Global quantitative indices reflecting provider process-of-care: data-base derivation

Background: Controversy has attended the relationship between risk-adjusted mortality and process-of-care. There would be advantage in the establishment, at the data-base level, of global quantitative indices subsuming the diversity of process-of-care. Methods: A retrospective, cohort study of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 1993-2003, at the level of geographic and ICU-level descriptors (n = 35), for both hospital survivors and non-survivors. Process-of-care indices were established by analysis of: (i) the smoothed time-hazard curve of individual patient discharge and determined by pharmaco-kinetic methods as area under the hazard-curve (AUC), reflecting the integrated experience of the discharge process, and time-to-peak-hazard (TMAX, in days), reflecting the time to maximum rate of hospital discharge; and (ii) individual patient ability to optimize output (as length-of-stay) for recorded data-base physiological inputs; estimated as a technical production-efficiency (TE, scaled [0,(maximum)1]), via the econometric technique of stochastic frontier analysis. For each descriptor, multivariate correlation-relationships between indices and summed mortality probability were determined. Results: The data-set consisted of 223129 patients from 99 ICUs with mean (SD) age and APACHE III score of 59.2(18.9) years and 52.7(30.6) respectively; 41.7% were female and 45.7% were mechanically ventilated within the first 24 hours post-admission. For survivors, AUC was maximal in rural and for-profit ICUs, whereas TMAX (≥ 7.8 days) and TE (≥ 0.74) were maximal in tertiary-ICUs. For non-survivors, AUC was maximal in tertiary-ICUs, but TMAX (≥ 4.2 days) and TE (≥ 0.69) were maximal in for-profit ICUs. Across descriptors, significant differences in indices were demonstrated (analysisof- variance, P ≤ 0.0001). Total explained variance, for survivors (0.89) and non-survivors (0.89), was maximized by combinations of indices demonstrating a low correlation with mortality probability. Conclusions: Global indices reflecting process of care may be formally established at the level of national patient databases. These indices appear orthogonal to mortality outcome.John L Moran, Patricia J Solomon and the Adult Database Management Committee (ADMC) of the Australian and New Zealand Intensive Care Society (ANZICS

A comparison of Child-Pugh, APACHE II and APACHE III scoring systems in predicting hospital mortality of patients with liver cirrhosis

BACKGROUND: The aim of this study was to assess the prognostic accuracy of Child-Pugh and APACHE II and III scoring systems in predicting short-term, hospital mortality of patients with liver cirrhosis. METHODS: 200 admissions of 147 cirrhotic patients (44% viral-associated liver cirrhosis, 33% alcoholic, 18.5% cryptogenic, 4.5% both viral and alcoholic) were studied prospectively. Clinical and laboratory data conforming to the Child-Pugh, APACHE II and III scores were recorded on day 1 for all patients. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under a ROC curve (AUC). Calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test. RESULTS: Overall mortality was 11.5%. The mean Child-Pugh, APACHE II and III scores for survivors were found to be significantly lower than those of nonsurvivors. Discrimination was excellent for Child-Pugh (ROC AUC: 0.859) and APACHE III (ROC AUC: 0.816) scores, and acceptable for APACHE II score (ROC AUC: 0.759). Although the Hosmer-Lemeshow statistic revealed adequate goodness-of-fit for Child-Pugh score (P = 0.192), this was not the case for APACHE II and III scores (P = 0.004 and 0.003 respectively) CONCLUSION: Our results indicate that, of the three models, Child-Pugh score had the least statistically significant discrepancy between predicted and observed mortality across the strata of increasing predicting mortality. This supports the hypothesis that APACHE scores do not work accurately outside ICU settings

Results from the national sepsis practice survey: predictions about mortality and morbidity and recommendations for limitation of care orders

Introduction: Critically ill patients and families rely upon physicians to provide estimates of prognosis and recommendations for care. Little is known about patient and clinician factors which influence these predictions. The association between these predictions and recommendations for continued aggressive care is also understudied. Methods: We administered a mail-based survey with simulated clinical vignettes to a random sample of the Critical Care Assembly of the American Thoracic Society. Vignettes represented a patient with septic shock with multi-organ failure with identical APACHE II scores and sepsis-associated organ failures. Vignettes varied by age (50 or 70 years old), body mass index (BMI) (normal or obese) and co-morbidities (none or recently diagnosed stage IIA lung cancer). All subjects received the vignettes with the highest and lowest mortality predictions from pilot testing and two additional, randomly selected vignettes. Respondents estimated outcomes and selected care for each hypothetical patient. Results: Despite identical severity of illness, the range of estimates for hospital mortality (5th to 95th percentile range, 17% to 78%) and for problems with self-care (5th to 95th percentile range, 2% to 74%) was wide. Similar variation was observed when clinical factors (age, BMI, and co-morbidities) were identical. Estimates of hospital mortality and problems with self-care among survivors were significantly higher in vignettes with obese BMIs (4.3% and 5.3% higher, respectively), older age (8.2% and 11.6% higher, respectively), and cancer diagnosis (5.9% and 6.9% higher, respectively). Higher estimates of mortality (adjusted odds ratio 1.29 per 10% increase in predicted mortality), perceived problems with self-care (adjusted odds ratio 1.26 per 10% increase in predicted problems with self-care), and early-stage lung cancer (adjusted odds ratio 5.82) were independently associated with recommendations to limit care. Conclusions: The studied clinical factors were consistently associated with poorer outcome predictions but did not explain the variation in prognoses offered by experienced physicians. These observations raise concern that provided information and the resulting decisions about continued aggressive care may be influenced by individual physician perception. To provide more reliable and accurate estimates of outcomes, tools are needed which incorporate patient characteristics and preferences with physician predictions and practices

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

Abnormal pancreatic enzymes and their prognostic role after acute paraquat poisoning

Ingestion of paraquat causes multi-organ failure. Prognosis is best estimated through measurement of blood paraquat concentrations but this facility is not available in most hospitals. We studied the prognostic significance of abnormal pancreatic enzymes for survival. Patients with acute paraquat poisoning were recruited. An extensive series of blood tests including serum amylase were serially checked. Patients were sorted according to their serum amylase activity (normal [<220 U/L], mildly elevated [220 to 660 U/L], elevated [>660 U/L]), and survival compared between groups. 177 patients were enrolled to the study, of whom 67 died and 110 survived. 122 (70.62%), 27 (15.25%) and 25 (14.13%) patients were in the normal, mildly elevated and elevated amylase activity groups, respectively. The case fatality in the elevated group was 100% compared to 17% in the normal group (P < 0.001). We found four independent factors for paraquat death prediction: amylase, PaCO(2), leukocyte number, and neutrophil percentage. Models using pancreatic enzyme activity showed good prediction power. We have found that abnormal pancreatic enzymes are useful prognostic marker of death after acute paraquat poisoning. Including serum amylase activity into a prognostic model provides a good prognostication