Fibrosis of Peritoneal Membrane as Target of New Therapies in Peritoneal Dialysis.

Peritoneal dialysis (PD) is an efficient renal replacement therapy for patients with end-stage renal disease. Even if it ensures an outcome equivalent to hemodialysis and a better quality of life, in the long-term, PD is associated with the development of peritoneal fibrosis and the consequents patient morbidity and PD technique failure. This unfavorable effect is mostly due to the bio-incompatibility of PD solution (mainly based on high glucose concentration). In the present review, we described the mechanisms and the signaling pathway that governs peritoneal fibrosis, epithelial to mesenchymal transition of mesothelial cells, and angiogenesis. Lastly, we summarize the present and future strategies for developing more biocompatible PD solutions.

Mild sodium reduction in peritoneal dialysis solution improves hypertension in end stage kidney disease: a case-report study.

INTRODUCTION: Blood Pressure (BP) control is largely unsatisfied in End Stage Kidney Disease (ESKD) principally due to sodium retention. Peritoneal Dialysis (PD) is the most common type of home dialysis, using a peritoneal membrane to remove sodium, though sodium removal remains challenging. METHODS: This is a case-study reporting two consecutive ESKD patients treated by a novel peritoneal PD solution with a mildly reduced sodium content (130 mmol/L) to treat hypertension. RESULTS: In the first case, a 78-year-old woman treated by Continuous Ambulatory PD (CAPD) with standard solution (three 4 h-dwells per day 1.36% glucose 132 mmol/L) showed resistant hypertension confirmed by ambulatory blood pressure monitoring (ABPM), reporting 24 h-BP: 152/81 mmHg, day-BP:151/83 mmHg and night-ABP: 153/75 mmHg, with inversion of the circadian systolic BP rhythm (1.01), despite use of three anti-hypertensives and a diuretic at adequate doses. No sign of hypervolemia was evident. We then switched from standard PD to low-sodium solution in all daily dwells. A six-months low-sodium CAPD enabled us to reduce diurnal (134/75 mmHg) and nocturnal BP (122/67 mmHg), restoring the circadian BP rhythm, with no change in ultrafiltration or residual diuresis. Diet and drug prescription were unmodified too. The second case was a 61-year-old woman in standard CAPD (three 5 h-dwells per day) suffering from hypertension confirmed by ABPM (mean 24 h-ABP: 139/84 mmHg; mean day-ABP:144/88 mmHg and mean night-ABP:124/70 mmHg). She was switched from 132-Na CAPD to 130-Na CAPD, not changing dialysis schedule. No fluid expansion was evident. During low-sodium CAPD, antihypertensive therapy (amlodipine 10 mg and Olmesartan 20 mg) has been reduced until complete suspension. After 6 months, we repeated ABPM showing a substantial reduction in mean 24 h-ABP (117/69 mmHg), mean diurnal ABP (119/75 mmHg) and mean nocturnal ABP (111/70 mmHg). Ultrafiltration and residual diuresis remained unmodified. No side effects were reported in either cases. CONCLUSIONS: This case-report study suggests that mild low-sodium CAPD might reduce BP in hypertensive ESKD patients.

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

OBJECTIVES: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. METHODS: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the "AmsterdamUMC database") admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients. RESULTS: The deep learning model had an area under the ROC curve (AUC) of 0,907 (± 0,007SE) with a sensitivity and specificity of 80% and 89%, respectively, for identifying oliguric AKI episodes. Logistic regression models had an AUC of 0,877 (± 0,005SE) with a sensitivity and specificity of 80% and 81%, respectively. These results were comparable to those obtained in the two US populations upon which the algorithms were previously developed and trained. CONCLUSION: External validation on the European sample confirmed the accuracy of the algorithms, previously investigated in the US population. The models show high accuracy in both the European and the American databases even though the two cohorts differ in a range of demographic and clinical characteristics, further underlining the validity and the generalizability of the two analytical approaches.

