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At least 703 records · Page 39Linked to original sources
PubMed2004

Serum phytoestrogens and prostate cancer risk in a nested case-control study among Japanese men.

The purpose of this study was to examine whether a high serum concentration of phytoestrogens reduces the risk of prostate cancer in a case-control study nested in a community-based cohort in Japan (Japan Collaborative Cohort (JACC) Study). Information on lifestyles and sera of the subjects were collected in 1988-90, and they were followed up to 1999. Incident and dead cases of prostate cancer and controls were matched for study area and age. Phytoestrogens and sex hormones in sera stored at - 80 degrees C were measured in 2002. Of 14,105 male subjects of the cohort who donated their sera, 52 cases and 151 controls were identified. Three datasets were analyzed; 1) all subjects, 2) 40 cases and 101 controls after excluding subjects with low testosterone levels who were suspected of having had medical intervention, and 3) 28 cases and 69 controls with prostate specific antigen level of </= 10.0 ng/ml. The odds ratio (OR) for the highest level to the lowest was 0.38 (95% confidence interval (CI); 0.13, 1.13) for genistein, 0.41 (0.15, 1.11) for daidzein, and 0.34 (0.11, 1.10) for equol for the second dataset. Genistein and daidzein showed similar findings in the third one. Equol and equol/daidzein ratio showed consistent findings in all three datasets (OR = 0.39, 95% CI; 0.13, 0.89, trend P = 0.02 for the first dataset). Their effects seemed to be independent of serum sex hormones. In conclusion, serum genistein, daidzein, and equol seemed to dose-dependently reduce prostate cancer risk.

Adult↗
PubMed2006

Exploiting the potential of routine data to better understand the disease burden posed by allergic disorders.

The Department of Health and Scottish Executive are currently undertaking independent reviews of allergy services in England (and Wales) and Scotland. Each review will assess the disease burden posed by allergic problems, involving secondary analyses of routine National Health Service (NHS) datasets. Major suggestions for re-structuring and/or re-focusing the NHS efforts to better deal with allergic disease are anticipated. The UK has some of the best datasets of routine health data in the world, but despite their strengths, they have important limitations. These include gaps in data collection, particularly in relation to monitoring of Accident & Emergency and out-patient consultations, and in-patient prescribing, thereby resulting in considerable under-estimates of hospital workload. The current gaps in service monitoring are likely to under-estimate the burden and workload associated with allergic problems, particularly in secondary care. One major limitation of existing data sources is the general inability to link individual patient level data between different datasets. By unlocking this potential there are very considerable potential gains to be made. Data linkage techniques currently being developed in the UK offer exciting new possibilities of looking across the primary-, secondary- and tertiary-care interfaces and also assessing short-and long-term social and educational outcomes in relation to allergic disorders. The current reviews of allergy services being undertaken need to be cognisant of these inherent limitations of existing data sources and would do well to recommend strategic initiatives that could enhance the availability, accessibility and quality of these datasets. Ideally, this should include investment in central data repositories staffed by teams with the necessary technical and statistical expertise, which would also take responsibility for progressing data linkage capabilities.

Drug Prescriptions↗
PubMed2006

The importance of independent risk-factors for long-term mortality prediction after cardiac surgery.

