Aims: In Intensive Care Units (ICUs), survival predictions essentially based on clinical data, while ElectroCardioGram (ECG) data, which are sys-tematically recorded upon patient admission, are often overlooked. This study aims to demonstrate that features derived from ECG can provide val-uable insights into patient prognosis.
Methods: We included 468 patients admitted ICUs for septic shock, using data from the French and euRopean Outcome reGistry in Intensive Care Units (FROG-ICU) study. In addition to comprehensive clinical information (e.g., medical history), which can be difficult to collect, 12-lead ECG features were collected. Here, only features extracted from the first ECG recorded during the ICU stay were included. To explore the prognostic value of ECG-derived features, patients were grouped into two clusters using the K-means algorithm, first based on ECG features alone, and then on non-ECG clinical variables. Survival analysis with the log-rank test was used to discriminate clusters.
Results: Based on ECG-derived features, a 12% difference (p-value = 0.0028) in survival at ICU discharge and 20% (p-value < 1e-4) one year later were observed between the two clusters. Compared to the high-risk group which have survival probabilities of 67% at ICU discharge and 44% after one year, the low-risk group shows 80% and 62%, respectively. For non-ECG clinical variables, a 12% difference (p-value = 0.0025) in survival at ICU discharge and 26% (p-value < 1e-4) at one year were observed between the two clusters. The high-risk group had survival probabilities of 70% at ICU discharge and 42% one year later, compared to 82% and 68% for the lower-risk group.
Conclusion: With only the first ECG recorded at ICU admission, high-risk patients can be identified as accurately as with clinical variables that are harder to obtain. Thus, the ECG offers great potential as an initial tool for assessing prognosis.