TY - JOUR
T1 - Development of a Clinical Prediction Model for In-hospital Mortality from the South African Cohort of the African Surgical Outcomes Study
AU - Kluyts, Hyla Louise
AU - Conradie, Wilhelmina
AU - Cloete, Estie
AU - Spijkerman, Sandra
AU - Smith, Oliver
AU - Alli, Ahmed
AU - Koto, Modise Z.
AU - Montwedi, Odisang D.
AU - Govender, Komalan
AU - Cronjé, Larissa
AU - Grobbelaar, Mariette
AU - Omoshoro-Jones, Jones A.
AU - Rorke, Nicolette F.
AU - Anderson, Philip
AU - Torborg, Alexandra
AU - Alphonsus, Christella
AU - Alexandris, Panagiotis
AU - Mallier Peter, Aunel
AU - Singh, Usha
AU - Diedericks, Johan
AU - Mrara, Busisiwe
AU - Reed, Anthony
AU - Davies, Gareth L.
AU - Davids, Jody G.
AU - Van Zyl, Hendrik A.
AU - Govindasamy, Vishendran
AU - Rodseth, Reitze
AU - Matos-Puig, Roel
AU - Bhat, Kajake A.P.
AU - Naidoo, Noel
AU - Roos, John
AU - Jaworska, Magdalena
AU - Steyn, Annemarie
AU - Dippenaar, Johannes M.
AU - Pearse, R. M.
AU - Madiba, Thandinkosi
AU - Biccard, Bruce M.
N1 - Publisher Copyright:
© 2020, Société Internationale de Chirurgie.
PY - 2021/2
Y1 - 2021/2
N2 - Background: Data on the factors that influence mortality after surgery in South Africa are scarce, and neither these data nor data on risk-adjusted in-hospital mortality after surgery are routinely collected. Predictors related to the context or setting of surgical care delivery may also provide insight into variation in practice. Variation must be addressed when planning for improvement of risk-adjusted outcomes. Our objective was to identify the factors predicting in-hospital mortality after surgery in South Africa from available data. Methods: A multivariable logistic regression model was developed to identify predictors of 30-day in-hospital mortality in surgical patients in South Africa. Data from the South African contribution to the African Surgical Outcomes Study were used and included 3800 cases from 51 hospitals. A forward stepwise regression technique was then employed to select for possible predictors prior to model specification. Model performance was evaluated by assessing calibration and discrimination. The South African Surgical Outcomes Study cohort was used to validate the model. Results: Variables found to predict 30-day in-hospital mortality were age, American Society of Anesthesiologists Physical Status category, urgent or emergent surgery, major surgery, and gastrointestinal-, head and neck-, thoracic- and neurosurgery. The area under the receiver operating curve or c-statistic was 0.859 (95% confidence interval: 0.827–0.892) for the full model. Calibration, as assessed using a calibration plot, was acceptable. Performance was similar in the validation cohort as compared to the derivation cohort. Conclusion: The prediction model did not include factors that can explain how the context of care influences post-operative mortality in South Africa. It does, however, provide a basis for reporting risk-adjusted perioperative mortality rate in the future, and identifies the types of surgery to be prioritised in quality improvement projects at a local or national level.
AB - Background: Data on the factors that influence mortality after surgery in South Africa are scarce, and neither these data nor data on risk-adjusted in-hospital mortality after surgery are routinely collected. Predictors related to the context or setting of surgical care delivery may also provide insight into variation in practice. Variation must be addressed when planning for improvement of risk-adjusted outcomes. Our objective was to identify the factors predicting in-hospital mortality after surgery in South Africa from available data. Methods: A multivariable logistic regression model was developed to identify predictors of 30-day in-hospital mortality in surgical patients in South Africa. Data from the South African contribution to the African Surgical Outcomes Study were used and included 3800 cases from 51 hospitals. A forward stepwise regression technique was then employed to select for possible predictors prior to model specification. Model performance was evaluated by assessing calibration and discrimination. The South African Surgical Outcomes Study cohort was used to validate the model. Results: Variables found to predict 30-day in-hospital mortality were age, American Society of Anesthesiologists Physical Status category, urgent or emergent surgery, major surgery, and gastrointestinal-, head and neck-, thoracic- and neurosurgery. The area under the receiver operating curve or c-statistic was 0.859 (95% confidence interval: 0.827–0.892) for the full model. Calibration, as assessed using a calibration plot, was acceptable. Performance was similar in the validation cohort as compared to the derivation cohort. Conclusion: The prediction model did not include factors that can explain how the context of care influences post-operative mortality in South Africa. It does, however, provide a basis for reporting risk-adjusted perioperative mortality rate in the future, and identifies the types of surgery to be prioritised in quality improvement projects at a local or national level.
UR - http://www.scopus.com/inward/record.url?scp=85094645035&partnerID=8YFLogxK
U2 - 10.1007/s00268-020-05843-1
DO - 10.1007/s00268-020-05843-1
M3 - Article
C2 - 33125506
AN - SCOPUS:85094645035
SN - 0364-2313
VL - 45
SP - 404
EP - 416
JO - World Journal of Surgery
JF - World Journal of Surgery
IS - 2
ER -