Lactate clearance in infants undergoing surgery for congenital heart disease

Relationship between the postoperative lactate dynamic levels, the acute gastrointestinal injury and the prognosis among patients who undergo surgical treatment for acute type A aortic dissection

Introduction

This study aimed to determine the relationship between the postoperative lactate dynamic levels, the postoperative acute gastrointestinal injury (AGI), and the prognosis among the patients who underwent surgical treatment for an acute Stanford type-A aortic dissection (aTAAD).

Methods

A total of 271 aTAAD patients were recruited and monitored. Of the 271 aTAAD patients, 29.2% developed an AGI and were designated as the AGI group (n = 79); the other patients (n = 192) were designated as the non-AGI group. According to the 2-year follow up, the aTAAD patients were also divided into the alive and death subgroups for further analysis.

Results

Binary logistic regression analysis revealed that the postoperative 4-h lactate (P4L) level, time-to-return to the normal blood lactate level (TRNL), postoperative 16-h lactate (P16L) level, and neutrophil granulocyte (NEU) count had a good predictive value for an AGI after aTAAD. The 8-week and 2-year mortality rates were higher in the AGI group than the non-AGI group (P < 0.05). Basic data and clinical characteristics were significantly different between the alive and death groups (P < 0.05). A higher AGI rate and mortality occurred in the P4L level ≥10.15 mmol/L subgroup, TRNL ≥21-h subgroup, P16L level ≥2.95 mmol/L subgroup, NEU count ≥10.9 × 10⁹/L subgroup, PaO₂ < 77.7 mmHg subgroup, WBC count ≥9.58 × 10⁹/L subgroup, and the operative time ≥427 min subgroup than the corresponding comparison subgroups (P < 0.05). The postoperative 0-h lactate (P0L) level, TRNL, postoperative 24-h lactate (P24L) level, D-dimer level, fibrinogen degradation products (FDP) level, duration of mechanical ventilation, and length of hospitalization were independent factors influencing the 30-day mortality rate in patients who underwent surgery for an aTAAD (P < 0.05). Cox regression multivariate analysis after univariate analysis of all-cause mortality showed the TRNL, postoperative 12-h lactate (P12L) level, P16L level, P24L level, D-dimer level, FDP level, and length of hospitalization were independently associated with the 2-year mortality rate in patients who underwent surgery for an aTAAD (P < 0.05).

Conclusion

The postoperative lactate changes and TRNL effectively predicted postoperative AGI and the mortality rate in patients with who underwent surgery for an aTAAD. The TRNL and P24L level were independent risk factors for the 30-day and 2-year mortality rates in patients who underwent surgery for an aTAAD.

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

BACKGROUND

Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics.

METHODS

Forty-eight post-operative children, median age 4 months (2.9-11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33-197), mean lactate level at admission of 22.3 mg/dL (range 6.3-71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance.

RESULTS

The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw.

CONCLUSIONS

The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.

Predictors of Increased Lactate in Neonatal Cardiac Surgery: The Impact of Cardiopulmonary Bypass

OBJECTIVE

Hyperlactatemia develops intraoperatively during cardiac surgery and is associated with postoperative mortality. This study aimed to determine the factors that lead to an increase in lactate during cardiopulmonary bypass (CPB) in neonates undergoing cardiac surgery.

DESIGN

Retrospective study from July 2015 to December 2018.

SETTING

Academic tertiary children's hospital.

PARTICIPANTS

The study comprised 376 neonates.

INTERVENTIONS

No interventions were performed.

MEASUREMENTS AND MAIN RESULTS

Lactate measurements at prebypass, upon initiation of CPB and before coming off CPB, last in the operating room, and first in the cardiac intensive care unit were collected. The changes in lactate levels were compared using the nonparametric Wilcoxon signed rank test for paired data. Univariate and multivariate median regression models of the change during CPB were determined. The cohort characteristics were male (60%), median age 5 days (range 1-30), and weight 3.2 kg (range 1.5-4.7). Most patients had a STAT score of 4 (45%) or 5 (23%). Significant increases in lactate were observed from pre-CPB to start of CPB (p < 0.001) and from start to end of CPB (p < 0.001). In the multivariate regression analysis, duration of circulatory arrest (coefficient = 1.216; 95% confidence interval [CI] 0.754-1.678; p < 0.001), duration of mean arterial pressure < 25 mmHg (coefficient = 0.423; 95% CI 0.196-to- 0.651; p < 0.001), and duration of mean arterial pressure between 35 and 39 mmHg (coefficient = -0.246; 95% CI -0.397 to -0.095; p = 0.001) were identified as significant independent predictors of the lactate change per 30- minutes duration.

CONCLUSION

These results emphasized the importance of blood pressure management during CPB and the importance of the duration of circulatory arrest.

Association Between Lactates, Blood Glucose, and Systemic Oxygen Delivery in Children After Cardiopulmonary Bypass

Objective: Lactate is often used as a surrogate marker of inappropriate oxygen delivery. It has been shown that hyperlactatemia is associated with worse clinical outcome in children after cardiac surgery. The purpose of this study is to evaluate the association of hyperlactatemia, low systemic oxygen delivery, and hyperglycemia, in children admitted to the pediatric critical care unit after cardiopulmonary bypass. Design: Secondary analysis of an observational cohort study. Setting: Tertiary pediatric critical care unit (PICU). Patients: Ninety-three patients, aged 6 months to 16 years, undergoing cardiac surgery with cardiopulmonary bypass. Interventions: None. Measurements and Main Results: Metabolic tests (blood glucose, lactate, lactate/pyruvate ratio, and ketones) and oxygen extraction (SaO₂-SvO₂) were performed before anesthesia, at the end of cardiopulmonary bypass, at PICU admission, and at 4 and 12 h after PICU admission. Four hours after PICU admission, 62% of the patients had hyperlactatemia (>2 mmol/L), of whom 55% had normal oxygen extraction (SaO₂-SvO₂ < 30%). There was no correlation between lactate and oxygen extraction (R = -0.09, p = 0.41) but there was a moderate correlation between lactate and blood glucose (R = 0.55, p < 0.001). Using a logistic regression model, hyperlactatemia at 4 h after PICU admission was independently associated with hyperglycemia (p = 0.007) and lactate/pyruvate ratio (p = 0.007) at the same timepoint, as well as with lactate at PICU admission (p = 0.002), but not with weight (p = 0.45), severity of the cardiac lesion (p = 0.85), duration of bypass (p = 0.16), or oxygen extraction, as evaluated by SaO₂-SvO₂ (p = 0.54). At 12 h after PICU admission, there was a very week correlation between lactate and blood glucose (R = 0.27, p = 0.007), but none between lactate and oxygen extraction (R = 0.13, p = 0.20). Conclusion: In children after cardiopulmonary bypass, lactates are not correlated with higher oxygen extraction, but are correlated with hyperglycemia, at both 4 and 12 h after PICU admission. Future research is warranted to better define this relationship.