Training in data definitions improves quality of intensive care data

Nurses experience increased clinical and organisational competence by working with a medical quality register, RevNatus - a qualitative study

Background

RevNatus is a consent-based, nationwide medical quality register that collects data on patients with inflammatory rheumatic diseases during pregnancy and one year postpartum. The entering of data takes place in outpatient clinics in rheumatology wards in hospitals. The aim of this study is to explore how rheumatology nurses experience organizing and working with the medical quality register RevNatus in addition to their normal clinical patient-care tasks.

Methods

Qualitative focus group interviews and individual in-depth interviews were conducted in 2018 to gain insights into how nurses organize performing quality register work and clinical work simultaneously. Data were analysed using systematic text condensation.

Results

The informants represented seven different rheumatology outpatient clinics in Norway. The analyses showed that working with RevNatus increased the nurses’ knowledge about pregnancy and rheumatic diseases, improved the content of their nurse consultations and found the ‘register form’ as a useful template to structure the nurse consultations. The nurses took the main responsibility for RevNatus, but lack of routines and uncoordinated collaboration with the rheumatologists and secretaries made the nurses spend too much time verifying the accuracy of data or post-registering missing data.

Conclusion

The nurses experienced work with RevNatus as time-consuming, but the register work increased both their clinical and organisational competences. Routines and collaboration within the registry team are important to ensure the data quality and reduce the workload.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08595-x.

Evaluating the influence of data collector training for predictive risk of death models: an observational study

BACKGROUND

Severity-of-illness scoring systems are widely used for quality assurance and research. Although validated by trained data collectors, there is little data on the accuracy of real-world data collection practices.

OBJECTIVE

To evaluate the influence of formal data collection training on the accuracy of scoring system data in intensive care units (ICUs).

STUDY DESIGN AND METHODS

Quality assurance audit conducted using survey methodology principles. Between June and December 2018, an electronic document with details of three fictitious ICU patients was emailed to staff from 19 Australian ICUs who voluntarily submitted data on a web-based data entry form. Their entries were used to generate severity-of-illness scores and risks of death (RoDs) for four scoring systems. The primary outcome was the variation of severity-of-illness scores and RoDs from a reference standard.

RESULTS

50/83 staff (60.3%) submitted data. Using Bayesian multilevel analysis, severity-of-illness scores and RoDs were found to be significantly higher for untrained staff. The mean (95% high-density interval) overestimation in RoD due to training effect for patients 1, 2 and 3, respectively, were 0.24 (0.16, 0.31), 0.19 (0.09, 0.29) and 0.24 (0.1, 0.38) respectively (Bayesian factor >300, decisive evidence). Both groups (trained and untrained) had wide coefficients of variation up to 38.1%, indicating wide variability. Untrained staff made more errors in interpreting scoring system definitions.

INTERPRETATION

In a fictitious patient dataset, data collection staff without formal training significantly overestimated the severity-of-illness scores and RoDs compared with trained staff. Both groups exhibited wide variability. Strategies to improve practice may include providing adequate training for all data collection staff, refresher training for previously trained staff and auditing the raw data submitted by individual ICUs. The results of this simulated study need revalidation on real patients.

Development of a Methodological Approach for Data Quality Ontology in Diabetes Management

The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition; (2) Conceptualization; (3) Semantic modeling; (4) Knowledge representation; and (5) Validation. The DQO was applied to the identification of patients with Type 2 Diabetes Mellitus (T2DM) in EHRs, which included an assessment of the DQ of the EHR. The five steps methodology is generalizable and reusable in other domains.

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.

Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature

PURPOSE

Effective use of routine data to support integrated chronic disease management (CDM) and population health is dependent on underlying data quality (DQ) and, for cross system use of data, semantic interoperability. An ontological approach to DQ is a potential solution but research in this area is limited and fragmented.

OBJECTIVE

Identify mechanisms, including ontologies, to manage DQ in integrated CDM and whether improved DQ will better measure health outcomes.

