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Li X, Wang Z, Zhao W, Shi R, Zhu Y, Pan H, Wang D. Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease. Ren Fail 2024; 46:2315298. [PMID: 38357763 PMCID: PMC10877653 DOI: 10.1080/0886022x.2024.2315298] [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: 05/24/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). METHODS After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. RESULTS This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. CONCLUSIONS In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.
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Affiliation(s)
- Xunliang Li
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhijuan Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenman Zhao
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Rui Shi
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuyu Zhu
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Haifeng Pan
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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Jing J, Wei Y, Dong X, Li D, Zhang C, Fang Z, Wang J, Wan X. Characteristics and Clinical Prognosis of Septic Patients With Persistent Lymphopenia. J Intensive Care Med 2024; 39:733-741. [PMID: 38225173 DOI: 10.1177/08850666241226877] [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] [Indexed: 01/17/2024]
Abstract
Background: Septic patients with persistent lymphopenia may be in an immunosuppressed state. Therefore, we evaluated and compared the clinical characteristics and outcomes of septic patients with persistent lymphopenia (≥2d) and those with nonpersistent lymphopenia. Methods: A retrospective cohort study was designed. A total of 1306 patients with sepsis who were attended to the First Affiliated Hospital of Dalian Medical University from March 2016 to August 2022 were included. The primary clinical outcome was 90d mortality. The secondary clinical outcomes were the length of stay, hospital mortality, 28d mortality, the incidence of secondary infection, and differences in clinical characteristics. Results: Among 1306 patients with sepsis, 913 (69.9%) patients developed persistent lymphopenia. Compared with patients with nonpersistent lymphopenia, patients with persistent lymphocytopenia were admitted to intensive care unit (75.7% vs 52.7%, P < .05), treated with mechanical ventilation (67.6% vs 39.2%, P < .05), positive rate of microbial culture pathogens (86.7% vs 71.2%, P < .05), SOFA [8.0 (6.0-10.0) vs 6.0 (4.0-8.0), P < .05], length of stay [17.0d (12.0-27.0) vs 13.0d (10.0-21.0), P < .05], hospital mortality (37.7% vs 24.2%, P < .05), 28d mortality (38.0% vs 22.9%, P < .05), and 90d mortality (51.2% vs 31.3%, P < .05) were higher. As the duration of lymphocytopenia increased, so did the mortality rate in hospital. In addition, the onset time of persistent lymphopenia was not associated with SOFA. But we found that the frequency of persistent lymphopenia during hospitalization was positively associated with SOFA. Conclusion: Septic patients with persistent lymphopenia have higher mortality, worse conditions, increased risk of secondary infection, and poor prognosis regardless of shock.
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Affiliation(s)
- Juanjuan Jing
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yushan Wei
- Department of Scientific Research, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xue Dong
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Dandan Li
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenyang Zhang
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhiyao Fang
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jia Wang
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xianyao Wan
- Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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Ma Q, Ding C, Wei W, Su C, Li B, Zhou Z, Chen C, Liu B, Zhang X, Wu J. The value of right ventricular pulmonary artery coupling in determining the prognosis of patients with sepsis. Sci Rep 2024; 14:15283. [PMID: 38961249 PMCID: PMC11222489 DOI: 10.1038/s41598-024-65738-2] [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: 02/07/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024] Open
Abstract
The outcomes of patients with sepsis are influenced by the contractile function of the right ventricle (RV), but the impact of cardiopulmonary interaction in ICU-mortality of sepsis patients remains unclear. This study aims to investigate the ICU-mortality impact of right ventricular-pulmonary artery (RV-PA) coupling in patients with sepsis. We employed echocardiography to assess patients with sepsis within the initial 24 h of their admission to the ICU. RV-PA coupling was evaluated using the tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP) ratio. A total of 92 subjects were enrolled, with 55 survivors and 37 non-survivors. TAPSE/PASP ratio assessed mortality with an area under the curve (AUC) of 0.766 (95% CI 0.670-0.862) and the optimal cutoff value was 0.495 mm/mmHg. We constructed a nomogram depicting the TAPSE/PASP in conjunction with IL-6 and Lac for the joint prediction of sepsis prognosis, and demonstrated the highest predictive capability (AUC = 0.878, 95% CI 0.809-0.948). In conclusion, the TAPSE/PASP ratio demonstrated prognostic value for ICU mortality in sepsis patients. The nomogram, which combines the TAPSE/PASP, IL-6, and LAC, demonstrated enhanced predictive efficacy for the prognosis of sepsis patients.
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Affiliation(s)
- Qiang Ma
- Department of Ultrasound, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China
| | - Caiyun Ding
- Department of Physiology, Wannan Medical College, Wuhu, People's Republic of China
| | - Wei Wei
- Department of Ultrasound, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China
| | - Chencheng Su
- Department of Ultrasound, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China
| | - Bozheng Li
- Department of Ultrasound, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China
| | - Zihao Zhou
- Department of Neurology Intensive Care Unit, First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Cui Chen
- Department of Neurology Intensive Care Unit, First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Biaohu Liu
- Department of Ultrasound, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China
| | - Xia Zhang
- Department of Ultrasound, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China.
| | - Jingyi Wu
- Department of Emergency Medicine, First Affiliated Hospital of Wannan Medical College, No.2 Zheshan West Road, Wuhu, 241001, Anhui, People's Republic of China.