The purpose of the present study was to determine independent predictors for long-term mortality after cardiac surgery. The European System for Cardiac Operative Risk Evaluation (EuroSCORE) was developed to score in-hospital mortality and recent studies have shown its ability to predict long-term mortality as well. We compared forecasts based on EuroSCORE with other models based on independent predictors. Medical records of patients with cardiac surgery who were discharged alive (n = 4852) were retrospectively reviewed. Their operative surgical risks were calculated according to EuroSCORE. Patients were randomly divided into two groups: training dataset (n = 3233) and validation dataset (n = 1619). Long-term survival data (mean follow-up 5.1 years) were obtained from the National Death Index. We compared four models: standard EuroSCORE (M1); logistic EuroSCORE (M2); M2 and other preoperative, intra-operative and post-operative selected variables (M3); and selected variables only (M4). M3 and M4 were determined with multivariable Cox regression analysis using the training dataset. The estimated five-year survival rates of the quartiles in compared models in the validation dataset were: 94.5%, 87.8%, 77.1%, 64.9% for M1; 95.1%, 88.0%, 80.5%, 64.4% for M2; 93.4%, 89.4%, 80.8%, 64.1% for M3; and 95.8%, 90.9%, 81.0%, 59.9% for M4. In the four models, the odds of death in the highest-risk quartile was 8.4-, 8.5-, 9.4- and 15.6-fold higher, respectively, than the odds of death in the lowest-risk quartile (P < 0.0001 for all). EuroSCORE is a good predictor of long-term mortality after cardiac surgery. We developed and validated a model using selected preoperative, intra-operative and post-operative variables that has better discriminatory ability.

Cardiac Surgical Procedures↗
PubMed2005

High-frequency oscillatory ventilation in adults with traumatic brain injury and acute respiratory distress syndrome.

BACKGROUND: This study observed adverse events of rescue treatment with high-frequency oscillatory ventilation (HFOV) in head-injured patients with acute respiratory distress syndrome (ARDS). METHODS: Data of five male patients with ARDS and traumatic brain injury, median age 28 years, who failed to respond to conventional pressure-controlled ventilation (PCV) were analyzed retrospectively during HFOV. Adjusted mean airway pressure at initiation of HFOV was set to 5 cm H2O above the last measured mean airway pressure during PCV. Frequency of pulmonary air leak, mucus obstruction, tracheal injury, and need of HFOV termination due to increased intracranial pressure, decreased cerebral perfusion pressure, or deterioration in P(a)CO2 were analyzed. RESULTS: During HFOV we found no complications. We recorded 390 datasets of intracranial pressure, cerebral perfusion pressure and P(a)CO2 simultaneously. Intracranial pressure increased (>25 mmHg) in 11 of 390 datasets, cerebral perfusion pressure was reduced (<70 mmHg) in 66 of 390 datasets, and P(a)CO2 variations (<4.7 kPa; >6.0 kPa) were observed in eight of 390 datasets after initiation of HFOV. All these alterations were responsive to treatment. P(a)O2/F(I)O2-ratio improved in four patients during HFOV. CONCLUSION: High-frequency oscillatory ventilation appears to be a promising alternative rescue treatment in head-injured patients with ARDS if continuous monitoring of intracranial pressure, cerebral perfusion pressure and P(a)CO2 are provided, in particular during initiation of HFOV.

Adolescent↗
PubMed2005

Predictors of mortality, length of stay and discharge destination in blunt trauma.

BACKGROUND: The present study explored a range of variables to identify predictors of mortality and morbidity and to develop prediction models based on these variables. METHODS: Tools for predicting mortality, hospital length of stay and a patient's destination post-hospital discharge were developed using logistic regression in one dataset (design) and evaluated for prediction performance in a separate dataset (validation). The performance of the mortality model was compared to the trauma and injury severity score (TRISS) and a severity characterization of trauma (ASCOT). RESULTS: The profile of variables contributing to the final prediction models developed from the design dataset varied across the different outcomes of interest although age, injury severity score, development of complications and triage category were common predictors of all three outcomes. The performance of the new mortality prediction model was superior to both TRISS and ASCOT in the validation dataset. Overall, the new models did not meet the prespecified performance criteria. CONCLUSIONS: The present study identified key predictors of mortality and morbidity (length of hospital stay and discharge destination). The newly developed mortality model out-performed published trauma scoring methods. However, further development and trial of the new prediction models is required before implementation as definitive audit and benchmarking tools could be recommended.

Adult↗
PubMed1993

Monitoring the targets of the St Vincent Declaration and the implementation of quality management in diabetes care: the DIABCARE initiative. The DIABCARE Monitoring Group of the St Vincent Declaration Steering Committee.

