Prognostic Utility of Lactate, Standard Base Excess, and Alactic Base Excess in Sepsis: A Retrospective Analysis of Critical Care Biomarkers
kilic, O.; Isevi, M.; Colak, O. Y.; Akman, T. S.
Show abstract
BackgroundLactate is an established prognostic marker in sepsis, but the additional predictive value of standard base excess (SBE) and alactic base excess (aBE) remains unclear. This study aimed to evaluate the prognostic utility of lactate, SBE, and aBE in predicting mortality among patients with sepsis and septic shock. MethodsThis retrospective cohort study included 218 adult patients admitted to the intensive care unit (ICU) with a diagnosis of sepsis or septic shock. Arterial blood gas parameters (lactate, SBE, and calculated aBE), severity scores (APACHE II, SOFA), and clinical outcomes were recorded. Patients were stratified into survivors and non-survivors. Receiver operating characteristic (ROC) curve analysis and multivariate logistic regression were used to assess the prognostic accuracy of the biomarkers. ResultsAmong 218 patients, 128 (58.7%) were non-survivors. Non-survivors had significantly higher lactate levels (median: 2.9 mmol/L vs. 1.2 mmol/L; p < 0.001). Lactate remained an independent predictor of mortality (OR: 1.40, 95% CI: 1.11-1.77; p = 0.005). SBE showed limited prognostic value and lost significance in multivariate analysis. aBE did not differ significantly between groups and was not associated with mortality. ROC analysis showed lactate had the highest area under the curve (AUC: 0.742), while SBE (AUC: 0.421) was a poor predictor. ConclusionsLactate is a superior independent predictor of ICU mortality in sepsis and septic shock. Neither SBE nor aBE provided additional prognostic value. These findings support the continued use of lactate for risk stratification, while highlighting the limited utility of SBE and aBE in this context.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Admission criteria in critically ill COVID-19 patients: a physiology-based approach 97%
- Clustering ICU patients with sepsis based on the patterns of their circulating biomarkers: a secondary analysis of the CAPTAIN prospective multicenter cohort study 96%
- Does the Antisecretory Peptide AF-16 modulate fluid balance and inflammation in experimental peritonitis induced sepsis? 96%
Similar papers in this journal
- COMPARISON OF sPLA2-IIA PERFORMANCE WITH HIGH-SENSITIVE CRP, NEUTROPHIL PERCENTAGE, PCT AND LACTATE TO IDENTIFY BACTERIAL INFECTION: A PROSPECTIVE STUDY 96%
- Calprotectin as a sepsis diagnostic marker in critical care: a retrospective observational study 96%
- Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure 95%
Similar papers in this journal
- Changes in non-linear and time-domain heart rate variability indices between critically ill COVID-19 and all-cause sepsis patients -a retrospective study 93%
- A Transcriptomic Severity Metric that Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients 92%
- Cardiovascular disease and severe hypoxemia associated with higher rates of non-invasive respiratory support failure in COVID-19 91%
Similar papers in this journal
- Evaluating the Association and Predictability of Complex Medication Regimen Scores with Clinical Outcomes Among the Critically Ill 97%
- Validation of SeptiCyte RAPID to discriminate sepsis from non-infectious systemic inflammation 93%
- Screening for Right Ventricular Dysfunction in the Emergency Department Using a Smartphone ECG Analysis Application: An External Validation Study with Acute Pulmonary Embolism Patients 92%
Similar papers in this journal
- Influence of body mass index on SAPS3 prognostic performance in critically ill patients from Brazil 94%
- ChatGPT achieves comparable accuracy to specialist physicians in predicting the efficacy of high-flow oxygen therapy 93%
- Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.