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OBJECTIVE: To identify pre-operative factors associated with in-hospital mortality following lung resection and to construct a risk model that could be used prospectively to inform decisions and retrospectively to enable fair comparisons of outcomes. METHODS: Data were submitted to the European Thoracic Surgery Database from 27 units in 14 countries. We analysed data concerning all patients that had a lung resection. Logistic regression was used with a random sample of 60% of cases to identify pre-operative factors associated with in-hospital mortality and to build a model of risk. The resulting model was tested on the remaining 40% of patients. A second model based on age and ppoFEV1% was developed for risk of in-hospital death amongst tumour resection patients. RESULTS: Of the 3426 adult patients that had a first lung resection for whom mortality data were available, 66 died within the same hospital admission. Within the data used for model development, dyspnoea (according to the Medical Research Council classification), ASA (American Society of Anaesthesiologists) score, class of procedure and age were found to be significantly associated with in-hospital death in a multivariate analysis. The logistic model developed on these data displayed predictive value when tested on the remaining data. CONCLUSIONS: Two models of the risk of in-hospital death amongst adult patients undergoing lung resection have been developed. The models show predictive value and can be used to discern between high-risk and low-risk patients. Amongst the test data, the model developed for all diagnoses performed well at low risk, underestimated mortality at medium risk and overestimated mortality at high risk. The second model for resection of lung neoplasms was developed after establishing the performance of the first model and so could not be tested robustly. That said, we were encouraged by its performance over the entire range of estimated risk. The first of these two models could be regarded as an evaluation based on clinically available criteria while the second uses data obtained from objective measurement. We are optimistic that further model development and testing will provide a tool suitable for case mix adjustment.
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BACKGROUND: Diffusion-weighted imaging (DWI) abnormalities can frequently be detected after carotid endarterectomy (CEA) and carotid angioplasty with stent placement (CAS) of the carotid arteries. We looked for possible predictors for the development of DWI lesions during the intervention. METHODS: We investigated 41 patients who underwent CAS without protection devices and 93 patients who underwent CEA. DWI studies were performed 1 day before and after the intervention. RESULTS: Ischemic complications consisted of two strokes (2.2%) in the CEA group and one stroke (2.4%) in the CAS group. DWI lesions were detected in 28.0% of all patients after intervention. Using a multivariate regression analysis, diabetes mellitus (DM), hyperlipidemia, symptomatic stenosis, age and CAS were found to be significant predictors for the occurrence of DWI lesions. CONCLUSIONS: DWI is an objective and highly sensitive method for monitoring interventions of the carotid arteries. Our results point to an increased risk of patients with diabetes and hyperlipidemia to develop DWI lesions during invasive therapy of the ICA.
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OBJECTIVE: To demonstrate that human overeating is not just a passive response to salient environmental triggers and powerful physiological drives; it is also about making choices. The ventromedial prefrontal cortex has been strongly implicated in the neural circuitry necessary for making advantageous decisions when various options for action are available. Decision-making deficits have been found in patients with ventromedial prefrontal cortex lesions and in those with substance dependence--impairments that reflect an inability to advantageously assess future consequences. That is, they choose immediate rewards in the face of future long-term negative consequences. RESEARCH METHODS AND PROCEDURES: We extended this research to the study of overeating and overweight, testing a regression model that predicted that poor decision making (as assessed by a validated computerized gambling task) and a tendency to overeat under stress would correlate with higher BMI in a group of healthy adult women (N = 41) representing a broad range of body weights. RESULTS: We found statistically significant main effects for both independent variables in the predicted direction (p < 0.05; R2 = 0.35). Indeed, the decision-making impairments across the 100 trials of the computer task were greater in those with high BMI than in previous studies with drug addicts. DISCUSSION: Findings suggested that cortical and subcortical processes, which regulate one's ability to inhibit short-term rewards when the long-term consequences are deleterious, may also influence eating behaviors in a culture dominated by so many, and such varied, sources of palatable and calorically dense sources of energy.
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OBJECTIVE: To estimate the relation between alcohol consumption and risk of death, the level of alcohol consumption at which risk is least, and how these vary with age and sex. DESIGN: Analysis using published systematic reviews and population data. SETTING: England and Wales in 1997. MAIN OUTCOME MEASURES: Death from any of the following causes: cancer of lip, oral cavity, pharynx, oesophagus, colon, rectum, liver, larynx, and breast, essential hypertension, coronary heart disease, stroke, cirrhosis, non-cirrhotic chronic liver disease, chronic pancreatitis, and injuries. RESULTS: A direct dose-response relation exists between alcohol consumption and risk of death in women aged 16-54 and in men aged 16-34. At older ages the relation is U shaped. The level at which the risk is lowest increases with age, reaching 3 units a week in women aged over 65 and 8 units a week in men aged over 65. The level at which the risk is increased by 5% above this minimum is 8 units a week in women aged 16-24 and 5 units a week in men aged 16-24, increasing to 20 and 34 units a week in women and men aged over 65, respectively. CONCLUSIONS: Substantially increased risks of all cause mortality can occur even in people drinking lower than recommended limits, and especially among younger people.
