Identifying vital nodes involves the task of pinpointing the most essential nodes within intricate networks. This challenge holds significant implications across different domains, including areas like viral marketing and managing the spread of viruses or rumors within real-world networks. Numerous techniques have been proposed for ranking influential nodes in complex networks, spanning from node centrality to diffusion-based processes. K-shell coreness centrality is employed in network analysis to evaluate the structural significance of nodes. The process in-volves k-shell decomposition, which categorizes nodes into shells according to their connectivity patterns. However, these measures are based on the coreness of direct nodes. We proposed an extended coreness score for finding the vital nodes based on the coreness of a node and its neighbors along with the degree. The foundation of degree centrality lies in the principle that the highly connected node is also the most central within the network. With the combination of the degree and isolating centralities, we proposed the isolating extended coreness score. We employ the SIR (Susceptible-Infected-Recovered) model to analyze the maximum information spread achieved by the proposed measure in comparison to conventional centralities. We apply the proposed centrality measure to various real-world networks to identify vital nodes. Additionally, we compare these results with existing basic centrality measures.