Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis
Jian Wei,
Ran Zeng,
Ruiyuan Liang,
Siying Liu,
Tianfeng Hua,
Wenyan Xiao,
Huaqing Zhu,
Yu Liu,
Min Yang
Affiliations
Jian Wei
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China
Ran Zeng
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Department of Intensive Care Unit, Fuyang Hospital of Anhui Medical University, 99 Huangshan Road, Fuyang, 236000, Anhui province, China
Ruiyuan Liang
Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui Province, China; School of Integrated Circuits, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui Province, China
Siying Liu
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China
Tianfeng Hua
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China
Wenyan Xiao
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China
Huaqing Zhu
Laboratory of Molecular, Biology and Department of Biochemistry, Anhui Medical University, 81 Meishan Road, Hefei, 230022, Anhui Province, China
Yu Liu
Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui Province, China; School of Integrated Circuits, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui Province, China; Corresponding author.
Min Yang
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, Anhui Province, China; Corresponding author.
Background: Septic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis. Methods: We extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness. Results: The study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786–0.834), 0.794 (95 % CI 0.756–0.831), and 0.723 (95 % CI 0.647–0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets. Conclusion: The constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.