The St Vincent Declaration, a joint initiative on diabetes care and research of the World Health Organization (Europe) and the International Diabetes Federation (Europe), includes 5-year targets for improvement in diabetes outcomes as a central tenet. Accordingly, the establishment of state of the art monitoring and control systems is urged as a basis for the implementation of quality management. As a prerequisite for both targets, a diabetes dataset (fields and definitions) has been agreed to allow common monitoring of diabetes throughout Europe. This dataset has been further developed as the foundation stone of DiabCare, an initiative for continuous quality development in diabetes care. In a formal consensus process using the Delphi method, over 130 European diabetologists from 21 countries contributed to the development of this dataset, which includes fields covering true patient outcomes, intermediate metabolic outcomes, markers of diabetes tissue damage, risk factors, pregnancy, and life-style. The tools for documentation of the quality of health status have been developed in three formats for use in different health care settings. These tools, the DiabCare Diabetes Dataset, the DiabCare Basic Information Sheet, and the DiabCare Computer Program, are designed to allow local feedback-driven improvement in the quality of care, but are also the subject of communication protocols to compare performance between centres, regions, and countries. Whether implemented with or without the benefits of modern information technology, these initiatives can be the basis for both monitoring the targets of the St Vincent Declaration and for implementation of continuing quality development in diabetes care.

Delivery of Health Care↗
PubMed2003

A submillimeter resolution fluorescence molecular imaging system for small animal imaging.

Most current imaging systems developed for tomographic investigations of intact tissues using diffuse photons suffer from a limited number of sources and detectors. In this paper we describe the construction and evaluation of a large dataset, low noise tomographic system for fluorescence imaging in small animals. The system consists of a parallel plate-imaging chamber and a lens coupled CCD camera, which enables conventional planar imaging as well as fluorescence tomography. The planar imaging data are used to guide the acquisition of a Fluorescence Molecular Tomography (FMT) dataset containing more than 106 measurements, and to superimpose anatomical features with tomographic results for improved visual representation. Experimental measurements exhibited good agreement with the diffusion theory models used to predict light propagation within the chamber. Tests of the instrument's capacity to quantitatively reconstruct fluorochrome distributions in three dimensions showed less than 5% errors between actual fluorochrome concentrations and FMT findings, and suggested a detection threshold of approximately 100 femptomoles for small localized objects. Experiments to assess the instrument's spatial resolution demonstrated the ability of the system to resolve objects placed at clear distances of less than 1 mm. This is a significant resolution increase over previously developed systems for animal imaging, and is primarily due to the large dataset employed and the use of inversion methods. Finally, the in vivo imaging capacity is showcased. It is expected that the large dataset collected can enable superior imaging of molecular probes in vivo and improve quantification of fluorescence signatures.

Animals↗
PubMed2005

Use and uncertainties of mutual information for computed tomography/ magnetic resonance (CT/MR) registration post permanent implant of the prostate.

Post-implant dosimetric analysis for permanent implant of the prostate benefits from the use of a computed tomography (CT) dataset for optimal identification of the radioactive source (seed) positions and a magnetic resonance (MR) dataset for optimal description of the target and normal tissue volumes. The CT/MR registration process should be fast and sufficiently accurate to yield a reliable dosimetric analysis. Since critical normal tissues typically reside in dose gradient regions, small shifts in the dose distribution could impact the prediction of complication or complication severity. Standard procedures include the use of the seed distribution as fiducial markers (seed match), a time consuming process that relies on the proper identification of signals due to the same seed on both datasets. Mutual information (MI) is more efficient because it uses image data requiring minimal preparation effort. A comparison of MI registration and seed-match registration was performed for twelve patients. MI was applied to a volume limited to the prostate and surrounding structures, excluding most of the pelvic bone structures (margins around the prostate gland were approximately 2 cm right-left, approximately 1 cm anterior-posterior, and approximately 2 cm superior-inferior). Seeds were identified on a 2 mm slice CT dataset using an automatic seed identification procedure on reconstructed three-dimensional data. Seed positions on the 3 mm slice thickness T2 MR data set were identified using a point-and-click method on each image. Seed images were identified on more than one MR slice, and the results used to determine average seed coordinates for MR images and matched seed pairs between CT and MR images. On average, 42% (19%-64%) of the seeds (19-54 seeds) were identified and matched to their CT counterparts. A least-squares method applied to the CT and MR seed coordinates was used to produce the optimum seed-match registration. MI registration and seed match registration angle differences averaged 0.5 degrees, which was not significantly different from zero. Translation differences averaged 0.6 (1.2 standard deviation) mm right-left, -0.5(1.5) mm posterior-anterior, and -1.2(2.0) mm inferior-superior. Registration error estimates were approximately 2 mm for both the MI and seed-match methods. The observed standard deviations in the offset values were consistent with propagation of error. Registration methods as applied here using mutual information and seed matching are consistent, except for a small systematic difference in the inferior-superior axis for a minority of cases (approximately 15%). Cases registered with mutual information and with bony anatomy misregistration of greater than approximately 5 mm should be evaluated for rescan or seed-match registration. The improvement in efficiency of use for the MI registration method is substantial, approximately 30 min compared to several hours using seed match registration.

