Longer pre-transplant dialysis duration is known to be associated with worse post-transplant outcomes. Our study aimed to cluster kidney transplant recipients with prolonged dialysis duration before transplant using an unsupervised machine learning approach to better assess heterogeneity within this cohort. We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 5092 kidney transplant recipients who had been on dialysis ≥ 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019. We characterized each assigned cluster and compared the posttransplant outcomes. Overall, the majority of patients with ≥10 years of dialysis duration were black (52%) or Hispanic (25%), with only a small number (17.6%) being moderately sensitized. Within this cohort, three clinically distinct clusters were identified. Cluster 1 patients were younger, non-diabetic and non-sensitized, had a lower body mass index (BMI) and received a kidney transplant from younger donors. Cluster 2 recipients were older, unsensitized and had a higher BMI; they received kidney transplant from older donors. Cluster 3 recipients were more likely to be female with a higher PRA. Compared to cluster 1, cluster 2 had lower 5-year death-censored graft  and patient survival . Clusters 1 and 3 had comparable death-censored graft and patient survival. Unsupervised machine learning was used to characterize kidney transplant recipients with prolonged pre-transplant dialysis into three clinically distinct clusters with variable but good post-transplant outcomes. Despite a dialysis duration ≥ 10 years, excellent outcomes were observed in most recipients, including those with moderate sensitization. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing kidney transplantation. End-stage kidney disease (ESKD) represents a significant global health challenge, imposing a considerable burden on both individuals and healthcare systems . While kThe life expectancy of ESKD patients on dialysis is generally poor. The United States Renal Data System (USRDS) reported that the overall death rate for ESKD patients between 2009 and 2019 was 19.7%, significantly higher than the 10.5% for kidney transplant recipients . Each exIn particular, the survival rates after kidney transplantation in patients with prolonged pre-transplant dialysis warrant specific attention. Patients who underwent dialysis for more than 10 years prior to transplantation exhibited notably lower survival rates compared to those with shorter durations of dialysis . For exaEven though research has focused on identifying recipients with long-term dialysis before transplant in order to reduce adverse outcomes, no machine learning (ML) approach has ever been utilized ,17,18,19In this study, kidney transplant recipients who had been on dialysis for at least 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019 were characterized by consensus cluster analysis using an unsupervised machine learning approach, including a comparison of the outcomes of each cluster.We used the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database to identify adult patients who received their first kidney-only transplant from 2010 to 2019 in the United States. We included patients who had been on dialysis for at least 10 years before their kidney transplant. We excluded (1) patients with prior kidney transplants, to avoid misclassifying their dialysis duration over multiple listings, and (2) multi-organ transplant patients, due to different organ allocation policies. The Mayo Clinic Institutional Review Board approved this study (IRB number 21-007698).We abstracted the comprehensive list of recipient-, donor-, and transplant-related characteristics, as shown in An unsupervised ML was applied by conducting a consensus clustering approach to categorize clinical phenotypes of kidney transplant recipients with prolonged pre-transplant dialysis duration . For thiPosttransplant outcomes included (1) death-censored graft failure and (2) patient death within 1 and 5 years. We defined death-censored graft failure as the need for dialysis or kidney retransplant, while censoring patients for death or at the last follow-up date reported to the OPTN/UNOS database.http://www.rstudio.com/, accessed on 1 September 2022); ConsensusClusterPlus package (version 1.46.0) for consensus clustering analysis, and the MICE command in R for multivariable imputation by chained equation [After consensus clustering analysis, we compared the characteristics and outcomes among the assigned clusters of patients with prolonged dialysis duration before kidney transplant. We tested the difference in clinical characteristics using analysis of variance (ANOVA) or the Kruskal–Wallis test, as appropriate, for continuous variables, and a Chi-squared test for categorical variables. We identified the key characteristics of each cluster using the standardized mean difference between each cluster and the overall cohort, with a pre-specified cut-off of >0.3. We demonstrated the risk of death-censored graft failure and patient death after kidney transplant using a Kaplan–Meier plot. We calculated the hazard ratio (HR) for death-censored graft failure and patient death using Cox proportional hazard analysis. We did not adjust the association of the assigned clusters with posttransplant outcomes for clinical characteristics because clinically distinct clusters were purposefully generated from the consensus clustering approach. We used R, version 4.0.3  had a prolonged dialysis duration of at least 10 years before receiving a kidney transplant. Therefore, we performed consensus clustering analysis on a total of 5092 kidney transplant patients. The mean age for this cohort was 50.5 years. Over half (52%) of recipients with prolonged dialysis were black and 25% were Hispanic. The median dialysis duration was 11.7 (IQR 10.7–13.9) years. A total of 88% of the recipients received kidney transplants from standard non-extended criteria donors (non-ECD).Recipient characteristics associated with lower death-censored graft survival included male sex, black race, higher body mass index (BMI), peripheral vascular disease and positive HIV serostatus, whereas donor characteristics associated with lower death-censored graft survival included ECD deceased donor, older donor age, female sex, black race, hypertensive donor and Kidney Donor Profile Index (KDPI) ≥ 85. In addition, more HLA mismatch, increased cold ischemia time, and delayed graft function were associated with lower death-censored graft survival. Meanwhile, recipient characteristics associated with lower patient survival were older age, male sex, higher BMI, longer pre-transplant dialysis duration, history of diabetes, malignancy, peripheral vascular disease, low functional status and lower serum albumin, whereas donor characteristics associated with lower patient survival were older donor age, hypertensive donor and KDPI ≥ 85. Delayed graft function was associated with lower patient survival .2) and were unlikely to be sensitized . Most recipients in this cluster (96%) received standard KDPI kidneys from younger (mean age 30.7 years) non-ECD donors  patients in cluster one, 2152 (42%) patients in cluster two, and 896 (18%) patients in cluster three. D donors .2). Cluster 2 recipients were not sensitized  and most (96%) were transplanted with standard KDPI kidneys from older (mean age 45 years) non-ECD donors  and older (mean recipient age 57 years). Most cluster 2 recipients were likely to be black (62%) or Hispanic (19%). They were more likely to be diabetic (38%) and had a higher BMI . The majority were black (52%) or Hispanic (25%). Recipients in cluster 3 were sensitized . Most (92%) received kidney transplants from standard KDPI donors with a lower number of total HLA mismatches (median 4), and less use of non-depleting induction (basiliximab 9%). Cluster 3 recipients were more likely to receive a kidney through national allocation (22%).Prolonged dialysis duration preceding kidney transplantation has been linked to inferior transplant outcomes ,10,11. HIn our study, we utilized an unsupervised machine learning technique to classify patients with extended dialysis durations before transplantation. These patients were drawn from the OPTN/UNOS database and categorized into three distinct clusters. Cluster 1 was comprised of younger, non-diabetic recipients who obtained kidneys from comparatively youthful donors. Individuals in cluster 2 were relatively older with higher BMIs; however, they were typically non-diabetic and received kidneys with a standard KDPI, and procured from slightly older donors. The majority of recipients in cluster 3 were female individuals with an elevated PRA. Despite the fact that these discrete recipient clusters exhibited varying clinical outcomes, including graft longevity and patient survival, the overall results across all clusters were positive.The proportion of black and Hispanic recipients having greater than 10 years of dialysis time highlights health care disparities for minorities and the need for improved access to kidney transplantation. This observed percentage of minorities is significantly higher as compared to the prevalent dialysis population . AlthougWhile delays in referral to transplantation likely play a role for many recipients in all three clusters, recipients in cluster 3 might have experienced additional delays due to moderate sensitization and a scarcity of compatible match offers. This is despite the current allocation system assigning some extra priority to those with moderate sensitization. Arguably, most recipients in cluster 3 were not highly sensitized and would not be expected to have such prolonged waiting times based on PRA alone. It is possible that additional barriers related to an overall lower number of black and Hispanic donors and lack of compatible matches for ethnic minorities may have further compounded this delay ,37,38,39This study has several limitations. Due to the nature of the national registry cohort, it is not possible to identify exact causes of graft loss and patient death. Comparative studies from centers able to provide more granular data would help to confirm these findings. In addition, data specific to patients listed but inactive on the waitlist remains unavailable. Similarly, data on qualifying time is unavailable for comparison. Consequently, it is assumed that delays in referral to transplantation were the primary factor contributing to these extended dialysis times. It is, however, possible that other factors played a role. Another potential limitation of our study is the exclusion of multi-organ transplant patients, including kidney–pancreas recipients, which might limit the broader applicability of our findings. Notably, cluster 2 recipients exhibited higher rates of diabetes, which might suggest that the consideration of pancreas transplantation could indeed have been a contributing factor. Future investigations addressing the outcomes and challenges specific to kidney–pancreas recipients could offer additional insights. In addition, there was a small amount of missing data which could have impacted our clustering results. To reduce the likelihood of bias, we used the multivariable imputation by chained equation (MICE) approach to replace the missing data, which generates plausible values for missing data while preserving the original variability and dataset distribution. While techniques like SMOTE have their merits, our priority was to handle missing data while maintaining the statistical integrity of the original dataset. To the best of our knowledge, this is the first machine learning approach specifically targeting kidney transplant recipients with extended dialysis durations preceding transplantation. The outcomes of this machine learning clustering allow for a better understanding of characteristics in recipients with prolonged dialysis vintage. Future studies are necessary to individualize pre- and post-transplant care for recipients with prolonged dialysis duration before transplant in order to improve their outcomes.Leveraging the novel insights offered by this machine learning approach, future studies will assume a crucial role in enhancing the optimization of the kidney transplantation process for patients who have undergone prolonged dialysis before transplantation. These studies should delve deeper into exploring and quantifying the distinct barriers that minorities and sensitized patients encounter during the kidney transplantation process. This is especially important, as these populations have been identified as experiencing extended wait times and reduced match rates. This will require an intersectional approach that blends demographic analysis with a comprehensive grasp of medical, social and systemic barriers. In addition, strategies should be developed to increase the pool of compatible donors for minority and sensitized patients. In terms of future implications, the identification of these unique patient clusters could help reshape the protocols around kidney transplantation. By tailoring the transplantation process based on the distinct requirements of various patient clusters, transplant providers might be able to reduce patients’ dialysis duration, enhance graft survival, and lower mortality rates. This strategy, in conjunction with policy modifications designed to tackle the obstacles encountered by minority and sensitized patients, has the potential to eventually foster a more equitable access to transplantation and yield improved overall outcomes.In this OPTN/UNOS database, using an unsupervised machine learning clustering approach, three clinically distinct clusters of kidney transplant recipients with prolonged dialysis duration prior to transplant were identified. Although different clinical outcomes, including mortality and graft survival, were observed between these transplant recipient clusters, most recipients had excellent outcomes. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing transplants.


