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Wei C, Wang X, He D, Huang D, Zhao Y, Wang X, Liang Z, Gong L. Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study. BMC Pulm Med 2024; 24:38. [PMID: 38233787 PMCID: PMC10795228 DOI: 10.1186/s12890-024-02852-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 01/07/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Severe community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients. METHODS The retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model. RESULTS Six factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719-0.768) in the training cohort and 0.731 (95% CI 0.694-0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality. CONCLUSIONS The nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission.
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Affiliation(s)
- Chang Wei
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Xinyu Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Dingxiu He
- Department of Emergency Medicine, The People's Hospital of Deyang, Deyang, Sichuan, China
| | - Dong Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Yue'an Zhao
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Xinyuan Wang
- Department of Orthopaedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zong'an Liang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China.
| | - Linjing Gong
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China.
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Zhang Y, Peng Y, Zhang W, Deng W. Development and validation of a predictive model for 30-day mortality in patients with severe community-acquired pneumonia in intensive care units. Front Med (Lausanne) 2024; 10:1295423. [PMID: 38259861 PMCID: PMC10801213 DOI: 10.3389/fmed.2023.1295423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background Based on the high prevalence and fatality rates associated with severe community-acquired pneumonia (SCAP), this study endeavored to construct an innovative nomogram for early identification of individuals at high risk of all-cause death within a 30-day period among SCAP patients receiving intensive care units (ICU) treatment. Methods In this single-center, retrospective study, 718 SCAP patients were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for the development of a predictive model. A total of 97 patients eligible for inclusion were included from Chongqing General Hospital, China between January 2020 and July 2023 for external validation. Clinical data and short-term prognosis were collected. Risk factors were determined using the least absolute shrinkage and selection operator (LASSO) and multiple logistic regression analysis. The model's performance was evaluated through area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results Eight risk predictors, including age, presence of malignant cancer, heart rate, mean arterial pressure, albumin, blood urea nitrogen, prothrombin time, and lactate levels were adopted in a nomogram. The nomogram exhibited high predictive accuracy, with an AUC of 0.803 (95% CI: 0.756-0.845) in the training set, 0.756 (95% CI: 0.693-0.816) in the internal validation set, 0.778 (95% CI: 0.594-0.893) in the external validation set concerning 30-day mortality. Meanwhile, the nomogram demonstrated effective calibration through well-fitted calibration curves. DCA confirmed the clinical application value of the nomogram. Conclusion This simple and reliable nomogram can help physicians assess the short-term prognosis of patients with SCAP quickly and effectively, and could potentially be adopted widely in clinical settings after more external validations.
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Affiliation(s)
- Yu Zhang
- Department of Infection Control, Chongqing Mental Health Center, Chongqing, China
| | - Yuanyuan Peng
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Wang Zhang
- Third Psychogeriatric Ward, Chongqing Mental Health Center, Chongqing, China
| | - Wei Deng
- Department of Nursing, Chongqing Mental Health Center, Chongqing, China
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Lu Y, Ren C, Wu C. In-Hospital Mortality Prediction Model for Critically Ill Older Adult Patients Transferred from the Emergency Department to the Intensive Care Unit. Risk Manag Healthc Policy 2023; 16:2555-2563. [PMID: 38024492 PMCID: PMC10676667 DOI: 10.2147/rmhp.s442138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 11/15/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose Studies on the prognosis of critically ill older adult patients admitted to the emergency department (ED) but requiring immediate admission to the intensive care unit (ICU) remain limited. This study aimed to develop an in-hospital mortality prediction model for critically ill older adult patients transferred from the ED to the ICU. Patients and Methods The training cohort was taken from the Medical Information Mart for Intensive Care IV (version 2.2) database, and the external validation cohort was taken from the Affiliated Dongyang Hospital of Wenzhou Medical University. In the training cohort, class balance was addressed using Random Over Sampling Examples (ROSE). Univariate and multivariate Cox regression analyses were performed to identify independent risk factors. These were then integrated into the predictive nomogram. In the validation cohort, the predictive performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, clinical utility decision curve analysis (DCA), and clinical impact curve (CIC). Results In the ROSE-balanced training cohort, univariate and multivariate Cox regression analysis identified that age, sex, Glasgow coma scale score, malignant cancer, sepsis, use of mechanical ventilation, use of vasoactive agents, white blood cells, potassium, and creatinine were independent predictors of in-hospital mortality in critically ill older adult patients, and were included in the nomogram. The nomogram showed good predictive performance in the ROSE-balanced training cohort (AUC [95% confidence interval]: 0.792 [0.783-0.801]) and validation cohort (AUC [95% confidence interval]: 0.780 [0.727-0.834]). The calibration curves were well-fitted. DCA and CIC demonstrated that the nomogram has good clinical application value. Conclusion This study developed a predictive model for early prediction of in-hospital mortality in critically ill older adult patients transferred from the ED to the ICU, which was validated by external data and has good predictive performance.
