Assessing Pneumatic Tube

The October 2011 issue of Clinical Chemistry highlights the importance of monitoring pneumatic tube systems (PTSs) to control preanalytical factors that may affect laboratory results (1, 2 ). Two important themes emerge: (a) the possible requirement of morefrequent monitoring if the PTS produces changes in the 3-axis acceleration (i.e., forces) and (b) consideration for specific populations of patients (i.e., hematology and/or oncology patients) whose blood samples may be more susceptible to PTS. As Felder noted, the work by Streichert et al. may usher in a new practice for monitoring PTS by means of data loggers (1, 2 ); however, in the interim, laboratories will be required to monitor PTSs with split-sample testing. In this regard, the number of studies and approaches that have assessed the impact of PTSs on the quality of samples has been surprisingly limited (3 ). Moreover, the available guidelines have mainly suggested that PTSs be evaluated and that certain analytes may be affected by the automated system (e.g., lactate dehydrogenase), whereas others (e.g., aspartate aminotransferase) may not (4 ). Additional questions arising from the study of Streichert et al. are whether samples from healthy volunteers are the most appropriate to test in PTSs and what criteria should be used to evaluate the acceptability of samples subjected to PTSs. While implementing a new PTS in our hospital, we wanted to determine if patient-specific attributes and laboratory-defined criteria could be used to assess the performance of the new PTS. Given that patients may be relocated to different hospital locations/wards, one of our goals was to evaluate the new PTS by using available leftover blood samples in the core laboratory that had been obtained from the different locations. For this evaluation, we randomly selected and resuspended lithium heparin blood samples (4-mL green-top Vacutainers; BD) from 5 different patients in each of 4 different hospital locations: the emergency department, the intensive care unit, surgical oncology and orthopedics, and hematology/ oncology. We pooled samples from each location and aliquoted them uniformly (1 mL) into 5 tubes (groups A–E) that were to be sent from the same station (the hematology/oncology ward) via PTS and tested with different interventions. The groups were as follows: group A, control (no PTS); group B, PTS (no intervention); group C, PTS and foam liner in tube only; group D, PTS and no foam, but tube caught at end destination without impact; and group E, PTS and foam with tube caught at end destination. The analytes chosen for this experiment were those that Streichert et al. found to produce different results when the PTS was used (potassium, lactate dehydrogenase, aspartate aminotransferase, phosphate), on the basis of the allowed relative deviation of QCs as specified by the German Federal Medical Council (2 ). An alternative approach, however, may be to use acceptable comparability testing for instruments within a single laboratory (5 ). In brief, comparability testing (i.e., instrument-toinstrument comparison) is a component of the quality-assurance process and may indicate that a measurement procedure needs to be reviewed for possible corrective action. One can assess comparability testing in various ways [CLSI C54 –1 guideline, within–patient sample pool biological variation (CVi)], and we have recently established our own laboratory-defined acceptable percentage differences (Table 1) (5 ). Samples from groups B–E were sent via PTS within a 20-min period, and all samples (groups A–E) were centrifuged together and analyzed in duplicate on the Roche P Modular System (overall CVs used for critical difference were 1% for potassium, 1% for lactate dehydrogenase, 3% for aspartate aminotransferase, and 2% for phosphate). The percentage differences in the mean analyte measurements between group A (control) and the different interventions were then assessed according to the different comparability criteria (Table 1). From these data, differences between the patient pools were seen, with the hematology/ oncology pool being more affected by the PTS than the intensive care unit pool (56% of the hematology/ oncology pool results and 13% of the intensive care unit pool results exceeded laboratory-defined limits; P 0.023). Moreover, it was evident that group E (foam liner and tube caught) had the least amount of cellular disruption, as indicated by both the potassium and aspartate aminotransferase results for the hematology/oncology pool. These investigations and dialogue with physicians led to further vendor modifications of the PTS with respect to both the impact and the speed of tubes during transit. These adjustments were made before any patient samples intended for clinical reporting were sent via the system. This approach of assembling different pools representative of different patient groups within a hospital may be useful for quickly ascertaining the suitability of PTS transport for chemistry testing and may be an option for monitoring PTSs after their initial validation. Furthermore, either laboratory-defined or guidelineClinical Chemistry 58:4 000 – 000 (2012) Letters to the Editor