios with central hemispheric WM as the reference ROI. Whether central WM is truly independent of venous back pressure from the ICVs is debatable—venous territories are less well defined than arterial.7Only the thalamic-central WM ADC ratio showed any significant changes with symptom severity (0.99 versus 0.96) and ICP pulsatile pressure (1.02 versus 0.98). Importantly, thalamic-central WM ADC ratio did not change after cyst removal. Going forwards, more advanced MR diffusion tensor imaging (DTI) techniques will provide a more comprehensive understanding of water movement in structures around and distant from the pineal recess. CSF reflux through the aqueduct was demonstrated with PC MRI (25% of cases) and intrathecal gadolinium MRI (40% of cases).FV was measured with PC MRI in the ICVs proximal to (upstream) and at the cyst, not downstream to the cyst before the ICVs enter the vein of Galen. There were no significant differences between surgical and conservative groups in per cent changes in ICV FVs at the cyst. In the combined groups, there was a small increase in ICV FV at the cyst of the order of 0.5 cm/s which was less as the cyst increased in size (TSC ratio). Without knowledge of the dimensions of the ICVs and venous anatomy, it is difficult to interpret such FV changes.8 There were no differences between FVs upstream between patients and controls. However, the average change in FV downstream in the transverse sinus in controls was 4.2 and 21.5 cm/s in the patients. For every increase of 10 cm/s in average FV, the cerebral venous gradient increased by 2.0 mmHg. 4D MRI displayed rather dramatic flow jets downstream in the patients. Unlike arterial pressure gradients, apparently small cerebral venous gradients may still be haemodynamically significant.It is instructive to compare these small changes with the much larger changes in trans-stenotic blood FVs  in relation to the cerebral venous gradient  across transverse sinus stenoses (IIH and pulsatile tinnitus).9 There is considerable debate about the exit route for large molecules.10The authors use the term ‘glymphatic enrichment’ to refer to CSF-mediated molecular enhancement in brain along extravascular pathways, specifically CSF influx along penetrating cortical arteries. They used intrathecal gadolinium MRI which lacks the spatial (1 mm) and temporal (hours) resolution required to demonstrate the postulated microscopic glymphatic machinery.Despite CSF reflux through the aqueduct in some patients, the tracer arrived in the cisterna magna and pineal recess and then the cortical subarachnoid space with much greater enrichment and more rapidly than into the ventricular system, using orbital fat as the reference. Tracer enhancement within the cerebral cortex was much greater than in subcortical WM and thalamus; all decreased with increasing crowding of the pineal recess. Curiously, this was only significant for the thalamic ADC ratio component, not for the TSC ratio. Notably, in the surgically treated patients who differed by severity of symptoms from the conservatively managed group, tracer enrichment was modestly reduced in the cerebral cortex and subcortical WM. This contrasts with the increased tracer enrichment reported by the Oslo group in IIH patients.There is growing awareness that there is a small cohort of patients with very disabling nhSPCs who have had positive and sustained outcomes after surgical decompression. A higher level of evidence is now required. This will require a multicentre registry to foster patient engagement, develop patient-reported and objective outcome assessments, facilitate exploratory pathophysiological studies and design a multicentre RCT.The Oslo group are to be congratulated on their thought-provoking hypothesis that will stimulate much further multidisciplinary study and independent verification for which the tools are now available.


Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of −0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of −4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field. Blood pressure is the main driver of blood flow through vessels, which leads to blood circulation in the body. It also marks the health of the human cardiovascular system . HyperteThe measurement of blood pressure can be divided into two major methods: invasive and noninvasive. In the invasive method, a catheter is inserted into the artery to measure the blood pressure accurately. However, this method has a high risk of causing complications such as blood clotting, bleeding , distal There are different techniques for measuring blood pressure noninvasively. The gold standard for over 100 years has been the mercury sphygmomanometer. In this method a cuff placed around the upper arm is inflated to control the blood flow in the brachial artery. A trained listener is required to recognize the Korotkoff sounds through a stethoscope that is placed above the brachial artery while the cuff is deflated, easing the blood flow in the artery . AnotherVolume clamping and tonometry are two other noninvasive blood pressure measurement techniques. Although these methods provide continuous blood pressure values, they are mainly used in research . Volume In recent years, cuffless blood pressure measurement methods, a generic term for blood pressure monitoring techniques that do not use a cuff, have seen increased interest due to limitations that other methods impose such as discomfort, inconvenience, high cost, complexity and lack of ability to provide continuous measurement . One of The PPG signal measure changes in blood volume in capillaries by monitoring the variation in light absorption on the skin . With thIn previous work, we investigated BP estimation based on information from dynamic changes in PPG signal while a mathematical model was used for calibration stage . In thisIn this work, we used mean point-to-point (mPTP) features derived from the morphology of the PPG pulses and averaged over all the pulses for the duration of the signal to develop our estimation model. This is a novel approach in BP estimation based on PPG morphological characteristics. Another innovation in this work is the use of PPG morphology features along with the information from the dynamics of these signals. Furthermore, to the best of our knowledge, there has been no previous research conducted on replacing the calibration stage in the BP estimation process with the PPG morphology features.We can divide our work into two major parts based on the PPG morphology feature extraction, where one uses a feedforward artificial neural network model and the other uses a deep learning model .In this study, we used two separate bio-signal datasets, one from the University of Queensland Vital Signs Dataset  and anotData from the University of Queensland includes ECG, PPG and noninvasive arterial BP waveforms recorded from 32 patients  in four operating rooms, and sampled at the rate of 100 Hz. The recording ranged from 13 min to 5 h with a median of 105 min. These data cover the complete duration of the anesthesia that patients go through, which means there could be rapid variations in vital signs in the induction and emergence phases of anesthesia and surgery. This makes the dataset unique compared to others, such as data collected from intensive care units .The original data are divided into fixed size signal blocks. Each block is processed through a simple averaging filter to smoothen the signal;Any block with irregular blood pressure value or heart rate is removed;Autocorrelation is calculated for PPG signal to indicate the degree of similarity between successive pulses in a block;Any block with high alteration between successive pulses, based on the calculated autocorrelation in the previous step, is removed .The second dataset, the one from UCI, is a subset of the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) data. MIMIC II is an open source multiparameter recording provided by the PhysioNet organization that is collected from 15,000 intensive care units (ICU) in multiple hospitals. The collected signals are sampled at 125 Hz with at least 8-bit resolution. The following process was followed in order to generate the subset of the MIMIC II dataset that is provided by UCI:In our work, we used PPG signals and noninvasive arterial BP signals from the University of Queensland data, and PPG signals and ABP signals from the UCI data. In the case of ABP, the reference values for systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated by averaging the values of the peaks and troughs of the waveform (respectively) for each patient over the duration of the test signal. For the data from the University of Queensland, the reference values were obtained by averaging the provided beat-to-beat noninvasive SBP and DBP values over the duration of the signal for each patient. In both cases we visually inspected the waveforms and chose the ones with minimal or no interruption. In the case of data from UCI, since the dataset comprises some signals with short duration, we only selected the ones with 8 to 10 min in length and excluded the signals that were short in duration. The resulting datasets consisted of signals from 200 patients for the data from UCI and 30 patients for the data from the University of Queensland. In addition, we set aside an extra set of signals, 25 randomly selected patients from the UCI dataset with an SBP range of 105.40 mmHg to 151.70 mmHg and a DBP range of 53.85 mmHg to 72.93 mmHg, to be used as never-seen test data in the final step of our work.For this part, we used both datasets mentioned earlier to estimate SBP and DBP. These are the same datasets that we used in our previous works ,17, and In , we usedIn , we usedIn this work, blood pressure was estimated based on the same features, as mentioned above (for both datasets), when the same methodology was required for result comparison.In this work, BP estimation from the PPG morphology was used in addition to extracted features from the PPG dynamics. For this purpose, 21 features were extracted based on the research conducted in . HoweverThe extracted features for the estimation of SBP and DBP are listed below and shown in Cardiac period;Systolic upstroke time;Diastolic time;Diastolic width at 10%, 25%, 33%, 50%, 66% and 75% of the pulse height;For example, the diastolic width at 10% is calculated based on the time difference