Post-injection delirium/sedation syndrome in patients with schizophrenia treated with olanzapine long-acting injection, I: analysis of cases

<p>Abstract</p> <p>Background</p> <p>An advance in the treatment of schizophrenia is the development of long-acting intramuscular formulations of antipsychotics, such as olanzapine long-acting injection (LAI). During clinical trials, a post-injection syndrome characterized by signs of delirium and/or excessive sedation was identified in a small percentage of patients following injection with olanzapine LAI.</p> <p>Methods</p> <p>Safety data from all completed and ongoing trials of olanzapine LAI were reviewed for possible cases of this post-injection syndrome. Descriptive analyses were conducted to characterize incidence, clinical presentation, and outcome. Regression analyses were conducted to assess possible risk factors.</p> <p>Results</p> <p>Based on approximately 45,000 olanzapine LAI injections given to 2054 patients in clinical trials through 14 October 2008, post-injection delirium/sedation syndrome occurred in approximately 0.07% of injections or 1.4% of patients (30 cases in 29 patients). Symptomatology was consistent with olanzapine overdose (e.g., sedation, confusion, slurred speech, altered gait, or unconsciousness). However, no clinically significant decreases in vital signs were observed. Symptom onset ranged from immediate to 3 to 5 hours post injection, with a median onset time of 25 minutes post injection. All patients recovered within 1.5 to 72 hours, and the majority continued to receive further olanzapine LAI injections following the event. No clear risk factors were identified.</p> <p>Conclusions</p> <p>Post-injection delirium/sedation syndrome can be readily identified based on symptom presentation, progression, and temporal relationship to the injection, and is consistent with olanzapine overdose following probable accidental intravascular injection of a portion of the olanzapine LAI dose. Although there is no specific antidote for olanzapine overdose, patients can be treated symptomatically as needed. Special precautions include use of proper injection technique and a post-injection observation period.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov ID; URL: <url>http://http//www.clinicaltrials.gov/</url>: NCT00094640, NCT00088478, NCT00088491, NCT00088465, and NCT00320489.</p

Unreliable numbers: error and harm induced by bad design can be reduced by better design

Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that many common, widely used systems are defective, and induce unnecessary human error. We show that Monte Carlo methods enable designers to explore the implications of normal and unexpected operator behaviour, and to design systems to be more resilient to use error. We demonstrate novel designs with improved resilience, implying that the common problems identified and the errors they induce are avoidable

Reducing number entry errors: solving a widespread, serious problem

Number entry is ubiquitous: it is required in many fields including science, healthcare, education, government, mathematics and finance. People entering numbers are to be expected to make errors, but shockingly few systems make any effort to detect, block or otherwise manage errors. Worse, errors may be ignored but processed in arbitrary ways, with unintended results. A standard class of error (defined in the paper) is an ‘out by 10 error’, which is easily made by miskeying a decimal point or a zero. In safety-critical domains, such as drug delivery, out by 10 errors generally have adverse consequences. Here, we expose the extent of the problem of numeric errors in a very wide range of systems. An analysis of better error management is presented: under reasonable assumptions, we show that the probability of out by 10 errors can be halved by better user interface design. We provide a demonstration user interface to show that the approach is practical. To kill an error is as good a service as, and sometimes even better than, the establishing of a new truth or fact.(Charles Darwin 1879 [2008], p. 229

Systematic derivation of an Australian standard for Tall Man lettering to distinguish similar drug names

Rationale, aims and objectives - Confusion between similar drug names can cause harmful medication errors. Similar drug names can be visually differentiated using a typographical technique known as Tall Man lettering. While international conventions exist to derive Tall Man representation for drug names, there has been no national standard developed in Australia. This paper describes the derivation of a risk-based, standardized approach for use of Tall Man lettering in Australia, and known as National Tall Man Lettering. Method - A three-stage approach was applied. An Australian list of similar drug names was systematically compiled from the literature and clinical error reports. Secondly, drug name pairs were prioritized using a risk matrix based on the likelihood of name confusion (a four-component score) vs. consensus ratings of the potential severity of the confusion by 31 expert reviewers. The mid-type Tall Man convention was then applied to derive the typography for the highest priority drug pair names. Results - Of 250 pairs of confusable Australian drug names, comprising 341 discrete names, 35 pairs were identified by the matrix as an ‘extreme’ risk if confused. The mid-type Tall Man convention was successfully applied to the majority of the prioritized drugs; some adaption of the convention was required. Conclusion - This systematic process for identification of confusable drug names and associated risk, followed by application of a convention for Tall Man lettering, has produced a standard now endorsed for use in clinical settings in Australia. Periodic updating is recommended to accommodate new drug names and error reports