End-expiratory occlusion (EEO) and end-inspiratory occlusion (EIO) tests have been successfully used to predict fluid responsiveness in various settings using calibrated pulse contour analysis and echocardiography. The aim of this study was to test if respiratory occlusion tests predicted fluid responsiveness reliably in cardiac surgical patients with protective ventilation. This single-centre, prospective study, included 57 ventilated patients after elective coronary artery bypass grafting who were indicated for fluid expansion. Baseline echocardiographic measurements were obtained and patients with significant cardiac pathology were excluded. Cardiac index (CI), stroke volume and stroke volume variation were recorded using uncalibrated pulse contour analysis at baseline, after performing EEO and EIO tests and after volume expansion (7 mL/kg of succinylated gelatin). Fluid responsiveness was defined as an increase in cardiac index by 15%. Neither EEO, EIO nor their combination predicted fluid responsiveness reliably in our study. After a combined EEO and EIO, a cut-off point for CI change of 16.7% predicted fluid responsiveness with a sensitivity of 61.8%, specificity of 69.6% and ROC AUC of 0.593. In elective cardiac surgical patients with protective ventilation, respiratory occlusion tests failed to predict fluid responsiveness using uncalibrated pulse contour analysis. Intravenous fluids are commonly administered in patients with acute circulatory failure to improve cardiac output (CO) and tissue perfusion. However, a significant CO increase is only observed in approximately 50% of patients, the so-called fluid responders ,2. FluidThe end-expiratory occlusion (EEO) test is a dynamic test based on heart-lung interactions. Mechanical ventilation interruption at the end of expiration transiently decreases intrathoracic pressure producing an internal fluid challenge and a temporary increase in CO in fluid-responsive subjects . Similar2O or peak inspiratory pressure > 30 cm H2O), any use of inotropes besides norepinephrine, poor echogenicity and an open thorax. Poor echogenicity was defined as the inability to visualise both ventricles and perform the required measurements.This prospective, single-centre study was approved by the Institutional Review Board . Informed consent was obtained from all participants. Patients scheduled for elective coronary artery bypass grafting in the General University Hospital in Prague between October 2019 and July 2022 were screened for enrolment. We included sedated and mechanically ventilated patients over 18 years of age with normal systolic function of the left and right ventricle, defined as left ventricular ejection fraction (LVEF) > 50% and right ventricular fractional area change (RV FAC) > 30%, in whom volume expansion was indicated by the attending physician based on any of the signs of suspected hypovolemia if study investigators were available. These parameters included central venous oxygen saturation < 65%, arterial lactate concentration > 2 mmol/L, any vasopressor support with norepinephrine, CVP < 5 mmHg and the presence of skin mottling. The exclusion criteria included spontaneous breathing activity interfering with a 15-s respiratory occlusion, any arrhythmias, moderate to severe valvular heart disease, aggressive mechanical ventilation (defined as positive end-expiratory pressure (PEEP) > 10 cm HTM/EV1000TM, Edwards Lifesciences, Irvine, CA, USA). All patients were equipped with a radial artery catheter and a central venous catheter in the right internal jugular vein. Standard monitoring included 5-lead electrocardiography, invasive blood pressure, central venous pressure, pulse oximetry and heart rate . Cardiac output, cardiac index (CI), stroke volume (SV) and SVV were measured continuously using uncalibrated pressure waveform analysis . From the apical 4-chamber view, we assessed LVEF and RV FAC. The LVEF was calculated using the monoplane Simpson’s method. All measurements were performed by two physicians (MP and MB) holding a certificate in echocardiography. Recordings were analysed offline by a single observer (MP) blinded to study data. 2, PEEP and respiratory rate based on the initial arterial blood gas analysis. In the study population, the PEEP was kept between 4 and 6 cm H2O, the respiratory rate between 10 and 14 per minute and FiO2 between 0.4 and 0.5. None of these parameters were adjusted during the study period.All patients were ventilated using the volume control/assist mode  with tidal volumes of 7 mL/kg of ideal body weight (IBW). The attending physician set the FiOPatients were transferred from the operating theatre at the end of surgery to the ICU and sedated by intravenous propofol infusion. At admission, mechanical ventilation was resumed using the settings mentioned above, baseline arterial and venous blood gas analyses were performed and a chest X-ray was taken. Mean arterial pressure was maintained between 65–80 mmHg with continuous infusion of norepinephrine. Patients were included in the study if the attending physician indicated volume expansion and sufficient echogenicity was confirmed. All patients were in the supine position with an elevated trunk of 15°. The first set of hemodynamic and echocardiographic measurements was taken at baseline. The second set was taken immediately before and the third after a 15-s EEO, performed as described in the original study by Monnet et al. . Both rep-value of <0.05 was considered statistically significant.R version 4.2.2  with graDuring the study period (October 2019 to July 2022), 106 patients met all inclusion criteria. Forty-four (41.5%) were excluded due to poor echogenicity, in agreement with previous studies ,31, threp < 0.05). All other hemodynamic parameters, including variations between individual baselines , did not differ between the groups  and stroke volume change  (p < 0.05) and SV  in responders than in non-responders. All changes in hemodynamic parameters induced by the respiratory tests and volume expansion are summarized in Before volume expansion, there was a trend towards a larger magnitude of changes in CI and SV induced by EIO, EEO and their combination in responders compared to non-responders, including the added effects of EEO and EIO on cardiac index  . HoweverNeither EEO, EIO nor their combination predicted fluid responsiveness reliably in our study. Univariate logistic regression analysis was performed for continuous and binarized variables that were analysed as predictors of fluid responsiveness. These included CI and SV changes during respiratory occlusion tests  and baseline SVV, showing no significant predictive ability . In ROC In the case of baseline SVV, the sensitivity and specificity were 58.8% and 69.6%, respectively, for a cut-off point of 14% with ROC AUC of 0.645. The ROC curves for the summary changes in CI and SV after an EEO and EIO as well as for baseline SVV are displayed in Our study did not confirm the previously reported accuracy of EEO for predicting fluid responsiveness in elective cardiac surgical patients in the ICU. Several explanations can be proposed for this discrepancy, as well as the limitations of this study. The EEO test has been increasingly used to predict fluid responsiveness with excellent results in the operating theatre ,20 and t2O, which may have decreased the magnitude of changes in venous return introduced by EEO and EIO [2O ventilated with high PEEP levels and low tidal volumes. The presence of ARDS with low lung compliance can also limit the transition of pressure changes from the airways to the cardiovascular system during mechanical ventilation. On the other hand, the original study by Monnet et al. [Nevertheless, several studies also report a failure of respiratory occlusion tests to predict fluid responsiveness reliably. In a study by Guinot et al. , failure and EIO . Even th and EIO , only tw and EIO ,18. Ther and EIO ,20. In g and EIO  showed tt et al.  demonstrTM/EV1000TM system to calculate CO, SV and SVV without external calibration in our study. However, most studies to date have relied on calibrated pulse contour analysis [TM/EV1000TM to assess the hemodynamic effects of EEO and EIO, as well as the first study to use an uncalibrated pulse contour analysis method in the ICU. Calibrated pulse contour analysis devices are considered more accurate than uncalibrated devices as their concordance with pulmonary artery catheter measurements is the highest [TM, VolumeViewTM) or lithium injections with a lithium dilution sensor (LiDCO PlusTM), and require frequent recalibration to maintain their precision [TM, LiDCO RapidTM) or only rely on dedicated sensors , thereby cutting costs and avoiding complications associated with arterial catheter re-insertion in patients where circulatory instability had not been expected initially and a thermistor-tipped catheter was not used. Despite being less accurate, uncalibrated systems seem to have sufficient precision for CO measurement and are favored in situations where the circulation is normo- or hypodynamic [TM/EV1000TM system has improved in its ability to adjust for changes over the years, especially in vascular tone. In our study, we used the latest, fourth generation software, which seems to have improved performance in scenarios with acute vascular resistance changes [TM algorithm displays CO, SV and SVV based on data averaged over 20-s periods [We used the uncalibrated pulse-contour analysis by the FloTracanalysis ,16,22 oranalysis ,19 for Canalysis ,20, but  highest , even th highest . Calibrarecision . On the odynamic  as well odynamic , althougodynamic ,40,41. T changes ,43. Howe changes ,44. Furt changes  while th periods ,46. Ther periods ,20,24. T periods . FurtherIn conclusion, individual respiratory occlusion tests or their combination failed in predicting fluid responsiveness in elective, protectively ventilated, cardiac surgical patients. The technical limitations of uncalibrated pulse contour analysis may play a crucial role in detecting subtle cardiac output changes during the tests.


Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (MLP) classifiers were developed. The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization. The second MLP classifier was built to estimate whether the patient will die in the following 10 years. The 5-year and 10-year mortality models were based on the following predictors: age; the University of Texas Staging System for Diabetic Foot Ulcers score; the Wagner–Meggitt classification; the Saint Elian Wound Score System; glomerular filtration rate; topographic aspects and the depth of the lesion; and the presence of foot ischemia, cardiovascular disease, diabetic nephropathy, and hypertension. The accuracy for the 5-year and 10-year models was 0.7717 and 0.7598, respectively (for the training set) and 0.7244 and 0.7087, respectively (for the test set). Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFUs using non-invasive and low-cost predictors. In recent years, there has been a rapid increase in the prevalence of diabetes mellitus and its associated complications, making it the leading cause of morbidity and death. Diabetes mellitus (DM) is a significant problem in today’s world, as there has been a substantial increase in the prevalence of individuals affected by this condition, with the number of diagnosed cases more than tripling over the course of the last two decades . AccordiThe diabetic foot is characterized by three significant pathophysiological pillars: neuropathy, ischemia, and infection. These factors, albeit intricate in nature, play a crucial role in the development and progression of diabetic foot complications. The incidence of diabetic neuropathy in adults with diabetes ranges from 16% to 87% ,4. AdditThe primary focus should be on preventing the onset of diabetic foot complications. This objective may be accomplished by implementing effective disease control measures and promptly identifying individuals at high risk, such as those with peripheral neuropathy, peripheral vascular disease, foot abnormalities, and the presence of calluses. The identification of specific risk factors among the predetermined predisposing factors is of utmost importance due to the significant rise in the occurrence of diabetic foot infections. The identification of the most significant risk factors among various variables is crucial for determining diagnostic and therapeutic protocols. This consideration plays a vital role in health management, treatment priority, and the enhancement of the patient’s quality of life.The probability of developing a diabetic foot ulcer (DFU) in individuals with DM fluctuates between 15% and 25% throughout the course of their lifetime, with a majority of these individuals needing amputation within four years of the initial diagnosis . In compClinical systems that include risk factors for DFU recurrence have been developed to classify diabetic foot conditions, such as the University of Texas Staging System for Diabetic Foot Ulcers; the Perfusion, Extent, Depth, Infection, and Sensation (PEDIS) Classification System and Score; the International Working Group on the Diabetic Foot (IWGDF) Guidelines on the Prevention and Management of Diabetes-Related Foot Disease; and the SINBAD  system ,13. HoweOur study proposes the first artificial intelligence (AI)/machine learning (ML) model to predict long-term mortality (5 and 10 years) in diabetic patients with foot ulcers. Enhancing mortality prediction in diabetes patients with foot ulcers will finally lead to the intensified control of modifiable risk factors and improved long-term prognostic. A single-center observational retrospective study was conducted, including a total of 659 individuals diagnosed with both type 1 and type 2 diabetes mellitus with associated DFUs. The Diabetes, Nutrition, and Metabolic Diseases Clinic, within the Emergency Clinical Hospital “Sf. Spiridon” in Iași, functions as a regional center for patients from eight counties in the north-eastern area of Romania. All individuals included in this study were admitted to the clinic over the period spanning from 2007 to 2017. The patients were monitored until the year 2022, until their death, or until the last date documented in the computerized case system. The patients who were hospitalized at our regional facility were either referred via the emergency department, by their diabetologist or general practitioner, or were transferred from other clinics. Inclusion criteria were the presence of diabetes mellitus and the presence of a discharge diagnostic code relevant for DFU according to the International Statistical Classification of Diseases, 10th Revision (ICD-10) . Cases wA comprehensive database was created in collaboration with the hospital’s statistics service, encompassing patients whose discharge records contained ICD-10 codes associated with diabetic foot pathology. A thorough investigation was conducted to determine the discharge diagnosis. Patients who did not have a diagnosis linked to diabetic foot ulcers (DFUs) were excluded from the database.3, neutrophils/mm3, plateles/mm3, fibrinogen (mg/dL), C reactive protein (CRP) mg/dL, glucose levels (mg/dL), HbA1c mmol/mol, urea (mg/dL), creatinine (mg/dL), alkaline reserve (mmol/L), sodium (mmol/L), potassium (mmol/L), aspartate aminotransferase (ASAT) (U/L), alanine aminotransferase (U/L), total proteins (g/L), albumin (g/L), sideremia (μg/dL), ferritin (ng/mL), total cholesterol (mg/dL), high-density lipoprotein—HDL (mg/dL), low-density lipoprotein—LDL (mg/dL), triglycerides (mg/dL), uric acid (mg/dL), and glomerular filtration rate (mL/min/1.73 m2).The data gathered for the study included numerous continuous variables: age, disease duration (years), date of death, year of admission, duration of hospitalization (days), readmissions, The University of Texas Staging System for Diabetic Foot Ulcers, the Saint Elian Wound Score System (SEWSS), hemoglobin (g/dL), hematocrit, white blood cells/mmNumerous categorical variables were also collected: sex, the presence of diabetic retinopathy, diabetic nephropathy, chronic kidney disease, cardiovascular disease, peripheral artery disease, sensorimotor polyneuropathy, autonomic neuropathy, Charcot foot, hypertension, dyslipidemia, hyperuricemia, obesity, metabolic decompensation at admission , treatment with metformin/insulin/or other classes of oral or injectable antidiabetics prior to admission, Wagner–Meggitt classification, variables from the Saint Elian Wound Score System , cellulite, fever, radiography osteolysis, and wound secretion.The outcomes to be predicted by our models are binary variables that estimate whether the patient will die in the 5 or 10 years following hospitalization.Documented continuous variables were normalized in the range . Records with missing values were removed, resulting in a database comprising 635 total records. Since the continuous parameters did not follow a normal distribution, we employed the Kruskal–Wallis rank sum test to assess potential associations between these continuous variables and the outcome. In cases where two of the selected continuous variables displayed high intercorrelation with a Pearson coefficient of ≥0.9, one of them was excluded.Similarly, we employed Pearson’s Chi-squared test of independence to investigate whether any connections existed between each categorical parameter and the outcome.p < 0.05), as determined by the Kruskal–Wallis and Pearson’s Chi-squared tests, were chosen as predictors for the machine learning models.Subsequently, the continuous and categorical parameters that demonstrated a statistically significant relationship with the outcome  and a test set comprising 127 records (20%). Two multilayer perceptron (MLP) classifiers were constructed. The models were developed using the caret::train function within R Studio (version 1.4.1106). To safeguard against overfitting, we implemented a ten-fold cross-validation approach. To address the challenge posed by imbalanced outcome classes, we integrated the synthetic minority over-sampling technique (SMOTE) into the caret::train function.The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization based on the selected variables. The second MLP classifier was built to estimate whether the patient will die in the following 10 years based on all the selected predictors. We assessed the performance of the ML models by measuring their classification accuracy on both the test and training datasets. Additionally, we calculated other performance metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).For both developed models, we applied the varImp function (caret library) in R to determine the importance of each variable in predicting the outcome. For MLP models, the varImp function will take as an argument the resulting MLP model and will sort the importance of the variables based on a “filter” approach .The present research received authorization from the Ethics Commission of the University of Medicine and Pharmacy “Gr. T. Popa”, Iaşi, under the reference number 2324/19.01.2017. The retrospective, observational nature of the investigation precluded the solicitation of informed consent from patients. The use of participants’ identity data for any other purposes has not been and will not be conducted.After excluding records with missing values, the study’s database comprised 635 diabetic patients, with 435 (69%) being male and 224 (31%) being female. The ages of the participants ranged from 19 to 89 years. p-values