Accurate identification of critical states and their primary driving factors in complex biological processes is crucial for providing early warning signals of catastrophic shifts. Although existing methods based on dynamic network biomarkers (DNBs) have made some progress, they often fall short in fully utilizing the information from single-sample networks and struggle with the computational challenges posed by ultrahigh-dimensional data. We introduce a comprehensive and rapid single-sample DNB method (crsDNB), which introduces a global community detection process and integrates it with a local network perspective to achieve self-adaptive identification of single-sample DNBs. In addition, it proposes targeted parallel optimization strategies to enhance computational efficiency. Validated using six transcriptomic datasets related to male aging and cancer, the crsDNB successfully identified critical states prior to decisive transitions, achieving significant improvements in both computational and biological significance metrics. Consequently, the crsDNB can identify personalized biomarkers for each sample more accurately and efficiently, providing a powerful new tool for determining critical states in complex biological processes.