Citations

Abstract Background Greater vasopressor requirements predict higher mortality in patients with cardiogenic shock (CS).(1) The extent to which CS phenotype modifies the relationship between vasopressor dose and mortality risk is unknown. Purpose We investigated the association between in-hospital mortality and vasopressor dosing (expressed as norepinephrine equivalents [NEE]) during the first 24 hours of hospitalization across distinct CS phenotypes. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. Peak combined vasopressor doses during the first 24 hours of hospitalization were recorded as NEE. High dose vasopressor (HDV) use was defined as peak 24 hour NEE >0.3 mcg/kg/min. Unsupervised machine learning was used to assign patients to one of three phenotypes (non-congested, cardiorenal, and haemometabolic), as reported previously by our group and others.(2,3) The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant covariates. Results A total of 1078 patients with CS (median age 69 years, 37% female, 59% with acute myocardial infarction) were admitted to our cardiac intensive care unit (CICU) between 2017 and 2015. The number of patients assigned to each CS phenotype were: haemometabolic (n=325, 30%), cardiorenal (n=314, 29%), non-congested (439, 41%). There was a strong incremental association between in-hospital mortality and NEE (Fig. 1, adjusted OR 1.104 per 0.1 mcg/kg/min, 95% CI 1.134–1.231, P<0.001), and patients receiving HDV (n=234, 22%) had significantly higher in-hospital mortality (57% vs 28%, p<0.001). Among patients who did not receive HDV, mortality was higher in haemometabolic CS (49%) compared to 24% in cardiorenal CS and 21% in non-congested CS (adjusted OR 3.58 relative to non-congested CS, 95% CI 2.46-5.23, p<0.001, Fig. 1). Among patients receiving HDV, hemometabolic CS was also associated with higher mortality (68%) than those with cardiorenal (46.2%) or non-congested CS (39.3%), (adjusted OR 3.26 relative to non-congested CS, 95% CI 1.75-6.18, P<0.001, Fig. 1). Conclusion Integration of CS phenotype with vasopressor requirements may provide incremental prognostic information in CS during the first 24 hours of admission, with haemometabolic CS portending a greater mortality risk even at lower NEE. Patients with HDV (particularly those requiring >=1 mcg/kg/min NEE) had high mortality irrespective of CS phenotype.Figure 1

Abstract Introduction Non-cardiovascular end-organ dysfunction is an important determinant of outcomes in cardiac intensive care unit (CICU) patients. We examined the association between in-hospital mortality with the severity and extent of admission liver function test (LFT) abnormalities in a heterogeneous CICU population. Purpose We hypothesized that a greater severity or extent of LFT abnormalities would be associated with higher in-hospital mortality. Methods We included consecutive unique adult CICU patients with available data for one or more of the admission LFT values of interest. Admission laboratory values were defined as those closest to CICU admission. We used the NIH Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 to categorize the severity of each LFT abnormality from Grade 0 (normal) to Grade 4 (life-threatening). We defined the extent of LFT abnormality as the number of individual LFTs meeting criteria for CTCAE Grade 1 or greater. The primary outcome of interest was all-cause in-hospital mortality. Odds ratio (OR) and 95% confidence interval (CI) values for in-hospital mortality were estimated using logistic regression, before and after multivariable adjustment. Results Of 12,428 unique CICU patients, 5,144 were excluded due to a lack of available data for any admission LFT values. The remaining 7,284 patients comprised the final study population. Among patients with available data for each individual LFT, abnormal values were present in: AST, 2,743/5,733 (47.8%); ALT 1,576/5,512 (28.6%); ALK 611/3,684 (16.6%); and TB 898/5,155 (17.4%). A total of 864 (11.9%) patients died during hospitalization. In-hospital mortality was higher for patients with one or more LFTs meeting criteria for CTCAE Grade 1 or greater (17.9% vs. 6.4%, adjusted OR 1.54 [1.27-1.86], p <0.001). A stepwise increase in in-hospital mortality was observed with increasing CTCAE grade, both overall (adjusted OR 1.25 per each higher CTCAE grade [1.16-1.34], p <0.001); (Figure 1A), and for each individual LFT (Figure 1B). Conclusion Cardiohepatic syndrome is an important predictor of prognosis in CICU patients, as the extent and severity of LFT abnormalities are strongly associated with in-hospital mortality. This is the first large-scale study to examine the association between the magnitude of hepatic biomarker derangement with in-hospital mortality in unselected CICU patients. Patients with markedly elevated LFT values exhibited the highest risk of in-hospital mortality. This analysis advocates for the inclusion of commonly obtained LFTs in future risk-prediction tools for enhanced prognostication.

Purpose of review This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology. Recent findings AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24–48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation. Summary The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.

The objective of this project was to develop a standardized list of renally eliminated and potentially nephrotoxic drugs that will help inform initiatives to improve medication safety. Several available lists of medications from the published literature including original research articles and reviews, and from regulatory agencies, tertiary references, and clinical decision support systems were compiled, consolidated, and compared. Only systemically administered medications were included. Medication combinations were included if at least 1 active ingredient was considered renally dosed or potentially nephrotoxic. The medication list was reviewed for completeness and clinical appropriateness by a multidisciplinary team of individuals with expertise in critical care, nephrology, and pharmacy. An initial list of renally dosed and nephrotoxic drugs was created. After reconciliation and consensus from clinical experts, a standardized list of 681 drugs is proposed. The proposed evidence-based standardized list of renally dosed and potentially nephrotoxic drugs will be useful to harmonize epidemiologic and medication quality improvement studies. In addition, the list can be used for clinical purposes with surveillance in nephrotoxin stewardship programs. We suggest an iterative re-evaluation of the list with emerging literature and new medications on an approximately annual basis.

Acute kidney injury (AKI) is a frequent complication in patients admitted to the intensive care unit (ICU), and it is known as an independent factor for adverse outcomes like increased length of hospital stay, the development of chronic kidney disease (CKD), and increased mortality with the associated high cost to healthcare systems. The use of AKI biomarkers and new tools such as point-of-care ultrasonography (POCUS) to perform a hemodynamic and volume status assessment has made it more feasible to detect or predict kidney damage in a very accurate way, thereby avoiding the progression of AKI. The design and implementation of a nephrology rapid response team (NRRT) should be considered to improve patient outcomes and healthcare costs. In this paper, we provide an overview of the implementation of an NRRT.