Clinical trial NCT04041076: Risk Calculators Validation for Elective Major General Surgery
Official title: Validation of Postoperative Mortality Prediction for Elective Major Surgical Operation With Existing Risk Calculators Based on Preoperative Parameters
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Surgery
Death
Keywords: preoperative parameters
risk prediction
postoperative mortality
elective major general surgery
Lead sponsor: Chinese University of Hong Kong
Sponsor class: OTHER
Collaborators: name: Tuen Mun Hospital
First posted: 2019-08-01
Enrollment: 5000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who received scheduled major surgical operation in Tuen Mun Hospital during from 01July 2012- 30June 2018

Brief summary: Nowadays, over 300 million surgical operations take place every year worldwide, which increase at a rate of 33.6% comparing data from 2005 to 2013. According to Surgical Outcomes Monitoring and Improvement Program (SOMIP) reports, which is an Hospital Authority-wide (HA-wide) audit on postoperative outcomes, a growth in major and ultra-major operations performed in our locality is also observed between 2008 and 2016, which leads to an increasing demand of high dependency and intensive care in the postoperative period. With the advancement in surgical technology, increasing surgical complexity and aging population have raised concerns towards perioperative costs and postoperative complications. Therefore, there is a need of an objective tool for risk stratification, which would be useful to guide clinical decision in terms of the magnitude of operation, level of intraoperative monitoring and postoperative placement plan.

Various risk scoring systems have been developed nowadays and each has its own limitations. As nowadays, the calculated risk score is commonly used in shared decision making process with patient and among the perioperative team. Risk calculation solely based on preoperative parameters will be more practical for daily clinical use. Therefore, in this study, the investigators would like to validate the postoperative mortality prediction with the risk calculators that are established merely using preoperative variables. Hopefully this would guide the future risk stratification in patients undergoing elective major surgical operation.

Detailed description: Nowadays, over 300 million surgical operations take place every year worldwide, which increase at a rate of 33.6% comparing data from 2005 to 2013. According to Surgical Outcomes Monitoring and Improvement Program (SOMIP) reports, which is an Hospital Authority-wide (HA-wide) audit on postoperative outcomes, a growth in major and ultra-major operations performed in our locality is also observed between 2008 and 2016, which leads to an increasing demand of high dependency and intensive care in the postoperative period. With the advancement in surgical technology, increasing surgical complexity and aging population have raised concerns towards perioperative costs and postoperative complications. An international prospective cohort study revealed that globally 1 in 6 patients experienced a complication before hospital discharge and 1 in 35 patients who experienced a complication subsequently died without leaving the hospital. Therefore, there is a need of an objective tool for risk stratification, which would be useful to guide clinical decision in terms of the magnitude of operation, level of intraoperative monitoring and postoperative placement plan.

There are a variety of risk stratification tools available for use in major non-cardiac surgery. Among all, the American Society of Anaesthesiology Physical Status (ASA-PS) evaluation scale is the most commonly used risk evaluation system in the assessment of patients' physical status in the preoperative period. Although ASA-PS is well-validated in previous studies and simple to use, inter-rater reliability and the lack of consideration in the surgical perspective have raised concerns towards the development of risk prediction models to supplement clinical judgements and strengthen operative mortality estimation. In 2013, a qualitative systematic review found that Portsmouth Variation of the Physiological and Operative Score for the enUmeration of Mortality and Morbidity (P-POSSUM) and Surgical Risk Scale (SRS) to be the most reliable multivariate risk scoring systems,, but both were noted to have limitations. P-POSSUM has overcome the issues of risk overestimation and inadequate generalization across various surgical specialties by POSSUM. But the calculation requires 12 physiological and 6 operative variables, some of which requires subjective interpretation e.g. chest X-ray. These makes P-POSSUM labour-intensive for clinical use. Whereas SRS requires fewer data for risk calculation, it has only been validated in a single centre study.

In recent years, newer risk prediction models like the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) model and Preoperative Score to Predict Postoperative Mortality (POSPOM) have been developed to provide a more comprehensive perioperative risk prediction for patients undergoing major operation. ACS-NSQIP model is developed based on high-quality clinical data from ACS-NSQIP and is described as a universal risk calculator, which includes a Surgeon Adjustment Score (SAS) that allows further score modification according to surgical performance. However, owing to the high dependence on preoperative laboratory results, ACS-NSQIP often encounters problems where these parameters are not readily available in emergency situations. POSSOM model involves 17 predictor variables. Together with its excellent discrimination and calibration properties demonstrated in its validation cohort and the easily referable rating system, POSSOM is considered a robust tool for 1-year postoperative mortality prediction. However, further reviews on its external validation are yet available.

