Development and External Validation of a Nomogram for Predicting One-Year Survival in Patients with Non-Traumatic Subarachnoid Hemorrhage

YIwei Lv¹Lu Zhongsheng^2*Menghui He¹Zihai Cheng¹Qiang Zhang²Xiaoqing Jin²Pei Han²

• ¹Qinghai University, Xining, China
• ²青海省人民医院, xining, China

Background: Subarachnoid hemorrhage (SAH), a critical cerebrovascular emergency characterized by acute bleeding into the subarachnoid space, is associated with permanent neurological deficits, substantial mortality rates, and unfavorable clinical outcomes. Survivors frequently develop long-term complications including cognitive impairment, memory loss, and neuropsychiatric issues like depression, anxiety, and PTSD, significantly reducing quality of life. Despite advancements in acute-phase management, the long-term survival prognosis for non-traumatic SAH patients remains poorly characterized in current clinical research. Identifying reliable prognostic biomarkers and developing validated predictive models are crucial for enabling risk-stratified care and personalized treatments, improving evidence-based clinical practice. Method: This study analyzed baseline and clinical data from 825 non-traumatic SAH patients in the MIMIC-IV ICU database. Kaplan-Meier analysis and multivariate Cox regression identified independent survival risk factors, followed by nomogram model construction. The model's performance was evaluated using C-index, ROC curve (AUC), calibration curve, and DCA to assess discrimination, calibration, and clinical utility. External validation was performed using 290 non-traumatic SAH patients from Qinghai Provincial People's Hospital. Result: Multivariate Cox regression identified 11 independent risk factors for non-traumatic SAH survival: hospital stay length, age, respiratory rate, red blood cell count, platelets, potassium, sodium, anion gap, urea nitrogen, blood glucose, and sepsis. A nomogram model based on these factors showed strong discrimination, stratifying patients into risk categories.In the training cohort, the model achieved an AUC of 0.844 (95% CI: 0.815–0.872) and a C-index of 0.827 (95% CI: 0.803–0.851). In the external validation set, the model exhibited acceptable discriminatory performance, with an AUC of 0.807 (95% CI: 0.758–0.856) and a C-index of 0.851 (95% CI: 0.825–0.875). Conclusion: In this study, the survival prognosis of patients with non-traumatic subarachnoid hemorrhage (SAH) was found to be associated with eleven factors: length of hospital stay, patient age, respiratory rate, red blood cell count, platelet count, potassium levels, sodium levels, anion gap, urea nitrogen, blood glucose levels, and the presence of sepsis. The nomogram model we developed demonstrates superior predictive accuracy and can serve as a valuable tool for clinicians in rapidly identifying high-risk patients, facilitating personalized risk assessment, and guiding targeted medical interventions.