Algorithms↗
PubMed2005

Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy.

In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological, and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4-30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10-30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4-10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity.

Biophysical Phenomena↗
PubMed2006

Extrapolation of preclinical pharmacokinetics and molecular feature analysis of "discovery-like" molecules to predict human pharmacokinetics.

The prediction of human pharmacokinetics from preclinical species is an integral component of drug discovery. Recent studies with a 103-compound dataset suggested that scaling from monkey pharmacokinetic data tended to be the most accurate method for predicting human clearance. Additionally, interrogation of the two-dimensional molecular properties of these molecules produced a set of associations which predict the likely extrapolative outcome (success or failure) of preclinical data to project human pharmacokinetics. However, a limitation of the previous analyses was the relative paucity of data for typical "discovery-like" molecules (molecular weight >300 and/or clogP >3). The objective of this investigation was to generate preclinical data required for extension of this dataset for additional discovery-like molecules and determine whether the aforementioned findings continue to apply for these molecules. In vivo nonrodent intravenous pharmacokinetic data were generated for 13 molecules, and data for 8 additional molecules were obtained from the literature. Additionally, the various scaling methodologies and molecular features analysis were applied to this new dataset to predict human pharmacokinetics. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from monkey liver blood flow continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Lastly, application of the molecular feature associations, particularly data-dependent associations, afforded an improved predictivity compared with the liver blood flow scaling approaches, and provides insight into the extrapolation of high clearance compounds in the preclinical species to human.

Animals↗
PubMed2000

Capture-recapture: a useful methodological tool for counting traffic related injuries?

INTRODUCTION: Although the capture-recapture technique is increasingly employed in studies of human populations to correct for under-ascertainment in traditional epidemiological surveillance, it has rarely been used in injury research. OBJECTIVES: To estimate the completeness of official data sources on traffic related injuries (TRIs) by using the capture-recapture technique and to calculate an ascertainment corrected number of fatal and serious TRIs among Scottish young people aged 15-24 years. The appropriateness of the approach in this context is also assessed. METHOD: A two sample capture-recapture technique was applied to two official sources of TRI data. Data on TRIs were obtained from the Scottish Health Service and the STATS19 dataset at the University of Essex Data Archive for 1995. Four standards (A-D) of matching were applied to fatalities and serious TRIs to allow plausible relaxation of matching standards within the context of the data collection setting. The completeness of each data source was assessed, and an ascertainment corrected number of fatalities and serious TRIs calculated. RESULTS: The ascertainment corrected number of TRI fatalities among 15-24 year olds using standard D was 104. This represents only a small increase in the number of fatalities using capture-recapture than when using each individual dataset. The completeness of the Scottish Health Service database for TRI fatalities was 93%. The STATS19 database was 95% complete. The ascertainment corrected number of TRI hospital admissions was 1969. The STATS19 and the Scottish Health Service databases were approximately two thirds and three quarters complete respectively for non-fatal TRIs requiring hospitalisation. CONCLUSIONS: Injury researchers have advocated the linkage of major datasets to supplement and improve the quality of injury data. Using capture-recapture we found that routine databases enumerate TRI fatalities accurately, in contrast to injury morbidity databases that do not. Capture-recapture is a potentially useful method of evaluating the completeness of data sources and identifying biases within datasets. However, ascertainment corrected rates should be viewed with caution. A number of requirements of the capture-recapture technique are unachieved in this study of injury in the human population.

