Adverse events examples

The Vaccine Adverse Event Reporting System (VAERS) is a national vaccine safety surveillance program co-sponsored by the Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA). VAERS collects and analyzes information from reports of adverse reactions after immunization. Anyone can report to VAERS, and reports are sent in by vaccine manufacturers, health care providers, and vaccine recipients and their parents or guardians. An example of the VAERS and instructions for completing the form are found in Appendix F. Any clinically significant adverse event that occurs after the administration of any vaccine should be reported. Individuals are encouraged to provide the information on the form even if the individual is uncertain if the event was related to the... 

The forms used for expedited reporting of adverse events are listed in Table 12.2. An example of mandatory reporting form FDA3500A is shotvn in Figure 12.9. 

Aleatory uncertainty —the roll of the die—describes risks that cannot practicably be predicted within the research process, for example, new failure modes, or modes that can only be detected in late stages of work, for example, humans. An example of aleatory uncertainty is the withdrawal in the UK of Bextra on the basis of two serious adverse events out of 40,000 patients this could only be discovered after launch [2]. This, without hindsight, was an uncontrollable risk. ... 

Expert opinion is a source, frequently elicited by survey, that is used to obtain information where no or few data are available. For example, in our experience with a multicountry evaluation of health care resource utilization in atrial fibrillation, very few country-specific published data were available on this subject. Thus the decision-analytic model was supplemented with data from a physician expert panel survey to determine initial management approach (rate control vs. cardioversion) first-, second-, and third-line agents doses and durations of therapy type and frequency of studies that would be performed to initiate and monitor therapy type and frequency of adverse events, by body system and the resources used to manage them place of treatment and adverse consequences of lack of atrial fibrillation control and cost of these consequences, for example, stroke, congestive heart failure. This method may also be used in testing the robustness of the analysis [30]. 

Adverse events need to be coded consistently with respect to letter case. Problems can occur when there is discordant coding using all capital letters, all lower-case letters, or combinations thereof, as computer software will interpret these capitalization variations as different events. Letter case sensitivity can be important when two or more words are used to describe an adverse event. For example, some databases utilizing the Medical Dictionary for Regulatory Activities (MedDRA) coding dictionary employ a coding system in which only the first letter of the first word of an adverse event is capitalized (e.g., Atrioventricular block complete ). Failing to adhere to uniform letter case conventions across the data can result in severe errors in data analysis. 

Additional examples of variability in data collection (which, in turn, affects data interpretation) include questionnaires and physical exam forms. Questionnaires often utilize open-ended questions that allow great variability in the type and extent of adverse event information gathered. Physical exam forms—even when designed in a checklist format—may elicit variable collection of adverse event data what is a serious event to one clinician may not be serious to another. 

Crucial factors affecting overall cost are the responsiveness to medication (for example, less than 70% of patients are lithium responders ), adherence to recommended treatment, and adverse events resulting from medication. A particular hazard of lithium treatment is the risk of rapid re-emergence of mania, which occurs in up to 50% of patients if the dmg is abruptly discontinued (see Cookson, 1997). Disappointingly, it has not been found that the introduction of widespread treatment with lithium has been associated with a reduction in the number of patients admitted and discharged from hospital with a diagnosis of mania. In order to achieve the best result with the available... 

The problem for the statistical programmer in categorizing data comes from text variables, or more specifically, free-text variables. A free-text variable is one that may contain any characters and is typically limited only in length. As an example, let s say you need to summarize the adverse events for a set of patients in a trial. The following SAS code shows the data and a quick summarization of the adverse events. 

There is only one good solution to handling free-text variables that are needed for statistical analysis. The free-text variables need to be coded by clinical data management in the clinical database. If the adverse events were coded with a dictionary such as MedDRA, the previous example might look like Program 2.3. 

The final meddra data set in this program contains the lower-level term code (llt code) that can then be merged with the adverse events or medical conditions database. By merging the MedDRA dictionary data with the disease data, you can match the verbatim event text captured on the case report form with the preferred term and associated body system. Then you can summarize these data by body system and preferred term you will see an example of this in Chapter 5. 

Often you want to redefine an already existing variable within a SAS DATA step. As simple as this may sound, it can lead to unexpected results if not done carefully. The following example displays some unexpected behavior that may occur when you redefine a variable within a DATA step. In this example you want to flag the subject who had the Fatal MI adverse event as having died (death =1). 

The following is a very simplistic example of what a time-to-event data set might look like when the event of interest is seizure. Here you assume that there is a seizure event form that collects whether a subject had a seizure and the date when the seizure occurred. You also assume that you do not need to search other ancillary data forms such as adverse events for seizure events. 

The following example relies on DATA step programming, a few SAS macro variables, and a final DATA NULL step with PUT statements for custom table presentation. Here are the adverse event summary annotated SAS program, notes for the program, and the output. 

These differences may become particularly germane if co-prescribing with some antipsychotics is undertaken. For example, in certain individuals, combinations of clozapine with benzodiazepines may lead to unexpected adverse events, including delirium and augmented respiratory depression (Jackson, Markowitz Brewer-ton, 1995 Grohmann et al, 1989). Presumably if there are additive or synergistic effects of ethnicity on clearance of one or both substances, adverse events may be enhanced. Similar interactions are theoretically possible with olanzapine, as adverse interactions have been described between olanzapine and benzodiazepines, at least in the elderly (Kryzhanovskaya etal, 2006). 

Risk characterization estimates the frequency and severity of adverse events and presents the results in a form useful to management, for example, in the form of various scenarios for emission abatement strategy on local or regional scale. 

Each time a patient moves from one setting to another, clinicians should compare previous medication orders with new orders and plans for care and reconcile any differences. If this process does not occur in a standardised manner designed to ensure complete reconciliation, medication errors may lead to adverse events and harm (IHI MedReconcilliation 2008). Several national organisations round the world have now produced help to reduce errors with medication reconciliation. Some examples are given below. 

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