Usefulness of intrinsic infection risk indexes as predictors of in-hospital death

Mortality attributable to hospital-acquired infections among surgical patients

We performed a multicenter prospective matched cohort study to evaluate the mortality attributable to hospital-acquired infections among 12,791 patients admitted to surgical departments. We were able to match 731 patients with 1 or more hospital-acquired infections (ie, case patients) with 731 patients without a hospital-acquired infection (ie, control patients) at a 1:1 ratio. Of the 731 case patients, 42 (5.7%) died; of the 731 control patients, 23 (3.1%) died--a significant difference of 2.6%.

Surgical site infection in patients submitted to digestive surgery: risk prediction and the NNIS risk index

BACKGROUND

Some problems have been reported with the power of the National Nosocomial Infection Surveillance (NNIS) risk index to predict the risk of surgical site infections (SSI) for specific procedures.

OBJECTIVES

To develop an alternative risk prediction index for SSI and to compare the performance with the NNIS index.

METHODS

A prospective cohort study was carried out with all (609) patients submitted to digestive tract surgery in 2 general teaching hospitals in the city of São Paulo, Brazil, from August 2001 through March 2002.

RESULTS

The final incidence rate of SSI was 24.5%; 149 cases of SSI were identified: 33 (22.1%) during hospitalization and 116 (77.9%) after discharge. Logistic multivariate analysis was used for construction of the model. Obesity, surgery risk, adjusted duration, and video laparoscopic surgery were statistically significant (P < .05) for all previous procedures. The performance of the NNIS model in this study showed a low predictive capacity for the occurrence of SSI as determined by the receiver operating characteristic (ROC) curve (0.627; 95% CI: 0.575-0.678) compared with the alternative model developed with this population (0.732; 95% CI: 0.685-0.779).

CONCLUSION

The presence of obesity, adjusted duration, and surgery risk significantly increased the risk for SSI. The NNIS risk index was not significant for SSI in the sample studied, and laparoscopic access was associated with a significant reduction in the risk for SSI. Although the NNIS index is a well-known and simple index, other models depicting variables related to SSI with a better sensitivity and specificity can be developed. Additional studies are required to confirm our results.

The use of economic modeling to determine the hospital costs associated with nosocomial infections

Hospital-associated infection is well recognized as a patient safety concern requiring preventive interventions. However, hospitals are closely monitoring expenditures and need accurate estimates of potential cost savings from such prevention programs. We used a retrospective cohort design and economic modeling to determine the excess cost from the hospital perspective for hospital-associated infection in a random sample of adult medical patients. Study patients were classified as being not infected (n=139), having suspected infection (n=8), or having confirmed infection (n=17). Severity of illness and intensive unit care use were both independently associated with increased cost. After controlling for these confounding effects, we found an excess cost of $6767 for suspected infection and $15,275 for confirmed hospital-acquired infection. The economic model explained 56% of the total variability in cost among patients. Hospitals can use these data when evaluating potential cost savings from effective infection-control measures.

Applicability of two surgical-site infection risk indices to risk of sepsis in surgical patients

OBJECTIVE

To compare the ability of the Study of the Efficacy of Nosocomial Infection Control (SENIC) and the National Nosocomial Infection Surveillance (NNIS) indices to predict the development of nosocomial sepsis in subjects undergoing surgery.

DESIGN

1-year prospective case-control study.

SETTING

A tertiary-care center in Spain.

PATIENTS

Cases were surgical patients with nosocomial sepsis defined using the criteria of the Consensus Conference on Sepsis, identified by daily prospective surveillance.

METHODS

Controls were randomly selected from the daily list of surgical inpatients. Data were prospectively collected. To determine whether either index added explanatory information to the other, two methods were used. The first method involved computing a set of residuals for both variables. Residuals and primary variables were introduced in logistic regression models. The second method evaluated both indices with the Goodman-Kruskal (G) nonparametric coefficient.

