Nikolaos Kroustalakis

Background: The relative merits of the Henderson–Hasselbalch (HH) versus Stewart frameworks for interpreting dialysis-associated acid–base shifts remain debated. Dialysis alters systemic pH through exogenous bicarbonate delivery, chloride displacement, and removal of organic anions. We compared these approaches across hemodialysis (HD) and peritoneal dialysis (PD). Methods: We studied 53 HD patients with paired pre/post-HD blood gas and chemistry (106 observations) and 41 PD patients cross-sectionally, totaling 147 datasets. Derived variables followed the Figge/Stewart implementation [apparent SID (SIDa), effective SID (SIDe), strong ion gap (SIG), albumin-corrected anion gap (AGc)]. For HD, changes in pH (ΔpH) were modeled using HH predictors (ΔHCO₃⁻, ΔPCO₂) and Stewart predictors (ΔSIDa, ΔATOT, ΔPCO₂). For cross-sectional data (pre-HD, post-HD, and PD), HH- and Stewart-based level models were fitted. Stewart-predicted pH was also computed using the Figge and the simplified Constable electroneutrality equation. Results: HD increased pH by 0.11, driven by ΔHCO₃⁻ = +5.7 mΕq/L, ΔCl⁻ = −2.3 mEq/L, and declines in unmeasured anions (ΔSIG = −3.9; ΔAGc = −3.3). SIDa increased only marginally (+1.3 mEq/L), whereas SIDe rose by +5.3 mEq/L and fully tracked the alkalinization. In Δ-models, HH explained 90% of variance in ΔpH (R² = 0.903) compared with 51% for Stewart (R² = 0.514). In level models, HH explained 96% of pH variance versus 36% for Stewart. Bland–Altman analysis showed systematic overestimation of pH by the Figge and Constable approach (bias + 0.111), most pronounced pre-HD. PD patients had consistently higher AGc and SIG values than HD patients, indicating a greater burden of unmeasured anions. Conclusions: Alkalinization during HD is primarily attributable to bicarbonate gain, chloride displacement, and organic-anion clearance. The HH framework provides superior predictive performance for ΔpH, while closed-system Stewart formulations based on SIDa underestimate alkalinization. However, a broader physicochemical interpretation using SIDe and SIG, which incorporate bicarbonate and unmeasured anions, coherently describes the observed physiology. Future applications of the Stewart approach in dialysis should emphasize SIDe and SIG to better reflect the open-system physiology of both HD and PD. Our findings suggest that the HH model remains more predictive of alkalinization, while SIDe and SIG refine the physicochemical understanding.

Background The relative merits of the Henderson–Hasselbalch (HH) versus Stewart frameworks for interpreting dialysis-associated acid–base shifts remain debated. Dialysis alters systemic pH through exogenous bicarbonate delivery, chloride displacement, and removal of organic anions. We compared these approaches across hemodialysis (HD) and peritoneal dialysis (PD). Methods We studied 53 HD patients with paired pre/post HD blood gas and chemistry (106 observations) and 41 PD patients cross-sectionally, totaling 147 datasets. Derived variables followed the Figge/Stewart implementation [apparent SID (SIDa), effective SID (SIDe), strong ion gap (SIG), albumin-corrected anion gap (AGc)]. For HD, changes in pH (ΔpH) were modeled using HH predictors (ΔHCO₃⁻, ΔPCO₂) and Stewart predictors (ΔSIDa, ΔATOT, ΔPCO₂). For cross-sectional data (HD-pre, HD-post, and PD), HH- and Stewart-based level-models were fitted. Stewart-predicted pH was also computed using the Figge and the simplified Constable electroneutrality equation. Results HD increased pH by 0.11, driven by ΔHCO₃⁻ = +5.7 mΕq/L, ΔCl⁻ = −2.3 mEq/L, and declines in unmeasured anions (ΔSIG = −3.9; ΔAGc = −3.3). SIDa increased only marginally (+1.3 mEq/L), whereas SIDe rose by +5.3 mEq/L and fully tracked the alkalinization. In Δ-models, HH explained 90% of variance in ΔpH (R² = 0.903) compared with 51% for Stewart (R² = 0.514). In level-models, HH explained 96% of pH variance versus 36% for Stewart. Bland–Altman analysis showed systematic overestimation of pH by the Figge and Constable approach (bias +0.111), most pronounced pre-HD. PD patients had consistently higher AGc and SIG values than HD patients, indicating a greater burden of unmeasured anions. Conclusions Alkalinization during HD is primarily attributable to bicarbonate gain, chloride displacement, and organic-anion clearance. The HH framework provides superior predictive performance for ΔpH, while closed-system Stewart formulations based on SIDa underestimate alkalinization. However, a broader physicochemical interpretation using SIDe and SIG, which incorporate bicarbonate and unmeasured anions, coherently describes the observed physiology. Future applications of the Stewart approach in dialysis should emphasize SIDe and SIG to better reflect the open-system physiology of both HD and PD.