METHODS

A realist review of English language studies (January 2001-March 2011) which addressed data quality, used ontology-based approaches and is relevant to CDM.

RESULTS

We screened 245 papers, excluded 26 duplicates, 135 on abstract review and 31 on full-text review; leaving 61 papers for critical appraisal. Of the 33 papers that examined ontologies in chronic disease management, 13 defined data quality and 15 used ontologies for DQ. Most saw DQ as a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. The majority of studies reported tool design and development (80%), implementation (23%), and descriptive evaluations (15%). Ontological approaches were used to address semantic interoperability, decision support, flexibility of information management and integration/linkage, and complexity of information models.

CONCLUSION

DQ lacks a consensus conceptual framework and definition. DQ and ontological research is relatively immature with little rigorous evaluation studies published. Ontology-based applications could support automated processes to address DQ and semantic interoperability in repositories of routinely collected data to deliver integrated CDM. We advocate moving to ontology-based design of information systems to enable more reliable use of routine data to measure health mechanisms and impacts.

Assessment and perceptions of intensive care data quality, reporting and use: a survey of ICU directors

It is becoming increasingly common for government bodies, healthcare providers, funders and consumers to seek measures of the quality of critical care. It is important to ensure the quality of intensive care unit (ICU) data is high so these stakeholders can confidently use quality of care measures in decision-making. This paper aims to evaluate the quality of data collected for and submitted to the Australian and New Zealand Intensive Care Society Adult Patient Database, and to investigate the perceptions of NSW ICU directors in relation to ICU data quality, reporting and usage. A survey tool was developed based on an existing framework that consisted of procedures for assessing data quality in medical registries. The survey was distributed to the directors of all NSW ICUs that submitted data in the 2007/2008 financial year. Overall, completeness of the data and its quality was perceived to be good. Participants were less likely to engage in activities involving the detection and correction of data errors, feedback of data or use of data for local purposes. A number of barriers and enablers to good quality ICU data as well as strategies to improve data quality were identified. Inadequate staff, training and resources for data collection were widespread concerns. NSW ICU directors believe more work is required to achieve high quality data and appropriate use of the data collected. Strategies targeting increased resources including updated technology and improved staffing and training, as well as low-cost solutions such as audit, feedback and clinician engagement, have been highlighted.

Evaluating the effectiveness of a tailored multifaceted performance feedback intervention to improve the quality of care: protocol for a cluster randomized trial in intensive care

BACKGROUND

Feedback is potentially effective in improving the quality of care. However, merely sending reports is no guarantee that performance data are used as input for systematic quality improvement (QI). Therefore, we developed a multifaceted intervention tailored to prospectively analyzed barriers to using indicators: the Information Feedback on Quality Indicators (InFoQI) program. This program aims to promote the use of performance indicator data as input for local systematic QI. We will conduct a study to assess the impact of the InFoQI program on patient outcome and organizational process measures of care, and to gain insight into barriers and success factors that affected the program's impact. The study will be executed in the context of intensive care. This paper presents the study's protocol.

METHODS/DESIGN

We will conduct a cluster randomized controlled trial with intensive care units (ICUs) in the Netherlands. We will include ICUs that submit indicator data to the Dutch National Intensive Care Evaluation (NICE) quality registry and that agree to allocate at least one intensivist and one ICU nurse for implementation of the intervention. Eligible ICUs (clusters) will be randomized to receive basic NICE registry feedback (control arm) or to participate in the InFoQI program (intervention arm). The InFoQI program consists of comprehensive feedback, establishing a local, multidisciplinary QI team, and educational outreach visits. The primary outcome measures will be length of ICU stay and the proportion of shifts with a bed occupancy rate above 80%. We will also conduct a process evaluation involving ICUs in the intervention arm to investigate their actual exposure to and experiences with the InFoQI program.

