Derivation and validation of an exclusive pre-operative risk evaluation system in HIV-infected patients undergoing surgery | Liu | Clinical Surgery Research Communications

Derivation and validation of an exclusive pre-operative risk evaluation system in HIV-infected patients undergoing surgery

Bao-chi Liu, Tie-nan Feng, Xiu-ling Feng, Xiao-dong Chen, Ying Wang, Yu-fang Shi

Abstract


Background: A broadly applicable scoring tool for risk evaluation in HIV-infected surgical patients is imperative for improving outcomes and patient safety. Because sepsis has been considered as the major cause of mortality for HIV-infected post-surgical patients, the objective of this study was to develop a pre-operative risk stratification model that predicts the incidence of sepsis after surgical operations in HIV-infected patients.

Methods: The scoring model was created by using inpatient databases, including 762 HIV-infected patients who underwent various surgical procedures from 2008 to 2014 in the Shanghai Public Health Clinical Center (SPHCC). A risk-point scale was developed with five variables for their predictive value of sepsis incidence using single and multi-factor logistical regressions. This model was validated with a receiver operating characteristic (ROC) curve with a Zhengzhou Sixth People Hospital (ZSPH) dataset (182 HIV-infected cases).

Results: Post-operative sepsis was identified in 256 patients in the SPHCC dataset. The average total scores of the sepsis group and the non-sepsis group were 7.22 and 11.62, respectively. Using the predictive model of multi-factor logistical regression, the sensitivity and specificity were 0.95 and 93.6, respectively. The area under curve (AUC) score was 0.98[0.97, 0.99]. The result was much larger than five single-factor predicting models. In the 30 days after surgery, 15 patients died, and the mortality rate was around 2% in SPHCC. Post-operative sepsis was identified in 41 patients in the ZSPH dataset, and four sepsis patients died in the 30 days following surgery. The mortality was also around 2% at ZSPH. The AUC score of the same model was 0.86[0.82, 0.93]. 

Conclusion: The risk scoring system with the predictive model has high predictive accuracy. This indicates that it can help surgeons evaluate the incidence and risk of post-surgical sepsis before surgical procedures on HIV-infected patients.




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