Renal hypomagnesemia syndromes involving CNNM2 protein pathogenic variants are associated with variable degrees of neurocognitive dysfunction and hypomagnesemia. Here, we report a family with a novel CNNM2 p.Pro482Ala variant, presenting with overt hypomagnesemia and mild neurological involvement (autosomal dominant renal hypomagnesemia 6, HOMG6, MIM# 613882). Using a bioinformatics approach, we showed that the p.Pro482Ala amino acid substitution causes a 3D conformational change in CNNM2 structure in the cystathionin beta synthase (CBS) domain and the carboxy-terminal protein segment. A novel finding was that aldosterone inhibition with spironolactone helped to alleviate hypomagnesemia and symptoms in the proband.

Background: Postoperative Acute Kidney Injury (AKI) is a common and serious complication associated with significant morbidity and mortality. While several pre- and intra-operative risk factors for AKI have been recognized in cardiac surgery patients, relatively few data are available regarding the incidence and risk factors for perioperative AKI in other surgical operations. The aim of the present study was to determine the risk factors for perioperative AKI in patients undergoing major abdominal surgery. Methods: This was a prospective, observational study of patients undergoing major abdominal surgery in a tertiary care center. Postoperative AKI was diagnosed according to the Acute Kidney Injury Network criteria within 48 h after surgery. Patients with chronic kidney disease stage IV or V were excluded. Logistic regression analysis was used to evaluate the association between perioperative factors and the risk of developing postoperative AKI. Results: Eleven out of 61 patients developed postoperative AKI. Four intra-operative variables were identified as predictors of AKI: intra-operative blood loss (p = 0.002), transfusion of fresh frozen plasma (p = 0.004) and red blood cells (p = 0.038), as well as high chloride load (p = 0.033, cut-off value > 500 mEq). Multivariate analysis demonstrated an independent association between AKI development and preoperative albuminuria, defined as a urinary Albumin to Creatinine ratio ≥ 30 mg·g⁻¹ (OR = 6.88, 95% CI: 1.43–33.04, p = 0.016) as well as perioperative chloride load > 500 mEq (OR = 6.87, 95% CI: 1.46–32.4, p = 0.015). Conclusion: Preoperative albuminuria, as well as a high intraoperative chloride load, were identified as predictors of postoperative AKI in patients undergoing major abdominal surgery.

INTRODUCTION AND AIMS: Acute kidney injury (AKI) occurs in 1% to 5% of all hospital admissions, and in the perioperative period has serious implications, being consistently associated with high mortality, morbidity and a more complicated hospital course with associated cost implications. Whereas peri-operative risk factors associated with AKI after cardiac surgery have been well described, this is not the case for major non-cardiac surgery. Hyperchloremia is often associated with over-zealous fluid resuscitation with 0.9% saline and has been associated with a decrease in renal blood flow. Recently, the impact of a high chloride load in the pathogenesis of AKI has been suggested. This study was performed in order to identify possible intra-operative risk factors linked to peri-operative AKI development in a group of non-cardiac surgery patients.