Colon or Colorectal Resection, Hepatectomy, Pancreaticoduodenectomy, Partial or Total Gastrectomy
Conditions
Brief summary
More than 8 millions surgical interventions are carried out each year in France. Postoperative complications, in particular infectious, can occur in 10 to 60% of cases and are the cause of postoperative revision in 30% of cases, an increase in mortality, length of stay, readmissions and lead to significant additional socio-economic costs. Currently, improvements in surgical practices have not reduced the incidence of surgical site complications. In this context, the development of predictive scores for the risk of post-operative complication becomes urgent in order to implement new interventions (pre-habilitation) or to modify surgical decisions (timing, approach) in order to reduce the risk of complications before surgery. Several recent studies highlights the importance of the immune response in postoperative prognosis. In particular, an imbalance between the adaptive and innate response involving MDSCs has been demonstrated in patients with postoperative complications.Thanks to new techniques for analyzing the immune system, in-depth analysis of the immune system before surgery is a very promising approach aimed at identifying predictive biomarkers of postoperative prognosis. Our team has developed and patented a multivariate model integrating mass cytometry data, proteomics and clinical data collected before surgery to accurately predict the occurrence of a surgical site complication (AUC = 0.94, p\<10e-7) in a monocentric cohort of 43 patients to major abdominal surgery (Stanford University). The objective of the present study is to generalize and validate this preoperative predictive score of infectious complications of the surgical site in the 30 days following major digestive surgery on a larger workforce within a multicenter cohort and to validate this score at using a machine learning method.
Detailed description
Research hypothesis and expected impact: Postoperative complications are frequent and associated with excess mortality and increased costs for the health system. But, it is possible to avoid a significant number of these complications through prehabilitation programs, in particular to prepare patients at risk, and to reduce these postoperative events by 30%. However, it is currently not possible to predict, before surgery, which patients are at risk of developing a complication. Current predictive clinical scores such as the one developed by the American College of Surgeons are unsatisfactory (AUC = 68%). This study will be a reference study to define the groups of patients at risk of complications in order to develop, in a second step, personalized patient pathways in order to optimize their health before surgery and thus improve post-operative results.
Interventions
BIOLOGICALPeripheral venous blood samples
10 ml in a sodium heparin tube and 5 ml in an EDTA tube
Sponsors
surge2surgery
Hopital Foch
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 99 Years
Healthy volunteers
No
Inclusion criteria
Patients will be included: * Aged 18 and over * Having undergone elective major digestive surgery: Major surgery defined according to the recent recommendations of the European Surgical Association - PMID: 32172309 by a rate of infectious or cognitive complications between 20 and 30% according to the ACS risk calculator * Having expressed their non-opposition to participate in the study * Being affiliated to a French health insurance
Exclusion criteria
Patients with the following criteria will not be included: * Aged under 18 * Having an ASA 4 or more, in palliative care * Having an expected duration of hospitalization \< 24 hours * Not speaking French, illiterate patient * Having expressed their opposition to participate in the study * Current pregnancy or breastfeeding * Absence of affiliation to social security plan * Being deprived of liberty or under guardianship
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of the preoperative prediction score for infectious complications of the surgical site. | 30 days | Defined as superficial or deep surgical site infection and organ as defined by CDC 2021. The performance of the score will be evaluate based on the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Performance of the postoperative prediction score for infectious complications of the surgical site. | 30 days | Defined as superficial or deep surgical site infection and organ as defined by CDC 2021. The performance of the score will be evaluate based on the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the preoperative prediction score for lung infections | 30 days | Defined by the prescription of antibiotics with one or more of the following elements: new or altered sputum, new or altered lung opacities on chest X-ray, fever \> 38°C, leukocytes \>12 × 109 /L. The performance of the score will be evaluate based on the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the preoperative prediction score for urinary tract infections | 30 days | As defined by CDC 2021. The performance of the score will be evaluate based on the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for the risk of post-operative septic shock | 30 days | Defined according to Sepsis-3 criteria: Sepsis-related Organ Failure Assessment score ≥ 2, persistent hypotension requiring vasopressors to maintain mean arterial pressure ≥ 65 mmHg and serum lactate \> 2 mmol/L despite adequate volume resuscitation. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for postoperative cardiovascular complications | 30 days | Defined as arrhythmia, cardiac arrest, acute coronary syndrome and acute heart failure. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for the risk of postoperative deep vein thrombosis or pulmonary embolism. | 30 days | Confirmed by imaging (angioscanner for pulmonary embolism and echo-doppler for deep vein thrombosis). The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for the risk of post-operative acute renal failure. | 30 days | Defined by an increase of creatinine \> 1.5 times of the baseline value or diuresis \< 0.5 ml/kg/h for 6 to 12 hours. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for the risk of postoperative occlusion or ileus. | 30 days | Defined as failure to resume transit within 72 hours of surgery. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for the risk of postoperative delirium | 30 days | Defined as disturbed attention and disturbed consciousness, with cognitive impairment not explained by a pre-existing neurological pathology. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Performance of the pre- and post-operative prediction score for the overall severity of postoperative complications. | 30 days | Based on the Comprehensive Complication Index (CCI), with a severity threshold at CCI ≥ 20. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
| Intra-hospital mortality | 30 days | Assessed from patient medical records |
| Length of hospital stay | 30 days | Assessed from patient medical records |
| Cost of stay | 30 days | From Groupe Homogène de Séjours ( GHS) collected in the medical information departments (DIM) based on the Programme de médicalisation des systèmes d'information (PMSI) of each establishment. |
| Score results | 30 days | The score is calculated using a machine learning method integrating immune, plasma protein and clinical data. The aim is to validate and generalize the score result (AUC = 0,94, p\<10e-7) of a multivariate model already developed in a monocentric cohort of 43 patients undergoing major abdominal surgery (Stanford University). |
| Performance of the pre- and post-operative prediction score for the risk of acute bleeding, hematoma or postoperative anemia | 30 days | Requiring surgical intervention or blood transfusion. The performance of the score will be evaluate based on the evaluation of the F1 score criterion and the AUROC. F1: score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score. AUROC: Score ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Performance of the global model | 30 days | An intermediate analysis will be performed after 165 inclusions (55% of the total cohort). The cohort will be randomized into training and validation cohorts. We will use a cross-validation training scheme on 135 patients. The trained algorithm will use logistic regression techniques including L1 and L2 regularization (Lasso and Elastic Net). |
Countries
France
Outcome results
None listed