Most PPG-based methods for extracting the respiratory rate (RR) rely on changes in the PPG signal's amplitude, baseline, or frequency. However, several other parameters may also contain the information required to compute RR with higher accuracy. In this study, we explored the capabilities of the respiratory-induced variations in successive systolic differences (RISSDV) to estimate RR. A publicly available dataset containing PPG and thoracic impedance respiratory signals from 53 critically-ill patients was used. We partitioned each recording into eight 1-minute segments and identified peaks and troughs of the PPG signals to quantify respiratory-induced variations in amplitude, baseline, frequency, and peak-to-peak amplitude differences. RR values were extracted by determining the peak frequency of the power spectral density of the four variations and the reference respiratory signal. We assessed each feature's performance by computing the root-mean-squared and the mean absolute errors, as well as Bland-Altman plots. RISSDV showed lower error values than those obtained with the most commonly referred variations and appeared less sensitive to absent or missed PPG pulses than respiratory-induced frequency variations. Nevertheless, further research is necessary to extrapolate these findings to subjects under ambulatory rather than stationary conditions, including pediatric and neonatal populations.