Pressure and oxygen debt on bypass - potential quality markers of perfusion?

The multivariable prognostic models for severe complications after heart valve surgery

Background

To provide multivariable prognostic models for severe complications prediction after heart valve surgery, including low cardiac output syndrome (LCOS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS).

Methods

We developed multivariate logistic regression models to predict severe complications after heart valve surgery using 930 patients collected retrospectively from the first affiliated hospital of Sun Yat-Sen University from January 2014 to December 2015. The validation was conducted using a retrospective dataset of 713 patients from the same hospital from January 2016 to March 2017. We considered two kinds of prognostic models: the PRF models which were built by using the preoperative risk factors only, and the PIRF models which were built by using both of the preoperative and intraoperative risk factors. The least absolute shrinkage selector operator was used for developing the models. We assessed and compared the discriminative abilities for both of the PRF and PIRF models via the receiver operating characteristic (ROC) curve.

Results

Compared with the PRF models, the PIRF modes selected additional intraoperative factors, such as auxiliary cardiopulmonary bypass time and combined tricuspid valve replacement. Area under the ROC curves (AUCs) of PRF models for predicting LCOS, AKI-rH and MODS are 0.565 (0.466, 0.664), 0.688 (0.62, 0.757) and 0.657 (0.563, 0.751), respectively. As a comparison, the AUCs of the PIRF models for predicting LOCS, AKI-rH and MODS are 0.821 (0.747, 0.896), 0.78 (0.717, 0.843) and 0.774 (0.7, 0.847), respectively.

Conclusions

Adding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valve surgery.

Designing a new scoring system (QualyP Score) correlating the management of cardiopulmonary bypass to postoperative outcomes

Aim:

The aim of this study was to ascertain if a score, directly derived from CPB records, could correlate to major postoperative outcomes.

Methods:

An additive score (QualyP Score) was created from 10 parameters: peak lactate value during CPB, peak VCO2i, lowest DO2i/VCO2i, peak respiratory quotient, CPB time, cross-clamp time, lowest CPB temperature, circulatory arrest, ultrafiltration during CPB, number of packed red cells transfused intraoperatively. The PerfSCORE was calculated, as well. Multivariable logistic regression models were built to detect the independent predictors of: peak lactate >3 mmol/L during the first three postoperative days; the incidence of acute kidney injury network (AKIN) 1-2-3; respiratory insufficiency; mortality.

Results:

The mean score was 4.8±2.6 (0-10). A QualyP Score ≥1 was predictive of postoperative acidosis (OR=1.595). A score ≥2 was predictive of AKIN 2 (OR=1.268) and respiratory insufficiency (OR=1.526). A score ≥5 was predictive of AKIN 3 (OR=1.848) and mortality (OR=1.497).

Conclusions:

QualyP Score may help to provide a quality marker of perfusion, emphasizing the need for goal-directed perfusion strategies.