[Use of the French medico-administrative database (PMSI) to detect nosocomial infections in the University hospital of Lyon]. / Intérêt de l'utilisation des données du Programme médicalisé des systèmes d'information (PMSI) pour la surveillance des infections nosocomiales aux Hospices Civils de Lyon.

BACKGROUND: Surveillance is an effective element in the fight against nosocomial infections, but the monitoring methods are often cumbersome and time consuming. The detection of infection in computerized databases is a means to alleviate the workload of health care teams. The objective of this study was to evaluate the performance of using discharge summaries in medico-administrative databases (PMSI) for the identification of nosocomial infections in surgery, intensive care and obstetrics. METHODS: The retrospective assessment study included patients who were hospitalized in general surgery, intensive care and obstetrics at different periods of time in 2006 and 2007 depending on the wards. Patients were monitored according to standard protocols which are coordinated at the regional level by the Southeast coordinating centre (CCLIN). The performance of identifying cases of nosocomial infection from discharge diagnoses coded by using the International Classification of Diseases (tenth revision) was evaluated by a study of sensitivity, specificity, positive and negative predictive values with their 95% confidence intervals. RESULTS: Using a limited number of diagnostic codes, the sensitivity and specificity were, respectively, 26.3% (95% CI 13.2-42.1) and 99.5% (95% 98.8-100.0) for the identification of surgical site infections. By expanding the number of diagnostic codes, the sensitivity and specificity were 78.9% (95% CI 65.8-92.1) and 65.7% (95% CI 61.0-70.3). The sensitivity and specificity for case identification of nosocomial infections in intensive care were 48.8% (95% CI 42.6-55.0) and 78.4% (95% CI 76.1-80.1), and were 42.9% (95% CI 25.0-60.7) and 87.3% (95% CI 85.2-89.3) for identification of postpartum infections. CONCLUSION: The PMSI is not a sufficiently efficient method in terms of sensitivity to be used in surveillance of nosocomial infections. A reassessment of the PMSI must be considered, with changes in coding of comorbidity that occurred in 2009.

Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000-2006.

The aim of this study was to evaluate seven different strategies for the automated detection of nosocomial infections (NIs) in an intensive care unit (ICU) by using different hospital information systems: microbiology database, antibiotic prescriptions, medico-administrative database, and textual hospital discharge summaries. The study involved 1,499 patients admitted to an ICU of the University Hospital of Lyon (France) between 2000 and 2006. The data were extracted from the microbiology laboratory information system, the clinical information system on the ward and the medico-administrative database. Different algorithms and strategies were developed, using these data sources individually or in combination. The performances of each strategy were assessed by comparing the results with the ward data collected as a national standardised surveillance protocol, adapted from the National Nosocomial Infections Surveillance system as the gold standard. From 1,499 patients, 282 NIs were reported. The strategy with the best sensitivity for detecting these infections using an automated method was the combination of antibiotic prescription or microbiology, with a sensitivity of 99.3% [95% confidence interval (CI): 98.2-100] and a specificity of 56.8% (95% CI: 54.0-59.6). Automated methods of NI detection represent an alternative to traditional monitoring methods. Further study involving more ICUs should be performed before national recommendations can be established.