Didier Nakache

Does catheter-associated urinary tract infection increase mortality in critically ill patients?

OBJECTIVE To produce an accurate estimate of the association between catheter-associated urinary tract infection (UTI) and intensive care unit (ICU) and hospital mortality, controlling for major confounding factors. DESIGN Nested case-control study in a multicenter cohort (the OutcomeRea database). SETTING Twelve French medical or surgical ICUs. METHODS All patients admitted between January 1997 and August 2005 who required the insertion of an indwelling urinary catheter. Patients who developed catheter-associated UTI (ie, case patients) were matched to control patients on the basis of the following criteria: sex, age (+/- 10 years), SAPS (Simplified Acute Physiology Score) II score (+/- 10 points), duration of urinary tract catheterization, and presence or absence of diabetes mellitus. The association of catheter-associated UTI with ICU and hospital mortality was assessed by use of conditional logistic regression. RESULTS Of the 3,281 patients who had an indwelling urinary catheter, 298 (9%) developed at least 1 episode of catheter-associated UTI. The incidence density of catheter-associated UTI was 12.9 infections per 1,000 catheterization-days. Crude ICU mortality rates were higher among patients with catheter-associated UTI, compared with those without catheter-associated UTI (32% vs 25%, P=.02); the same was true for crude hospital mortality rates (43% vs 30%, P<.01). After matching and adjustment, catheter-associated UTI was no longer associated with increased mortality (ICU mortality: odds ratio [OR], 0.846 [95% confidence interval {CI}, 0.659-1.086]; P=.19 and hospital mortality: OR, 0.949 [95% CI, 0.763-1.181]; P=.64). CONCLUSION After carefully controlling for confounding factors, catheter-associated UTI was not found to be associated with excess mortality among our population of critically ill patients in either the ICU or the hospital.

Reliability of diagnostic coding in intensive care patients

IntroductionAdministrative coding of medical diagnoses in intensive care unit (ICU) patients is mandatory in order to create databases for use in epidemiological and economic studies. We assessed the reliability of coding between different ICU physicians.MethodOne hundred medical records selected randomly from 29,393 cases collected between 1998 and 2004 in the French multicenter Outcomerea ICU database were studied. Each record was sent to two senior physicians from independent ICUs who recoded the diagnoses using the International Statistical Classification of Diseases and Related Health Problems: Tenth Revision (ICD-10) after being trained according to guidelines developed by two French national intensive care medicine societies: the French Society of Intensive Care Medicine (SRLF) and the French Society of Anesthesiology and Intensive Care Medicine (SFAR). These codes were then compared with the original codes, which had been selected by the physician treating the patient. A specific comparison was done for the diagnoses of septicemia and shock (codes derived from A41 and R57, respectively).ResultsThe ICU physicians coded an average of 4.6 ± 3.0 (range 1 to 32) diagnoses per patient, with little agreement between the three coders. The primary diagnosis was matched by both external coders in 34% (95% confidence interval (CI) 25% to 43%) of cases, by only one in 35% (95% CI 26% to 44%) of cases, and by neither in 31% (95% CI 22% to 40%) of cases. Only 18% (95% CI 16% to 20%) of all codes were selected by all three coders. Similar results were obtained for the diagnoses of septicemia and/or shock.ConclusionIn a multicenter database designed primarily for epidemiological and cohort studies in ICU patients, the coding of medical diagnoses varied between different observers. This could limit the interpretation and validity of research and epidemiological programs using diagnoses as inclusion criteria.

Text Categorization for Multi-label Documents and Many Categories

In this paper, we propose a new classification method that addresses classification in multiple categories of textual documents. We call it Matrix Regression (MR) due to its resemblance to regression in a high dimensional space. Experiences on a medical corpus of hospital records to be classified by ICD (International Classification of Diseases) code demonstrate the validity of the MR approach. We compared MR with three frequently used algorithms in text categorization that are k-Nearest Neighbors, Centroide and Support Vector Machine. The experimental results show that our method outperforms them in both precision and time of classification.