These results support the view that EIM has the potential of serving as an effective outcome measure for assessing disease progression in ALS, using the more simply obtained measure θz-max. This was true both in comparison to MMT data from the same group of patients and for historical data for ALSFRS-R and MVICT.
The primary EIM variable that we evaluate here is the phase, in the forms of θavg
. Although the reactance and resistance can also be examined separately, both are much more dependent on muscle shape and size, being approximately inversely proportional to the cross-sectional area of the measured region. However, these dependences tend to cancel in the calculation of the phase, and thus changes over time for a given subject predominantly reflect variation in the inherent characteristics of the tissue itself rather than simple size or shape effects. We note also that the reduction in phase with increasing disease severity is not specific to neurogenic disorders, since myopathic disorders show a similar change (Tarulli et al., 2005
) as does muscle disuse [unpublished results]. In fact, it is possible that the reductions in phase observed here are in part related to axon loss and in part related to the superimposed disuse that affects the muscle as it becomes increasingly functionally impaired, due to both central and peripheral disease effects.
EIM is a new technique that is still under development and is likely unfamiliar to most neurologists; thus, several salient points are worth reviewing. First, as applied in this study, the technique measures the group of muscles underlying the voltage electrodes and not a single muscle. Other EIM approaches may be useful for the study of individual muscles, but those methods are still in relatively early stages of development. Second, the measurements are made from specified landmarks and the electrodes placed accordingly; no specific optimization procedure is required for this form of testing. Third, EIM is quick to perform. Using just 2 voltage electrodes to perform a measurement takes no more than about 3 minutes and the entire group of 5 muscles studied here can be completed within 15–20 minutes. Fourth, EIM can be applied successfully to the cranial nerve muscles (e.g. masseter) or thoracic muscles (e.g. thoracic paraspinal muscles), and thus be of use in patients with weakness affecting those regions, a definite advantage over conventional outcome measures.
An established characteristic of ALS is the linear behavior of commonly used outcome measures as functions of time, as a muscle or group of muscles deteriorates. Correspondingly, the effect of a hypothetical drug treatment can be taken as the percentage change in the slope of the chosen outcome measure, e.g. the phase megascore in the case of EIM, as was done here. In power calculations, the important factors for determining sample sizes for a specified effect would then be patient-to-patient variations in slope and the variations of slope with time for individual patients. The first of these simply represents the different rates of disease progression in ALS, as measured by the given technique, while the second reflects a combination of measurement errors and genuine departures from strict linear behavior. In addition, since some muscles will be in the process of active deterioration while others will not, there is a risk of underestimating the slope of decline in any given patient by mixing the effects of those actively deteriorating with those muscles that are yet to be affected or those in which no functioning motor units remain. This issue, of course, applies to the hypothetical EIM drug test power calculations presented here as well as to the global MMT results.
However, there is in principle no reason why the reliance on muscle-averaged slopes must inevitably be invoked. For this reason, we also evaluated a single-muscle approach to disease assessment, which would be considerably simpler to implement than a multiple-muscle approach. Although this implies representing the disease course for the entire body by the behavior of a single muscle, such an approach is taken routinely in motor unit number estimation where a single hand muscle is assessed repeatedly over time. Of course, such a single-muscle approach would demand very high reproducibility, but EIM has been shown to have extremely high reproducibility at the single-muscle level in biceps, quadriceps, and tibialis anterior (Rutkove et al., 2006
), and with careful technique it is likely to demonstrate high reproducibility elsewhere as well. In contrast, single-muscle reproducibility for MMT and MVICT is relatively low, necessitating data collection from multiple muscles and averaging of the results to help reduce scatter. We should note that in the present single-muscle comparisons, it is not clear why tibialis anterior fared better than the other muscles, but that may relate to the possibility of disease progression occurring most substantially in that region during the study period in these patients. In any case, further EIM research may consider alternatives to the megascore approach, such as following multiple separate muscles phases normalized to their first visit values, which would allow the active stage of deterioration in individual muscles to be compared between patients.
Like any measure, EIM has its limitations, and a variety of factors can affect the reliability of the results. For example, edema and artificial joints may produce spuriously low values for the phase, and thus limbs impacted by these factors will need to be excluded from study. We also note that EIM is position-dependent and that major changes in the angle of a joint at the time of measurement may cause considerable variability. Similarly, placing the voltage electrodes in the same positions at each measurement session can be very important. For example, phase may change up to 11% for a 1 cm distal-proximal shift of the electrode array (Rutkove et al., 2005b
). The use of small tattoos to assist in repeated electrode placements can certainly help overcome this problem. These factors, in truth, may account for much of the observed variability in the EIM measurements seen in , since none of these patients improved clinically. We also note that the phase changes reported here are far beyond those found for normal subjects followed over comparable time periods (Aaron et al., 2006
, Rutkove et al., 2006
), so are highly unlikely to be due to an effect of aging alone.
A more general area of interest concerns the nature of the relationship between muscle deterioration and changes in the EIM phase. First, it is likely that the changes in θz-max
observed in ALS patients are predominantly due to muscle fiber denervation and atrophy and not that due to reinnervation, but this is yet to be proven and is currently the subject of investigation in rats (see Nie et al., 2006
). Still, one cannot exclude the possibility that accompanying changes in the connective tissue, the state of hydration, or even the blood supply to the muscle may contribute to the reductions in θz-max
that occur with disease progression. One point, however, is clear. Despite the measurements being taken via surface electrodes, the skin and subcutaneous fat contribute minimally to the EIM data. The four-electrode technique, coupled with distant placement of the current electrodes and high-impedance input circuitry, reduces the contribution of the skin and the subcutaneous fat to a negligible level (Shiffman et al, 1999
). Moreover, of all tissues in a limb, muscle contributes by far the greatest extent to the EIM data since is has a very low resistivity (Faes et al., 1999
) and relativity large volume, thus serving as the preferential path for electrical current flow.
Regardless of the actual mechanism underlying EIM phase reduction, this study shows that θz-max has substantial potential to serve as an outcome measure in ALS clinical trials. Given the robustness of the data, our next step will be to perform a dedicated investigation prospectively comparing EIM against existing outcome measures of disease progression, including survival, ALSFRS-R, MVICT, and MUNE, using a larger group of patients at multiple centers and with several testing sessions per patient. The results should help determine whether EIM can be effectively and meaningfully used as a new outcome measure for ALS clinical trials work.