Usefulness of intrinsic infection risk indexes as predictors of in-hospital death.
OBJECTIVE
Comparison of two measures of intrinsic infection risk for predicting in-hospital mortality risks among subjects undergoing general surgery: the Study on the Efficacy of Nosocomial Infection Control (SENIC) index and the National Nosocomial Infection Surveillance (NNIS) index.


METHODS
Prospective cohort study on 1483 patients admitted to the service of general surgery of a tertiary hospital. The main outcome measure was in-hospital death. Relative risks, crude and multiple-risk factor adjusted for by logistic regression analysis, and their 95% CIs were estimated.


RESULTS
During follow-up, 33 patients (2.2%) died. Both the SENIC and the NNIS indexes appeared related to in-hospital mortality risk in crude data. After several confounders (age, sex, severity of illness, American Society of Anesthesiologists score, serum creatinine, serum albumin, stay at the intensive care unit, length of operation, type of surgical wound, and preoperative stay) were controlled for, the SENIC index showed a borderline significant trend with mortality (p = 0.052), whereas the trend was significant for the NNIS index (p = 0.026). The NNIS index also showed a linear trend with both crude and adjusted for (SENIC index) risk of death. The SENIC index did not exhibit any linear trend with adjusted for (NNIS index) risk of surgical wound infection. To delineate whether the SENIC index added explanatory information to the NNIS index (or vice versa), we regressed the SENIC index on the NNIS index (and vice versa) and computed a set of residuals for both indexes. In logistic regression analyses, the residuals of NNIS index added meaningful information to the SENIC index, whereas the residuals of the SENIC index did not add any relevant information to the NNIS index. These results remained unchanged after controlling for several confounders.


CONCLUSIONS
Both the SENIC and the NNIS indexes are good predictors of in-hospital mortality risk. The NNIS index had greater capability for discriminating and predicting risk of dealth.
Delgado-Rodríguez
M
M
Division of Preventive Medicine and Public Health, School of Medicine, University of Cantabria, Santander, Spain.
Sillero-Arenas
M
M
Medina-Cuadros
M
M
Martínez-Gallego
G
G
D000328:Adult; D000368:Aged; D015331:Cohort Studies; D016001:Confidence Intervals; D003428:Cross Infection / Q000453:epidemiology / Q000401:mortality*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D015994:Incidence; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D018570:Risk Assessment; D012720:Severity of Illness Index; D017678:Sex Distribution; D013030:Spain / Q000453:epidemiology; D013503:Surgery Department, Hospital / Q000706:statistics & numerical data*