DISCUSSION

The results of this study will inform those involved in providing ICU care on the feasibility of a tailored multifaceted performance feedback intervention and its ability to accelerate systematic and local quality improvement. Although our study will be conducted within the domain of intensive care, we believe our conclusions will be generalizable to other settings that have a quality registry including an indicator set available.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN50542146.

Comparison of sequential organ failure assessment (SOFA) scoring between nurses and residents

PURPOSE

We aimed to evaluate differences in the interobserver reliability and accuracy of sequential organ failure assessment (SOFA) scoring between nurses and residents.

METHODS

Eight nurses and eight residents independently scored 24 randomly selected patients. Intraclass correlation coefficients (ICCs) for the reliability of total SOFA scoring were calculated. The residents' and nurses' SOFA scores were compared with a gold standard to assess accuracy.

RESULTS

The overall ICC of the total SOFA score was 0.87 (nurses 0.89, residents 0.86) for a single measurement. Residents tended to assign higher total SOFA scores than did nurses, without a statistically significant difference (7.01 ± 4.43 vs. 6.72 ± 4.27, P > 0.05). The mean bias between the nurses' and the gold standard total SOFA scores was -0.16 ± 1.86 and the 95% confidence limit of agreement was -3.8 to +3.49. The mean bias between the residents' and the gold standard total SOFA scores was -0.39 ± 1.81, and the 95% confidence limit of agreement was -3.95 to +3.16. The percentage of accurate data for the total SOFA score was 47.4% for nurses and 51% for residents (P > 0.05). Although not statistically significant, the major error rate (≥2 point deviation from the gold standard score) was higher for nurses than for residents (29.16 and 23.43%, P > 0.05). Accuracy of scoring individual organ systems was similar for the two groups; however, the major error rate in the cardiovascular system score was higher for nurses.

CONCLUSION

Interobserver reliability was good and mean SOFA scores were not significantly different between nurses and residents. The accuracy of SOFA scoring was moderate for both groups; however, although the difference was not statistically significant, the major error rate was higher for nurses than for residents.

Perfusion Downunder Collaboration Database--data quality assurance: towards a high quality clinical database

Maintaining a high quality clinical database is critical to obtain reliable information upon which to base clinical and institutional decisions, and to preserve the public and the user's confidence in the quality of the data. The success of the Perfusion Downunder Collaboration (PDUC) Database, a dataset for cardiopulmonary bypass procedures, can only be guaranteed through the assurance of the quality of its data. This paper presents the evaluation of the data quality in the PDUC Database. Three participating centers located in Adelaide, Australia were audited: Flinders Private Hospital (FPH), Flinders Medical Center (FMC), and Ashford Hospital (AH). Ten perCent of the cases submitted from the first year of data harvest were audited (2008: FPH and FMC, 2009: AH). A total of 57 variables were reviewed and rates of discrepancies (inaccurate, missing, not entered, cannot be validated) categorized as 0-25%, 25-50%, 51-75%, and 75-100% of cases (% = cases with discrepancy/total cases audited) evaluated. Sixty randomly selected cases were audited, comprising of 13 cases from FPH, 31 cases from FMC, and 16 cases from AH. Of a total of 3420 data points evaluated, 6.9% were found to be inaccurate and 3.2% were missing. For each participating center, the great majority of variables have discrepancies in few (0-25%) of the cases audited. The discrepancies found can be attributed to systematic errors (e.g., error in date difference calculation for length of stays, data transformation error for postoperative dialysis) and random errors (e.g., use of incorrect unit for creatinine, transcription error for discharge date). The PDUC Database is currently reasonably accurate and complete. This evaluation is part of a complex system of data quality assurance, and when conducted routinely, could provide a continuous feedback loop towards a high quality PDUC Database.

Hospital mortality is associated with ICU admission time

Introduction

Previous studies have shown that patients admitted to the intensive care unit (ICU) after “office hours” are more likely to die. However these results have been challenged by numerous other studies. We therefore analysed this possible relationship between ICU admission time and in-hospital mortality in The Netherlands.

