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OBJECTIVES: Conduct a systematic review and meta-analysis to assess prevalence and timing of acute kidney injury (AKI) development after acute respiratory distress syndrome (ARDS) and its association with mortality. DATA SOURCES: Ovid MEDLINE(R), Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Ovid PsycINFO database, Scopus, and Web of Science thought April 2023. STUDY SELECTION: Titles and abstracts were screened independently and in duplicate to identify eligible studies. Randomized controlled trials and prospective or retrospective cohort studies reporting the development of AKI following ARDS were included. DATA EXTRACTION: Two reviewers independently extracted data using a pre piloted abstraction form. We used Review Manager 5.4 software (Cochrane Library, Oxford, United Kingdom) and Open Meta software (Brown University, Providence, RI) for statistical analyses. DATA SYNTHESIS: Among the 3646 studies identified and screened, 17 studies comprising 9359 ARDS patients met the eligibility criteria and were included in the meta-analysis. AKI developed in 3287 patients (40%) after the diagnosis of ARDS. The incidence of AKI at least 48 hours after ARDS diagnosis was 20% (95% CI, 0.18–0.21%). The pooled risk ratio (RR) for the hospital (or 30-d) mortality among ARDS patients who developed AKI was 1.93 (95% CI, 1.71–2.18). AKI development after ARDS was identified as an independent risk factor for mortality in ARDS patients, with a pooled odds ratio from multivariable analysis of 3.69 (95% CI, 2.24–6.09). Furthermore, two studies comparing mortality between patients with late vs. early AKI initiation after ARDS revealed higher mortality in late AKI patients with RR of 1.46 (95% CI, 1.19–1.8). However, the certainty of evidence for most outcomes was low to very low. CONCLUSIONS: While our findings highlight a significant association between ARDS and subsequent development of AKI, the low to very low certainty of evidence underscores the need for cautious interpretation. This systematic review identified a significant knowledge gap, necessitating further research to establish a more definitive understanding of this relationship and its clinical implications.

ABSTRACT Purpose: The aim of the study is to evaluate whether serial assessment of shock severity can improve prognostication in intensive care unit (ICU) patients. Materials and Methods: This is a retrospective cohort of 21,461 ICU patient admissions from 2014 to 2018. We assigned the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Stage in each 4-h block during the first 24 h of ICU admission; shock was defined as SCAI Shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: The admission SCAI Shock stages were as follows: A, 39.0%; B, 27.0%; C, 28.9%; D, 2.6%; and E, 2.5%. The SCAI Shock stage subsequently increased in 30.6%, and late-onset shock developed in 30.4%. In-hospital mortality was higher in patients who had shock on admission (11.9%) or late-onset shock (7.3%) versus no shock (4.3%). Persistence of shock predicted higher mortality (adjusted OR = 1.09; 95% CI = 1.06–1.13, for each ICU block with shock). The mean SCAI Shock stage had higher discrimination for in-hospital mortality than the admission or maximum SCAI Shock stage. Dynamic modeling of the SCAI Shock classification improved discrimination for in-hospital mortality (C-statistic = 0.64–0.71). Conclusions: Serial application of the SCAI Shock classification provides improved mortality risk stratification compared with a single assessment on admission, facilitating dynamic prognostication.

Introduction: Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) augmented electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. Hypothesis: We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond the serum potassium measurement. Methods: We included 11 234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who had both a 12 lead ECG and blood potassium (K) level obtained at admission with K>=5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for probability of hyperkalemia (probability >=0.5 defined as positive). Hospital mortality was analyzed using logistic regression and survival to one year was estimated using Kaplan-Meier and Cox analysis. Results: The mean age was 70 years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20%. The mean K value was 4.2 mEq/L, and 13% (n=1460) had hyperkalemia. AI-ECG predicted hyperkalemia in 34% (n=3820). In-hospital mortality was higher in patients with laboratory and/or AI-ECG hyperkalemia, and these patients had lower survival out to 1 year. Mortality was lowest in patients with normal K and negative AI-ECG and highest in patients with both laboratory-confirmed hyperkalemia and AI-ECG predicted hyperkalemia. Patients with isolated laboratory hyperkalemia (false-negative AI-ECG) had higher mortality than those with isolated AI-ECG hyperkalemia (false-positive AI-ECG). Conclusions: AI-ECG probability of hyperkalemia, even with a normal laboratory K, was associated with higher short-term and long-term mortality in CICU patients. The laboratory K value and AI-ECG probability of hyperkalemia are complementary risk factors. The AI algorithm can detect prognostically important ECG abnormalities which may reflect the effects of K levels on the myocardium.

