Severity of oEsophageal Anastomotic Leak in patients after oesophagectomy: the SEAL score

Background

Anastomotic leak (AL) is a common but severe complication after oesophagectomy. It is unknown how to determine the severity of AL objectively at diagnosis. Determining leak severity may guide treatment decisions and improve future research. This study aimed to identify leak-related prognostic factors for mortality, and to develop a Severity of oEsophageal Anastomotic Leak (SEAL) score.

Methods

This international, retrospective cohort study in 71 centres worldwide included patients with AL after oesophagectomy between 2011 and 2019. The primary endpoint was 90-day mortality. Leak-related prognostic factors were identified after adjusting for confounders and were included in multivariable logistic regression to develop the SEAL score. Four classes of leak severity (mild, moderate, severe, and critical) were defined based on the risk of 90-day mortality, and the score was validated internally.

Results

Some 1509 patients with AL were included and the 90-day mortality rate was 11.7 per cent. Twelve leak-related prognostic factors were included in the SEAL score. The score showed good calibration and discrimination (c-index 0.77, 95 per cent c.i. 0.73 to 0.81). Higher classes of leak severity graded by the SEAL score were associated with a significant increase in duration of ICU stay, healing time, Comprehensive Complication Index score, and Esophagectomy Complications Consensus Group classification.

Conclusion

The SEAL score grades leak severity into four classes by combining 12 leak-related predictors and can be used to the assess severity of AL after oesophagectomy.

Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects

There is no objective way to monitor mechanical loading characteristics during exercise for bone health improvement. We developed accelerometry-based equations to predict ground reaction force (GRF) and loading rate (LR) in normal weight to severely obese subjects. Equations developed had a high and moderate accuracy for GRF and LR prediction, respectively, thereby representing an accessible way to determine mechanical loading characteristics in clinical settings.

INTRODUCTION

There is no way to objectively prescribe and monitor exercise for bone health improvement in obese patients based on mechanical loading characteristics. We aimed to develop accelerometry-based equations to predict peak ground reaction forces (pGRFs) and peak loading rate (pLR) on normal weight to severely obese subjects.

METHODS

Sixty-four subjects (45 females; 84.6 ± 21.7 kg) walked at different speeds (2-6 km·h^-1) on a force plate-equipped treadmill while wearing accelerometers at lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Actual and predicted values at different speeds were compared by repeated measures ANOVA.

RESULTS

Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration transient rate for pLR prediction. All pGRF equation coefficients of determination were above 0.89, a good agreement between actual and predicted pGRFs, with a mean absolute percent error (MAPE) below 6.7%. No significant differences were observed between actual and predicted pGRFs at each walking speed. Accuracy indices from our equations were better than previously developed equations for normal weight subjects, namely a MAPE approximately 3 times smaller. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF.

CONCLUSION

Walking pGRF and pLR in normal weight to severely obese subjects can be predicted with moderate to high accuracy by accelerometry-based equations, representing an easy and accessible way to determine mechanical loading characteristics in clinical settings.