Methods

This article relates time of ICU admission to hospital mortality for all patients who were included in the Dutch national ICU registry (National Intensive Care Evaluation, NICE) from 2002 to 2008. We defined office hours as 08:00–22:00 hours during weekdays and 09:00–18:00 hours during weekend days. The weekend was defined as from Saturday 00:00 hours until Sunday 24:00 hours. We corrected hospital mortality for illness severity at admission using Acute Physiology and Chronic Health Evaluation II (APACHE II) score, reason for admission, admission type, age and gender.

Results

A total of 149,894 patients were included in this analysis. The relative risk (RR) for mortality outside office hours was 1.059 (1.031–1.088). Mortality varied with time but was consistently higher than expected during “off hours” and lower during office hours. There was no significant difference in mortality between different weekdays of Monday to Thursday, but mortality increased slightly on Friday (RR 1.046; 1.001–1.092). During the weekend the RR was 1.103 (1.071–1.136) in comparison with the rest of the week.

Conclusions

Hospital mortality in The Netherlands appears to be increased outside office hours and during the weekends, even when corrected for illness severity at admission. However, incomplete adjustment for certain confounders might still play an important role. Further research is needed to fully explain this difference.

Electronic supplementary material

The online version of this article (doi:10.1007/s00134-010-1918-1) contains supplementary material, which is available to authorized users.

Designing and implementing an Australian and New Zealand intensive care data audit study

PURPOSE

The aim of this pilot audit study is to develop and test a model to examine existing adult patient database (APD) data quality.

DESIGN/METHODOLOGY/APPROACH

A database was created to audit 50 records per site to determine accuracy. The audited records were randomly selected from the calendar year 2004 and four sites participated in the pilot audit study. A total of 41 data elements were assessed for data quality--those elements required for APACHE II scoring system.

FINDINGS

Results showed that the audit was feasible; missing audit data were an unplanned problem; analysis was complicated owing to the way the APACHE calculations are performed and 50 records per site was too time-consuming.

ORIGINALITY/VALUE

This is the first audit study of intensive care data within the ANZICS APD and demonstrates how to determine data quality in a large database containing individual patient records.

[Abdominal compartment sydrome in trauma patients]

Intra-abdominal compartment syndrome (ACS) are increasingly recognised to be a contributing cause of organ dysfunction and mortality in critically ill patients. The term abdominal compartment syndrome (ACS) describes the clinical manifestations of the pathologic elevation of the intra abdominal pressure (IAP). This syndrome is most commonly observed in the setting of severe abdominal trauma. ACS affects mainly the respiratory, cardiovascular, renal, gastrointestinal and central nervous system. Preventing ACS by the identification of patients at risk and early diagnosis is paramount to its successful management. Because of the frequency of this condition, routine measurement of intra abdominal pressure should be performed in high risk patients in the intensive care unit. Surgical decompression is definitive treatment of fully developed abdominal compartment syndrome, but nonsurgical measures can often effectively affect lesser degrees of IAH and ACS.

Accuracy of Sequential Organ Failure Assessment (SOFA) scoring in clinical practice

BACKGROUND

The Sequential Organ Failure Assessment (SOFA) score is used to quantify the severity of illness daily during intensive care. Our aim was to evaluate how accurately SOFA is recorded in clinical practice, and whether this can be improved by a refresher course in scoring rules.

METHODS

The scores recorded by physicians in a university hospital intensive care unit (ICU) were compared with the gold standard determined by two expert assessors. Data concerning all consecutive patients during two 6-week-long observation periods (baseline and after the refresher course) were compared.