In 2014, a new risk stratification tool, Surgical Outcome Risk Tool (SORT) was developed in the UK to predict 30-day mortality after non-cardiac surgery in adults, based on post hoc analysis of data in the Knowing the Risk study from the observational National Confidential Enquiry into Patient Outcome and Death (NCEOPD). SORT is a multivariate risk scoring system, which includes 6 variables: 1) American Society of Anesthesiologists Physical Status (ASA-PS) grade, 2) urgency of surgery, 3) surgical specialty, 4) surgical magnitude, 5) cancer or non-cancer surgery and 6) age.

In 2018, the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator has been developed based on Singapore local data, which makes use of 9 preoperative parameters namely: 1) age, 2) gender, 3) ASA classification, 4) surgical risk group, 5) emergency surgery, 6) anaemia status, 7) red cell distribution width (RDW), 8) ischaemic heart disease, , 9) congestive heart failure for prediction of postsurgical mortality and need for intensive care unit admission.

When the investigators look into each of these existing risk stratification tools, each of the risk calculators possesses its drawbacks when coming into clinical applications. As nowadays, the calculated risk score is commonly used in shared decision making process with patient and among the perioperative team. Risk calculation solely based on preoperative parameters will be more practical for daily clinical use. Therefore, in this study, the investigators would like to validate the postoperative mortality prediction with the risk calculators that are established merely using preoperative variables. Hopefully this would guide the future risk stratification in patients undergoing elective major surgical operation.

Eligibility criteria: Inclusion Criteria:

1. patient aged 18 years or over
2. undergoing elective major surgical operation\*
3. requiring a planned overnight admission

Exclusion Criteria:

* Patients undergoing day case surgery, obstetric procedures, neurosurgery, cardiac or transplant surgery were excluded.
* Patient were also excluded if any of the key variables were missing