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Affiliation(s)
- Yan Lu
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China
| | - Chaoxiang Ren
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China
| | - Chaolong Wu
- Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100, People’s Republic of China
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Pan J, Bu W, Guo T, Geng Z, Shao M. Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit. BMC Pulm Med 2023; 23:303. [PMID: 37592285 PMCID: PMC10436447 DOI: 10.1186/s12890-023-02567-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 07/17/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients. METHODS As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA). RESULTS Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838-0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems. CONCLUSIONS We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.
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Affiliation(s)
- Jingjing Pan
- Department of Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Respiratory Intensive Care Unit, Anhui Chest Hospital, Hefei, China
| | - Wei Bu
- Department of Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tao Guo
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, China
| | - Zhi Geng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China.
| | - Min Shao
- Department of Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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Lv C, Pan T, Shi W, Peng W, Gao Y, Muhith A, Mu Y, Xu J, Deng J, Wei W. Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study. Sci Rep 2023; 13:12432. [PMID: 37528213 PMCID: PMC10393957 DOI: 10.1038/s41598-023-39542-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/26/2023] [Indexed: 08/03/2023] Open
Abstract
Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).
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Affiliation(s)
- Chunxin Lv
- Oncology Department, Shanghai Punan Hospital of Pudong New District, No 279, Linyi Road, Pudong, Shanghai, China
| | - Teng Pan
- Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, China
- Faculty of Life Sciences and Medicine, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Wen Shi
- Department of Dermatology, Shanghai Punan Hospital of Pudong New District, No 279, Linyi Road, Shanghai, China
| | - Weixiong Peng
- Hunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China
| | - Yue Gao
- Hunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China
| | - Abdul Muhith
- Department of Oncology, Royal Marsden Hospital, London, UK
| | - Yang Mu
- Hunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China
| | - Jiayi Xu
- Geriatric Department, Minhang Hospital, Fudan University, No 170, Xinsong Road, Shanghai, China
| | - Jinhai Deng
- Hunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China.
- Richard Dimbleby Department of Cancer Research, Comprehensive Cancer Centre, Kings College London, London, SE1 1UL, UK.
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, China.
| | - Wei Wei
- Oncology Department, Shanghai Punan Hospital of Pudong New District, No 279, Linyi Road, Pudong, Shanghai, China.
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A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature. Aging (Albany NY) 2022; 14:9951-9968. [PMID: 36534449 PMCID: PMC9831738 DOI: 10.18632/aging.204432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. However, the ubiquitin-proteasome system (UPS) genes have not yet been established as a prognostic predictor for MM, despite their potential applications in other cancers. METHODS RNA sequencing data and corresponding clinical information were acquired from Multiple Myeloma Research Foundation (MMRF)-COMMPASS and served as a training set (n=787). Validation of the prediction signature were conducted by the Gene Expression Omnibus (GEO) databases (n=1040). To develop a prognostic signature for overall survival (OS), least absolute shrinkage and selection operator regressions, along with Cox regressions, were used. RESULTS A six-gene signature, including KCTD12, SIAH1, TRIM58, TRIM47, UBE2S, and UBE2T, was established. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients with high-risk conditions had a significantly worse prognosis than those with low-risk conditions. Furthermore, UPS-related signature is associated with a positive immune response. For predicting survival, a simple to use nomogram and the corresponding web-based calculator (https://jiangyanxiamm.shinyapps.io/MMprognosis/) were built based on the UPS signature and its clinical features. Analyses of calibration plots and decision curves showed clinical utility for both training and validation datasets. CONCLUSIONS As a result of these results, we established a genetic signature for MM based on UPS. This genetic signature could contribute to improving individualized survival prediction, thereby facilitating clinical decisions in patients with MM.
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