Shih‑Hong Chena,b,c, Yi‑Chia Wanga, Anne Chaoa, Chih‑Min Liua, Ching‑Tang Chiua, Ming‑Jiuh Wanga, Yu‑Chang Yeha*
aDepartment of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan; bDepartment of Anesthesiology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan; cInstitute of Molecular Medicine, National Tsing Hua University, Hsinchu, Taiwan
Open Access funded by Buddhist Compassion Relief Tzu Chi Foundation
Abstract
Objectives: Sepsis is a major cause of death around the world. Complicated scoring systems require time to have data to predict short‑term survival. Intensivists need a tool to predict survival in sepsis patients easily and quickly. Materials and Methods: This retrospective study reviewed the medical records of adult patients admitted to the surgical intensive care units between January 2009 and December 2011 in National Taiwan University Hospital. For this study, 739 patients were enrolled. We recorded the demographic and clinical variables of patients diagnosed with sepsis. A Cox proportional hazard model was used to analyze the survival data and determine significant risk factors to develop a prediction model. This model was used to create a nomogram for predicting the survival rate of sepsis patients up to 3 months. Results: The observed 28‑day, 60‑day, and 90‑day survival rates were 71.43%, 52.53%, and 46.88%, respectively. The principal risk factors for survival prediction included age; history of dementia; Glasgow Coma Scale score; and lactate, creatinine, and platelet levels. Our model showed more favorable prediction than did Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment at sepsis onset (concordance index: 0.65 vs. 0.54 and 0.59). This model was used to create the nomogram for predicting the mortality at the onset of sepsis. Conclusion: We suggest that developing a nomogram with several principal risk factors can provide a quick and easy tool to early predict the survival rate at different intervals in sepsis patients.
Keywords: Cox proportion hazard model, Nomogram, Sepsis, Survival