Introduction: A single assessment of the SCAI shock classification robustly predicts mortality, and a repeat assessment may improve prognostication. Hypothesis: We hypothesized that frequent serial assignment of the SCAI shock stage could further improve risk stratification. Methods: Unique AHA Level 1 cardiac intensive care unit (CICU) admissions at a single center from 2015 to 2018 were reviewed retrospectively. Time-stamped electronic health record data were used to assign the SCAI shock stage in each 4-hour block during the first 24 hours of CICU admission. Shock was defined as SCAI shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: Among 2,918 CICU patients, 1,537 (52.7%) met criteria for shock during >=1 block and 266 (9.1%) died in hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; E, 3.3%. Patients with worsening SCAI shock stage after admission (first 4 hours) were at higher risk of mortality ( Figure A ), as were patients who met SCAI criteria for shock (particularly those with shock on admission, Figure B ). The pattern was consistent in patients with acute coronary syndromes, heart failure, or cardiac arrest (who had very high mortality). Each higher admission (aOR 1.36, 95% CI 1.18-1.56, AUC 0.70), maximum (aOR 1.59, 95% CI 1.37-1.85, AUC 0.73) and 24 hour mean (aOR 2.42, 95% CI 1.99-2.95, AUC 0.78) SCAI shock stage were incrementally associated with increasing in-hospital mortality. Discrimination was highest for the mean SCAI shock stage (p <0.05). Each additional 4 hour block meeting SCAI criteria for shock was associated with higher mortality (aOR 1.15, 95% CI 1.07-1.24). Conclusions: Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients. Cumulative time in shock was a strong mortality risk indicator, highlighting the area under the curve of shock severity as an important determinant of outcomes.

Introduction: An elevated shock index (SI) predicts worse outcomes in multiple clinical arenas. We aimed to determine whether the SI can aid in mortality risk stratification in unselected CICU patients. Methods: We included admissions to the Mayo Clinic from 2007 to 2015 and stratified them based on admission SI, defined as heart rate divided by systolic blood pressure. The primary outcome was in-hospital mortality, and predictors of in-hospital mortality were analyzed using multivariable logistic regression. Results: We included 9,939 unique CICU patients with available data for SI. The mean age was 69 years old (37% female). Median HR, SBP, and shock index for our cohort were 79 beats per minute (BPM), 121 mmHg, and 0.65 BPM/mmHg, respectively. Patients were grouped by SI as follows: < 0.6, 3,973 (40%); 0.6-0.99, 4,810 (48%); and ≥ 1.0, 1,156 (12%). After multivariable adjustment, both HR (adjusted OR 1.06 per 10 BPM higher; CI 1.02-1.10; p-value 0.005) and SBP (adjusted OR 0.94 per 10 mmHg higher; CI 0.90-0.97; p-value < 0.001) remained associated with higher in-hospital mortality. As SI increased there was an incremental increase in in-hospital mortality (adjusted OR 1.07 per 0.1 BPM/mmHg higher, CI 1.04-1.10, p-Value < 0.001). A higher SI was associated with increased mortality across all examined admission diagnoses, including acute coronary syndromes, heart failure, and cardiogenic shock. Conclusions: The SI is a simple and universally available bedside marker that can be used at the time of admission to predict in-hospital mortality in CICU patients across a variety of cardiac conditions.

