Counterintuitively, the initial use of elongated multi-pass cIM routines resulted in underperformance relative to 1-pass cIM data. Manual inspection of multi-pass data revealed the presence of many highquality peptide ions which remained unidentified after default HDMS E processing in PLGS. This was likely due to "wrap-around" derived loss of correlation between apparent ion drift time and drift FWHM. This, in turn, prevents Apex3D from applying appropriate kernel smoothing to all IM data. To address this issue, we developed a novel HDMS E data processing approach which uses user-defined drift FWHM trendlines that are more appropriate for smoothing cIM multi-pass data and better handle multipass data. This led to substantial increases in peptide identification when using multi-pass routines, with some examples exhibiting data quality comparable to 1-pass. While the 11-14 mobility peak width trendline improved peptide identification across all targets in this study, it's probable that this trendline will not be applicable to all targets and/or multi-pass cyclic routines. Thus, we recommend optimisation of mobility peak width trendlines when analysing new targets and/or using new multi-pass cyclic sequences, as exemplified in this manuscript.