Predictive Model and Risk Score for In-Hospital Mortality in Patients with All-Cause Cardiogenic Shock.

Cardiogenic shock (CS) is a condition associated with high morbidity and mortality. Our study aimed to perform a risk score for in-hospital mortality that allows for stratifying the risk of death in patients with CS.This is a retrospective analysis, which included 135 patients from a Spanish university hospital between 2011 and 2020. The Santiago Shock Score (S3) was created using clinical, analytical, and echocardiographic variables obtained at the time of admission.The in-hospital mortality rate was 41.5%, and acute coronary syndrome (ACS) was the responsible cause of shock in 60.7% of patients. Mitral regurgitation grade III-IV, age, ACS etiology, NT-proBNP, blood hemoglobin, and lactate at admission were included in the score. The S3 had good accuracy for predicting in-hospital mortality area under the receiver operating characteristic curve (AUC) 0.85 (95% confidence interval (CI) 0.78-0.90), higher than the AUC of the CardShock score, which was 0.74 (95% CI 0.66-0.83). Predictive power in a cohort of 131 patients with profound CS was similar to that of CardShock with an AUC of 0.601 (95% CI 0.496-0.706) versus an AUC of 0.558 (95% CI 0.453-0.664). Three risk categories were created according to the S3: low (scores 0-6), intermediate (scores 7-10), and high (scores 11-16) risks, with an observed mortality of 12.9%, 49.1%, and 87.5% respectively (P < 0.001).The S3 score had excellent predictive power for in-hospital mortality in patients with nonprofound CS. It could aid the initial risk stratification of patients and thus, guide treatment and clinical decision making in patients with CS.

Fast track triage for COVID-19 based on a population study: The soda score.

BACKGROUND: Healthcare systems are under prominent stress due to the COVID-19 pandemic. A fast and simple triage is mandatory to screen patients who will benefit from early hospitalization, from those that can be managed as outpatients. There is a lack of all-comers scores, and no score has been proposed for western-world population. AIMS: To develop a fast-track risk score valid for every COVID-19 patient at diagnosis. METHODS: Single-center, retrospective study based on all the inhabitants of a healthcare area. Logistic regression was used to identify simple and wide-available risk factors for adverse events (death, intensive care admission, invasive mechanical ventilation, bleeding > BARC3, acute renal injury, respiratory insufficiency, myocardial infarction, acute heart failure, pulmonary emboli, or stroke). RESULTS: Of the total healthcare area population, 447.979 inhabitants, 965 patients (0.22%), were diagnosed with COVID-19. A total of 124 patients (12.85%) experienced adverse events. The novel SODA score (based on sex, peripheral O2 saturation, presence of diabetes, and age) demonstrated good accuracy for adverse events prediction (area under ROC curve 0.858, CI: 0.82-0.98). A cut-off value of ≤2 points identifies patients with low risk (positive predictive value [PPV] for absence of events: 98.9%) and a cut-off of ≥5 points, high-risk patients (PPV 58.8% for adverse events). CONCLUSIONS: This quick and easy score allows fast-track triage at the moment of diagnosis for COVID-19 using four simple variables: age, sex, SpO2, and diabetes. SODA score could improve preventive measures taken at diagnosis in high-risk patients and also relieve resources by identifying very low-risk patients.