displayed in To identify the continuous and categorical variables significantly associated with the study’s outcomes, we applied Kruskal–Wallis rank sum and Chi-square tests, with the corresponding p < 0.05): age, hemoglobin, creatinine, urea, duration of hospitalization, Texas classification, ischemia from the SWESS, neuropathy from the SWESS, glomerular filtration rate, cardiovascular disease, SEWSS score, Wagner classification, topographic aspects of the lesion from the SWESS, hypertension, diabetic nephropathy, peripheral artery disease, hyperuricemia, and depth of lesion from the SWESS. For the 10-year prediction model, the same set of predictors was selected, with the exception of peripheral artery disease and hyperuricemia.The feature selection process consequently pinpointed the following predictors for utilization in training the 5-year prediction model (There were no high inter-correlations (Pearson coefficient ≥ 0.9) between the selected continuous variables .Using the variables identified as predictors through the feature selection process, we proceeded to train two ML models.We developed the 5-year mortality model to predict whether the patient will die in the following 5 years after hospitalization. The final values used for the model were: three hidden layers with four neurons in the first layer, one neuron in the second layer, and five neurons in the third layer. The ROC curve obtained by the 5-year model both on the training and test sets is illustrated in The variable importance determined by the varImp function is graphically represented in The 10-year mortality classifier was built to estimate whether the patient will die within 10 years after hospital admission. The variable importance corresponding to the 10-year prediction model is graphically illustrated in Our research has proposed an ML method that can predict long-term mortality in diabetic patients with foot ulcers. To our knowledge, this is the first study in the literature to propose an ML model aimed to estimate long-term mortality for diabetic patients with diabetic foot ulcers. Our findings indicate that it is possible to predict with good accuracy whether the patient will die in the following 5 or 10 years using non-invasive and low-cost clinical and biological predictors.Initially, the study undertook the process of feature selection, which led to the identification of several non-invasive predictors related to the 5- and 10-year mortality outcome: age, hemoglobine, creatinine, urea, length of hospitalization, The University of Texas Staging System for Diabetic Foot Ulcers score, ischemia from the Saint Elian Wound Score System (SEWSS), neuropathy from the SEWSS, glomerular filtration rate, cardiovascular disease, SEWSS score, the Wagner–Meggitt classification of foot ulcers grade, topographic aspects of the lesion from the SEWSS, hypertension, diabetic nephropathy, peripheral artery disease, hyperuricemia, and depth of lesion from the SEWSS. This is in accordance with the scientific literature as, to date, many of these variables were studied as mortality predictors in diabetes mellitus patients ,18,19,20The predictive model for 5-year mortality, which aimed to determine the likelihood of patient death within five years after hospitalization, incorporated the following variables in descending order of significance: age, The University of Texas Staging System for Diabetic Foot Ulcers score, the Wagner–Meggitt classification of foot ulcers grade, the Saint Elian Wound Score System (SEWSS), glomerular filtration rate, the presence of cardiovascular disease, creatinine serum value, urea serum value, the depth of lesion from the SEWSS, duration of hospitalization, the presence of peripheral artery disease, the value of hemoglobin, topographic aspects of the lesion, diabetic nephropathy, the presence of high blood pressure, the presence of ischemia according to the SEWSS, and the presence of hyperuricemia. The predictors included in the 10-year mortality model, listed in order of importance, were age, the presence of neuropathy, glomerular filtration rate, The University of Texas Staging System for Diabetic Foot Ulcers score, serum urea level, the Wagner–Meggitt classification of foot ulcers grade, creatinine serum level, presence of diabetic nephropathy, presence of cardiovascular disease, duration of hospitalization, the Saint Elian Wound Score System (SEWSS), presence of high blood pressure, depth of lesion, hemoglobin value, topographic aspects of the lesion, and the presence of ischemia according to the SEWSS. The disparity between the 5-year and 10-year prediction models lies in the inclusion of peripheral artery disease and hyperuricemia in the former, while the latter includes the grading of diabetic neuropathy based on the SEWSS. The findings of our study provide empirical support for the need to adopt a multidisciplinary strategy in the management of patients with diabetic foot ulcers (DFUs). The implementation of a multidisciplinary treatment paradigm for individuals with diabetic foot conditions has the potential to provide positive results and enhance overall health . A study2, which corresponds to stage 3 CKD [Diabetic chronic kidney disease (CKD) leads to gradual injuries of the skeletal and vascular systems, giving rise to a complex condition referred to as chronic kidney disease-mineral bone disorder (CKD-MBD) ,24. The ge 3 CKD . In a stge 3 CKD .Ischemic heart disease is often seen in individuals with DFU, with a prevalence rate of up to 60% of cases . A recenThe findings of our study underscore the need to adopt an interdisciplinary strategy when managing patients with diabetic foot ulcers (DFUs). Our analysis revealed a significant association between fatality risk and both the severity of ulcers and the coexistence of cardiovascular and renal chronic conditions. The enhancement of survival rates for patients demands collaborative efforts among diabetologists, cardiologists, nephrologists, and surgeons.In individuals diagnosed with diabetes and a foot ulcer, it is recommended by guidelines to utilize either the SINBAD  system or the WIfI  system for effective communication among healthcare providers. It is important to note that in both cases, the specific components of these systems should be described individually rather than relying solely on a cumulative score . The ‘GuIn a previous study based on conventional survival analysis, we showed that patients with deep ulcers involving the tendon, joint, or bone had a higher mortality risk . In thisA retrospective study was performed on a consecutive sample of patients hospitalized at the Diabetes, Nutrition, and Metabolic Diseases Clinic at “Sf. Spiridon” Hospital, Iaşi. The chosen study strategy facilitated the collection of data from a significant sample size of patients over a lengthy period of time. This is the first examination using machine learning techniques to evaluate the long-term mortality rates among individuals with diabetic foot ulcers (DFUs). The findings of our research provide further empirical evidence to reinforce the prediction of extended mortality in persons diagnosed with diabetic foot ulcers (DFUs), therefore enhancing the current corpus of academic literature on this particular subject.The research was conducted at a single facility, namely “Sf. Spiridon” Hospital in Iaşi, which serves a wide geographical area in the north-eastern part of Romania. This hospital has departments specializing in general and vascular surgery, which allowed for the mitigation of selection bias. One primary constraint of our dataset is that the independent test set originates from the same center. Consequently, it is essential to conduct comprehensive external validation using data from many centers in future studies. One further concern is the unequal distribution of result classes, which has the potential to introduce computation biases. Nevertheless, efforts have been made to mitigate these biases via the use of the synthetic minority over-sampling technique (SMOTE) function in the R programming language.One of the primary limitations of our study was the failure to include the specific therapeutic approach, whether medical or surgical, used for the treatment of diabetic foot ulcers (DFUs).Furthermore, the use of a prospective study design would enhance the appeal of our research by allowing for the incorporation of additional variables that were previously unavailable to us. Furthermore, the implementation of surveys at multiple intervals would provide a more accurate assessment of the influence of glycemic control on our sample of patients.In addition, the analysis of the association between DFUs and the cause of death was not conducted due to the limited availability of electronic case histories, which did not include this information. The Public Health Department in our area regularly communicates the registration of deceased patients, mitigating the potential for reporting bias in mortality data.Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFU in the following 5 or 10 years using non-invasive and low-cost clinical and biological predictors. Our study emphasizes the significance of a comprehensive strategy in managing patients with diabetic foot ulcers (DFUs), including thorough local examination, the consideration of comorbidities, and the identification of risk factors. Such an approach is best achieved within the framework of a multidisciplinary team.