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

BACKGROUND: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions. METHODS: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to  the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model. RESULTS: The deep learning model defined an area under the curve (AUC) of 0.89 (± 0.01), sensitivity = 0.8 and specificity = 0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12 h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI. CONCLUSION: In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12 h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated in the ICU setting to better manage, and potentially prevent, AKI episodes.

Association between brain natriuretic peptide and extracellular water in hemodialysis patients.

BACKGROUND: Brain natriuretic peptide (BNP) is a hormone released by the left ventricle (LV) as a consequence of pressure or volume load. BNP increases in left ventricle hypertrophy (LVH), LV dysfunction, and it can also predict cardiovascular mortality in the general population as well as those undergoing hemodialysis (HD). We investigated the association between BNP and volume load in HD patients. METHODS: We studied 32 HD patients (60 +/- 17.1 years) treated thrice-weekly for at least 6 months. Exclusion criteria were: LV dysfunction, atrial fibrillation, malnutrition. Blood chemistries and BNP were determined on mid-week HD day. Blood pressure (BP) and cardiac diameters were determined on mid-week inter-HD day by using 24-hour ambulatory blood pressure monitoring and echocardiography. Bioimpedance was performed after HD and extracellular water (ECW%), calculated as a percentage of total body water, was considered as the index of volume load. RESULTS: Patients were divided into quartiles of 8 patients depending on the BNP value: 1st qtl BNP < or =45.5 pg/ml (28.4 +/- 10.9 pg/ml), 2nd qtl BNP > 45.5 pg/ml and < or =99.1 pg/ml (60.9 +/- 15.8 pg/ml), 3rd qtl BNP > 99.1 pg/ml and < or =231.8 pg/ml (160.5 +/- 51.8 pg/ml), 4th qtl BNP > 231.8 pg/ml (664.8 +/- 576.6 pg/ml). No inter-quartile differences were reported in age, HD age, body mass index spKt/V, or blood chemistries. As expected patients in the 4th BNP quartile showed the highest values of 24-hour pulse pressure (PP) and LV mass index (LVMi). The study of body composition revealed significant differences in ECW%, which was higher in the 4th quartile when compared to the others (4th q: 50 +/- 9.6%, vs 1st q. 40.1 +/- 2.4%, 2nd q. 41.9 +/- 5%, 3rd q. 42.8 +/- 6.9%). Using multiple stepwise linear regression where BNP was the dependent variable, and PP and ECW% the independent variables, only ECW% maintained statistical significance as a predictor of BNP levels (PP: Beta = 0.86, p = 0.58; ECW%: Beta = 0.64, p < 0.001 p < 0.001). CONCLUSIONS: Few studies have investigated the relationship between plasma BNP and volume load, and direct evidence is lacking. We used bioimpedance and the determination of ECW% to assess volume state in HD patients finding an association between BNP and ECW. The increased synthesis and release of BNP from the LV in HD patients appear to be mainly related to volume stress rather than to pressure load.

Lack of isoprene overproduction during peritoneal dialysis.

OBJECTIVE: Isoprene is the constitutive unit of isoprenoid lipids and sterols. However, it is also a potential toxic and carcinogenic agent. Recent findings of a marked and prolonged isoprene overproduction induced by hemodialysis sessions raises the question of isoprene behavior in patients on peritoneal dialysis. DESIGN: A study with repeated measures per patient and healthy control. SETTING: Nephrology and Dialysis Unit and Perugia University Medical School. PATIENTS: Sixteen consecutive patients on regular continuous ambulatory peritoneal dialysis (CAPD) were evaluated. Endogenous isoprene was analyzed using gas chromatographic assay of breath isoprene, collected at set times before and after dialysis fluid exchange. RESULTS: No significant variations were found in breath isoprene concentrations in the different samples from each patient, and levels were almost stable within the normal range of healthy controls. CONCLUSION: These results show that CAPD, unlike hemodialysis, has little or no effect on isoprene and isoprenoid-related lipid turnover. This lack of increased endogenous isoprene synthesis, in addition to being a distinctive metabolic feature of CAPD, could have important pathophysiological and clinical implications.