Accidents, Traffic↗
PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29&#x2009;709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85)&#x2009;and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗
PubMed2006

ON the influence of parameter theta- on performance of RBF neural networks trained with the dynamic decay adjustment algorithm.

The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks (RBFNs) and probabilistic neural networks (PNNs). The algorithm has two parameters, namely, theta(+) and theta(-). The papers which introduced DDA argued that those parameters would not heavily influence classification performance and therefore they recommended using always the default values of these parameters. In contrast, this paper shows that smaller values of parameter theta(-) can, for a considerable number of datasets, result in strong improvement in generalization performance. The experiments described here were carried out using twenty benchmark classification datasets from both Proben1 and the UCI repositories. The results show that for eleven of the datasets, the parameter theta(-) strongly influenced classification performance. The influence of theta(-) was also noticeable, although much less, on six of the datasets considered. This paper also compares the performance of RBF-DDA with theta(-) selection with both AdaBoost and Support Vector Machines (SVMs).

Algorithms↗
PubMed2006

ANGLE: a sequencing errors resistant program for predicting protein coding regions in unfinished cDNA.

In the process of making full-length cDNA, predicting protein coding regions helps both in the preliminary analysis of genes and in any succeeding process. However, unfinished cDNA contains artifacts including many sequencing errors, which hinder the correct evaluation of coding sequences. Especially, predictions of short sequences are difficult because they provide little information for evaluating coding potential. In this paper, we describe ANGLE, a new program for predicting coding sequences in low quality cDNA. To achieve error-tolerant prediction, ANGLE uses a machine-learning approach, which makes better expression of coding sequence maximizing the use of limited information from input sequences. Our method utilizes not only codon usage, but also protein structure information which is difficult to be used for stochastic model-based algorithms, and optimizes limited information from a short segment when deciding coding potential, with the result that predictive accuracy does not depend on the length of an input sequence. The performance of ANGLE is compared with ESTSCAN on four dataset each of them having a different error rate (one frame-shift error or one substitution error per 200-500 nucleotides) and on one dataset which has no error. ANGLE outperforms ESTSCAN by 9.26% in average Matthews's correlation coefficient on short sequence dataset (< 1000 bases). On long sequence dataset, ANGLE achieves comparable performance.

Algorithms↗
PubMed2006

Prediction of intravenous immunoglobulin unresponsiveness in patients with Kawasaki disease.

BACKGROUND: In the present study, we developed models to predict unresponsiveness to intravenous immunoglobulin (IVIG) in Kawasaki disease (KD). METHODS AND RESULTS: We reviewed clinical records of 546 consecutive KD patients (development dataset) and 204 subsequent KD patients (validation dataset). All received IVIG for treatment of KD. IVIG nonresponders were defined by fever persisting beyond 24 hours or recrudescent fever associated with KD symptoms after an afebrile period. A 7-variable logistic model was constructed, including day of illness at initial treatment, age in months, percentage of white blood cells representing neutrophils, platelet count, and serum aspartate aminotransferase, sodium, and C-reactive protein, which generated an area under the receiver-operating-characteristics curve of 0.84 and 0.90 for the development and validation datasets, respectively. Using both datasets, the 7 variables were used to generate a simple scoring model that gave an area under the receiver-operating-characteristics curve of 0.85. For a cutoff of 0.15 or more in the logistic regression model and 4 points or more in the simple scoring model, sensitivity and specificity were 86% and 67% in the logistic model and 86% and 68% in the simple scoring model. The kappa statistic is 0.67, indicating good agreement between the logistic and simple scoring models. CONCLUSIONS: Our predictive models showed high sensitivity and specificity in identifying IVIG nonresponders among KD patients.