Many elder patients with hip fractures also suffered from hypertension. This study aims to explore the relationship between the use of ACEI or ARB and the outcomes of geriatric hip fractures.All the patients were divided into four groups: non-users without hypertension, non-users with hypertension, ACEI users, and ARB users. The outcomes of patients in different groups were compared. LASSO regression and univariable Cox analysis were used for variable screening. Then Cox models and Logistics models were established to identify the relationships between the use of RAAS inhibitors and outcomes.p = 0.016) and ARB users (p = 0.027) had a significantly lower survival probability than the non-users with hypertension. Non-users without hypertension, ACEI users, and ARB users may face lower 6-month and 1-year mortalities and higher 6-month and 1-year free walking rates compared with non-users with hypertension.ACER users (Patients with the use of ACEI or ARB may face a better prognosis of hip fractures. Hip fractures as one of the most severe diseases have attracted more and more attention from doctors and researchers around the world due to their high mortality and disability rate, especially in older adults . Most hiAngiotensin-Converting Enzyme Inhibitors (ACEI) and Angiotensin II Receptor Blockers (ARB) as two kinds of Renin–angiotensin–aldosterone system (RAAS) inhibitors were widely used for patients with hypertension . Many stThis study was a retrospective cohort study conducted in Emergency Trauma Center, Nanyang Second People’s Hospital. We declare that this study, as an observational study, was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Nanyang Second People’s Hospital. All the information about patient privacy was well protected, and informed consent was obtained from all patients enrolled in this study. Patients with hip fractures  in our department between January 2014 and January 2021 were extracted from our hospital's electronic medical record system. The inclusion criteria were set as below: a. aged ≥ 50 years; b. underwent hip surgeries due to hip fractures. c. with low-energy injury. The exclusion criteria were: a. with pathological fractures; b. without available data about ACEI or ARB; c. with severe kidney diseases. All the patients were divided into four groups according to their use of RAAS inhibitors and hypertension: non-users without hypertension, non-users with hypertension, ACEI users, and ARB users. All surgeries were conducted by a senior doctor or in his presence and direction. All the patients were followed up for 1 year and the outcomes of patients in four groups were compared. To reduce the bias caused by co-variables, LASSO analysis was used to screen the variables and the multi-variable models including Cox models and Logistics models were established.Baseline features of patients enrolled in our study were collected and summarized, including age, sex,body mass index (BMI), type of fracture, fracture history, smoking history, alcoholism history, polytrauma, surgical procedures, anesthesia, time from injury to surgery, and so on. The fracture types were summarized as femoral neck fracture and intertrochanteric fracture, and the surgical procedures were divided into internal fixation and arthroplasty. All the total arthroplasty, bipolar and unipolar hemiarthroplasty were defined as arthroplasty. All the patients with polytrauma were not underwent surgeries for secondary injuries. The hospital examination including electrocardiogram, chest radiograph, red blood count (RBC), hemoglobin (Hb), blood glucose (GLU), and albumin (ALB) was also collected. Major comorbidities were summarized to calculate the Charlson comorbidity index (CCI), which may evaluate the pre-operative status of patients conveniently . The diaAll of the individuals enrolled in our study were followed up for 1 year by telephone. Death was defined as all-cause death, and free walking was identified if the individuals were able to carry out activities of daily living independently. The patients who survived > 1 year were defined as censored data. The primary outcomes were mortality rates at 3 months, 6 months, and 1 year after surgery, and the second outcomes were the free walking abilities at 3 months, 6 months, and 1 year.Continuous variables were expressed as mean ± standard deviation and categorical variables were presented as count (percent). CCI score was transformed into a binary variable according to the cut-off value of four. Normally distributed variables were analyzed by the ANOVA test, while non-normally distributed variables were evaluated by Kruskal–Wallis test. Categorical data were assessed using Chi-squared tests.Least Absolute Shrinkage and Selection Operator (LASSO) models were established to identify and screen the possible variables related to the 1-year mortality and 1-year free walking rate of geriatric hip fractures by using R packages “glmnet”. Firstly, the relationships between the coefficients of punishment (λ) and the weight coefficient were calculated and summarized to explore the most suitable λ. Then the λ with the strongest stability was selected by calculating partial likelihood deviance for 1-year mortality and by using the AUC method for 1-year free walking rate. Then the variables included in the LASSO models were summarized and then included in the Cox and Logistics models following.Kaplan–Meier curves were established and the Log-rank test was used to compare the survival between groups with Bonferroni correction. Then the univariate Cox models were established and those variables that were significant were included in Cox model 1, while variables screened by LASSO models were included in Cox model 2. Similarly, the variables screened by LASSO models were also included in Logistics models to reduce the impact caused by co-variables. Due to the multicollinearity between hypertension and groups, the hypertension variable was excluded in the multi-variable models. All analyses were conducted by using R software version 4.2.2 .n = 361), non-users with hypertension (n = 123), ACEI users (n = 209), ARB users (n = 132). Then the baseline features and outcomes were compared between groups and summarized in Table p < 0.001), fracture history (p = 0.015), hypertension (p < 0.001), and polytrauma (p = 0.045) in the comparison of baseline data between groups. In the part of outcomes, patients in different groups had significantly different 1-year mortality (p = 0.005), 6-month independent walking rate (p < 0.001), and 1-year independent walking rate (p < 0.001). The Kaplan–Meier curves and the Log-rank test showed that the patients in different groups had significantly different survival probabilities  and ARB users (p = 0.027), while there was no significant difference in non-users without hypertension compared with non-users with hypertension (p = 0.876), ACER users (p = 0.250) and ARB users (p = 0.314), as well as ACEI compared with ARB users (p > 0.999).Finally, 825 patients who underwent hip surgeries due to hip fractures between January 2014 and January 2021 in our hospital met the inclusion and exclusion criteria and were enrolled in our study (Fig. The direct comparison between groups may ignore the influence caused by co-variables such as ages and the bias may affect the conclusion. To identify the relationships between the use of RAAS inhibitors and the prognosis of hip fractures and to reveal the potential co-factors, LASSO models were established for 1-year mortality and 1-year free walking rate. The changes of coefficients and partial likelihood deviance with the increase of λ in LASSO models of 1-year mortality were shown in Fig. Based on the screened variables, two Cox models were established to identify the relationship between RASS inhibitors and the prognosis of geriatric patients with hip fractures. Cox model 1 was adjusted for age and groups, and Cox model 2 was adjusted for age, groups, sex, electrocardiograph, chest radiograph, smoking history, surgical procedures, and anesthesia. As shown in Table To reduce the bias caused by co-factors and identify the prognostic value of RAAS inhibitors, Logistics models for mortality and free walking rates at 3 months, 6 months, and 1 year were established and adjusted for variables screened in LASSO models Table . As showIn this study, the relationships between the use of ACEI and ARB were identified: the use of RAAS inhibitors in geriatric patients who underwent hip surgeries for hip fractures may relate to a better prognosis in both mobility and survival. To reduce the impact of co-variables and to keep the stability of Cox and Logistics models, a procedure of variable screening was conducted by using LASSO analysis. Just as the results of multi-variable regression, besides the use of ACEI and ARB, many variables were associated with the outcomes, such as age an electrocardiogram, and even after the adjustment of multi-variable models, the non-users without hypertension, ACEI users, and ARB users may also have significantly more satisfactory outcomes, which indicated that the status of RASS using and hypertension may be the independent risk factors for the prognosis of geriatric hip fractures.Age and sex were two of the most significant risk factors for the incidence of hip fractures: the risk of hip fractures increased with age, and women may have a higher incidence of hip fractures compared with men , 21. UndThe primary outcomes of our study were all-cause death. As we know, the hip fracture itself rarely causes death directly, and usually, the development of complications of hip fractures and aggravation of comorbidity may be the major cause of death in older patients with hip fractures . HyperteThe relation and pathway between hypertension and bone metabolism may be achieved via the RAAS , 32, andThe effects of RAAS inhibitors were also reported in many studies. The mice with bilateral orchiectomy may have a significantly increased trabecular bone area after the treatment of captopril . SimilarOur study had several limitations. First, this study as a single-center retrospective study based on a small number of samples may cause bias. Next, the loss of data and follow-up may also reduce the clinical evidence grade. Thirdly, some factors that may affect the prognosis were not included in this study, and the missing co-factors may also affect the outcomes of our patients enrolled in this study. However, the co-variables which may relate to the outcomes significantly, such as ages and sex, had been included in our multi-variable models, and we believed that this may reduce the bias caused by co-variables. Lastly, the dose of RAAS inhibitors was not collected in our study, which may cause some differences in outcomes.Our study showed that the uses of ACEI and ARB may relate to higher survival and free-walking abilities. Consistent with our study, many studies based on animals had reported the effect of RAAS inhibitors on bone healing and metabolism. We hope more experimental population studies may be conducted to prove our conclusion with a high level of evidence.Patients with the use of ACEI or ARB may face a better prognosis of hip fractures.


