Key result
Seven-indicator clinico-hematological model predicts severe perioperative symptom distress in cervical cancer with ~0.86 AUC.
Why the study?
The study aimed to clarify perioperative symptom cluster characteristics in cervical cancer patients, identify influencing factors and hematological indicators, and develop a risk prediction model.
Observational (n=317)
No
Effect estimate: AUC 0.863 (95% CI 0.820-0.907)
A seven-indicator nomogram prediction model effectively stratifies cervical cancer patients into high- and low-risk groups for severe perioperative symptom distress.
May support risk stratification before cervical cancer surgery; leaves open prospective validation before clinical use.
Background: This prospective longitudinal quantitative study aimed to clarify perioperative symptom cluster characteristics in cervical cancer patients, identify influencing factors and hematological indicators, and develop a clinically applicable risk prediction model. Methods: A prospective longitudinal study was conducted at a tertiary cancer hospital in Northwest China (April 2024–June 2025). Symptoms were assessed using the MDASI-PeriOp-GYN scale at preoperative, postoperative, and pre-discharge time points. Exploratory PCA identified symptom cluster structures. Based on Linear Mixed Model we explore heterogeneous symptom trajectories. Independent predictors were screened using logistic regression and restricted cubic splines. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis. Results: Five stable symptom clusters were determined: a general somatic symptom cluster, a psycho-neurological symptom cluster, a gastrointestinal motility and energy cluster, an emotional and eating disorder cluster, and a consciousness and sedation-related cluster; and patients were classified into three trajectories: well-recovered, moderately persistent and severely distressed types. Age, operative duration, fibrinogen, neutrophil-to-lymphocyte ratio and D-dimer were risk factors, while serum albumin and serum potassium were protective factors. The seven-indicator combined model had an AUC of 0.863, with sensitivity 68.4%, specificity 87.8% and accuracy 82.0%, possessing superior predictive effect compared with single indexes. Conclusion: Patients in the severe distress trajectory showed rapid symptom changes around discharge, representing a high-risk group for perioperative management. The Nomogram prediction model effectively stratifies patients into high- and low-risk groups for severe symptom distress, enabling precise interventions, improving transitional care, and enhancing perioperative symptom management.
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Zhang et al. (2026) conducted an observational in Cervical cancer (n=317). Seven-indicator combined prediction model (Fbg, NLR, D-D, operative duration, age, ALB, K) vs. Single clinical indicators was evaluated on Severe symptom distress trajectory (AUC 0.863, 95% CI 0.820-0.907). A seven-indicator combined model incorporating clinical and hematological factors effectively predicted the risk of severe perioperative symptom distress in cervical cancer patients with an AUC of 0.863.
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