between the point at 10% of the peak amplitude that coincides in time with the peak  activation function is used for both LSTM and GRU approaches.The blood pressure estimation result from the PPG morphology based on the above-mentioned methodologies was considered an extra feature to be added to the ones extracted from cardiovascular dynamics. The dataset was partitioned based on the leave-one-out method, where all features from one patient were set aside to be used as the test data, while the remaining features were split into 85% training and 15% validation. This process was repeated for all patients to cover the entire dataset. The purpose of setting aside 15% of the dataset for validation was to use the early stopping technique in order to avoid overfitting .To estimate both systolic and diastolic blood pressure, an ANN with a two-layer feedforward network structure was used for regression. The two layers consisted of a sigmoid layer followed by a linear output layer. Based on our previous work with the same datasets ,17 and tThe estimation performance was evaluated based on the mean error (ME), the mean absolute error (MAE), and the standard deviation of error (SDE) obtained with the test data. The ME and MAE are calculated using the following equations:The SDE was calculated using the following equation:The BP estimation results in this paper are based on three different datasets: PPG and noninvasive BP from 30 patients (University of Queensland dataset), the PPG and invasive arterial blood pressure waveform from 200 patients (UCI dataset) and the PPG and invasive arterial blood pressure waveform from 25 new patients (UCI dataset).Features from cardiovascular dynamics extracted from PPG signal. This is a calibration-free method that we developed in ;Information based on PPG morphology features. Estimation for both SBP and DBP was performed based on 21 extracted morphology features;A calibrated mathematical model. This is part of our previous work , where wIn this part, we estimated both systolic and diastolic blood pressure from 30 patients using the following three methods:Results from the proposed methods are shown in Fusion technique;Feature combination.We also combined the estimation methodologies mentioned above to obtain higher accuracy. This was completed through two different approaches:In fusion technique, we used the sensor fusion method to combine different estimation results. Sensor fusion is a process used to combine data derived from different sources in order to reduce uncertainty that would otherwise have come from each of the sources, and it is calculated using (4)x3=σ3In the second method, we used blood pressure estimation from PPG morphology or the calibrated mathematical model as extra features and added them to the PPG IBIs, one at a time or both together (having three combinations that were the same as the ones in the fusion process). We then used wrapper subset evaluation with the forward greedy stepwise search method and determined the best features for the ANN estimator. The blood pressure estimation results for SBP and DBP obtained using this method are shown in cov is covariance, X, and Y.Comparing the two previously mentioned methods (fusion process and feature combination) shows that the fusion process resulted in lower accuracy. As for the results obtained from combining PPG dynamics features and estimation from PPG morphology and the calibrated mathematical model, they showed that while adding either results from morphology or calibration to the PPG IBI features improved the estimation results, adding both at the same time did not have much of an improvement, especially for diastolic blood pressure. Considering this new finding, we decided to compare the correlations between the estimation results obtained using PPG dynamics, PPG morphology and the PPG mathematical calibration model by calculating the correlation coefficient:The resulting correlation coefficients showed that while there is a low level of correlation between the estimation results from IBI and morphology or IBI and the calibration method, the blood pressure estimation values from morphology and calibration methods are highly correlated. Correlation results were similar for both SBP and DBP. These correlations are shown in Given the finding of high correlation between BP estimation results obtained from PPG morphology features and the calibration model, we used the estimation from the PPG morphology features as a substitute for the calibration model in our remaining experiments.In this part, we estimated both the systolic and diastolic blood pressure of 200 patients using features from cardiovascular dynamics and PPG morphology. As was mentioned in In this part, we tested our proposed estimation model with the data from 25 patients that were not seen by the model before. Our feature set for the proposed ANN model consisted of the seven PPG dynamics features  trend in the error, with increase in the mean of the BP estimated using our models and the reference BP. A similar effect has been found in the past in Bland–Altman analyses of BP pressure estimated using other methods ,36. The The lower errors found for DBP vs. SBP also follow the same trend that we found with different methods, and it could be due to the strong relation between HRV indices and both SBP and DBP in females, while this relation is limited to DBP for males . The datThis study also had some limitations. First, the datasets did not include some useful information regarding characteristics such as race, age, gender and weight that have an impact on blood pressure levels and that could provide valuable information contributing to the accuracy of the estimation results ,40. SecoIn this paper, we focused on a cuffless blood pressure estimation based solely on information carried by PPG signals. We demonstrated that the cardiovascular dynamics carry useful information that can help to improve the accuracy of BP estimation when features based on pulse morphology are also used. Another finding of this work was that we were able to replace a calibration method (that was introduced in our previous work ) with anSince in this paper adding information from cardiovascular dynamics to a number of different BP estimation techniques based on pulse morphology showed considerable improvement in the estimation accuracy, this approach may also be useful for improving the BP estimation accuracy of other methods, such as the widely used ones that are based on the pulse transit time.


