title: The European thoracic data quality project: An Aggregate Data Quality score to measure the quality of international multi-institutional databases.
abstract: OBJECTIVE
  To describe the methodology for the development of data quality metrics in multi-institutional databases, deriving a cumulative data quality score [Aggregate Data Quality score (ADQ)]. The ESTS database was used to create and apply the metrics. The Units contributing to the ESTS database were ranked for the quality of data uploaded using the ADQ.
  
  
  METHODS
  We analysed data obtained from 96 Units contributing with at least 100 major lung resections (January 2007 to December 2014). The Units were anonymized assigning a casual numeric code. The following metrics were developed for measuring the data quality of each Unit: (i) record Completeness (COM); rate of present variables on 16 expected variables for all the records uploaded [1 - ('null values'/total expected values for the Unit) × 100, the concept of 'null value' was defined for each variable]; (ii) record Reliability (REL); rate of consistent checks on 9 checks tested for all the records uploaded [1 - (valid controls/total possible controls for the Unit) × 100, specific reliability control queries were defined]. These two metrics were rescaled using the mean and standard deviation of the entire dataset and summed, obtaining: (iii) ADQ score: [COM rescaled + REL rescaled]; it measures the cumulative data quality of a given dataset. The ADQ was used to rank the contributors.
  
  
  RESULTS
  The COM of ESTS database contributors varied from 98.6 to 43% and the REL from 100 to 69%. Combining the rescaled metrics, the obtained ADQ ranged between 2.67 (highest data quality) and -7.85 (lowest data quality). Comparing the rating using just the COM value to the one obtained using the ADQ, 93% of Units changed their position. The major change was the drop of 66 positions considering the ADQ list.
  
  
  CONCLUSIONS
  We described a reproducible method for data quality assessment in clinical multi-institutional databases. The ADQ is a unique indicator able to describe data quality and to compare it among centres. It has the potential of objectively guiding projects of data quality management and improvement.
authors.affiliation: Unit of Thoracic Surgery, AOU Ospedali Riuniti, Ancona, Italy michelesalati@hotmail.com.
authors.forename: Michele
authors.initials: M
authors.lastname: Salati
authors.affiliation: Department of Thoracic Surgery, Nouvel Hopital Civil, Strasbourg, France.
authors.forename: Pierre-Emmanuel
authors.initials: PE
authors.lastname: Falcoz
authors.affiliation: Department of Thoracic Surgery, University Hospital, Leuven, Belgium.
authors.forename: Herbert
authors.initials: H
authors.lastname: Decaluwe
authors.affiliation: Department of Thoracic Surgery, National Cancer Institute, Pascale Foundation, Naples, Italy.
authors.forename: Gaetano
authors.initials: G
authors.lastname: Rocco
authors.affiliation: Department of Thoracic Surgery, University Hospital, Leuven, Belgium.
authors.forename: Dirk
authors.initials: D
authors.lastname: Van Raemdonck
authors.affiliation: Department of Thoracic Surgery, University Hospital, Salamanca, Spain.
authors.forename: Gonzalo
authors.initials: G
authors.lastname: Varela
authors.affiliation: Department of Thoracic Surgery, St James's University Hospital, Leeds, UK.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Brunelli
mesh_terms: D000068598:Data Accuracy; D016208:Databases, Factual / Q000592:standards*; D005060:Europe; D006801:Humans; D012042:Registries / Q000592:standards*; D019616:Thoracic Surgical Procedures
keywords: Data quality; Database management systems; Quality indicators; Registry