Laboratory errors

Without good laboratory error explanations to ward off REJECTED labels, batch failure rates are likely to increase under the strict interpretation. The health authorities are waiting The rejection rate is a criterion they use to judge the trustworthiness of the companies they inspect and license. 

Figure 4.41. Trend analysis over 12 batches of a bulk chemical. The sieve analysis shows that over time crystals larger than 250 /urn were reduced from a weight contribution in the range of a few percent of the total to about 1% in favor of smaller sizes. Impurity C appears to follow the trend given by the lead compound for the competing side reaction 1. The very low moisture found for sample 3 could be due to a laboratory error because during drying one would expect ethanol to be driven off before water. Methanol is always below the detection limit.

One assay falls out of trend by about 4% this could be a laboratory error (see Section 4.32) that, by chance, does not generate an OOS result and so goes undetected. This out-of-trend (OOT) result could... 

ASSAYl.dat As part of a cross-validation of a modification of a given analytical method, 20 samples were run on either method. There is at least one result that is out of trend (OOT), and another two to three are indicative of laboratory errors. 

Since many ion exchange columns exhibit mixed-mode interactions with analytes, factor analysis has been found to be useful in optimization.84 A 3-year, comprehensive review of inter-laboratory errors in determinations of the anions chloride, nitrate, and sulfate and the cations sodium, potassium, magnesium, and calcium suggested that multipoint calibration is essential and nonlinear calibration desirable.102 The need for nonlinear calibration was confirmed by an extended quality assurance study of chloride, sulfate, and nitrate in rainwater.103... 

We will address aspects of reproducibility, which has previously been defined as, the precision between laboratories . It has also been defined as total between-laboratory precision . This is a measure of the ability of different laboratories to evaluate each other. Reproducibility includes all the measurement errors or variances, including the within-laboratory error. Other terms include precision, defined as the closeness of agreement between independent test results obtained under stipulated conditions [3] and repeatability, or the precision for the same analyst within the same laboratory, or within-laboratory precision . Note that for none of these definitions do we require the true value for an analytical sample . In practice we do not know the true analyte value unless we have created the sample, and then it is only known to a given certainty (i.e., within a determined uncertainty). 

A variety of factors may be responsible for apparent lack of response to therapy. It is possible that the disease is not infectious or nonbacterial in origin, or there is an undetected pathogen. Other factors include those directly related to drug selection, the host, or the pathogen. Laboratory error in identification and/or susceptibility testing errors are rare. 

Bll. Besch, P. K., Steroid determinations, inherent clinical and laboratory errors. J. Amer. Med. Tecknol. 23, 1-6 (1961). 

As discussed above, the greatest source of error in NIR calibration is usually reference laboratory error, sample nonhomogeneity, and nonrepresentative sampling in the learning (training) set or calibration set population. Instrument quality and equation selection account for only a fraction of the variance or error attributable to NIR analytical technique in current routine application. 

Some laboratory errors are more obvious than others, but there is error associated with every measurement. There is no way to measure the true value of anything. The best we can do in a chemical analysis is to carefully apply a technique that experience tells us is reliable. Repetition of one method of measurement several times tells us the precision (reproducibility) of the measurement. If the results of measuring the same quantity by different methods agree with one another, then we become confident that the results are accurate, which means they are near the true value. 

If no growth is observed in both of the challenged containers, one repeat test may be conducted to rule out laboratory error. On the repeat test, both containers must support growth. 

Non-sampling errors can be categorized into laboratory error and data management error, with laboratory error further subdivided into measurement, data interpretation, sample management, laboratory procedure and methodology errors. 

We can easily quantify measurement error due to existence of a well-developed approach to analytical methods and laboratory QC protocols. Statistically expressed accuracy and precision of an analytical method are the primary indicators of measurement error. However, no matter how accurate and precise the analysis may be, qualitative factors, such as errors in data interpretation, sample management, and analytical methodology, will increase the overall analytical error or even render results unusable. These qualitative laboratory errors that are usually made due to negligence or lack of information may arise from any of the following actions ... 

The worst mistake a laboratory can make is to circumvent the internal review process altogether. Internal review process is the last line of defense against laboratory errors and it enables laboratories to authenticate the technical and legal defensibility of their data. The absence of the internal review process is an indicator of a careless approach to laboratory analysis that opens doors to all sorts of laboratory errors and fraudulent data manipulation. 

Understanding how the calculations are conducted is important for the assessment of data quality, as the recalculation of results from raw data may disclose an undetected laboratory error. In this chapter, we will review common internal and external standard calculations. 

A great variety of data qualifiers, each signifying a specific deviation from acceptance criteria, may be used in data validation. The trends in deviation type and incidence may be important for identifying systematic field and laboratory errors. However, they are not as important to the data user who only needs to know whether... 

Batch failure of the drug product does not necessarily occur when an individual test result does not meet the specifications outlined in the United States Pharmacopeia (USP), manufacturers new drug application (NDA), or abbreviated new drug application (ANDA). Additionally, an OOS result identified through a thorough laboratory investigation as laboratory error is not necessarily a batch failure. 

Criteria for determining whether OOS results were caused by sampling or laboratory error... 

Should work along with the analyst to attempt to quickly identify whether the cause of the OOS result is laboratory error or whether a problem has occurred in the manufacturing of the batch under test [i.e., process-related or non-process (operator) -related error]... 

For a clearly identified laboratory error (i.e., assignable cause or determinant error found), the retest results would substitute for the original results. The original results, however, should be retained and the explanation recorded in a laboratory investigation report. 

In cases in which there is no laboratory error clearly identified, there is no scientific basis for invalidation of the original test results. The guidance recommends that all results, both passing and suspect, be reported and considered in batch release decisions. 

For identified laboratory error or operator-related error, documentation of corrective action(s) required and deadline for completion. 

The aberrant result may still have been derived from laboratory error therefore, a different multidisciplinary approach, both within and outside the laboratory must be taken. 

Further, if the OOS is found to be caused by a laboratory error, other samples run along with the sample in question should be investigated, irrespective of whether they were acceptable or not. 

It is quite possible that until this particular condition with its specific biochemical abnormalities was recognized, the occurrence of hypercalcemia in infants could have been dismissed as due to laboratory errors. ... 

Out-of-specification laboratory results have been given additional emphasis by the FDA, particularly after the Barr v. FDA court case [55]. An out-of-specification result falls into three catogories laboratory error, non-process-related or operator error, and process-related or manufacturing process error. Retesting of the same sample is appropriate when the analyst error can documented. An outlier test on some chemical assays, particularily those involving extensive sample preparation and manipulation, is justifiable but is not a routine approach to rejecting results [56]. 

---