RESULTS

SOFA was accurate on 75/158 (48%) patient days at baseline. The cardiovascular, coagulation, liver, and renal component scores showed excellent accuracy (>or=82%, weighted kappa >or=0.92), while the neurological score showed only moderate (70%, weighted kappa 0.51) and the respiration score showed good accuracy (75%, weighted kappa 0.79). After the refresher course, the number of >or=2 point errors decreased (P<0.01). Sedation precluded neurological evaluation on 135/311 (43%) days. The accuracy of the assumed neurological scores was lower than those based on timely data: 89/135 (66%, weighted kappa 0.55) vs. 125/176 (71%, weighted kappa 0.81) (P<0.01).

CONCLUSION

Only half of the SOFA scores were accurate. In most cases, they were accurate enough to allow the recognition of organ failure and detection of change. The component scores showed good to excellent accuracy, except the neurological score. After the refresher course, the results improved slightly. The moderate accuracy of the neurological score was not amended. A simpler neurological classification tool than the Glasgow Coma Scale is needed in the ICU.

Quantifying data quality for clinical trials using electronic data capture

Background

Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes.

Methods and Principal Findings

The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions.

Conclusions

Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.

Use of the All Patient Refined-Diagnosis Related Group (APR-DRG) Risk of Mortality Score as a Severity Adjustor in the Medical ICU

Objective:

To evaluate the performance of APR-DRG (All Patient Refined—Diagnosis Related Group) Risk of Mortality (ROM) score as a mortality risk adjustor in the intensive care unit (ICU).

Design:

Retrospective analysis of hospital mortality.

Setting:

Medical ICU in a university hospital located in metropolitan New York.

Patients:

1213 patients admitted between February 2004 and March 2006.

Main results:

Mortality rate correlated significantly with increasing APR-DRG ROM scores (p < 0.0001). Multiple logistic regression analysis demonstrated that, after adjusting for patient age and disease group, APR-DRG ROM was significantly associated with mortality risk in patients, with a one unit increase in APR-DRG ROM associated with a 3-fold increase in mortality.

Conclusions:

APR-DRG ROM correlates closely with ICU mortality. Already available for many hospitalized patients around the world, it may provide a readily available means for severity-adjustment when physiologic scoring is not available.

External validation of prognostic models for critically ill patients required substantial sample sizes

OBJECTIVE

To investigate the behavior of predictive performance measures that are commonly used in external validation of prognostic models for outcome at intensive care units (ICUs).

STUDY DESIGN AND SETTING

Four prognostic models (Simplified Acute Physiology Score II, the Acute Physiology and Chronic Health Evaluation II, and the Mortality Probability Models II) were evaluated in the Dutch National Intensive Care Evaluation registry database. For each model discrimination (AUC), accuracy (Brier score), and two calibration measures were assessed on data from 41,239 ICU admissions. This validation procedure was repeated with smaller subsamples randomly drawn from the database, and the results were compared with those obtained on the entire data set.

RESULTS

Differences in performance between the models were small. The AUC and Brier score showed large variation with small samples. Standard errors of AUC values were accurate but the power to detect differences in performance was low. Calibration tests were extremely sensitive to sample size. Direct comparison of performance, without statistical analysis, was unreliable with either measure.

CONCLUSION

Substantial sample sizes are required for performance assessment and model comparison in external validation. Calibration statistics and significance tests should not be used in these settings. Instead, a simple customization method to repair lack-of-fit problems is recommended.

Reliability and accuracy of Sequential Organ Failure Assessment (SOFA) scoring

OBJECTIVE

The Sequential Organ Failure Assessment (SOFA) score was developed to quantify the severity of patients' illness, based on the degree of organ dysfunction. This study aimed to evaluate the accuracy and the reliability of SOFA scoring.

DESIGN

Prospective study.

SETTING

Adult intensive care unit (ICU) in a tertiary academic center.

SUBJECTS

Thirty randomly selected patient cases and 20 ICU physicians.

