Lactate-to-albumin ratio is associated with in-hospital mortality in patients with spontaneous subarachnoid hemorrhage and a nomogram model construction - Zhang, Guo-Guo; Hao, Jia-Hui; Yong, Qi; Nie, Qian-Qian; Yuan, Gui-Qiang; Zheng, Zong-Qing; Li, Jin-Quan (Frontiers in Neurology, 2022)
Introduction: Subarachnoid hemorrhage (SAH) is a severe hemorrhagic stroke with high mortality, while there is a lack of clinical tools for predicting in-hospital mortality in clinical practice. LAR is a novel clinical marker that has demonstrated significant prognostic significance in a variety of diseases. Methods: Critically ill patients diagnosed with SAH in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) were included in our study. Multivariate logistic regression was utilized to establish the nomogram. Results: A total of 244 patients with spontaneous SAH in the MIMIC-IV database were eligible for the study as a training set, and 83 patients in eICU-CRD were included for external validation. Data on clinical characteristics, laboratory parameters and outcomes were collected. Univariate and multivariate logistic regression analysis identified age (OR: 1.042, P value: 0.003), LAR (OR: 2.592, P value: 0.011), anion gap (OR: 1.134, P value: 0.036) and APSIII score (OR: 1.028, P value: <0.001) as independent predictors of in-hospital mortality and we developed a nomogram model based on these factors. The nomogram model incorporated with LAR, APSIII score, age and anion gap demonstrated great discrimination and clinical utility both in the training set (accuracy: 77.5%, AUC: 0.811) and validation set (accuracy: 75.9%, AUC: 0.822). Conclusion: The LAR is closely associated with increased in-hospital mortality of spontaneous SAH patients as a novel clinical marker. The nomogram model combined with LAR, APSIII score, age and anion gap present good predictive performance and clinical practicability.