Abstract Background Vasoactive medications and Mechanical Circulatory Support (MCS) are both central to the contemporary management of cardiogenic shock (CS). There are limited data on the interaction between vasoactive medication dosing and MCS in patients with CS. Purpose We investigated the association between in-hospital mortality and vasoactive medication dosing among patients with CS who did and did not receive MCS during hospitalization. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. The peak combined vasoactive medication dose during the first 24 hours of CICU admission was recorded and converted to norepinephrine equivalents (NEE)(references 1 & 2). The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant variables and calculate adjusted odds ratios (OR). Results Of 1078 patients with CS (median age 69.0 years [Interquartile Range 59.1-77.3], 36.8% female, 59% with acute myocardial infarction), 721 (66.9%) received vasoactive medications in the first 24 hours and 510 (47.3%) received MCS (87% intra-aortic balloon pump). Among those receiving vasoactive medications during the first 24 hours, the median NEE was 0.16 (Interquartile Range 0.06-0.40) mcg/kg/min. In hospital mortality was 34.4% overall, and was lower among those who received MCS (26.1% versus 41.9%, p&lt;0.001; adjusted OR 0.71 95% CI 0.50-0.99) and higher among those who received vasoactive medications (38.1% versus 26.9%, p&lt;0.001). A gradient of mortality was observed according to the use of vasoactive medications and MCS (Fig. 1). We found a strong incremental association between in-hospital mortality and NEE (adjusted OR 1.11 per 0.1 mcg/kg/min, 95% CI 1.06–1.16, P&lt;0.001; Fig. 2). Mortality was lower across the spectrum of NEE for patients who received MCS (Fig. 2). There was no statistically significant difference in the slope of the relationship between in-hospital mortality and NEE in patients who did and did not receive MCS (p = 0.86). Conclusion Increasing vasoactive medication requirement during the first 24 hours of CICU admission is incrementally associated with in-hospital mortality in CS, representing a simplified marker of shock severity. Patients who received MCS were at lower risk of mortality across the spectrum of vasopressor requirements, emphasizing the potential synergy between these two complementary approaches to hemodynamic support.Figure 1Figure 2

Background One‐time assessment of the Society for Cardiovascular Angiography and Interventions (SCAI) shock classification robustly predicts mortality in the cardiac intensive care unit (CICU). We sought to determine whether serial SCAI shock classification could improve risk stratification. Methods and Results Unique admissions to a single academic level 1 CICU from 2015 to 2018 were included in this retrospective cohort study. Electronic health record data were used to assign the SCAI shock stage during 4‐hour blocks of the first 24 hours of CICU admission. Shock was defined as hypoperfusion (SCAI shock stage C, D, or E). In‐hospital death was evaluated using logistic regression. Among 2918 unique CICU patients, 1537 (52.7%) met criteria for shock during ≥1 block, and 266 (9.1%) died in the hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; and E, 3.3%. Patients who met SCAI criteria for shock on admission (first 4 hours) and those with worsening SCAI shock stage after admission were at higher risk for in‐hospital death. Each higher admission (adjusted odds ratio, 1.36 [95% CI, 1.18–1.56]; area under the receiver operating characteristic curve, 0.70), maximum (adjusted odds ratio, 1.59 [95% CI, 1.37–1.85]; area under the receiver operating characteristic curve, 0.73) and mean (adjusted odds ratio, 2.42 [95% CI, 1.99–2.95]; area under the receiver operating characteristic curve, 0.78) SCAI shock stage was incrementally associated with a higher in‐hospital mortality rate. Discrimination was highest for the mean SCAI shock stage ( P <0.05). Each additional 4‐hour block meeting SCAI criteria for shock predicted a higher mortality rate (adjusted odds ratio, 1.15 [95% CI, 1.07–1.24]). Conclusions Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients by quantifying the magnitude and duration of shock.

AbstractStudy ObjectiveTo develop and validate a model for predicting acute kidney injury (AKI) after high‐dose methotrexate (HDMTX) exposure.DesignRetrospective analysis.SettingMultisite integrated health system throughout Minnesota and Wisconsin.PatientsAdult patients with lymphoma who received HDMTX as a 4‐h infusion.Measurements and Main ResultsLASSO methodology was used to identify factors available at the outset of therapy that predicted incident AKI within 7 days following HDMTX. The model was then validated in an independent cohort. The incidence of AKI within 7 days following HDMTX was 21.6% (95% confidence interval (CI) 18.4%‐24.8%) in the derivation cohort (435 unique patients who received a total of 1642 doses of HDMTX) and 15.6% (95% CI 5.3%‐24.8%) in the validation cohort (55 unique patients who received a total of 247 doses of HDMTX). Factors significantly associated with AKI after HDMTX in the multivariable model included age ≥ 55 years, male sex, and lower HDMTX dose number. Other factors that were not found to be significantly associated with AKI on multivariable analysis, but were included in the final model, were body surface area, Charlson Comorbidity Index, and estimated glomerular filtration rate. The c‐statistic of the model was 0.72 (95% CI 0.69‐0.75) in the derivation cohort and 0.72 (95% CI 0.60‐0.84) in the validation cohort.ConclusionThis model utilizing identified sociodemographic and clinical factors is predictive of AKI following HDMTX administration in adult patients with lymphoma.