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

Breen, Thomas; Bennett, Courtney; Diepen, Sean van; Katz, Jason N.; Anavekar, Nandan S.; Murphy, Joseph G.; Bell, Malcolm R.; Barsness, Gregory W.; Jentzer, Jacob C.

doi:10.1016/j.mayocpiqo.2020.12.009

Cited by 5 publications

(6 citation statements)

References 45 publications

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“…One of the scores we have considered, M-CARS, has shown a role in predicting criticalcare resources, suggesting that its implementation could facilitate the triage of critically ill cardiac patients. 23 In our population, a correlation has been found among general ICU mortality or severity scores and only one risk score, to our knowledge, developed and validated specifically for the ICCU in a single academic tertiary-care centre. Performance evaluation of M-CARS in contemporary ICCU populations may confirm the applicability of this model, but more extensive validation is necessary before routine use in contemporary ICCUs.…”

Section: Utility Of Risk Scoresmentioning

confidence: 80%

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

Troisi,

Guida,

Vitulano

et al. 2024

Journal of Cardiovascular Medicine

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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.

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Section: Utility Of Risk Scoresmentioning

confidence: 80%

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

Troisi,

Guida,

Vitulano

et al. 2024

Journal of Cardiovascular Medicine

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“…Clinical outcomes in patients requiring MV can be influenced by factors other than clinical variables. These factors include the assessment of frailty, the perspectives of the patient's family, communications between physicians and families, and the allocation of medical resources [30][31][32]. Furthermore, the presence and sophistication of critical care resources in university hospitals can significantly affect prognoses.…”

Section: Discussionmentioning

confidence: 99%

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

Jang,

Yoo,

Seong

et al. 2024

Medicina

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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.

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“…Variables are collected at the time of CICU admission with a score ranging from 0 to 10. In addition, the M-CARS on CICU admission can predict the likelihood of requiring critical care interventions during the CICU stay (Breen et al, 2021a ). An M-CARS <2 correlates with a patient that typically does not require CICU level of care based on low mortality (<1% in hospital) and infrequent need for critical care resources (Jentzer et al, 2019a ; Breen et al, 2021a ).…”

Section: Existing Cicu Mortality Prediction Modelsmentioning

confidence: 99%

“…In addition, the M-CARS on CICU admission can predict the likelihood of requiring critical care interventions during the CICU stay (Breen et al, 2021a ). An M-CARS <2 correlates with a patient that typically does not require CICU level of care based on low mortality (<1% in hospital) and infrequent need for critical care resources (Jentzer et al, 2019a ; Breen et al, 2021a ). A score >6 is associated with a high in-hospital mortality risk that generally exceeds 50%, with high resource utilization (Jentzer et al, 2019a ; Breen et al, 2021a ).…”

Section: Existing Cicu Mortality Prediction Modelsmentioning

confidence: 99%

“…An M-CARS <2 correlates with a patient that typically does not require CICU level of care based on low mortality (<1% in hospital) and infrequent need for critical care resources (Jentzer et al, 2019a ; Breen et al, 2021a ). A score >6 is associated with a high in-hospital mortality risk that generally exceeds 50%, with high resource utilization (Jentzer et al, 2019a ; Breen et al, 2021a ). Knowing these risks early in the patient's hospital course will allow for additional interventions and collaborations, such as with palliative care.…”

Section: Existing Cicu Mortality Prediction Modelsmentioning

confidence: 99%

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

Rafie

Jentzer

Noseworthy

et al. 2022

Front. Artif. Intell.

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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.

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

Cited by 5 publications

References 45 publications

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

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

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

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

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