SurgeryRCVEMGNCT04041076
Risk Calculators Validation for Elective Major General Surgery
An observational study of Surgery and Death, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- What is being tested?
- Major surgical operation
- Type of treatment
- Surgery or procedure
- How many people
- 5,000 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Nov 2019to 31 Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who received scheduled major surgical operation in Tuen Mun Hospital during from 01July 2012- 30June 2018
Who may be able to join
- patient aged 18 years or over
- undergoing elective major surgical operation*
- requiring a planned overnight admission
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site, 1 recruiting
- Recruiting
Department of Anaesthesia and Intensive Care, New Territories West Cluster, Hospital Authority
Hong Kong
Matthew TV Chan, MBBS, +852 91363821, mtvchan@cuhk.edu.hk
Carmen KM Lam, MBBS, +852 90804633, carmenlam1013@gmail.com
About this study
Official title: Validation of Postoperative Mortality Prediction for Elective Major Surgical Operation With Existing Risk Calculators Based on Preoperative Parameters
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.
Read the 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.
- Being tested
- Major surgical operation
- Keywords
- preoperative parameters, risk prediction, postoperative mortality, elective major general surgery
- Medical subject headings
- Death
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Major surgical operationProcedure or surgery
Surgical operation with magnitude defined as major or ultra-major
| Study group | Role | What they receive |
|---|---|---|
| Elective Surgical Patients in Tuen Mun Hospital | Patients who received elective surgical operation in Tuen Mun Hospital from 1July 2012 to 30June 2018 Receives: Procedure: Major surgical operation |
What is being measured
The primary outcome is the main question the study is designed to answer.
30-day mortality rate
The Rate of Mortality at or within 30 days after the elective major surgical operation
Measured: 30 days postoperatively
Secondary outcomes (1)
1-year mortality rate
The Rate of Mortality at or within 1 year after the elective major surgical operation
Measured: 1 year postoperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Aug 2019
- Study start1 Nov 2019
- Primary completion31 Dec 2020 (estimated)
- Study completion31 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (22)Papers the sponsor has linked to this study.
- Other trials for SurgeryStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- Tuen Mun Hospital
- Responsible party
- Principal investigator, Matthew Chan, Chinese University of Hong Kong
- Investigators
- Matthew TV Chan, MBBS, Principal investigator, Department of Anaesthesia and Intensive Care, CUHK
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04041076
- Sponsor's protocol ID
- RCVEMG Protocol V2.0
- Acronym
- RCVEMG
- Status last verified
- Nov 2019
- First submitted
- 30 Jul 2019
- Last updated
- 12 Nov 2019
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 26313057Weiser 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.
PubMed 18582931Davenport 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.
PubMed 16192806Makary 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.
PubMed 20510798Devereaux 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.
PubMed 26630144Kristensen 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.
PubMed 25086026International 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.
PubMed 27799174Sobol 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.
PubMed 21457500Moonesinghe 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.
PubMed 24195875Cuvillon 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.
PubMed 21912242Yurtlu 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.
PubMed 27043696Sankar 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.
PubMed 24727705Lupei 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.
PubMed 24781569Liao 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.
PubMed 12957431Barnett 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.
PubMed 21257993
Study at a glance
From the registry record, last updated by the sponsor on 12 Nov 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of Postoperative Mortality Prediction for Elective Major Surgical Operation With Existing Risk Calculators Based on Preoperative Parameters
- ClinicalTrials.gov ID
- NCT04041076
- Acronym / sponsor ID
- RCVEMG / RCVEMG Protocol V2.0
- Keywords
- preoperative parameters, risk prediction, postoperative mortality, elective major general surgery
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Collaborators
- Tuen Mun Hospital
- Study start
- 1 Nov 2019 (actual)
- Primary completion
- 31 Dec 2020 (estimated)
- Study completion
- 31 Dec 2020 (estimated)
- First posted
- 1 Aug 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Elective Surgical Patients in Tuen Mun Hospital | – | Procedure: Major surgical operationPatients who received elective surgical operation in Tuen Mun Hospital from 1July 2012 to 30June 2018 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who received scheduled major surgical operation in Tuen Mun Hospital during from 01July 2012- 30June 2018
Key inclusion criteria (3 listed)
- patient aged 18 years or over
- undergoing elective major surgical operation*
- requiring a planned overnight admission
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Major surgical operationSurgical operation with magnitude defined as major or ultra-major | Procedure or surgery | Elective Surgical Patients in Tuen Mun Hospital |
Primary outcome
30-day mortality rate
Time frame: 30 days postoperatively
The Rate of Mortality at or within 30 days after the elective major surgical operation
1 secondary outcome
1-year mortality rate
Time frame: 1 year postoperatively
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Nov 2019.
- Background: PubMed 26313057Weiser 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.
- Background: PubMed 18582931Weiser 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.
- Background: PubMed 16192806Davenport 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.
- Background: PubMed 20510798Makary 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.
- Background: PubMed 26630144Devereaux 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.
- Background: PubMed 25086026Kristensen 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.
- Background: PubMed 27799174International 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.
- Background: PubMed 21457500Sobol 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.
- Background: PubMed 24195875Moonesinghe 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.
- Background: PubMed 21912242Cuvillon 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.
- Background: PubMed 27043696Yurtlu 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.
- Background: PubMed 24727705Sankar 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.
- Ismail, H., Cormie, P., Burbury, K. et al. Curr Anesthesiol Rep (2018) 8: 375. https://doi.org/10.1007/s40140-018-0300-7Link provided by the sponsor.
- Individual participant dataSharing planned: No
Related trials for Surgery
Not yet recruiting
The Contribution of Accelerated Recovery (mERAS) Approach to Postoperative Recovery in Pediatric Laparoscopic Surgery Cases
mERAS Intervention, The ASA (American Society of Anesthesiologists) Score
RecruitingObservational study
Label-free Femtosecond Laser Imaging Combined With the Fast Lung Artificial Intelligence Model for Rapid Intraoperative Diagnosis of Lung Surgical Specimens: A Multicentre, Prospective, Parallel-workflow, Non-inferiority Study.
Lung Cancer
RecruitingPhase 3
Dinutuximab With Chemotherapy, Surgery and Stem Cell Transplantation for the Treatment of Children With Newly Diagnosed High Risk Neuroblastoma
Ganglioneuroblastoma, Nodular, Neuroblastoma
Not yet recruiting
The Effect of Compound Chamomile and Lidocaine Hydronchloride Gel as an Endotracheal Tube Lubricant on Postoperative Pharyngeal and Laryngeal Complications in Thoracic Surgery Patients
Postoperative Sore Throat
Active, not recruitingObservational study
Nerve Transfer Surgery to Restore Upper-limb Function After Cervical Spinal Cord Injury
Spinal Cord Injury, Spinal Cord Injury at C5-C7 Level
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Matthew TV Chan, MBBS principal investigator
Department of Anaesthesia and Intensive Care, CUHK
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Surgery and Death, run by Chinese University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Nov 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Carmen KM Lam, MBBS (+85290804633, carmenlam1013@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
30-day mortality rate (time frame: 30 days postoperatively). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04041076, which also lists 22 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Nov 2019.