The Challenge of Predicting Outcomes After Cardiac Arrest in the Pre-hospital Field

Resumen

Out-of-hospital cardiac arrest has an average global incidence of 55 per 100,000 person-years, with high mortality rates and poor outcomes (1). The survival chances are extremely scarce, with studies indicating that survival to hospital discharge ranges from 1% to 31% globally, and often averages below 10% (2,3). Main reported factors associated with survival comprise bystander intervention, emergency medical services response, and the initial rhythm of arrest (4). These factors, however, vary by demographic, with research highlighting persistent gender gaps in out-of-hospital cardiac arrest outcomes (5). Conventional cardiopulmonary resuscitation (CPR) remains the standard of care, but its effectiveness is limited. While bystander CPR significantly improves survival chances, especially in cases with a shockable rhythm, its rates remain low, a challenge that can be traced back through history (6,7). Extracorporeal life support (ECLS) offers a potential rescue for out-of-hospital cardiac arrests when conventional intervention fails, but only a small percentage of patients are eligible due to the need for rapid field decisions and strict criteria (8,9).

Identifying which patients may benefit most from ECLS remains a key challenge, requiring individualized tools to guide decision-making in cardiac arrest patients (8,10,11). Jeong et al.’s study addresses this by focusing on the metabolic derangements that occur during cardiac arrest. Cardiac arrest leads to metabolic changes such as acidosis, electrolyte imbalances, and hypoxia-related organ damage. These derangements, reflecting the body’s physiological response to impaired circulation and hypoxia, offer important prognostic insights (12–14).  They may help identify patients who could benefit from advanced therapies like ECLS (8,10,11). Jeong et al. aim to develop a reliable survival and neurologic recovery predictor, for out-of-hospital cardiac arrest patients, based on these metabolic markers, using standard blood laboratory tests.

In this study, Jeong et al. conducted a retrospective analysis using data from the Korean Cardiac Arrest Research Consortium, a multicenter registry from 2015 to 2021, with 65 participating hospitals in South Korea. The primary outcome was 30-day mortality or poor neurological function, measured by the Cerebral Performance Category scale. The authors developed a Metabolic Derangement Score based on routine blood tests, including pH, oxygen and carbon dioxide levels, potassium, sodium, lactate, glucose, hemoglobin, and creatinine. A total of 5,100 patients were analyzed. The Metabolic Derangement Score was calculated by counting significant metabolic parameters, with patients scoring 3 or higher benefiting from ECLS, showing a significantly lower risk of death or poor neurologic outcomes. In contrast, those scoring 2 or lower showed no difference, regardless of ECLS. Additionally, the Metabolic Derangement Score demonstrated superior predictive accuracy compared to other validated scoring systems, such as the OHCA, the CAHP and the NULL-PLEASE scores, with an area under the curve of 0.91(15). This positions the Metabolic Derangement Score as a promising tool for assessing post-cardiac arrest patient prognosis and guiding ECLS decisions.

The study has several strengths. The Metabolic Derangement Score is a practical tool that can be applied quickly in the emergency setting using standard laboratory tests, allowing for timely prognostication. The large, multicenter dataset of 5,100 patients used for score development and validation strengthen its credibility. With an excellent predictive accuracy for poor outcomes and an area under the curve of 0.91 in both the training and validation cohorts, the MDS outperformed existing prognostic scores, highlighting its potential for guiding patient prognosis and ECLS decisions. However, due to the retrospective design, the findings may be vulnerable to inherent biases, such as selection bias and missing data. Prospective validation in diverse populations and healthcare systems is needed to confirm its generalizability. Additionally, while the Metabolic Derangement Score relies on objective metabolic parameters, it should complement rather than replace clinical judgment, as metabolic status can change rapidly after return of spontaneous circulation.

Moving ahead, the Metabolic Derangement Score is a promising tool for improving decision-making in out-of-hospital cardiac arrest management, especially guiding patient prognosis for ECLS use. Given that it is based on accessible patient characteristics, it could be widely implemented. Future research should prioritize prospective validation in diverse settings and explore integrating the Metabolic Derangement Score with other tools, such as neuroimaging or clinical assessments, to enhance its predictive accuracy and provide a more comprehensive evaluation of cardiac arrest prognosis.