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Cancer risk models and their relationship to ionizing radiation are discussed. There are many model assumptions and risk factors that have a large quantitative impact on the cancer risk estimates. Other health end points such as mental retardation may be an even more serious risk than cancer for those with in utero exposures.
Competing events concerning individual subjects are of interest in many medical studies. For example, leukemia-free patients surviving a bone marrow transplant are at risk of developing acute or chronic graft-versus-host disease, or they might develop infections. In this situation, competing risks models provide a natural framework to describe the disease. When incorporating covariates influencing the transition intensities, an obvious approach is to use Cox's proportional hazards model for each of the transitions separately. A practical problem then is how to deal with the abundance of regression parameters. Our objective is to describe the competing risks model in fewer parameters, both in order to avoid imprecise estimation in transitions with rare events and in order to facilitate interpretation of these estimates. Suppose that the regression parameters are gathered into a p x K matrix B, with p and K as the number of covariates and transitions, respectively. We propose the use of reduced rank models, where B is required to be of lower rank R, smaller than both p and K. One way to achieve this is to write B = AGamma(intercal) with A and Gamma matrices of dimensions p x R and K x R, respectively. We shall outline an algorithm to obtain estimates and their standard errors in a reduced rank proportional hazards model for competing risks and illustrate the approach on a competing risks model applied to 8966 leukemia patients from the European Group for Blood and Marrow Transplantation.
Adoption of a Bayesian framework for risk characterization permits the seamless integration of different kinds of information available in order to choose and parameterize risk models. It also becomes easy to disentangle uncertainty from variability, through hierarchical statistical modeling. Appropriate numerical techniques can be found, for example, in the recently developed arsenal of Markov chain, Monte Carlo simulations. The developments in this area can actually be viewed as extensions of the traditional or standard Monte Carlo methods for uncertainty analysis. Following a brief review of the techniques, examples of Bayesian analyses of physiologically-based pharmacokinetic models are presented for tetrachloroethylene and dichloromethane. The discussion touches on some open problems and perspectives for the proposed methods.
The objectives of this analysis were to develop and validate simplified risk score models for predicting the risk of major in-hospital complications after percutaneous coronary intervention (PCI) in the era of widespread stenting and use of glycoprotein IIb/IIIa antagonists. We then sought to compare the performance of these simplified models with those of full logistic regression and neural network models. From January 1, 1997 to December 31, 1999, data were collected on 4,264 consecutive interventional procedures at a single center. Risk score models were derived from multiple logistic regression models using the first 2,804 cases and then validated on the final 1,460 cases. The area under the receiver operating characteristic (ROC) curve for the risk score model that predicted death was 0.86 compared with 0.85 for the multiple logistic model and 0.83 for the neural network model (validation set). For the combined end points of death, myocardial infarction, or bypass surgery, the corresponding areas under the ROC curves were 0.74, 0.78, and 0.81, respectively. Previously identified risk factors were confirmed in this analysis. The use of stents was associated with a decreased risk of in-hospital complications. Thus, risk score models can accurately predict the risk of major in-hospital complications after PCI. Their discriminatory power is comparable to those of logistic models and neural network models. Accurate bedside risk stratification may be achieved with these simple models.
BACKGROUND: We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. METHODS: Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. MAIN RESULTS: For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. CONCLUSIONS: Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.
Empirical studies have established that when pairs of similar cars collide, the relative injury risk between pairs of different size is inversely related to their mass ratio. Further empirical studies have shown that in frontal collisions between dissimilar cars, relative injury risk is inversely proportional to mass ratio raised to the power of n. The value of the exponent n increases with impact speed, with n approximately 1 at low speeds and n ranging from 2.81 to 3.74 for fatalities. In this paper a theory is derived which explains relative injury risk in terms of three parameters: length (or size) ratio, mass ratio and the ratio of collision energy absorption between the colliding vehicles. It is proposed that the ratio of collision energy absorption between colliding vehicles is a function of the structural collapse forces imposed at maximum dynamic crush. The theory shows that the fundamental factor in collisions between pairs of similar cars is size, i.e. length. For collisions between two dissimilar cars, Monte-Carlo simulations using generalised characterisations for the car population yield theoretical predictions that match empirical findings ranging from minor injuries (AISI +) to fatal (AIS6) injuries.