Kashani, Kianoush

Background: Little is known about the epidemiology and prognostic significance of acid-base disturbances in the cardiovascular intensive care unit (CICU). This study examines the association of acid-base status at admission with in-hospital mortality among CICU patients. Methods: We conducted a retrospective analysis of adults admitted to the Mayo Clinic CICU from 2007-2018 who had available blood gas data, utilizing laboratory values obtained closest to the time of CICU admission. Arterial pH, serum bicarbonate, base excess, and partial pressure of carbon dioxide (PaCO 2 ) were examined as predictors of in-hospital mortality. Logistic regression models were used to assess associations, with adjustment for demographics, comorbidities, illness severity, and interventions. A composite acidosis score (CAS, range 0–5), incorporating predefined cut-offs for arterial pH, base excess, and anion gap was calculated to quantify acid-base disturbance severity. Results: A total of 3,229 patients were included for analysis. Of all acid-base variables, acidemia (pH<7.35) emerged as the strongest predictor of in-hospital mortality (adjusted odds ratio [aOR] 1.60, 95% confidence interval [CI] 1.29-1.98, p <0.003). Metabolic acidosis (HCO 3 <20 mEq/L) aOR 1.55, 95% CI 1.24-1.95, p <0.001) and respiratory acidosis (PaCO 2 >45 mmHg, aOR 1.44, 95% CI 1.14-1.81, p =0.002) were associated with worse adjusted in-hospital mortality, whereas alkalemia, metabolic alkalosis, and respiratory alkalosis were not (Fig. 1A-C). After adjustment, lower pH and more negative base excess were associated with higher in-hospital mortality (both p <0.001), whereas HCO 3 and PaCO 2 were not ( p =0.053&p=0.051, respectively; Fig. 2). Patients with combined metabolic and respiratory acidosis had the highest in-hospital mortality (56.3%), followed by patients with isolated or compensated metabolic acidosis (Fig.1D). Patients with severe acidosis (CAS>1) had higher mortality (aOR 2.34, 95% CI 1.88–2.90; p<0.001, Fig. 3AB). In-hospital mortality rose with increasing CAS (aOR 1.36 per point higher, 95% CI 1.26–1.47; p<0.001), with consistent effects across admission diagnoses (Fig. 3C). Conclusions: Acidemia, especially in the context of combined metabolic and respiratory acidosis, strongly predicts in-hospital mortality in the CICU. Incorporating combined acid-base disorders into risk stratification scores may facilitate earlier recognition and prevent progression of hemometabolic shock, thereby improving prognosis.

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: ( 1 ) rigorous multicenter validation focused on clinical outcomes, ( 2 ) integration of uncertainty quantification and causal modeling into AI tools, ( 3 ) development of clinician-centered interfaces that minimize cognitive load, and ( 4 ) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.

Abstract Background: Thrombocytopenia commonly occurs in critically ill patients with acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT) and is associated with poor outcomes. While previous studies have examined static platelet counts and relative declines, the prognostic significance of platelet trajectory patterns before and after CRRT initiation remains unclear. This study investigates the association between platelet count trajectories and in-hospital mortality in critically ill adult patients with AKI receiving CRRT. Methods: This study is a single-center retrospective cohort study utilizing electronic health record (EHR) data from critically ill adults (≥18 years) with AKI who received CRRT at the University of Alabama at Birmingham between January 2012 and December 2023. Platelet trajectories were assessed using mixed-effects models incorporating all platelet measurements within seven days before and after CRRT initiation. The slopes of platelet count change were categorized as descending (≤ -10 × 10³/μL/day), stable (-10 to 10), or ascending (≥ 10), creating a total of nine trajectory patterns by combining the three pre-CRRT and three during-CRRT categories. The primary outcome was in-hospital mortality, which was analyzed in relation to these trajectory patterns using logistic regression models adjusted for demographics, comorbidities, Sequential Organ Failure Assessment (SOFA) score, medications, and use of other organ support devices as covariates. Results: The study included 2,226 critically ill adults with AKI on CRRT, encompassing 8,569 patient-days before and 11,699 patient-days after CRRT initiation for platelet slope calculation. The median age was 59 years (IQR 48–69) and the median SOFA score at CRRT initiation was 12 (9–14). The in-hospital mortality rate was 57.8%. Before CRRT initiation, the most common trajectory was descending (46.2%), while after CRRT initiation, the stable pattern was most prevalent (58.3%). All patterns with a descending platelet trajectory during-CRRT were significantly associated with higher in-hospital mortality (aOR 1.92 [95% CI: 1.16–3.21] for ascending-descending; 1.49 [1.07–2.08] for stable-descending; 1.52 [1.04–2.23] for descending-descending). In contrast, all patterns with stable or increasing platelet counts during-CRRT tended to have lower mortality. Conclusions: A descending platelet trajectory after CRRT initiation was independently associated with increased in-hospital mortality.

Background: Sepsis-associated acute kidney injury (AKI) is linked to increased mortality and prolonged hospital stays. The exact relationship between central venous pressure (CVP) and AKI remains unclear. We explored the correlation between CVP and AKI in septic shock patients. Methods: This retrospective study included adult patients with septic shock admitted to Mayo Clinic Rochester between 2006 and 2018. CVP levels were measured at 6, 12, 24, and 48 h after the diagnosis of sepsis, and patients were stratified into two groups based on CVP levels (CVP < 8 or ≥8 mmHg). Results: Of 5600 patients with septic shock, 3128 patients without AKI on admission are included. One-thousand-and-ninety-eight patients (35.1%) developed AKI within a median of 4.4 days. The median CVP levels and frequency of elevated CVP at 6, 12, 24, and 48 h are significantly higher in the AKI group. Elevated CVP (≥8 mmHg) at 6, 12, 24, and 48 h is associated with AKI incidence, even after adjusting for mean arterial pressure (MAP) levels. This association, after multivariable adjustments, only remains significant at 12 h with an odds ratio (OR) of 1.60 (95% CI, 1.26–2.05), p < 0.001 and 48 h with an OR of 1.60 (95% CI, 1.29–1.99), p < 0.001. Conclusions: Our findings indicate that CVP ≥ 8 mmHg is strongly associated with an increased risk of AKI, even after adjusting for MAP at the 12 and 48 h time points. These findings underscore a critical 12 or 48h window for interventions to lower CVP.