Up to one-fifth of patients continue to have poor quality of life after transcatheter aortic valve implantation (TAVI), with an additional similar proportion not surviving 1 year after the procedure. We aimed to assess the value of a new method based on an integrated analysis of left ventricular outflow tract flow velocity and aortic pressure to predict objective functional improvement and prognosis after TAVI.In a cohort of consecutive patients undergoing TAVI, flow velocity–pressure integrated analysis was obtained from simultaneous pressure recordings in the ascending aorta and flow velocity recordings in the left ventricular outflow tract by echocardiography. Objective functional improvement 6 months after TAVI was assessed through changes in a 6-min walk test and NT-proBNP levels. A clinical follow-up was conducted at 2 years.p = 0.001). In multivariate analysis, parameter “(Pressure at Vmax − Pressure at Vo)/Vmax” was found to be an independent predictor for objective improvement. The C-statistic was 0.70 in the overall population and 0.78 in the low-gradient subgroup. All echocardiographic parameters and the valvuloarterial impedance showed a C-statistic of <0.6 for the overall and low-gradient patients. In a validation cohort of 119 patients, the C-statistic was 0.67 for the total cohort and 0.76 for the low-gradient subgroup.Of the 102 patients studied, 82 (80.4%) showed objective functional improvement. The 2-year mortality of these patients was significantly lower (9% vs. 44%, This new method allows predicting objective functional improvement after TAVI more precisely than the conventional parameters used to assess the severity of aortic stenosis, particularly in low-gradient patients. Transcatheter aortic valve implantation (TAVI) substantially improves survival and quality of life in most patients with severe aortic stenosis (AS). Nonetheless, up to one-fifth of patients continue to have poor quality of life after TAVI, with an additional similar proportion not surviving 1 year after the procedure .Nonetheless, it is important to explore and understand the factors associated with a sub-optimal outcome to inform decisions regarding the optimal timing of TAVI and/or adjunctive interventions that may improve the outcomes after TAVI such as particular medications and rehabilitation. Furthermore, the identification of such predictors is important to help in the decision-making when the indication for TAVI is uncertain because of the risk of futility due to frailty or relevant comorbidities and in certain cases of low-flow/low-gradient AS , 4.AS represents a complex, multifaceted set of syndromes that may present in a range of manners and is not isolated to calcific degeneration of the aortic valve alone. Each component part of the system, from the ventricle proximal to the valve to the vasculature distal, can impact signs and symptoms –8. With A previous study by our group identified a series of variables related to the aortic valve, left ventricle, cardiac rhythm, and arterial pulse wave that showed a high predictive value for functional recovery in patients undergoing TAVI .Continuous cardiac afterload monitoring based on a combined analysis of flow velocity signal recorded by Doppler and aortic pressure, the velocity–pressure (VP) loops, has been suggested. This analysis appeared to provide insights into arterial mechanics with standard hemodynamic signals recorded in the operating room –12.We designed an original diagnostic approach based on the integrated analysis of left ventricular outflow tract flow velocity–aortic pressure. In this study, we aimed to assess the value of this approach to predict the objective functional improvement after TAVI and thus to estimate the AS burden.The present study was performed in a subgroup of population included in a larger study previously published by our group .All consecutive patients scheduled for TAVI in our institution who met the inclusion criteria were prospectively included in the study. The criteria for inclusion are as follows: (1) diagnosis of symptomatic severe AS (according to guidelines) without significant regurgitation, (2) indication for TAVI established by the institutional Heart Team, and (3) undergoing a TAVI procedure through femoral artery access. Patients who did not consent or who exhibited cognitive impairment that prevented them from properly understanding the investigational procedures were excluded.Patients who were initially included but presenting severe periprocedural complications such as coronary obstruction, annulus rupture, or stroke were finally excluded from the analysis, given their relevant effect on physiologic measurements and functional recovery after the procedure. For the same reason, patients who required permanent pacemaker implantation after the procedure and showed pacemaker dependence were excluded.All the procedures were performed in the appropriate setting of a catheterization laboratory dedicated to structural heart interventions. The local TAVI program was started in 2009 and was mostly based on balloon-expandable prosthetic valves. The study was approved by the corresponding Institutional Review Board, and all participating patients signed the informed