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Infection and Drug Resistance Volume 16, 2023 - Issue
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ORIGINAL RESEARCH

An Individualized Nomogram for Predicting Mortality Risk of Septic Shock Patients During Hospitalization: A ten Years Retrospective Analysis

Mengqi Wang1 Department of Neurology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China
,
Yunzhen Shi2 Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China
,
Xinling Pan3 Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, People’s Republic of China
,
Bin Wang4 Department of Emergency, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China
,
Bin Lu2 Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of ChinaCorrespondence[email protected]
&
Jian Ouyang4 Department of Emergency, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of ChinaCorrespondence[email protected]
Pages 6247-6257 | Received 21 Jul 2023, Accepted 14 Sep 2023, Published online: 20 Sep 2023

Figures & data

Table 1 Baseline Characteristics of the Modeling Group and Validation Groupa

Table 2 Univariate Analysis Between Survivors and No Survivors in Modeling Groupa

Table 3 Stepwise Regression Analysis of Independent Risk Factors for Death in Patients with Septic Shock

Figure 1 The nomogram of established model for predicting mortality risk in septic shock patients during hospitalization. The enrolled variables were collected for the first time after admission. A patient was displayed as an example, with detailed enrolled variables labelled by red dots. The variables labelled of asterisk indicated significance in the model, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 1 The nomogram of established model for predicting mortality risk in septic shock patients during hospitalization. The enrolled variables were collected for the first time after admission. A patient was displayed as an example, with detailed enrolled variables labelled by red dots. The variables labelled of asterisk indicated significance in the model, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 2 ROC curves for the logistic model with the modeling groups (A) and the validation groups (B).

Figure 2 ROC curves for the logistic model with the modeling groups (A) and the validation groups (B).

Figure 3 Calibration curves for the modeling groups (A) and the validation groups (B).

Figure 3 Calibration curves for the modeling groups (A) and the validation groups (B).

Figure 4 Decision-curve analysis for the modeling groups (A) and the validation groups (B).

Figure 4 Decision-curve analysis for the modeling groups (A) and the validation groups (B).

Figure 5 ROC curves for the logistic model, sofa model and five machine learning models to predict in-hospital mortality risk.

Abbreviations: Sofa, sequential organ failure assessment; SVM, Support Vector Machine; Xgboost, extreme gradient boosting.
Figure 5 ROC curves for the logistic model, sofa model and five machine learning models to predict in-hospital mortality risk.

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