Chronic kidney disease (CKD) is a significant public health concern associated with significant morbidity and has become one of the foremost global causes of death in recent years. A frequent comorbidity of CKD is secondary hyperparathyroidism (SHPT), exemplified by high serum parathyroid hormone (PTH) levels. The mineral metabolism disturbances resulting from CKD and progression to SHPT are currently considered part of the definition of chronic kidney disease–mineral and bone disorder (CKD-MBD). However, CKD-MBD does not only include abnormalities in laboratory-measured parameters; it is a complex condition characterized by dysregulation of bone turnover, mineralization, growth and strength, accompanied by vascular or another soft-tissue calcification. Together, this increases the risk of bone fractures, cardiovascular disease, and overall mortality in CKD-MBD patients. Monitoring serum markers is essential in diagnosing SHPT and CKD-MBD, and there are several recognized indicators for prognosis, optimal clinical management and treatment response in late-stage kidney disease patients receiving dialysis. However, far fewer markers have been established for patients with non-dialysis CKD. This review provides an overview of current and emerging markers and tools used in the diagnosis and management of CKD-MBD in non-dialysis adult patients. Chronic kidney disease (CKD) is a significant public health concern associated with significant morbidity. It has become one of the foremost global causes of mortality over the past 30 years . An estiSHPT is an adaptive and typically detrimental condition that develops due to declining kidney function and the subsequent dysregulation of calcium and phosphate homeostasis. This leads to increased serum phosphate, elevated fibroblast growth factor-23 (FGF-23) levels, and reduced synthesis of klotho (the FGF-23 co-receptor) and the active form of vitamin D, calcitriol. These alterations directly and indirectly cause elevated production and secretion of PTH and parathyroid hyperplasia, further exacerbating the condition .The mineral metabolism disturbances resulting from CKD and rising PTH levels are currently considered part of the definition of chronic kidney disease–mineral and bone disorder (CKD-MBD) . HoweverMonitoring of serum markers, including calcium, phosphate, PTH, vitamin D and alkaline phosphatase activity, as well as other diagnostic tools, are essential in the management of SHPT and CKD-MBD, and treatment decisions should be based on a combined assessment of these parameters . AlthougBiochemical anomalies are common in CKD ,13, and,The benchmark for diagnosis of renal osteodystrophy is bone biopsy with histomorphometric analysis, which is the only technique capable of providing a full evaluation of dynamic bone parameters ,16. HoweFor the diagnosis of cardiovascular calcifications in CKD-MBD, lateral abdominal radiography is recommended to detect VC, while an echocardiogram can be used to assess the degree of valvular calcification ,18. OtheThere are several recognized markers for prognosis, optimal clinical management and treatment response in late-stage kidney disease patients receiving dialysis; however, fewer markers have been established for CKD patients not receiving dialysis. This review aims to provide an overview of current and emerging markers or risk factors for disease progression, cardiovascular dysfunction, and bone turnover in non-dialysis patients with CKD-MBD. Markers described include parameters that can be assessed via blood samples. The functions of markers typically detected in systemic circulation are summarized in Abnormalities of mineral metabolism are very common in patients with CKD. As previously mentioned, variations in laboratory-measured levels of circulating markers may begin in CKD stage 3 or earlier, and monitoring is essential for effective management . The folAlthough monitoring of serum phosphate levels is standard practice for the diagnosis and management of CKD-MBD, there is evidence to suggest a role for phosphate as a marker of disease progression and outcomes. For example, research has shown that high serum phosphate levels in non-dialysis CKD patients are associated with renal function decline and an increased risk of end-stage kidney disease and death ,25,26,27Parathyroid hormone is a peptide secreted by the parathyroid glands that regulates serum calcium concentration through interaction with the kidneys, bones and gastrointestinal tract . The actWhile a consensus exists that rising PTH levels serve as an indication for treatment initiation, it has been suggested that complete normalization of PTH levels is to be avoided ,36,37, dThere is a balance to be found when managing rising PTH levels, as parathyroid hyperplasia, persistent elevation of PTH, and the progression of SHPT resulting from delayed therapeutic interventions are accompanied by continuing reductions in sensitivity to vitamin D and calcium homeostasis, subsequently posing a risk of treatment resistance in later stages of the disease . AdditioDespite some of the challenges with PTH measurement, it remains a key marker for monitoring CKD-MBD . As ment2D , which is generated in the kidney after another hydroxylation step. In clinical settings and epidemiological studies, 25(OH)D is the most widely used marker of vitamin D status [2D lack standardization, its short half-life complicates accurate measurement, and exogenous administration of calcitriol and vitamin D analogs can artificially influence results [Individuals can obtain vitamin D through multiple sources: endogenous synthesis from UV exposure to the skin, dietary intake, or supplementation. Upon entering the bloodstream, vitamin D undergoes hepatic hydroxylation, resulting in the formation of 25(OH)D . 25(OH)D is biologically less active compared to 1,25(OH)D status . Calcitr results .Vitamin D deficiency (defined by most experts as serum 25(OH)D serum levels <20 ng/mL)  is commoGuidelines differ regarding recommendations to supplement low 25(OH)D levels. Australian and US guidance states that levels < 30 ng/mL should be corrected using appropriate treatment strategies even in earlier stages of CKD, whereas KDIGO advised that adult non-dialysis patients with stage 3–5 CKD and vitamin D deficiency should be treated with nutritional vitamin D. In contrast, calcitriol and vitamin D analogs should not be routinely prescribed, reserving these for stage 4–5 CKD patients with severe and progressive hyperparathyroidism . HoweverSeveral studies have identified relationships between dysregulation of serum calcium levels and outcomes in non-dialysis patients. For example, results from two observational studies show that low serum calcium levels are associated with renal function decline and progression to renal replacement therapy. Together, these studies suggest that serum calcium could be considered in a predictive model of patient prognosis ,49. A moFGF-23 is a 32 kDa glycoprotein mainly produced in bone by osteoblasts and osteocytes. The primary action of FGF-23 is the inhibition of phosphate reabsorption from the urine and the suppression of calcitriol synthesis in the kidney . FGF-23 Increases in serum FGF23 have been demonstrated as one of the early indicators of mineral dysregulation in non-dialysis patients, compared with phosphate and PTH, with high FGF-23 and low 25(OH)D levels identified as independent predictors of poor renal outcome . Furtherp = 0.05), as well as both fatal and non-fatal cardiovascular events  and all-cause mortality  [p < 0.001), cardiovascular disease , and renal events , including progression to ESKD or the initiation of dialysis [In a later study, elevated FGF-23 was associated with risk of progression to renal replacement therapy  . Similardialysis . Finallydialysis ,54, amondialysis .Klotho is a protein produced mainly in the kidney that can exist in a membrane-bound state or a soluble, circulating form . The memEvidence shows that a decline in serum klotho level can occur as early as CKD stage 2 and continues to reduce as CKD progresses ,63,64. RVascular calcification (VC) is a highly complex process involving multiple signaling pathways; there are many promoters as well as inhibitors of calcification . There aVC is associated with several adverse clinical outcomes, including cardiac events, cardiovascular mortality and all-cause mortality . GuideliPatients with CKD exhibit an elevated cardiovascular risk, and cardiovascular disease, rather than ESKD, is the leading cause of death in patients with advanced CKD. In CKD patients not receiving dialysis, the incidence and prevalence of cardiovascular events are also significantly higher compared with the general population . This seSeveral imaging methods have been developed to evaluate the extent and severity of VC in patients with CKD, many of which do not differentiate between intimal and medial calcification .Coronary artery calcification (CAC) scoring is a technique to assess the likelihood of coronary heart disease and mortality in the general population , and is While the CAC score is the gold standard to measure VC, the technique delivers high radiation doses and can incur significant costs . ConsequThe Kauppila score is a validated grading system used to assess the severity of abdominal aortic calcification based on lateral abdominal radiography . Aortic Adragao scoring is another simple VC score based on plain radiographical imaging of the pelvis and hands . VC asseKauppila and Adragrao scoring are semi-quantitative, relying on a visual assessment and, therefore, are more susceptible to human errors. Recently, an X-ray-based, computer-assisted abdominal aorta VC score has been developed with improved precision versus the Kauppila score. Ongoing studies will determine whether this new scoring method may be beneficial in detecting the progression of VC . FinallyPulse wave velocity (PWV) is a non-invasive method for analyzing central arterial stiffness based on the rate at which blood pressure pulses travel through the circulatory system. It is a straightforward and reproducible technique and is validated and widely used in clinical settings throughout Europe. Changes in PWV have independent predictive value for cardiovascular risk in the general population .2: 62.6%) of