Propensity analysis and adjusted outcomes by cohorts of timing of
DSWI diagnosis
Given the significant differences in baseline characteristics between
the early and late diagnosis groups, five baseline variables were used
to create a logistic regression model for selection to an early or late
diagnosis of DSWI. Figure 3 shows the results of the logistic
regression model predicting selection to the early or late diagnosis of
DSWI group. Those more likely to be in the early diagnosis group were
those with positive wound cultures (odds ratio 0.06, 95% CI 0.01-0.69,
p=0.24) and those more likely to be in the late diagnosis group were
females (OR 8.75, 95% CI 2.0-38.4, p=0.004) and those requiring urgent
DSWI procedures (OR 9.25, 95% CI 1.86-45.9, p=0.007). The area under
the receiver operator curve (c-statistic) was 0.84 and the
Hosmer-Lemeshow goodness-of-fit test was not statistically significant
(p=0.62), suggesting good model discrimination and limited collinearity
and interactions (Supplemental Figure 1) .
Propensity score adjusted multivariable Cox proportional hazard
regression outcomes for mortality are shown in Figure 4 . Early
diagnosis of DSWI and initial attempted medical management were both
strongly associated with increased mortality (hazard ratio 7.48, 95% CI
1.38-40.4, p=0.019 and hazard ratio 7.76, 95% CI 1.67-35.9, p=0.009,
respectively). This was independent of initial operation (flap or
negative pressure wound therapy) or whether any flap was eventually
performed.