The Mayo Cardiac Intensive Care Unit Admission Risk Score is Associated with Medical Resource Utilization During Hospitalization

Clinical complexity of an Italian cardiovascular intensive care unit: the role of mortality and severity risk scores

AIMS

The identification of patients at greater mortality risk of death at admission into an intensive cardiovascular care unit (ICCU) has relevant consequences for clinical decision-making. We described patient characteristics at admission into an ICCU by predicted mortality risk assessed with noncardiac intensive care unit (ICU) and evaluated their performance in predicting patient outcomes.

METHODS

A total of 202 consecutive patients (130 men, 75 ± 12 years) were admitted into our tertiary-care ICCU in a 20-week period. We evaluated, on the first 24 h data, in-hospital mortality risk according to Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score 3 (SAPS 3); Sepsis related Organ Failure Assessment (SOFA) Score and the Mayo Cardiac intensive care unit Admission Risk Score (M-CARS) were also calculated.

RESULTS

Predicted mortality was significantly lower than observed (5% during ICCU and 7% at discharge) for APACHE II and SAPS 3 (17% for both scores). Mortality risk was associated with older age, more frequent comorbidities, severe clinical presentation and complications. The APACHE II, SAPS 3, SOFA and M-CARS had good discriminative ability in distinguishing deaths and survivors with poor calibration of risk scores predicting mortality.

CONCLUSION

In a recent contemporary cohort of patients admitted into the ICCU for a variety of acute and critical cardiovascular conditions, scoring systems used in general ICU had good discrimination for patients' clinical severity and mortality. Available scores preserve powerful discrimination but the overestimation of mortality suggests the importance of specific tailored scores to improve risk assessment of patients admitted into ICCUs.

Variation in risk-adjusted cardiac intensive care unit (CICU) length of stay and the association with in-hospital mortality: An analysis from the Critical Care Cardiology Trials Network (CCCTN) registry

BACKGROUND

Previous studies have suggested that there is wide variability in cardiac intensive care unit (CICU) length of stay (LOS); however, these studies are limited by the absence of detailed risk assessment at the time of admission. Thus, we evaluated inter-hospital differences in CICU LOS, and the association between LOS and in-hospital mortality.

METHODS

Using data from the Critical Care Cardiology Trials Network (CCCTN) registry, we included 22,862 admissions between 2017 and 2022 from 35 primarily tertiary and quaternary CICUs that captured consecutive admissions in annual 2-month snapshots. The primary analysis compared inter-hospital differences in CICU LOS, as well as the association between CICU LOS and all-cause in-hospital mortality using a Fine and Gray competing risk model.

RESULTS

The overall median CICU LOS was 2.2 (1.1-4.8) days, and the median hospital LOS was 5.9 (2.8-12.3) days. Admissions in the longest tertile of LOS tended to be younger with higher rates of pre-existing comorbidities, and had higher Sequential Organ Failure Assessment (SOFA) scores, as well as higher rates of mechanical ventilation, intravenous vasopressor use, mechanical circulatory support, and renal replacement therapy. Unadjusted all-cause in-hospital mortality was 9.3%, 6.7%, and 13.4% in the lowest, intermediate, and highest CICU LOS tertiles. In a competing risk analysis, individual patient CICU LOS was correlated (r2 = 0.31) with a higher risk of 30-day in-hospital mortality. The relationship remained significant in admissions with heart failure, ST-elevation myocardial infarction and non-ST segment elevation myocardial infarction.

CONCLUSIONS

In a large registry of academic CICUs, we observed significant variation in CICU LOS and report that LOS is independently associated with all-cause in-hospital mortality. These findings could potentially be used to improve CICU resource utilization planning and refine risk prognostication in critically ill cardiovascular patients.

Development of a Prognostic Scoring System for Tracheostomized Patients Requiring Prolonged Ventilator Care: A Ten-Year Experience in a University-Affiliated Tertiary Hospital

Background and Objectives: This study aimed to assess the value of a novel prognostic model, based on clinical variables, comorbidities, and demographic characteristics, to predict long-term prognosis in patients who received mechanical ventilation (MV) for over 14 days and who underwent a tracheostomy during the first 14 days of MV. Materials and Methods: Data were obtained from 278 patients (66.2% male; median age: 71 years) who underwent a tracheostomy within the first 14 days of MV from February 2011 to February 2021. Factors predicting 1-year mortality after the initiation of MV were identified by binary logistic regression analysis. The resulting prognostic model, known as the tracheostomy-ProVent score, was computed by assigning points to variables based on their respective ß-coefficients. Results: The overall 1-year mortality rate was 64.7%. Six factors were identified as prognostic indicators: platelet count < 150 × 103/μL, PaO2/FiO2 < 200 mmHg, body mass index (BMI) < 23.0 kg/m2, albumin concentration < 2.8 g/dL on day 14 of MV, chronic cardiovascular diseases, and immunocompromised status at admission. The tracheostomy-ProVent score exhibited acceptable discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.786 (95% confidence interval: 0.733-0.833, p < 0.001) and acceptable calibration (Hosmer-Lemeshow chi-square: 2.753, df: 8, p = 0.949). Based on the maximum Youden index, the cut-off value for predicting mortality was set at ≥2, with a sensitivity of 67.4% and a specificity of 76.3%. Conclusions: The tracheostomy-ProVent score is a good predictive tool for estimating 1-year mortality in tracheostomized patients undergoing MV for >14 days. This comprehensive model integrates clinical variables and comorbidities, enhancing the precision of long-term prognosis in these patients.

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.