The CMPD performs well against the criteria for clinical databases defined by DoCDat, and can be considered a high-quality clinical database. The summary statistics presented for the case mix, outcome and activity of admissions in the CMPD are therefore representative of the country and are accurate.
The authors would encourage any persons considering organising a similar database to pay close attention to the DoCDat criteria and to consider carefully how to address these important issues to ensure their database is representative and accurate.
Determining scores for some elements of the DoCDat evaluation is necessarily subjective (e.g. deciding what constitutes 'good evidence' rather than 'some evidence' that the database is representative of the population, or whether the 'major known confounders' have been included). However, the scores presented for the CMPD were determined by DoCDat and not by the authors.
Particular strengths of the CMPD include its wide coverage, making it highly representative of the population, and explicit definitions for all variables and data collection rules. Collection of raw data enables risk adjustment models to be derived using standard algorithms across all units, allowing for better comparability of risk-adjusted outcomes between units. The main weakness identified by the DoCDat criteria is in the reliability of data collection. While there is no reason to believe that the reliability should be poor, only small-scale reliability studies in individual units have been carried out. The size of the CMP makes formal assessment of reliability across the entire programme a resource-intensive, mammoth task.
Lack of clear instruction in the timing of data collection [34
] and the definition of variables [35
] have been shown to be sources of interobserver variability in the collection of APACHE II data. The CMP uses data collection training, the data collection manual and a precise dataset specification to minimise this variability. Training in data definitions has been shown effective in improving the quality of intensive care data [36
Previous work on the inter-rater reliability of the ICM for coding reasons to admission has shown agreement of 79% for the specific condition and of 88% for the body system [19
]. This compares favourably with a reliability study from the US Project IMPACT database [38
], which showed agreement of 52% and 62% for the specific condition and of 71% and 69% for the body system for reasons for admission to two critical care units coded using the Project IMPACT coding system.
High-quality clinical databases provide the opportunity to perform studies of high generalisability on large numbers of patients at comparatively low cost [39
]. Data from multi-centre, high-quality clinical databases can be used for many purposes, including comparative audit, aiding clinical practice, informing health-service management and evaluating health technologies [1
]. Data from the CMP are used to provide comparative reports to each unit on a 6-monthly basis, and to provide additional ad hoc
reports on specific questions as required by the units. In addition, these data have been used to explore the effects of patient gender [40
] and socioeconomic status (Hutchings A, personal communication, 2002), of day and time of admission to critical care [41
], of time of discharge from critical care [42
] and of end-of-life decision-making [43
] on critical care outcomes.
The use of a detailed system to code the reason for admission to the critical care unit enables identification of groups of patients with specific conditions. This can be of interest not only for common conditions, but also for rare conditions where a meaningful sample can only be obtained using a large multicentre database [1
]. When reporting the prevalence of different conditions in the CMPD (Fig. ), it is important to consider potential sources of variability. These may include over-representation or under-representation of units admitting certain types of patients in the CMP, and the level of detail to which certain conditions are defined in the coding method (e.g. aortic aneurysm surgery would not be the most common reason for admission if the conditions of bacterial pneumonia and pneumonia with no organism isolated were considered a single category).
The results from the CMPD are consistent with the results reported from other multicentre databases of UK critical care admissions (Table ). They are based on more than twice the number of admissions of the other studies combined, and cover a much wider geographical region than any other single database.
Hospital mortality following admission to intensive care varies widely in different countries (range 16–34%; Table ). This is in contrast to the results from UK databases that were fairly consistent (range 27–33%; Table ). The hospital mortality in the CMPD lies towards the top end of that observed internationally, with only the studies from Portugal [32
] and Brazil [27
] reporting higher rates. Methods of case mix (risk) adjustment also varied considerably among the international studies, with only two studies reporting APACHE II hospital mortality probabilities [29
]. Most other studies reported either APACHE III score or SAPS II probabilities, while the two studies that did not had an emphasis on re-estimating the mortality equation of an established model in a new population [26
] or evaluating the discrimination and calibration of several models [22
]. While we have concentrated on the APACHE II model in this paper, as it was the most widely used in the large UK studies, it is important that the CMPD contains sufficient data to be able to calculate a number of different models to facilitate comparison with other studies.
Risk adjustment has its limitations when used to compare critical care unit outcomes. Methods that rely on the worst values of data recorded over the first 24 hours following admission (e.g. APACHE II and APACHE III, SAPS II) are unable to distinguish between a very sick patient admitted to a good unit and a less sick patient whose condition deteriorates over the first 24 hours due to poor management [44
]. Other methods (e.g. MPM II) have similar drawbacks due to relying on variables reflecting treatment (e.g. mechanical ventilation, vasoactive drug treatment). Methods based on data at or around the time of admission (e.g. MPM II0
) have other limitations in that they assume all admissions take place at the same time point in the continuum of critical illness. In addition, all the methods have various exclusion criteria, and the exclusions applied in practice are even more varied. The 'observed' mortality of a unit may change considerably depending on exactly which exclusion criteria are applied [45
]. As this study was largely descriptive, we applied no exclusion criteria except in the calculation of APACHE II scores and probabilities, where standard exclusions were applied (Table ).
Accurate comparisons between databases, both within the UK and internationally, can be problematic due to differences in methods of data collection and reporting. Even something as superficially straightforward as applying the exclusion criteria for a risk adjustment method can result in varied interpretation [45
]. Precise variable definitions and clear reporting of collection methods can assist in identifying these differences to improve interpretation of results.
This paper forms the baseline for a series of articles on specific conditions in critical care, providing essential background on the data collection, data validation and overall case mix, outcome and activity for all critical care admissions to set those for specific conditions in context. Baseline statistics (case mix, outcome, length of stay) on specific conditions in critical care provide useful and practical information for working clinicians.