consent after proper explanation of the investigational procedures. Database was completely anonymized.All patients were monitored in a specific structural cardiology office, where the clinical follow-up was performed, tests were applied, and the medical treatment of each patient was optimized as much as possible.The workflow of the study is shown in A systematic protocol-specific transesophageal and transthoracic echocardiographic examination was performed during the TAVI procedure in all patients before and after prosthetic valve implantation. The intraprocedural echocardiographic examinations were performed simultaneously with the invasive central pressure measurements. The following parameters are obtained: left ventricular ejection fraction (LVEF), stroke volume, maximal and mean aortic valve gradients, energy loss index, pulse Doppler recording at the left ventricular outflow tract (LVOT), and continuous Doppler recording through the aortic valve. The Doppler recordings were generally obtained from the transthoracic echocardiography except for those few cases in which the transgastric view provided a more adequate recording.Invasive pressure measurements in the ascending aorta were performed with a 5- or 6-Fr pigtail catheter attached to a fluid-filled manometer system. The catheter was inserted through a femoral 6-Fr introducer sheath  with its tip steadily positioned in the middle portion of the ascending aorta, at least 3 cm over the cusps of the aortic valve. Invasive aortic pressure recordings were taken at two different intraprocedural moments, pre- and post-TAVI, simultaneously with the echocardiographic examinations. An average of 20 cardiac cycles was used to render the final pressure measurements.The first step was to align both simultaneous VP records and create a mesh to obtain the same number of coincident points in time for velocity and pressure . The resThe objective functional improvement of a patient with AS after the TAVI procedure was defined as the achievement at 6 months of an increase of at least 10% in the distance covered during the 6-min walk test or a reduction of at least 50% in NT-proBNP blood levels with respect to pre-TAVI when this 10% increase was not evident. In this way, with both criteria, the potential presence of factors that limit the speed of gait and are not related to cardiovascular capacity was taken into consideration.For those patients who died before the 6-month landmark and after the 30-day evaluation, improvement was based on this evaluation, but this was considered negative if the patients suffered from or died of heart failure afterward. Patients who died before the 30-day follow-up, patients who died because of heart failure were considered without improvement, and the rest of the patients who died from other causes were excluded from the analysis since no functional evaluation was available. Subjective improvement was considered if patients reported a positive change in at least one class of the NYHA classification and/or an increase in at least 10 points in the KCCQ. All patients underwent a systematic clinical follow-up at 2 years. Baseline staging of cardiac damage was conducted according to the classification proposed by Généreux et al. .t-test if they followed a normal distribution and by non-parametric tests when this was not the case. The categorical variables were compared with the chi-squared test or Fisher’s exact test, as required.Continuous variables are presented as means ± standard deviations or medians (interquartile ranges) according to the type of distribution, and categorical variables are presented as percentages. Distribution was assessed for each variable with the Shapiro–Wilk test. Accordingly, continuous variables were compared using the Student's p < 0.2) were entered into the multivariable logistic regression model. Then, a stepwise elimination analysis was performed to define a useful subset of predictors.Multivariable logistic regression analysis identified independent predictors of objective functional improvement post-TAVI. Among all the clinical, echocardiographic, hemodynamic, and VP loop-related variables, those that showed a univariate relationship with an outcome , and the other will be used to test the estimated model (test data). The R createDataPartition command allowed us to obtain these sets from the original sample.The Hosmer–Lemeshow test or the likelihood ratio test was used to evaluate the goodness of fit, that is, the overall significance of the model. The Nagelkerke Random Over-Sampling Examples), which allowed us to deal with binary classification problems in the presence of unbalanced samples, was used. Because the sample partitions used for the model estimation phase were random and different from each other, they led to different estimates of the prediction model. That is why, to finish with the model estimation phase, it is necessary to carry out machine learning techniques known as assembled models. There are different ways to obtain the assembled model, either by using different algorithms or by varying the training data obtained by bootstrapping, for the same algorithm. In this last procedure, each model has different parameters since it has