Lactate improves SAPS 3 prognostication

Lactate as a Predictor of 30-Day Mortality in Cardiogenic Shock

Background/Objectives: This study sought to evaluate the efficacy of various lactate measurements within the first 24 h post-intensive care unit (ICU) admission for predicting 30-day mortality in cardiogenic shock patients. It compared initial lactate levels, 24 h levels, peak levels, and 24 h clearance, alongside the Simplified Acute Physiology Score 3 (SAPS3) score, to enhance early treatment decision-making. Methods: A retrospective analysis of 64 patients assessed the prognostic performance of lactate levels and SAPS3 scores using logistic regression and AUROC calculations. Results: Of the baseline parameters, only the SAPS3 score predicted survival independently. The lactate level after 24 h (LL) was the most accurate predictor of mortality, outperforming initial levels, peak levels, and 24 h-clearance, and showing a significant AUROC. LL greater than 3.1 mmol/L accurately predicted mortality with high specificity and moderate sensitivity. Conclusions: Among lactate measurements for predicting 30-day mortality in cardiogenic shock, the 24 h lactate level was the most effective one, suggesting its superiority for early prognostication over initial or peak levels and lactate clearance.

Initial Blood pH, Lactate and Base Deficit Add No Value to Peri-Arrest Factors in Prognostication of Neurological Outcome After Out-of-Hospital Cardiac Arrest

Background: In cardiac arrest survivors, metabolic parameters [pH value, lactate concentration, and base deficit (BD)] are routinely added to peri-arrest factors (including age, sex, bystander cardiopulmonary resuscitation, shockable first rhythm, resuscitation duration, adrenaline dose) to enhance early outcome prediction. However, the additional value of this strategy remains unclear.

Methods: We used our resuscitation database to screen all patients ≥18 years who had suffered in- or out-of-hospital cardiac arrest (IHCA, OHCA) between January 1st, 2005 and May 1st, 2019. Patients with incomplete data, without return of spontaneous circulation or treatment with sodium bicarbonate were excluded. To analyse the added value of metabolic parameters to prognosticate neurological function, we built three models using logistic regression. These models included: (1) Peri-arrest factors only, (2) peri-arrest factors plus metabolic parameters and (3) metabolic parameters only. Receiver operating characteristics curves regarding 30-day good neurological function (Cerebral Performance Category 1-2) were analysed.

Results: A total of 2,317 patients (OHCA: n = 1842) were included. In patients with OHCA, model 1 and 2 had comparable predictive value. Model 3 was inferior compared to model 1. In IHCA patients, model 2 performed best, whereas both metabolic (model 3) and peri-arrest factors (model 1) demonstrated similar power. PH, lactate and BD had interchangeable areas under the curve in both IHCA and OHCA.

Conclusion: Although metabolic parameters may play a role in IHCA, no additional value in the prediction of good neurological outcome could be found in patients with OHCA. This highlights the importance of accurate anamnesis especially in patients with OHCA.

Equine simplified acute physiology score: Personalised medicine for the equine emergency patient

BACKGROUND

Scoring models are useful tools that guide the attending clinician in gauging the severity of disease evolution and in evaluating the efficacy of treatment. There are few tools available with this purpose for the non-human patient, including horses. We aimed (i) to adapt the simplified acute physiology score 3 (SAPS-3) model for the equine species, reaching a margin of accuracy greater than 75% in the calculation of the probability of survival/death and (ii) to build a decision tree that helps the attending veterinarian in assessment of the clinical evolution of the equine patient.

METHODS

From an initial pool of 5568 medical records from University-based Veterinary Hospitals, a final cohort of 1000 was further mined manually for data extraction. A set of 19 variables were evaluated and tested by five machine learning data mining algorithms.

RESULTS

The final scoring model, named EqSAPS for equine simplified acute physiology score, reached 91.83% of correct estimates (post hoc) for probability of death within 24 hours upon hospitalization. The area under receiver operating characteristic curve for outcome 'death' was 0.742, while for 'survival' was 0.652. The final decision tree was able to refine prognosis of patients whose EqSAPS score suggested 'death'.

CONCLUSION

EqSAPS is a useful tool to gauge the severity of the clinical presentation of the equine patient.