MEASUREMENTS AND MAIN RESULTS

Each physician scored 15 patient cases. The intraclass correlation coefficient was .889 for the total SOFA score. The weighted kappa values were moderate (0.552) for the central nervous system, good (0.634) for the respiratory system, and almost perfect (>0.8) for the other organ systems. To assess accuracy, the physicians' scores were compared with a gold standard based on consensus of two experts. The total SOFA score was correct in 53% (n = 158) of the cases. The mean of the absolute deviations of the recorded total SOFA scores from the gold standard total SOFA scores was 0.82. Common causes of errors were inattention, calculation errors, and misinterpretation of scoring rules.

CONCLUSIONS

The results of this study indicate that the reliability and the accuracy of SOFA scoring among physicians are good. We advise implementation of additional measures to further improve reliability and accuracy of SOFA scoring.

Redesign of diagnostic coding in pediatrics: from form-based to discharge letter linked

Diagnostic coding after hospital discharge is mainly based on abstracting of paper medical records by medical record coders. Studies show that the quality of these data is often moderate, possibly because discharge registries play no role in daily patient care. Timely writing of discharge letters is needed to support continuity of care, at least in the Netherlands. This article describes the redesign and evaluation of diagnosis registration and discharge letter writing at a Dutch pediatric department.Formerly, pediatricians at this department completed discharge forms. However, many forms were completed with insufficient information or not at all. Pediatricians now provide diagnoses with codes in a special heading of the discharge letter. The medical record coder checks and corrects this diagnosis heading. A list of diagnoses for pediatrics, based on ICD-9-CM, was developed and alphabetically ordered into one booklet used by pediatricians when dictating discharge letters. A reminder system for in-time writing of letters was implemented. Since 1995, this discharge letter-linked registration has proven to be applicable in daily care. How accurately pediatricians filled in the diagnosis heading was analyzed during two periods. In 1995, 25 percent of the diagnoses were initially (before adjustments made by the medical record coder) not coded or incorrectly coded; nine percent of these shortcomings could be attributed to the pediatricians. In 1997, 67 percent of the diagnoses were initially not coded or incorrectly coded; 37 percent of these shortcomings were attributable to pediatricians. Initially, only half of the letters were written within six weeks after discharge. The correction function of the medical record coder is indispensable.

Case mix, outcome and length of stay for admissions to adult, general critical care units in England, Wales and Northern Ireland: the Intensive Care National Audit & Research Centre Case Mix Programme Database

INTRODUCTION

The present paper describes the methods of data collection and validation employed in the Intensive Care National Audit & Research Centre Case Mix Programme (CMP), a national comparative audit of outcome for adult, critical care admissions. The paper also describes the case mix, outcome and activity of the admissions in the Case Mix Programme Database (CMPD).

METHODS

The CMP collects data on consecutive admissions to adult, general critical care units in England, Wales and Northern Ireland. Explicit steps are taken to ensure the accuracy of the data, including use of a dataset specification, of initial and refresher training courses, and of local and central validation of submitted data for incomplete, illogical and inconsistent values. Criteria for evaluating clinical databases developed by the Directory of Clinical Databases were applied to the CMPD. The case mix, outcome and activity for all admissions were briefly summarised.

RESULTS

The mean quality level achieved by the CMPD for the 10 Directory of Clinical Databases criteria was 3.4 (on a scale of 1 = worst to 4 = best). The CMPD contained validated data on 129,647 admissions to 128 units. The median age was 63 years, and 59% were male. The mean Acute Physiology and Chronic Health Evaluation II score was 16.5. Mortality was 20.3% in the CMP unit and was 30.8% at ultimate discharge from hospital. Nonsurvivors stayed longer in intensive care than did survivors (median 2.0 days versus 1.7 days in the CMP unit) but had a shorter total hospital length of stay (9 days versus 16 days). Results for the CMPD were comparable with results from other published reports of UK critical care admissions.

CONCLUSIONS

The CMP uses rigorous methods to ensure data are complete, valid and reliable. The CMP scores well against published criteria for high-quality clinical databases.