Citations

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.

Background: Patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS) often require vasoactive medications to maintain organ perfusion. However, there are limited data on the real-world use of vasoactive medications in this population. Methods: We used the Vizient® Clinical Data Base to identify patients aged ≥18 years who were admitted between 2015-2019 with AMI-CS and required mechanical ventilation. We report the proportion of patients receiving vasoactive medications by day as well as site-level variability for vasoactive medication strategies using the median odds ratio (MOR), which quantifies the odds that a patient receives a strategy at two randomly selected sites after adjusting for covariates. Results: We identified 10,758 patients from 160 centers admitted with AMI-CS. The median age was 65.8 years, 29.8% were female, 83.6% received vasoactive medications in the first 5 days, and 56.9% received temporary mechanical support. The most used vasoactive medication on day 1 was norepinephrine (48.2%), followed by epinephrine (31.5%), and dopamine (16.9%) ( Figure ). On day 1, 65.9% and 36.2% of patients receiving vasoactive medications were prescribed ≥2 or ≥3 different medications, respectively, including 111 different combinations. Among patients receiving inotropes (N=4,485), there was a nearly 3-fold difference in milrinone use across sites after accounting for differences in patient characteristics (MOR 2.89; 95% confidence interval [CI]: 2.39-3.38). For patients receiving either norepinephrine, dopamine, or epinephrine on day 1 (N=3,224), there was a nearly 2-fold difference in the use of norepinephrine as a first-line vasopressor across sites (MOR 1.84; 95% CI: 1.59-2.06). Conclusion: Norepinephrine is the most commonly used vasoactive medication in patients with AMI-CS. However, there is substantial site-level variability in vasoactive medication strategies, which require further study.

Background: Robust models exist to predict risk-adjusted mortality following congenital heart surgery. It is known that underlying diagnosis modifies predicted risk of operations, and institution-level outcomes vary based on their unique case mix. We therefore evaluated risk-adjusted outcomes for a subset of diagnosis-procedure (D-P) combinations within the Society of Thoracic Surgeons' (STS) congenital heart surgery database (CHSD). Methods: A clinician panel identified the most frequently encountered D-P combinations in CHSD between 07/2017 and 06/2021. Candidate modeling techniques were compared in a random 80% development cohort. Model performance was assessed using internal and cross-validation calibration, discrimination plots and optimism adjusted C-statistics. The final selected model was evaluated in the 20% validation sample. Results: The chosen subset of D-P combinations represented 45,384 of 87,589 (52%) unique episodes of surgical care in CHSD during this time-period. The development cohort (36,350 episodes) had 1.8% mortality (667 deaths). All candidate models had excellent discrimination with C-statistics ranging from 0.862 to 0.876. The model with the best combination of discrimination, calibration, and clinical face validity was a modified version of the current STS mortality risk model. This version included all current risk model variables with the addition of indicator variables for D-P combinations. In the 20% validation sample (9,034 episodes), this model was well calibrated (Figure) and had excellent discrimination (C = 0.879). Conclusion: We describe a cohort of D-P combinations widely encountered in the STS CHSD. Further, we refined the existing STS CHSD risk model for application to these D-P dyads to derive empirical risk-adjusted benchmark outcomes. This approach will benefit quality improvement efforts at an institutional level and may have implications for publicly reporting congenital heart surgery outcomes.

Introduction: Sepsis and its associated systemic inflammatory response syndrome in the heart can lead to septic cardiomyopathy. We aim to explore new onset cardiac dysfunction in septic shock patients and its association of right heart dysfunction with hospital outcomes. Hypothesis: Early development of correct ventricular abnormalities due to septic shock may lead to worse hospital outcomes. Methods: We conducted a retrospective study of 102 patients who were hospitalized at the Mayo Clinic between August 2018 and September 2022 with septic shock and had at least one pre-sepsis echo (within one year before admission) and one post-sepsis echo (within seven days of access). Information on patient demographics, comorbidities, echo parameters, and outcomes was collected from our sepsis database and retrospective chart review. Categorical variables were described as percentages and compared by Chi-square test, and continuous variables as medians and compared using an independent sample t-test. Statistical analysis was performed using BlueSky Statistics v10.3.1. Results: Our study included 102 patients with a median age of 68 (55, 78) years, the majority being men (68%) and Caucasians (80%). Between pre- and post-sepsis echos, there was a noticeable decrease in LVEF (60% vs 50.5 %; p=0.002). Moreover, there was an increase in reduced RV function (31.6% vs. 62.6%, p<0.001), reduced LV function (56.5% vs. 85.2%, p=0.048), and RV dilatation (35.8% vs. 51.5%, p=0.03). There was also a noticeable decrease in IVC collapsibility of more than 50% (65.9% vs 22.9%, p<0.001). There was no significance in terms of demographics, comorbidities, and baseline vitals and labs among patients with RV dysfunction and without RV dysfunction except for a lower LVEF (49% vs 62 %, p= 0.004) and decreased >50 % IVC collapsibility (14.5% vs 48%, p=0.005) in RV dysfunction group. We found no differences in hospital mortality, ICU and hospital LOS, or mechanical ventilation requirements amongst patients with RV abnormalities in function and size. Conclusions: Our retrospective study shows that abnormalities in RV function and RV size are commonly observed in the initial week of septic shock. However, such RV abnormalities did not translate into worse hospital outcomes.

Introduction: Predictive algorithms using multiple clinical variables can identify patients at higher risk for abdominal aortic aneurysms (AAA), however they are cumbersome and cannot used directly by patients. A simple, cheap, noninvasive, and readily available (virtual) screening tool for identification of AAA could aid in screening algorithms. Aims: We sought to determine if a machine learning algorithm could predict the presence of an AAA by using a image of the face Methods: Diagnostic imaging studies were extracted from the electronic health record (EHR) from 5/7/2018 through 1/1/2023 and analyzed by a natural language processing algorithm previously validated to identify abdominal aortic aneurysms. Patient EHR profile pictures were extracted and matched to imaging studies. Various deep neural network (DNN) architectures were explored, all trained as classifiers on the face images along with clinical variables to predict clinical outcomes. All models used were CNNs with an EfficientNet architecture. Model performance was evaluated by standard metrics such as the area under the curve (AUC), specificities (Sp), and sensitivities (Sn). All performance metrics reported results from the test subset of the primary dataset and did not contain any observations considered during training. Results: A total of 5522 patients with facial images and AAA diagnostic studies were analyzed, among whom 1314 (23.8%) had a AAA. The optimal model had an AUC of 0.72 (Sn 0.44, Sp 0.83) for predicting AAA and remained similar in women (AUC 0.7), men (AUC 0.65), age >65 (AUC 0.63), and age <=65 (AUC 0.72) (Table). Conclusions: Using only an image of a patients face, the presence of an AAA could be identified with moderate performance in men, women, and in those less than 65. The model’s performance in demographics not typically screened for AAA may allow for targeted screening programs in these groups.

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.