all-cause mortality [In non-dialysis patients with CKD, several studies have correlated high PWV, indicative of increased arterial stiffness, with deterioration of renal function ,94,95. Aortality . TogetheFetuin-A is a multifunctional, circulating glycoprotein that scavenges calcium phosphate via negatively charged amino acids β-sheet of the N-terminal domain ,100,101.Low serum fetuin-A is typically linked to mineralization imbalance and increased likelihood of death in advanced kidney disease . HoweverMatrix Gla protein (MGP) is a powerful inhibitor of VC, and carboxylation by vitamin K is required to trigger the active, inhibitory state . CorrespAs an essential co-factor for MGP activation, poor vitamin K status may contribute to a high VC burden in CKD patients . VitaminPrimary CPPs are small, spherical complexes of amorphous calcium phosphate and other regulatory proteins. Primary CPPs develop into larger, needle-shaped secondary CPPs containing crystalline calcium phosphate . Our undn = 2785) study of patients receiving hemodialysis [In addition, the T50 calcification inhibition test has recently been developed to determine the overall tendency for calcification of a patient’s serum. This in vitro diagnostic method evaluates the maturation of primary to secondary CPPs in serum, with T50 being the half-maximum time in minutes for this conversion . As T50 dialysis . Additiodialysis . A furthdialysis .These studies suggest that serum T50 may be advantageous as a marker to monitor treatments against VC; however, future studies are needed to further evaluate T50 in non-dialysis patients and establish whether therapies to improve calcification promoter/inhibitor regulation might benefit patients ,120.Osteocalcin is a vitamin K-dependent hormone derived from osteoblasts and has roles in bone mineralization, bone cell activity, and regulation of glucose and lipid metabolism ,122. AltOsteocalcin correlated negatively with renal function in a small study of non-dialysis CKD patients , and othDuring normal kidney function, a reduction in calcium causes calcium-sensing receptors in the parathyroid glands to release PTH, causing the kidney’s reabsorption of calcium from urine into the blood and increased excretion of phosphate. PTH also stimulates the formation and release of calcitriol to increase calcium uptake from the small intestine to the blood, especially calcium release from bone, returning circulating calcium levels to the basal range and achieving homeostasis ,8,127.In CKD patients, when the glomerular filtration rate falls below normal retrievable levels, the kidneys cannot increase calcium uptake from urine to blood and stimulate the production of calcitriol to increase calcium absorption from the gut, leading to hypocalcemia. This further stimulates PTH secretion via calcium-sensing receptors, causing bones to release calcium (bone resorption) to regulate serum calcium levels, weakening them and causing symptoms including bone pain in the lower back, hips and legs, with a significant fracture risk correlating to increasing PTH levels ,8,24,127These bone quality and strength abnormalities are observed in most patients with CKD stage 3–5 and in all patients treated with dialysis . ConsequSerum alkaline phosphatase (ALP) is a hydrolytic enzyme that catalyzes the removal of phosphate groups from protein and nucleotides. Although it is produced by many tissues, it is typically concentrated in the bone and liver . Bone-spIn non-dialysis CKD patients, BSAP has been identified as an independent predictor of total hip BMD; furthermore, monitoring the B/I isoform of BSAP predicts osteopenia in the hip and the distal third of the radius in stage 3 CKD . In anotTartrate-resistant acid phosphatase 5b (TRAP5b) is an enzyme secreted by osteoclasts to catabolize bone matrix. Therefore, elevated serum levels indicate enhanced osteoclastic activity , and stuIn non-dialysis patients, an early study identified significant negative correlations with renal function and a positive correlation with serum bone markers and the development of secondary hyperparathyroidism . In a suAn additional study reported a significant correlation between elevated TRAP5b concentrations, reduced BSAP levels and higher arterial stiffness, suggesting that the disparity between bone resorption and formation in CKD, inferred by the TRAP5b:BSAP ratio, is prognostic of vascular stiffness in non-dialysis patients . TRAP5b Procollagen type 1 N-terminal propeptide (P1NP) is released during the extracellular cleavage of type 1 collagen and is a marker of bone formation . Higher Sclerostin is a protein primarily produced by osteocytes, which plays a crucial role in regulating bone metabolism . By bindIn addition, studies have established significant links between sclerostin levels and aortic, carotid and coronary artery calcification ,154,155 Up to half of patients with moderate kidney failure have irregular bone histology, suggesting that alterations to the skeleton may begin years before symptoms become apparent . Bone biAssessment of bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA) is suggested by the current KDIGO guidelines for patients with stage 3–5 CKD showing evidence of CKD-MBD, as the lack of bone biopsy may not be sufficient justification for reserving antiresorptive therapy in patients with a high fracture risk . Among nAlthough bone strength, inferred by its density, can be established by DXA, bone quality (as opposed to quantity) relating to factors such as remodeling, microdamage, and microarchitecture cannot be determined using this method . FurtherPeripheral quantitative computed tomography (pQCT) can measure both trabecular and cortical volumetric density , and is A further non-invasive measurement of bone structure can be achieved by determining the trabecular bone score (TBS), which evaluates texture variations and bone quality from images acquired from lumbar spine DXA . Recent Despite advances in the field, optimal management of CKD-MBD and SHPT in non-dialysis adult CKD patients remains challenging. While there are established markers for prognosis and treatment response in late-stage kidney disease patients receiving dialysis, far fewer markers have been identified for CKD-MBD patients not receiving dialysis. Recent evidence highlights several promising tools and markers for the prediction of disease progression and risk of adverse clinical outcomes in non-dialysis CKD patients; however, many of them are not currently available in everyday clinical practice and further research is needed to define parameters that inform optimal clinical management or likely treatment responses.Cardiovascular and bone disease are common complications in CKD patients, highlighting the complex relationship between bone turnover and vascular calcification in CKD patients. In addition to biochemical markers, bone and vascular imaging should be considered important tools to guide treatment decisions for non-dialysis CKD patients and the focus of future research.


Chronic kidney disease (CKD) represents a public health burden worldwide and is associated with significant morbidity and mortality. Most patients with CKD are managed by primary care practitioners and this educational series hope to improve knowledge and delivery of care to this high-risk patient population with CKD. BMC Nephrology and BMC Primary Care are teaming up to bring a much-needed series for the primary care practitioner serving as an educational update of chronic kidney disease (CKD). This series will feature articles focused on the identification, diagnosis, management of complications, and treatment of patients with CKD geared towards the primary care practitioner perspective. CKD is a leading cause of medical illness worldwide with an estimated 8 to 16% of the world’s population and 15% of the US population affected [2 and normal albuminuria is defined as urine albumin-to-creatinine ratio (ACR) < 30 mg/gram or < 3 mg/mmol. Decreasing GFR and increasing albuminuria define the various stages of CKD [affected . CKD is s of CKD . The lifCKD is associated with many biochemical and physiologic complications, most importantly being an independent risk factor for cardiovascular disease and mortality . PatientAlthough screening for CKD has not yet been advocated by The Unites States Preventative Services Task Force, screening is recommended for patients with increased risk by several kidney organizations, including the National Kidney Foundation in the United States . ApproprCKD is associated with many complications in addition to the increased risk of cardiovascular disease. Cardiovascular complications in CKD are the leading cause of death in all stages of disease including transplant patients , 13. Mos2 or if urine ACR is greater than 300 mg/gram [https://kidneyfailurerisk.com). Optimal preparation for renal replacement therapy is essential to limit complications to patients, improve their sense of control, and to increase use of home dialysis modalities and potentially pre-emptive transplantation. Kidney transplants offer patients not only a better quality of life compared to dialysis but also a significantly extended life.Currently there are about 37 million US adults living with CKD, that’s more than 1 in 7 adults, with the majority in CKD stages 1 to 3 not being followed by a nephrologist . This tr mg/gram . PrognosToday is a very exciting time in nephrology and primary care as we now have many more tools in our armamentarium to significantly limit the risk of CKD progression to ESKD, reduce cardiovascular morbidity and improve overall survival, above and beyond the well-known renin-angiotensin-aldosterone system blockade agents. Several new medications such as the sodium–glucose cotransporter-2 inhibitors (SGLT2is) have been developed, proven in several multicenter international randomized clinical trials and approved by the Food and Drug Administration to benefit both diabetic and non-diabetic CKD patients. Similar benefits among diabetic CKD patients have been shown with the new nonsteroidal mineralocorticoid receptor antagonist, finerenone. This series will highlight the evidence and recent advances in nephrology to assist our primary care colleagues optimize the care of CKD patients, improve their quality of life and survival.


