Baselines baseline risk

Human health risk assessment estimates the likelihood of health problems occurring if no cleanup action were ttikcn at the site. To cstiimite the baseline risk at a site, the following four-step process should be taken to detennine Uie possible human risk which will then detennine what sort of corrective action should be employed ... 

The first step in preventing PONV is reducing baseline risk factors when appropriate.6 Regional anesthesia rather than general anesthesia should be used. Opioids should be replaced... 

Evaluating the results of data analysis to characterize the site and develop a baseline risk assessment... 

A draft RI report should be produced for review by the support agency and submitted to the Agency for Toxic Substances and Disease Registry (ATSDR) for its use in preparing a health assessment and also to serve as documentation of data collection and analysis in support of the FS. The draft RI report can be prepared any time between the completion of the baseline risk assessment and the completion of the draft FS. Therefore, the draft RI report should not delay the initiation or execution of the FS. 

The exposure pathways of concern identified during the baseline risk assessment include direct contact, with the possible ingestion of contaminated soil (1 x 10 3 4 associated excess cancer risk), and potential ingestion of contaminated groundwater in the future through existing or newly installed offsite wells (2 x 11 0 2 associated excess cancer risk). 

Table 16.9 lists five remedial alternatives and their primary components. The nonaction alternative (Alternative 1) provides a baseline for comparison of other alternatives. Because no remedial activities will be implemented with the nonaction alternative, long-term human health and environmental risks for the site essentially will be the same as those identified in the baseline risk assessment. All other action alternatives with action have four common components ... 

No definitive conclusions can be drawn concerning a possible role of rifaximin in preventing major complications of diverticular disease. Double-blind placebo-controlled trials with an adequate sample size are needed. However, such trials are difficult to perform considering the requirement of a large number of patients. Assuming a baseline risk of complications of diverticular disease of 5% per year [2], a randomized controlled trial able to detect a 50% risk reduction in complications should include 1,600 patients per treatment group considering a power of 80% (1 - (3) and an a error of 5%. 

Teriparatide is contraindicated in patients at baseline increased risk for osteosarcoma (e.g., Paget s bone disease, unexplained alkaline phosphatase elevations, pediatric patients, young adults with open epiphyses, or patients with prior radiation therapy involving the skeleton). 

Ziprasidone prolonged the QTc interval about one-half as much as thioridazine. Ziprasidone s effect on the ECG is probably without clinical sequelae except in patients with baseline risk factors. 

Compounds of the same therapeutic class may profoundly differ as to QT prolonging potential in addition, the assessment of the proarrhythmic risk must take into account pharmacokinetic aspects including metabolism, since a very low baseline proarrhythmic risk may become clinically important in case of drug interactions leading to higher than expected plasma levels. 

Indirect comparisons are potentially subject to methodological flaws, mainly because, outside the confines of a single randomized controlled clinical trial, one cannot be sure that the patients enrolled in the various trials are equivalent in terms of baseline risk, the settings for the trials are comparable, and that endpoints are measured in the same way. Therefore, an apparent superiority for one therapy over another, in an indirect comparison, may be as much due to differences in the trials as to differences between the therapies themselves. 

Block randomisation therefore forces the required balance in terms of the numbers of patients in the treatment groups, but things can still go wrong. For example, let s suppose in an oncology study with time to death as the primary endpoint that we can measure baseline risk (say in terms of the size of the primary tumour) and classify patients as either high risk (H) or low risk (L) and further suppose that the groups turn out as follows ... 

Now suppose that the mean survival times are observed to be 21.5 months in A and 27.8 months in group B. What conclusions can we draw It is very difficult the difference we have seen could be due to treatment differences or could be caused by the imbalance in terms of differential risk across the groups, or a mixture of the two. Statisticians talk in terms of confounding (just a fancy way of saying mixed up ) between the treatment effect and the effect of baseline risk. This situation is very difficult to unravel and we avoid it by stratified randomisation to ensure that the case mix in the treatment groups is comparable. 

There may be other factors that we wish to adjust, for example, age, sex, baseline risk and so on and there are several reasons why we might want to do this. 

Using several different statistical methods, for example, an unpaired t-test, an analysis adjusted for centre effects, ANCOVA adjusting for centre and including baseline risk as a covariate, etc., and choosing that method which produces the smallest p-value is another form of multiplicity and is inappropriate. 