Primary outcomes:
measure: 30-day mortality rate
timeFrame: 30 days postoperatively
Secondary outcomes:
measure: 1-year mortality rate
timeFrame: 1 year postoperatively
Other outcomes:
Locations:
city: Hong Kong
country: Hong Kong
facility: Department of Anaesthesia and Intensive Care, New Territories West Cluster, Hospital Authority
Study officials:
affiliation: Department of Anaesthesia and Intensive Care, CUHK
name: Matthew TV Chan, MBBS
role: PRINCIPAL_INVESTIGATOR
References:
citation: Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM, Uribe-Leitz T, Fu R, Azad T, Chao TE, Berry WR, Gawande AA. Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes. Lancet. 2015 Apr 27;385 Suppl 2:S11. doi: 10.1016/S0140-6736(15)60806-6. Epub 2015 Apr 26.
citation: Weiser TG, Regenbogen SE, Thompson KD, Haynes AB, Lipsitz SR, Berry WR, Gawande AA. An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet. 2008 Jul 12;372(9633):139-144. doi: 10.1016/S0140-6736(08)60878-8. Epub 2008 Jun 24.
citation: Davenport DL, Henderson WG, Khuri SF, Mentzer RM Jr. Preoperative risk factors and surgical complexity are more predictive of costs than postoperative complications: a case study using the National Surgical Quality Improvement Program (NSQIP) database. Ann Surg. 2005 Oct;242(4):463-8; discussion 468-71. doi: 10.1097/01.sla.0000183348.15117.ab.
citation: Makary MA, Segev DL, Pronovost PJ, Syin D, Bandeen-Roche K, Patel P, Takenaga R, Devgan L, Holzmueller CG, Tian J, Fried LP. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010 Jun;210(6):901-8. doi: 10.1016/j.jamcollsurg.2010.01.028. Epub 2010 Apr 28.
citation: Devereaux PJ, Sessler DI. Cardiac Complications in Patients Undergoing Major Noncardiac Surgery. N Engl J Med. 2015 Dec 3;373(23):2258-69. doi: 10.1056/NEJMra1502824. No abstract available.
citation: Kristensen SD, Knuuti J, Saraste A, Anker S, Botker HE, Hert SD, Ford I, Gonzalez-Juanatey JR, Gorenek B, Heyndrickx GR, Hoeft A, Huber K, Iung B, Kjeldsen KP, Longrois D, Luscher TF, Pierard L, Pocock S, Price S, Roffi M, Sirnes PA, Sousa-Uva M, Voudris V, Funck-Brentano C; Authors/Task Force Members. 2014 ESC/ESA Guidelines on non-cardiac surgery: cardiovascular assessment and management: The Joint Task Force on non-cardiac surgery: cardiovascular assessment and management of the European Society of Cardiology (ESC) and the European Society of Anaesthesiology (ESA). Eur Heart J. 2014 Sep 14;35(35):2383-431. doi: 10.1093/eurheartj/ehu282. Epub 2014 Aug 1. No abstract available.
citation: International Surgical Outcomes Study group. Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries. Br J Anaesth. 2016 Oct 31;117(5):601-609. doi: 10.1093/bja/aew316.
citation: Sobol JB, Wunsch H. Triage of high-risk surgical patients for intensive care. Crit Care. 2011;15(2):217. doi: 10.1186/cc9999. Epub 2011 Mar 22. No abstract available.
citation: Moonesinghe SR, Mythen MG, Das P, Rowan KM, Grocott MP. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review. Anesthesiology. 2013 Oct;119(4):959-81. doi: 10.1097/ALN.0b013e3182a4e94d.
citation: Cuvillon P, Nouvellon E, Marret E, Albaladejo P, Fortier LP, Fabbro-Perray P, Malinovsky JM, Ripart J. American Society of Anesthesiologists' physical status system: a multicentre Francophone study to analyse reasons for classification disagreement. Eur J Anaesthesiol. 2011 Oct;28(10):742-7. doi: 10.1097/EJA.0b013e328348fc9d.
citation: Yurtlu DA, Aksun M, Ayvat P, Karahan N, Koroglu L, Aran GO. Comparison of Risk Scoring Systems to Predict the Outcome in ASA-PS V Patients Undergoing Surgery: A Retrospective Cohort Study. Medicine (Baltimore). 2016 Mar;95(13):e3238. doi: 10.1097/MD.0000000000003238.
citation: Sankar A, Johnson SR, Beattie WS, Tait G, Wijeysundera DN. Reliability of the American Society of Anesthesiologists physical status scale in clinical practice. Br J Anaesth. 2014 Sep;113(3):424-32. doi: 10.1093/bja/aeu100. Epub 2014 Apr 11.
citation: Lupei MI, Chipman JG, Beilman GJ, Oancea SC, Konia MR. The association between ASA status and other risk stratification models on postoperative intensive care unit outcomes. Anesth Analg. 2014 May;118(5):989-94. doi: 10.1213/ANE.0000000000000187.
citation: Liao L, Mark DB. Clinical prediction models: are we building better mousetraps? J Am Coll Cardiol. 2003 Sep 3;42(5):851-3. doi: 10.1016/s0735-1097(03)00836-2. No abstract available.
citation: Barnett S, Moonesinghe SR. Clinical risk scores to guide perioperative management. Postgrad Med J. 2011 Aug;87(1030):535-41. doi: 10.1136/pgmj.2010.107169. Epub 2011 Jan 21.
citation: Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kmiecik TE, Ko CY, Cohen ME. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013 Nov;217(5):833-42.e1-3. doi: 10.1016/j.jamcollsurg.2013.07.385. Epub 2013 Sep 18.
citation: Protopapa KL. Is there a place for the Surgical Outcome Risk Tool app in routine clinical practice? Br J Hosp Med (Lond). 2016 Nov 2;77(11):612-613. doi: 10.12968/hmed.2016.77.11.612. No abstract available.
citation: Older P, Hall A. Clinical review: how to identify high-risk surgical patients. Crit Care. 2004 Oct;8(5):369-72. doi: 10.1186/cc2848. Epub 2004 Mar 31.
citation: Haskins IN, Maluso PJ, Schroeder ME, Amdur RL, Vaziri K, Agarwal S, Sarani B. A calculator for mortality following emergency general surgery based on the American College of Surgeons National Surgical Quality Improvement Program database. J Trauma Acute Care Surg. 2017 Jun;82(6):1094-1099. doi: 10.1097/TA.0000000000001451.
citation: Le Manach Y, Collins G, Rodseth R, Le Bihan-Benjamin C, Biccard B, Riou B, Devereaux PJ, Landais P. Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation. Anesthesiology. 2016 Mar;124(3):570-9. doi: 10.1097/ALN.0000000000000972.
citation: Protopapa KL, Simpson JC, Smith NC, Moonesinghe SR. Development and validation of the Surgical Outcome Risk Tool (SORT). Br J Surg. 2014 Dec;101(13):1774-83. doi: 10.1002/bjs.9638.
citation: Chan DXH, Sim YE, Chan YH, Poopalalingam R, Abdullah HR. Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: a single-center retrospective study. BMJ Open. 2018 Mar 23;8(3):e019427. doi: 10.1136/bmjopen-2017-019427.