BACKGROUND : To assess the efficacy of a centralised review of a voluntary low-budget head injury database with a retrospective analysis of data before and after a centralised review. METHOD: A computerised data collection (Neurolink) on traumatic brain injury cases admitted to three neuro-intensive care units in Milan (Italy): analysis of a three-year period (1999-2001).Data from 499 patients (epidemiology, type of lesion, clinical course, monitoring, treatment, complications and outcome).The audit involved a review of forms relating to patients enrolled in the three-year period, with the aim of improving the quality of data entry. Missing data in all empty fields were identified; evident errors and contradictory data were identified and corrected; missing and final data were analysed to test the efficacy of the review. FINDINGS: The total post-review missing data rate was significantly lower than the paired pre-review missing data rate (p = 0.001).The same was confirmed for each of the 3 years (p = 0.001 for each year). The missing data rate significantly improved over the three-year period (p = 0.001). Data for the pre-hospitalisation period had the highest missing rates; data regarding the ICU stay showed the greatest improvement after the review. A total of 407 items (0.44%) were identified as errors. CONCLUSIONS: Data quality is fundamental to avoid information bias in database analysis. This study indicates that it is possible to generate a serious data collection without significant resources. Audit seems to be an important tool before the final data is used for scientific projects.

Improving the quality of data entry in a low-budget head injury database / L. Beretta, V. Aldovrandi, E. Grandi, G. Citerio, N. Stocchetti. - In: ACTA NEUROCHIRURGICA. - ISSN 0001-6268. - 149:9(2007), pp. 903-909.

Improving the quality of data entry in a low-budget head injury database

N. Stocchetti
Ultimo
2007

Abstract

BACKGROUND : To assess the efficacy of a centralised review of a voluntary low-budget head injury database with a retrospective analysis of data before and after a centralised review. METHOD: A computerised data collection (Neurolink) on traumatic brain injury cases admitted to three neuro-intensive care units in Milan (Italy): analysis of a three-year period (1999-2001).Data from 499 patients (epidemiology, type of lesion, clinical course, monitoring, treatment, complications and outcome).The audit involved a review of forms relating to patients enrolled in the three-year period, with the aim of improving the quality of data entry. Missing data in all empty fields were identified; evident errors and contradictory data were identified and corrected; missing and final data were analysed to test the efficacy of the review. FINDINGS: The total post-review missing data rate was significantly lower than the paired pre-review missing data rate (p = 0.001).The same was confirmed for each of the 3 years (p = 0.001 for each year). The missing data rate significantly improved over the three-year period (p = 0.001). Data for the pre-hospitalisation period had the highest missing rates; data regarding the ICU stay showed the greatest improvement after the review. A total of 407 items (0.44%) were identified as errors. CONCLUSIONS: Data quality is fundamental to avoid information bias in database analysis. This study indicates that it is possible to generate a serious data collection without significant resources. Audit seems to be an important tool before the final data is used for scientific projects.
Settore MED/41 - Anestesiologia
2007
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/42580
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