Introduction: Transcatheter atrial fibrillation (AF) ablation is still carried out with continuous invasive radial arterial blood pressure (IBP) monitoring in many centers. Continuous noninvasive blood pressure (CNBP) measurement using the volume-clamp method is a noninvasive alternative method used in ICU. No data on CNBP reliability are available in the electrophysiology lab during AF ablation, where rhythm variations are common. Background: The objective of the present study was to compare continuous noninvasive arterial pressure measured with the ClearSight device  with invasive radial artery pressure used as the reference method during AF ablation. Methods: We prospectively enrolled 55 consecutive patients  undergoing transcatheter AF ablation  at our center. Standard of care IBP monitoring via a radial cannula and a contralateral noninvasive finger volume-clamp CNBP measurement device were positioned simultaneously in all patients for the entire procedure. Bland-Altman analysis was used to analyze the agreement between the two techniques. Results: A total of 1219 paired measurements for systolic, diastolic, and mean arterial pressure were obtained in 55 subjects, with a mean (SD) of 22 (9) measurements per patient. The mean bias (SD) was −12.97 (13.89) mmHg for systolic pressure , −1.85 (8.52) mmHg for diastolic pressure  and 2.31 (8.75) mmHg for mean pressure . Conclusion: In patients undergoing AF ablation, CNBP monitoring with the ClearSight device showed acceptable agreement with IBP monitoring. Larger studies are needed to confirm the potential clinical implications of continuous noninvasive BP monitoring during AF ablation. Catheter ablation for atrial fibrillation (AF) has emerged as an important rhythm-control strategy and is by far the most common cardiac ablation procedure performed worldwide. Despite the fact that many technological advances have been incorporated into clinical practice in order to improve success rates and minimize complications, hemodynamic status monitoring remains mandatory to monitor potential ablation complications such as anesthesia-related hypotension and/or cardiac tamponade . NoninvaClearSight™ (CS)  is a noninvasive hemodynamic monitoring system that uses a digital cuff and “volume clamp” technology to obtain a continuous arterial waveform. The system measures continuous noninvasive blood pressure (CNBP) with an improved method based on the Penaz and Wesseling studies ,5 througAlthough the CS has been validated and investigated in different settings, including critical patients, ref.  surgicalThe aim of this study was to investigate the feasibility and accuracy of the CNBP in comparison with the current gold standard CIBP in patients undergoing transcatheter AF ablation.We conducted a single-center prospective validation study from March 2021 to August 2021. Patients aged 18 years or more with an indication for transcatheter AF ablation  were enrolled. Radiofrequency ablation, as well as cryoablation procedures, were included in this study. The Declaration of Helsinki was adequately addressed, and the study was approved by the local ethics committee.In all patients, the following clinical, echocardiographic, and procedural characteristics were collected: age, body mass index (BMI), left ventricular ejection fraction (LVEF), moderate-severe valvulopathy, hypertension (HTN), hyperlipidemia, coronary artery disease (CAD), diabetes mellitus (DM), and chronic kidney disease (CKD).Standard of care CIBP monitoring was performed via sterile positioning of a 16 G radial cannula using the modified Seldinger technique under local anesthesia. For CNBP measurement, the ClearSight™ system was placed on the opposite arm from the IABP cannula, using the recommended finger cuff size, and placed on the intermediate phalanx of the first or second finger. A,B After2 analysis or Fisher exact test, as appropriate.Descriptive statistics are reported as mean ± SD for normally distributed continuous variables or as a median with a range for skewed distributions. Categorical data are expressed as percentages, and differences in proportions were compared by χAgreement between measurements of BP measurements was assessed by Bland-Altman plot analysis ,12. To eWe enrolled a total of 55 consecutive patients with a prevalence of males (80%). The median age was 62 ± 11 years, with a heterogeneous cardiac risk profile and almost normal BMI. Regarding procedural indication, 62% of patients had a paroxysmal AF form, while 38% had a persistent form. Fifty-three procedures (96.4%) were conducted with the radiofrequency ablation technique and only two (3.6%) with cryoablation. According to the indication, the ablation strategy included PVI with additional substrate modification when required. The median overall procedural duration was 195.5 [IQ 163.5–318] minutes.One major complication (cardiac tamponade) and three minor complications  occurred during the study period. No procedural-related deaths were observed.Baseline patient characteristics and procedural results are shown in A total of 1219 paired measurements for SBP, DBP, and MBP were obtained in 55 subjects, with a mean (SD) of 22 (9) and a median of 22 (25–75° interquartile range: 16–29) measurements per patient. Total measurements per patient ranged from 4 to 41. In the overall population, mean bias (SD) was −12.97 (13.89) mmHg for SBP , −1.85 (8.52) mmHg for DBP  and 2.31 (8.75) mmHg for MBP  . PercentA sub-analysis was also performed to assess agreement between CNBP and IBP during sinus rhythm and during atrial fibrillation. A total of 726 paired measurements for SBP, DBP, and MBP were obtained during sinus rhythm. The mean bias (SD) was 13.18 (12.92) mmHg for SBP , −1.93 (8.01) mmHg for DBP  and 2.32 (8.48) mmHg for MBP . A total of 455 paired measurements for SBP, DBP, and MBP were obtained during atrial fibrillation. The mean bias (SD) was 12.63 (15.38) mmHg for SBP , −1.72 (9.31) mmHg for DBP  and 2.29 (9.21) mmHg for MBP .The present study demonstrated for the first time the accuracy of the CNBP device during AF ablation, a special playground where many factors may account for multiple and unpredictable blood pressure changes.In patients with AF undergoing ablation, SBP, DBP, and MAP measurements monitored noninvasively with the CNBP were accurate and precise compared with invasive radial arterial catheter-derived measurements.CNBP results were accurate both in SR and in AF.CNBP results were accurate both under sedation and general anesthesia.Specifically, the main results of the study were:Although the safety, efficacy, and effectiveness of AF ablation are consistently improved in the last decade, ,14 proceTraditionally hemodynamic monitoring during AF ablation has been performed via invasive femoral or radial arterial access. However, IBP monitoring continues to account for procedural lengthening and potential access site complications ,20. As rUnfortunately, unlike in the USA, where it is usually performed under general anesthesia with ICE monitoring, in Europe, conscious sedation without ICE is the predominant approach,  demandinTM demonstrated to be accurate and reliable in various clinical settings [The CNBP monitoring system has been previously studied and validated in multiple small-scale studies. The first Nexfin generation and, later, the Clearsightsettings ,24, suchsettings , cardiacsettings , and TAVsettings .The originality of the present study is the special scenario studied. Indeed, AF ablation represents a heterogenous playground where many different factors may account for BP variations and measurements.First, the AF ablation patient population may include many different sub-populations characterized by different volume-loading conditions, such as obesity and elderliness.Secondly, the anesthesiologic regimes may space from slight sedation to general anesthesia, with different related risks of hypotension. Third, the concomitant anticoagulation needs to control hypertension minimizing any potential intracranial bleeding. Fourth, fluid injection and heart rhythm instability (including ectopies and irregular AF-related cycle length variations) may definitely be responsible for sudden BP change.  Finally,In line with previously published data,  our studSpecifically, according to Bland-Altman analysis , resultsIntraoperative blood pressure is usually measured intermittently using noninvasive oscillometric devices every 3–5 min. Continuous monitoring facilitates early diagnoses of hypotension, thus potentially promoting timely treatment, especially when ICE monitoring is not routinely used. If intraoperative hypotension may cause cerebral hypoperfusion, which can result in hypoxia and subsequently cerebral ischemia, similarly intraoperative hypertension may lead to cerebral hyperperfusion with hemorrhagic stroke when not timely recognized and treated.Recently, a new ClearSight™ system algorithm to predict hypotension has been developed based on machine learning that uses arterial waveform to predict intraoperative hypotension  . FurtherThe results of this study should be interpreted considering some limitations.It was a single-center prospective observational study with a relatively small sample size of patients undergoing AF ablation. Thus, results might not be generalizable to other clinical settings or extrapolated for other scenarios. In certain populations, such as critical ICU patients, patients under hemodialysis, or Reynaud syndromes, CS has been shown to lack accuracy and precision compared with the invasive arterial catheter, and thus, it cannot be recommended.Similarly, according to the low sample size, the number of complications was very low, and any conclusions remain beyond the scope of the present study. Although a reduction of vascular complications commonly related to vascular access of IBP, larger studies should be carried out to further analyze CNBP in complications during EP procedures.Another limitation of our study was the moderate density of coupled measurements obtained. Coupled data sampling was arbitrarily performed every 5 min, although, in a recent prospective observational study , authorsRegarding data and correlations, the predefined acceptable range for bias (20%) and mean error (30%) were chosen based on previously published data. Nevertheless, the SBP was notably less accurate than the DBP and MAP. A small number of outliers was found; however, no complications or procedural variations from usual practice happened in those cases. Further study will evaluate the more accurate index to be used during the BP monitoring for AF ablation.In patients undergoing AF ablation, noninvasive finger volume-clamp continuous BP monitoring with the ClearSight™ device showed acceptable agreement with standard of care invasive BP monitoring. Larger studies are needed to confirm these results and assess the overall safety of a noninvasive-only approach.