RESULTS

99 cases and 97 controls were included. After controlling for confounders, both the SENIC index (P<.001) and the NNIS index (P=.04) showed a significant trend. Residuals of the SENIC index added discriminating ability to the NNIS index, whereas residuals of the NNIS index did not improve the prediction ability of the SENIC index. Similar results were yielded by the G statistic: the SENIC index showed higher predictive power than the NNIS index (G=0.56 vs G=0.41).

CONCLUSIONS

Both indices performed about equally well for discriminating risk of nosocomial sepsis. The SENIC index had a somewhat better ability than the NNIS index only when the number of discharge diagnoses (not truly a predictive factor) were involved in the calculation of the SENIC index.

Risk-adjusted in-hospital death rates for peer hospitals in rural and urban regions

The purpose of this research project was to compare inpatient mortality rates for rural hospitals with mortality rates of urban hospitals of given sizes and ranges of service. Statistical adjustments for risk were made in the probability of death during hospitalization for 43,000 patients across 166 hospitals by age, gender, principal diagnosis, principal surgical procedure, characteristics of the secondary diagnoses, and whether or not cancer was a secondary diagnosis. Eighty-three small hospitals that had a relatively unspecialized range of services constituted the study group. Patient characteristics of this study group were moderately representative of the national population. A standardized score was calculated for each hospital using a formula based on the actual hospital death rate and the death rate expected for a given hospital with patients of the same demographic and medical characteristics. Patients admitted to hospitals in nonmetropolitan areas had a mortality rate of 0.41 percent compared with a mortality rate of 0.66 percent in peer hospitals in metropolitan areas. After mortality rates were risk-adjusted and converted to z scores, nonmetropolitan areas had an average z of +0.16, and metropolitan areas had an average z of -0.25, where positive z scores reflect a lower-than-average adjusted mortality rate. The metropolitan-nonmetropolitan (urban-rural) difference was not statistically significant, but it is meaningful in that rural hospitals tended to have a lower adjusted mortality rate than urban hospitals of the same size and type, indicating that rural hospitals had the same or lower adjusted mortality rates. The possibility of urban hospitals having riskier patients was minimized but could not be definitively ruled out. Taken together with other studies, the data are consistent with the view that small rural hospitals generally make appropriate transfer decisions for severely ill patients and provide quality care for retained patients.

Nosocomial infection, indices of intrinsic infection risk, and in-hospital mortality in general surgery

The objectives of this paper are to assess whether two indices of intrinsic infection risk (the SENIC and the NNIS index) predict in-hospital mortality and the attributable in-hospital mortality due to nosocomial infection in surgical patients. A prospective study on 4714 patients admitted to three hospitals has been carried out. The relative risk and its 95% confidence interval (CI) were estimated. Multiple-risk factors adjusted for odds ratios (OR) were yielded by logistic regression analysis. Overall, 119 patients (2.5%) died before hospital discharge. Both the SENIC and the NNIS indices were related to in-hospital mortality in crude data. After controlling for several variables (age, sex, ASA score, cancer, renal failure, diabetes mellitus, stay at the ICU), the SENIC index did not show any significant trend with mortality (P = 0.252), whereas the trend was significant for the NNIS index (P < 0.001). Risk of death in patients with one nosocomial infection was 7.5%, and in patients developing more than one nosocomial infection was 17.1%. After adjusting for several confounding variables, the development of an organ/space surgical site infection was significantly related to mortality (OR = 4.5, 95% CI 1.5-15.6) as was blood infection (OR = 17.3, 95% CI 3.5-87.0). The association of a surgical site infection and either a respiratory tract infection or a blood infection also increased significantly the risk of in-hospital mortality (OR = 3.3, 95% CI 1.2-8.7). In conclusion, the NNIS index is a good predictor of in-hospital mortality. Patients developing an organ/space surgical site infection and/or a blood infection have an increased risk of in-hospital mortality.