Multidimensional analysis of postoperative nursing challenges in oral cancer surgery: a retrospective study of 245 patients and proposal of the PROTECT mode.

purposeThe quality of postoperative care for oral tumors critically influences patient prognosis and quality of life; however, current nursing systems for oral cancer often lack standardization, personalization, and continuity.

methodsA retrospective analysis was conducted on 245 patients with oral cancer who underwent tumor resection combined with flap reconstruction between February 2022 and February 2025. The study focused on four core dimensions: complication management, nutritional support, functional rehabilitation, and continuity of care. Statistical analyses were performed using SPSS 25, including chi-square tests, t-tests, and multivariable logistic regression.

resultsOf 218 patients with flap reconstruction, 184 (75.1%) received pedicled flaps and 34 (13.9%) free flaps. Free flaps were associated with a significantly higher overall complication rate (82.4% vs. 40.2%, P < 0.001), particularly pulmonary infections, flap necrosis/venous crisis (both P < 0.05). Operative time was the sole independent risk factor for complications (OR = 1.54, P = 0.016). Only 30.2% (74 cases) completed NRS-2002 within 72 h postoperatively. Malnutrition showed a bimodal pattern (acute insufficient intake, chronic metabolic disorders), and 42% (103 cases) had "swallowing-nutrition mismatch" requiring enteral/parenteral nutrition. Dysphagia (31.8%), speech impairment (29%), and limited mouth opening (20%) were common, correlating with surgical extent and requiring 1-4 months of rehabilitation. At 2 weeks post-discharge, 30% (74 cases) lost home-care skills. Of 13 readmissions within 30 days (5.3%), 30.8% (4 cases) were avoidable due to preventable airway/nutrition/infection issues.

conclusionCurrent postoperative nursing for oral cancer exhibits significant gaps in flap-specific interventions, dynamic nutritional assessment, personalized rehabilitation, and continuity support. To address these challenges, we propose the PROTECT model as a conceptual framework for future prospective evaluation. This framework is designed to integrate four core components: (1) multimodal machine learning algorithms for identifying high-risk patients, (2) mobile health apps to standardize early nutrition screening, (3) AI-driven tools for generating personalized rehabilitation plans, and (4) smart nurse-physician collaboration platforms to reinforce home-care skills and enable rapid emergency response. The model aims to provide a structured approach to improve precision, continuity, and long-term quality of life in postoperative care, pending empirical validation through future studies.