Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study
Objective: Sudden Death (SD) is a high-mortality emergency event that typically occurs within one hour of symptom onset. Accurate risk prediction is essential for optimizing post-resuscitation care. This study aims to enhance the survival rate of patients experiencing sudden death by developing and...
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MDPI AG
2025-03-01
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| Series: | Emergency Care and Medicine |
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| Online Access: | https://www.mdpi.com/2813-7914/2/1/15 |
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| author | Yu Li Zhen Chen Xin Guo Yifan Liang Jueyan Wang Jinglei Li Xianting Yang Fen Ai |
| author_facet | Yu Li Zhen Chen Xin Guo Yifan Liang Jueyan Wang Jinglei Li Xianting Yang Fen Ai |
| author_sort | Yu Li |
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| description | Objective: Sudden Death (SD) is a high-mortality emergency event that typically occurs within one hour of symptom onset. Accurate risk prediction is essential for optimizing post-resuscitation care. This study aims to enhance the survival rate of patients experiencing sudden death by developing and validating a risk prediction model for in-hospital mortality following successful resuscitation. Method: This study is a retrospective analysis of data that were collected prospectively from a standardized clinical database. All data were recorded at the time of patient admission using a predefined protocol to ensure consistency and accuracy. We retrospectively analyzed the data collected from 295 patients who experienced sudden death and achieved successful resuscitation at Wuhan Central Hospital from January 2017 to June 2024. The patients were assigned to groups using a randomization process into training and validation sets using k-fold cross-validation and further categorized within these sets based on in-hospital mortality as the outcome. A prediction model was constructed, and its efficacy was validated using logistic regression analysis, which was visualized with nomograms. Results: The results of this regression analysis of the training set demonstrated the actual length of hospital stay, in-hospital norepinephrine dosage, post-resuscitation respiratory rate, and sinus rhythm after resuscitation as independent influencing factors (<i>p</i> < 0.05), which formed the basis of the prediction model. The analysis of the training set exhibited high discriminative ability, with an area under the ROC curve (AUC) of 0.860, which exceeds the commonly accepted threshold for good classification performance, and the calibration, applicability, and reasonableness were all favorable. When the model was applied to the validation set, the AUC was 0.758, and the discrimination, calibration, applicability, and reasonableness of the validation set were also satisfactory. Conclusions: the main conclusion is that a risk prediction model for in-hospital mortality following resuscitation from sudden death was successfully developed and internally validated, offering a significant advancement in clinical decision-making support. |
| format | Article |
| id | doaj-art-492302a4c9044eec94b97895fdf8ed5f |
| institution | Kabale University |
| issn | 2813-7914 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Emergency Care and Medicine |
| spelling | doaj-art-492302a4c9044eec94b97895fdf8ed5f2025-08-20T03:43:30ZengMDPI AGEmergency Care and Medicine2813-79142025-03-01211510.3390/ecm2010015Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control StudyYu Li0Zhen Chen1Xin Guo2Yifan Liang3Jueyan Wang4Jinglei Li5Xianting Yang6Fen Ai7Department of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Cardiovascular, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Cardiovascular, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, ChinaObjective: Sudden Death (SD) is a high-mortality emergency event that typically occurs within one hour of symptom onset. Accurate risk prediction is essential for optimizing post-resuscitation care. This study aims to enhance the survival rate of patients experiencing sudden death by developing and validating a risk prediction model for in-hospital mortality following successful resuscitation. Method: This study is a retrospective analysis of data that were collected prospectively from a standardized clinical database. All data were recorded at the time of patient admission using a predefined protocol to ensure consistency and accuracy. We retrospectively analyzed the data collected from 295 patients who experienced sudden death and achieved successful resuscitation at Wuhan Central Hospital from January 2017 to June 2024. The patients were assigned to groups using a randomization process into training and validation sets using k-fold cross-validation and further categorized within these sets based on in-hospital mortality as the outcome. A prediction model was constructed, and its efficacy was validated using logistic regression analysis, which was visualized with nomograms. Results: The results of this regression analysis of the training set demonstrated the actual length of hospital stay, in-hospital norepinephrine dosage, post-resuscitation respiratory rate, and sinus rhythm after resuscitation as independent influencing factors (<i>p</i> < 0.05), which formed the basis of the prediction model. The analysis of the training set exhibited high discriminative ability, with an area under the ROC curve (AUC) of 0.860, which exceeds the commonly accepted threshold for good classification performance, and the calibration, applicability, and reasonableness were all favorable. When the model was applied to the validation set, the AUC was 0.758, and the discrimination, calibration, applicability, and reasonableness of the validation set were also satisfactory. Conclusions: the main conclusion is that a risk prediction model for in-hospital mortality following resuscitation from sudden death was successfully developed and internally validated, offering a significant advancement in clinical decision-making support.https://www.mdpi.com/2813-7914/2/1/15sudden deathcardiopulmonary resuscitationreturn of spontaneous circulationin-hospital mortalitypredictive model |
| spellingShingle | Yu Li Zhen Chen Xin Guo Yifan Liang Jueyan Wang Jinglei Li Xianting Yang Fen Ai Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study Emergency Care and Medicine sudden death cardiopulmonary resuscitation return of spontaneous circulation in-hospital mortality predictive model |
| title | Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study |
| title_full | Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study |
| title_fullStr | Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study |
| title_full_unstemmed | Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study |
| title_short | Establishing and Validating a Predictive Model for the Risk of In-Hospital Mortality Post-Resuscitation in Patients with Sudden Death, as Well as Conducting Clinical Analysis Research: A Case-Control Study |
| title_sort | establishing and validating a predictive model for the risk of in hospital mortality post resuscitation in patients with sudden death as well as conducting clinical analysis research a case control study |
| topic | sudden death cardiopulmonary resuscitation return of spontaneous circulation in-hospital mortality predictive model |
| url | https://www.mdpi.com/2813-7914/2/1/15 |
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