Adolescent↗
PubMed1984

MRIDIR--a system for integrating research databases.

Lederle's Medical Research Integrated Database Information Retrieval (MRIDIR) system provides timely and facile access to a growing body of computerized, in-house data relating to pharmaceutical discovery and development. It was created in response to a proliferation of special-purpose programs, each dealing with a specific dataset and each with its own query syntax. Written in FORTRAN and interfacing to the System-1022 Database Management System, MRIDIR provides relational links between these datasets and, from the user's viewpoint, integrates them into a single database with a simple query language. A special dataset called the "data dictionary" describes these linkages, and alterations to the database structure (eg, addition of new datasets) are smoothly accomplished through changes to the data dictionary.

Drug Evaluation↗
PubMed2005

Impact of operator expertise on collection of the APACHE II score and on the derived risk of death and standardized mortality ratio.

We assessed the impact of operator expertise on collection of the APACHE II score, the derived risk of death and standardized mortality ratio in 465 consecutive patients admitted to a multi-disciplinary tertiary hospital ICU. Research coordinators and junior clinical staff independently collected the APACHE II variables; experts (senior clinical staff) rescored 20% of the records. Agreement was moderate between junior clinical staff and research coordinators or senior clinical staff for most variables of the acute physiology score (weighted kappa<0.6); agreement between research coordinators and senior clinical staff data collectors was good (weighted kappa >0.75). The APACHE II score and its derived risk of death (ROD) were significantly lower using the junior clinical staff dataset compared to research coordinators and senior clinical staff (APACHE II score: 13.4+/-9.2 vs 16.8+/-8.5 vs 17.1+/-7 7, P<0.001; ROD: 14.7%+/-22.4% vs 21.6%+/-22.6% vs 20.8%+/-22.4%, P<0.01 respectively). The discriminative capacity was not altered by the lack of agreement (area under Receiver Operator Characteristic curve >0.8) but calibration of ROD from the junior clinical staff dataset was poor (Goodness-of-fit: P= 0.001). The standardized mortality ratio (SMR) was higher with the junior clinical staff dataset (SMR: 1.22, 95% CI: 0.96-1.52 vs 0.87, 95% CI: 0.70-1.06 vs 0.76, 95% CI: 0.40-1.3 calculated from junior clinical staff research coordinators and senior clinical staff datasets respectively). We conclude that the expertise of data collectors significantly influences the APACHE II score, the derived risk of death and the standardized mortality ratio. Given the importance of such scores, ICUs should be provided with sufficient resources to train and employ dedicated data collectors.

APACHE↗
PubMed1999

A survey of Australasian obstetric anaesthesia audit.

In order to develop a minimal obstetric anaesthesia dataset based on current Australasian clinical audit best practice, we carried out a postal survey of 69 Australasian anaesthetic departments covering an obstetric service. We asked about data being collected, specifically concerning the high risk obstetric patient, epidural analgesia and postoperative anaesthetic review. Examples of any data collection forms were requested. Of the 66 responses, 35 departments (53%) were not collecting any audit data. Twenty-six of the 31 departments (84%) performing obstetric anaesthesia audit responded to our follow-up telephone survey. Eighteen departments believed that there had been an improvement in patient care as a result of their audit and 13 felt that the benefits outweighed the costs involved. However, only six departments (9%) had performed an audit cycle. The importance of feedback to patients or hospital staff and the incidence of post dural puncture headache (PDPH) were cited by some as priorities for obstetric anaesthesia audit. There was however no consistency as to what data should be collected. Many responses suggested a perceived need to collect clinical data without knowing what to do with it. Our survey has highlighted confusion between three distinct objectives; a dataset for obstetric anaesthesia record keeping, data required for continuing patient management in hospital and, a specific minimal dataset for clinical audit purposes. We conclude that current Australasian obstetric anaesthesia audit strategies are inadequate to develop a minimal dataset for cost-effective clinical audit.

Analgesia, Epidural↗