been estimated from a different sample in each case. This is the method that has been chosen to solve the problem that was raised above. Assembled models are used to more robustly predict an outcome from multiple models.In the presence of unbalanced samples, it is necessary to balance the set with which the model is to be estimated, that is, the train data set. To solve this problem, R function ROSE  were used during the course of the study.The discriminating power of the parameters for predicting objective functional improvement was assessed by considering the area under the curve from the receiver operating characteristic (ROC) analysis. Box and whisker plots were built to show the baseline and post-TAVI evolution of variables according to improvement subgroups. Kaplan–Meier curves for event-free survival were obtained for each group and compared using the log-rank test and the hazard ratios with 95% confidence intervals. Finally, among the 105 eligible patients, 102 consecutive patients who underwent TAVI and met the inclusion criteria were included in the study. The baseline characteristics of the patients are listed in The hemodynamic, echocardiographic, and VP loop-derived parameters at baseline for both groups, with and without objective functional improvement, are listed in The baseline values of the parameters derived from the VP loops are also listed in The respective VP loops of patients with and without objective functional improvement, showing the remarkably larger values for the ALPHA angle and the P(Vmax) − P(Vo) difference in the patients experiencing improvement, are illustrated in p = 0.03). Replacing the ALPHA angle value for the equivalent parameter, “P(Vmax) − P(Vo)/Vmax,” resulted in this being a unique independent predictor.In the multivariate regression analysis, the only variable that resulted in an independent predictor of objective functional improvement was the ALPHA angle . The cutoff value for [P(Vmax) − P(Vo)]/Vmax was 0.1 in the overall and low-gradient groups.The discriminative performance of different parameters to predict the objective functional improvement after TAVI is described in p = 0.001)  .p < 0.001).The predictive value of the VP loop-derived parameter [P(Vmax) − P(Vo)]/Vmax was assessed in a validation cohort of 119 patients. The clinical and procedural characteristics of the validation cohort are described in The summarized results are graphically illustrated in In the present study, the parameters derived from the integrated analysis of LVOT flow velocity and ascending aorta pressure resulted in the only independent predictors for objective clinical improvement at 6 months in patients with severe aortic stenosis undergoing TAVI.Namely, the parameter “[P(Vmax) − P(Vo)]/Vmax” resulted in a predictor for objective improvement in the overall population and was even more accurate in the low-gradient subgroup. This parameter outperformed all other conventional parameters used in clinical practice to estimate AS severity, particularly in the challenging subgroup of patients with low-gradient AS.The calculation of this parameter is not complex since both aortic pressure and flow velocity recordings include a simultaneous electrocardiographic recording in the same lead. Thus, the pressure at Vmax is estimated through the time between peak R-wave and peak Doppler flow velocity applied to the pressure curve, and the pressure at Vo is equivalent to the diastolic pressure. We do not have a clear pathophysiological explanation for the relationship between this parameter and the true hemodynamic load imposed by aortic stenosis . However, we are convinced that this parameter reflects the interaction, throughout the ventricular ejection phase, between the flow velocity proximal to the valve (at the LVOT) and the dynamics of pressure just distal to the valve (aortic root). This multidimensional integral nature, which contemplates pressure, velocity, and time, explains its additional value with respect to pressure parameters such as the gradient, which are unidimensional.Vallée and colleagues described aortic velocity–pressure loops and a series of angles derived from them  as an estimate of ventriculoaortic coupling that is easily monitored during surgical interventions, obtaining a continuous measurement of left ventricular afterload –12. In tIn our study, an original new design was proposed to assess the effect of aortic stenosis in which flow is measured in the LVOT and pressure in the ascending aorta, so it is plausible that the ALPHA angle or [P(Vmax) − P(Vo)]/Vmax could be largely representing the effect of the valvular component of the afterload. Thus, patients with clinical improvement at 6 months had significantly higher values at baseline than those who did not improve.The conventional echocardiographic parameters such as the indexed aortic valve area, energy loss index, and peak or mean aortic gradients were not significant predictors. The delayed time to peak velocity also did not show any independent predictive value .Briand et al. described the concept of valvuloarterial impedance (Zva) to quantify the global afterload in aortic stenosis since this variable took into account the valve and arterial load components . HoweverPatients with low-gradient/low-flow AS, particularly with low ejection fraction, have a significantly worse medium-term to long-term survival compared with all other patients undergoing TAVI . In thisIn our study, the patients with low-gradient AS who underwent TAVI had had diagnostic confirmation of the severity of the stenosis, in some cases after dobutamine stress echo or after considering a high degree of valve calcification on tomography. Nonetheless, in these patients, valve calcification had no predictive value for functional improvement after TAVI. Remarkably, the VP loop parameters showed a notable discriminatory value in the low-gradient population regardless of the stroke volume, clearly better than the yielded by valve-related echocardiographic measurements or the valvuloarterial impedance.The contribution of this study is original and provides an additional value with respect to the previous study of our group since this study investigates the value of an integrated analysis of LVOT flow velocity and aortic pressure, measured non-invasively and invasively, respectively, in the assessment of aortic stenosis. This true novel approach could have by itself a high potential value in defining the hemodynamic burden imposed by aortic stenosis. In contrast, the previous study included a series of conventional parameters well known in clinical practice . Thus, tThese findings should be prospectively validated in a larger population of patients currently treated with TAVI. The development of software capable of facilitating the integrated analysis of pressure and Doppler tracings would be welcome. In addition, the use of central pressure recordings estimated by non-invasive techniques would allow a broader implementation of this type of analysis , 21.This study contains several limitations. First, the sample size of the cohorts limits the statistical power of the study. The fact that most patients presented clinical improvement at 6 months has meant that the sample is unbalanced, which required machine learning techniques to try to correct the regression analysis outcomes. Anyway, the predictive superiority of the VP loop parameters over the conventional metrics used to estimate AS severity was evident in this study. In addition, the predictive power of these parameters was prospectively validated.The definition of objective functional improvement was specific to the study and, although well thought out, may be questionable. However, it was sufficiently precise and, at the same time, conservative, as confirmed by seeing how the group considered without improvement showed even worse post-TAVI performance in the walking test and the absence of changes in heart failure biomarkers. In addition, the classification also showed important prognostic implications. The assessment time of 6 months can be discussed; however, we know from previous studies that the improvement after TAVI is rapid, being evident even at 30 days , 23. On These results are certainly applicable to the profile of patients included in the study, who are patients with degenerative-calcified aortic stenosis, most of whom have tricuspid anatomy and a minority have bicuspid anatomy. Therefore, the results could obviously be applicable to patients undergoing aortic valve replacement surgery. The validity of the method in bicuspid stenosis or in rheumatic stenosis could be defended taking into account that the pathophysiological effects of aortic valve stenosis would be comparable.In methods, it is indicated that patients who were initially included but presenting severe periprocedural complications such as coronary obstruction, annulus rupture, or stroke were finally excluded from the analysis, given their relevant effect on physiologic measurements and functional recovery after the procedure. The aim of the study was to establish pre-procedural predictors of objective functional improvement since these are the ones that would be of value to help in the decision-making when the indication for TAVI is uncertain. With regard to comorbidity and frailty, all were included in the predictive model. The fact is that the clinical selection process prior to indication already excludes those with very high frailty or severe comorbidities that seriously compromise the patient's short-term future and make the transcatheter aortic valve implantation (TAVI) procedure futile.The rate of missing data was extremely low. This is a series of limited sizes, from a single center with a systematic prospective database. All patients belong to our regional public health system and are therefore perfectly traceable. In addition, all patients in the TAVI program are followed up in our department.The application of this method in clinical practice would require the use of specific software, but it is not complex and could be developed without great difficulty.The integrated analysis of the left ventricular outflow tract flow velocity and aortic pressure allows us to predict the degree of objective functional improvement after TAVI and thus to estimate the aortic stenosis burden more precisely than the conventional parameters used to assess the severity of aortic stenosis, particularly in low-gradient patients.