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Background There is limited evidence on the epidemiology and prognostic significance of acid-base disorders 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 with available blood gas data, utilizing values obtained closest to CICU admission. Arterial pH, serum bicarbonate, base excess, and partial pressure of carbon dioxide (PaCO 2 ) were examined as predictors of in-hospital mortality. Multivariable logistic regression was used to assess associations, with adjustment for demographics, comorbidities, illness severity, and interventions. Results Among 3229 patients included for analysis, 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 < .003). Metabolic acidosis (HCO3 < 20 mEq/L, aOR 1.55, 95% CI 1.24-1.95, P < .001) and respiratory acidosis (PaCO2 > 45 mm Hg, aOR 1.44, 95% CI 1.14-1.81, P = .002) were associated with higher in-hospital mortality, whereas metabolic and respiratory alkalosis were not. After adjustment, lower pH and more negative base excess were associated with higher in-hospital mortality (both P < .001), whereas HCO3 and PaCO2 were not ( P = .053 and P = .051, respectively). Patients with combined metabolic and respiratory acidosis had the highest in-hospital mortality (56.3%). Conclusions Short-term survival in CICU patients decreases progressively with worse acidemia, especially in the context of combined metabolic and respiratory acidosis. Incorporating metabolic acid-base disorders as key therapeutic targets in randomized cardiogenic shock trials may improve outcomes in this complex population by addressing hemometabolic shock.

Purpose of review The diuretic agents are commonly used in ICUs for various purposes. Despite the widespread utilization of diuretics, there remains substantial ambiguity regarding their indications, ICU-related changes in their pharmacodynamics and pharmacokinetics, dosing, form, and timing of administration, the differences in their utilities based on each organ failure, and their monitoring and safety issues. Recent findings In the recent past, there have been several clinical trials and large registries or systematic reviews with a focus on the use of diuretic agents in ICUs. Summary In this review article, we outline the essential changes in drug behavior during critical illnesses and organ failures, describe the indications of their use in ICUs (management of fluid overload, electrolyte imbalances, or diagnostic or prognostic tests), assess the impact of different organ failures on the utility and effectiveness of diuretics, review major recent clinical trials related to diuretic comparison, use of multiple classes of diuretics, and monitoring and safety of their use. We also provide some information regarding the safety and adverse effects of diuretic use in the ICU and outline the importance of individualizing their use during critical illnesses.

Background: Paroxysmal atrial fibrillation (AF) is a common arrhythmogenic complication in the intensive care unit (ICU) associated with significant costs and morbidity. AF detection models in the literature have utilized entropic analyses of telemetry to diagnose AF, but they have not been utilized to forecast its onset. Hypothesis: Deep learning techniques applied to high-frequency telemetry data can be utilized to forecast AF with clinically significant lead time. Methods: Telemetry data were collected from 72 ICU monitored beds in a tertiary care center from 4/2018-12/2023. Coefficient of sample entropy (COSEn), a metric of heart rate variability complexity, was calculated on successive 1-minute windows and utilized to identify areas of AF initiation as well as control windows without AF. ECG lead II (256Hz) and beat-to-beat (B2B) measurements were analyzed by the MOMENT time-series foundational model to generate representation embeddings. A binary classification multilayer perceptron (MLP) was trained on these embeddings to forecast AF onset 5m in advance. All results were reported on separate test sets. Group-based trajectory modelling (GBTM) was performed on risk score trajectories from the 20m before AF onset or absence. Results: Over 230,000 hours of B2B data from 2263 patients was analyzed using COSEn to identify 287 transitions to AF from 136 patients. The same number of AF-absent windows were randomly selected. COSEn alone as a forecaster produced an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI: 0.61-0.69). The MOMENT + MLP model (MMM) achieved an AUC of 0.81 (0.78-0.83) (Fig. 1). COSEn and MMM were subsequently applied to the two hours preceding AF onset or absence. For COSEn, the mean risk trajectory dipped in the baseline period whereas the MMM risk trajectory sustained a high level through the baseline, highlighting its capacity to forecast AF (Fig. 2). For AF patients, GBMT yielded two types of trajectories: one with elevated risk throughout, and another with a rising trend 5 minutes prior to AF onset (Fig. 3). Conclusions: Deep learning techniques can forecast the onset of AF with up to 5 minutes of lead-time and GBTM allowed for the identification of different pre-AF risk trajectory phenotypes.The ability to forecast AF onset has potential to clinically prevent poor outcomes in the ICU, though further refinements to augment the amount of lead time are warranted.

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.