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| Name | Class |
|---|---|
| surge2surgery | UNKNOWN |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with major elective digestive surgery | Other | The size of the cohort is 300 patients Population: Patients with major elective digestive surgery (eg, colon or colorectal resection, partial or total gastrectomy, pancreaticoduodenectomy, hepatectomy). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Peripheral venous blood samples | Biological | 10 ml in a sodium heparin tube and 5 ml in an EDTA tube |
|
| Measure | Description | Time Frame |
|---|---|---|
| Performance of the preoperative prediction score for infectious complications of the surgical site. | 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. | 30 days |
| Measure | Description | Time Frame |
|---|---|---|
| Performance of the postoperative prediction score for infectious complications of the surgical site. | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Performance of the global model | 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). | 30 days |
Inclusion Criteria:
Patients will be included:
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
Exclusion Criteria:
Patients with the following criteria will not be included:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| La pitiè Salpâtrière Hospital | Paris | France | ||||
| Saint Antoine Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30229870 | Background | Storesund A, Haugen AS, Hjortas M, Nortvedt MW, Flaatten H, Eide GE, Boermeester MA, Sevdalis N, Softeland E. Accuracy of surgical complication rate estimation using ICD-10 codes. Br J Surg. 2019 Feb;106(3):236-244. doi: 10.1002/bjs.10985. Epub 2018 Sep 18. | |
| 21817889 | Background | Hawn MT, Vick CC, Richman J, Holman W, Deierhoi RJ, Graham LA, Henderson WG, Itani KM. Surgical site infection prevention: time to move beyond the surgical care improvement program. Ann Surg. 2011 Sep;254(3):494-9; discussion 499-501. doi: 10.1097/SLA.0b013e31822c6929. |
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| 30 days |
| Performance of the preoperative prediction score for lung infections | 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. | 30 days |
| Performance of the preoperative prediction score for urinary tract infections | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the risk of post-operative septic shock | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for postoperative cardiovascular complications | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the risk of postoperative deep vein thrombosis or pulmonary embolism. | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the risk of post-operative acute renal failure. | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the risk of acute bleeding, hematoma or postoperative anemia | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the risk of postoperative occlusion or ileus. | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the risk of postoperative delirium | 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. | 30 days |
| Performance of the pre- and post-operative prediction score for the overall severity of postoperative complications. | 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. | 30 days |
| Intra-hospital mortality | Assessed from patient medical records | 30 days |
| Length of hospital stay | Assessed from patient medical records | 30 days |
| Cost of stay | 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. | 30 days |
| Score results | 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). | 30 days |
| Paris |
| France |
| Saint Joseph Hospital | Paris | France |
| FOCH Hospital | Suresnes | France |
| 28816846 | Background | Gaudilliere B, Angst MS, Hotchkiss RS. Deep Immune Profiling in Trauma and Sepsis: Flow Is the Way to Go! Crit Care Med. 2017 Sep;45(9):1577-1578. doi: 10.1097/CCM.0000000000002594. No abstract available. |
| 20643302 | Background | Zhu X, Herrera G, Ochoa JB. Immunosupression and infection after major surgery: a nutritional deficiency. Crit Care Clin. 2010 Jul;26(3):491-500, ix. doi: 10.1016/j.ccc.2010.04.004. |
| 25253674 | Background | Gaudilliere B, Fragiadakis GK, Bruggner RV, Nicolau M, Finck R, Tingle M, Silva J, Ganio EA, Yeh CG, Maloney WJ, Huddleston JI, Goodman SB, Davis MM, Bendall SC, Fantl WJ, Angst MS, Nolan GP. Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med. 2014 Sep 24;6(255):255ra131. doi: 10.1126/scitranslmed.3009701. |