Infection Control & Hospital Epidemiology


Table 1. Results from Analysis of Spanish ICU Data NI Etiology


SE Method


APACHE 2nd Quartilee Full Cohortf Traditionalg IPW KM IPW GLM


APACHE 3rd Quartilee Full Cohortf Traditionalg IPW KM IPW GLM


APACHE 4th Quartilee Full Cohortf Traditionalg IPW KM IPW GLM


Antibiotich Full Cohortf Traditionalg IPW KM IPW GLM


HR (95% CI)a


1.58 (1.14–2.20) 1.39 (0.90–2.13) 1.46 (0.96–2.23) 1.29 (0.84–1.99)


2.03 (1.47–2.80) 2.16 (1.38–3.37) 2.33 (1.52–3.55) 2.09 (1.36–3.21)


2.14 (1.57–2.93) 2.10 (1.39–3.18) 2.28 (1.52–3.55) 2.09 (1.39–3.16)


0.75 (0.60–0.93) 0.62 (0.45–0.86) 0.63 (0.48–0.84) 0.61 (0.45–0.82)


(Log HR)b 0.17 HR (95% CI)a 0.62 (0.59–0.66)


0.57 (0.40–0.80) 0.67 (0.51–0.88)


0.45 (0.41–0.48)


0.44 (0.26–0.77) 0.52 (0.39–0.70)


0.41 (0.38–0.44)


0.28 (0.20–0.39) 0.43 (0.33–0.55)


0.64 (0.60–0.69)


0.64 (0.44–0.95) 0.65 (0.53–0.81)


1199


Death or Discharge Etiology SE (Log HR)b 0.03


0.17 0.14


0.04


0.28 0.15


0.04


0.17 0.13


0.04


0.20 0.11


RR (95% CI)c 2.86 (2.07–3.94)


0.22 …… … 0.22 0.22


0.16


2.73 (1.55–4.81) 1.84 (1.17–2.89)


5.66 (4.15–7.70)


0.23 …… … 0.22 0.21


0.16


5.89 (3.05–11.39) 3.65 (2.27–5.86)


6.92 (5.14–9.32)


0.21 …… … 0.21 0.21


0.11


9.08 (5.27–15.65) 4.48 (2.84–7.07)


1.17 (0.95–1.45)


0.16 …… … 0.14 0.15


0.99 (0.65–1.51) 0.82 (0.57–1.18)


NI Prediction SE (Log RR)b


0.16 …


0.29 0.23


0.16 …


0.34 0.24


0.15 …


0.28 0.23


0.11 …


0.22 0.19


Cohort Sized


6,563 760 760 760


6,563 760 760 760


6 563 760 760 760


6 563 760 760 760


Note. NI, nosocomial infection; HR, hazard ratio; CI, confidence interval; SE, standard error; RR, risk ratio; IPW, Cox partial likelihood with inverse probability weighting; KM, Kaplan Meier


weights; GLM, logistic regression weights. aCause-specific hazard ratio for exposure bCalculated with estimated standard errors for Cox regression and conditional logistic regression, calculated with robust standard errors for inverse probability weighting and log


binomial model. cUsing log binomial model. dDistinct patients. eFirst APACHE quartile reference for second, third, and fourth APACHE quartiles. fCox regression. gConditional logistic regression. hAntibiotic treatment within 48 hours of admission.


Discussion


In this study, we adapted existing methods to perform a complete competing risks analysis on the occurrence of hospital acquired infections. This adapted method of reusing controls not only matched the accuracy and precision of traditional cause specific analyses for an event of interest but also extended it to provide competing event etiological and event-of-interest prediction analysis, which are 2 substantial improvements. Although the KM and GLM weights produced similar results, Fig. 2 illustrates that the nonparametric KM weights are more prone to extreme values, whereas the GLM weights have a smoothing effect on the weight distribution. We therefore recommend plotting and studying the weights of different approaches; extreme weights impact the robustness of the model. The only additional information


required for this extension analysis was follow-up and event-type data that are routinely recorded for hospital patients. Considering that this information was likely recorded for previously conducted NCC studies, one could easily revisit these studies and enhance their results. The method of reusing controls can be extended in several ways.


Matching controls to cases on additional variables can both adjust for confounding and improve efficiency. For example, we could have matched controls in our Spanish ICU cohort by sex or age at admission. In reviewing the role of matching in case-control studies of antimicrobial resistance, Cerceo et al12 emphasize the importance of accounting for study design matching in the analysis. The matching can be resolved in the inclusion probabilities and/or the regression analysis. Stoer and Samulesen13 addressed this question by introducing strong correlations between matching variables and


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