EPA (1991a). U.S. Environmental Protection Agency. Role of Baseline Risk Assessment in Superfund Remedy Selection Decisions, OSWER Directive 9355.0-30 (National Technical Information Service, Springfield, Virginia). 

The major features of treatment guidelines concern indications for treatment, the particular treatments to be used, and in the case of biomedical risk factors, target levels following intervention. Figure 2 shows how these aspects are informed by clinical trials. In particular, net benefit depends on the absolute risk reduction (related to both baseline risk and relative risk reduction) and safety of the treatment. The greater the risk of the patient group or individual for future events, the greater is the absolute risk reduction with therapy (Fig. 3). Health policy decisions are informed not only by outcome data but also by cost-effectiveness analyses, Cost-effectiveness in turn relates to the absolute benefits that are observed. 

The baseline absolute risk of an Ml during normal daily life is low—one chance in a million per hour for a healthy adult, and 10 chances in a million per hour for a patient with documented cardiac disease. Therefore, during the two hours post-sex, the risk increases to 2.5 in a million for a healthy adult and 25 in a million for a patient with documented cardiac disease, but, importantly, there is no risk... 

Because of the uncertain relevance of the rat osteosarcomas finding to humans, teriparatide should be prescribed only to patients for whom the potential benefits are considered to outweigh the potential risk. Teriparatide should not be prescribed for patients who are an increased baseline risk for osteosacrcoma (including those with Paget s disease of bone or unexplained... 

During the hour after cocaine is used, the risk of myocardial infarction is 24 times the baseline risk (53). Cocaine users have a lifetime risk of non-fatal myocardial infarction that is seven times the risk in non-users, and cocaine use accounts for up to 25% of cases of acute myocardial infarction in patients aged 18 15 years (54). In 2000, there were 175 000 cocaine-related visits to emergency departments in the USA (55), with chest discomfort in 40% of the patients (56), 57% of whom were admitted to the hospital and had an admission lasting an average of 3 days (57), involving huge costs (58). 

Model 1 includes baseline risk factor and demographic data model 2 incorporates event characteristics, and model 3 adds investigation results. Note that the simplest model has very similar discriminatory power, as measured by the AUC-ROC, as the more complex models. 

However, such large treatment effects are rare. Most treatments used in medicine have smaller effects that require assessment in randomized controlled trials if they are to be reliably quantified. Specifically, randomization has two main advantages over a non-randomized comparison. First, it ensures that clinicians do not know which treatment the patient will receive, and cannot select certain types of patient for one particular treatment. Second, it tends to result in an equal balance of baseline risk across the treatment groups. 

Patients with an acute ischemic stroke have a higher baseline risk of intracranial bleeding, and medium-dose aspirin produced an excess of about two intracranial bleeds per 1000 during a treatment period of just a few weeks in two recent, large, randomized trials (47,48). However, it is clear from these trials that any increased risk of intracranial bleeding is still substantially outweighed by the associated reductions in recurrent stroke, death and disability (particularly in the absence of concomitant heparin use). 

Where a baseline risk is low, a statement of relative risk alone is particularly misleading as it implies big benefit where actual benefit is small. Thus a reduction of risk from 2% to 1% is 50% relative risk reduction, but it saves only one patient for every 100 patients treated. But where the baseline is high, say 40%, a 50% reduction in relative risk saves 20 patients for every 100 treated. 

Calculation of a frequency (or rate) of the number of reports per 1000 patients, prescriptions, DDD/ lOOOp / day or another available denominator. From there several external comparisons can be made with similar frequencies based on number of cases in an unexposed population, also coined as baseline risk or background frequency, or the number of cases in a population exposed to another medicine from the same therapeutic category. This approach is only possible when reliable denominator data are present. Moreover the head-to-head comparisons require limited under-, over- or selective reporting in the two frequencies of observed possible induced drug problems. 

Another feature of this third wave of pharmacovigilance learning is the growing notion that populations exposed to certain drugs carry specific baseline risks. There is increasing evidence that the hkehhood of the majority of the problems we face in pharmacovigilance is in certain patients more at risk than others. Therefore a critical part of pharmacovigilance is seen... 

Was the potential impact of different baseline risk in the treatment population estimated on costs and outcomes ... 

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