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Orsatti VN, Ribeiro VST, de Oliveira Montenegro C, Costa CJ, Raboni EA, Sampaio ER, Michielin F, Gasparetto J, Telles JP, Tuon FF. Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities. Med Intensiva 2024; 48:263-271. [PMID: 38575400 DOI: 10.1016/j.medine.2024.03.005] [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] [Indexed: 04/06/2024]
Abstract
OBJECTIVE In this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score. DESIGN This retrospective cohort study was conducted between 2016 and 2021. SETTING Two university hospitals in Brazil. PARTICIPANTS Patients with sepsis. INTERVENTIONS Several clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities. MAIN VARIABLE OF INTEREST In-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis. RESULTS A total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction. CONCLUSIONS Several risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.
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Affiliation(s)
- Vinicius Nakad Orsatti
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Victoria Stadler Tasca Ribeiro
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Carolina de Oliveira Montenegro
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Clarice Juski Costa
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Eduardo Albanske Raboni
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Eduardo Ramos Sampaio
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Fernando Michielin
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Juliano Gasparetto
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - João Paulo Telles
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil
| | - Felipe Francisco Tuon
- Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil.
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Yuan Y, Meng Y, Li Y, Zhou J, Wang J, Jiang Y, Ma L. DEVELOPMENT AND VALIDATION OF A NOMOGRAM FOR PREDICTING 28-DAY IN-HOSPITAL MORTALITY IN SEPSIS PATIENTS BASED ON AN OPTIMIZED ACUTE PHYSIOLOGY AND CHRONIC HEALTH EVALUATION II SCORE. Shock 2024; 61:718-727. [PMID: 38517232 DOI: 10.1097/shk.0000000000002335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
ABSTRACT Purpose : The objective of this study is to establish a nomogram that correlates optimized Acute Physiology and Chronic Health Evaluation II (APACHE II) score with sepsis-related indicators, aiming to provide a robust model for early prediction of sepsis prognosis in clinical practice and serve as a valuable reference for improved diagnosis and treatment strategies. Methods : This retrospective study extracted sepsis patients meeting the inclusion criteria from the MIMIC-IV database to form the training group. An optimized APACHE II score integrated with relevant indicators was developed using a nomogram for predicting the prognosis of sepsis patients. External validation was conducted using data from the intensive care unit at Lanzhou University Second Hospital. Results : The study enrolled 1805 patients in the training cohort and 203 patients in the validation cohort. A multifactor analysis was conducted to identify factors affecting patient mortality within 28 days, resulting in the development of an optimized score by simplifying evaluation indicators from APACHE II score. The results showed that the optimized score (area under the ROC curve [AUC] = 0.715) had a higher area under receiver operating characteristic curve than Sequential Organ Failure Assessment score (AUC = 0.637) but slightly lower than APACHE II score (AUC = 0.720). Significant indicators identified through multifactor analysis included platelet count, total bilirubin level, albumin level, prothrombin time, activated partial thromboplastin time, mechanical ventilation use and renal replacement therapy use. These seven indicators were combined with optimized score to construct a nomogram based on these seven indicators. The nomogram demonstrated good clinical predictive value in both training cohort (AUC = 0.803) and validation cohort (AUC = 0.750). Calibration curves and decision curve analyses also confirmed its good predictive ability, surpassing the APACHE II score and Sequential Organ Failure Assessment score in identifying high-risk patients. Conclusions : The nomogram was established in this study using the MIMIC-IV database and validated with external data, demonstrating its robust discriminability, calibration, and clinical practicability for predicting 28-day mortality in sepsis patients. These findings aim to provide substantial support for clinicians' decision making.
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Affiliation(s)
| | - Yanfei Meng
- Department of Critical Care Medicine, The Second Hospital of Lanzhou University, Lanzhou, China
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Rusev S, Thon P, Rahmel T, Ziehe D, Marko B, Nowak H, Ellger B, Limper U, Schwier E, Henzler D, Ehrentraut SF, Bergmann L, Unterberg M, Adamzik M, Koos B, Rump K. The Association between the rs3747406 Polymorphism in the Glucocorticoid-Induced Leucine Zipper Gene and Sepsis Survivals Depends on the SOFA Score. Int J Mol Sci 2024; 25:3871. [PMID: 38612684 PMCID: PMC11011808 DOI: 10.3390/ijms25073871] [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: 03/08/2024] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/14/2024] Open
Abstract
The variability in mortality in sepsis could be a consequence of genetic variability. The glucocorticoid system and the intermediate TSC22D3 gene product-glucocorticoid-induced leucine zipper-are clinically relevant in sepsis, which is why this study aimed to clarify whether TSC22D3 gene polymorphisms contribute to the variance in sepsis mortality. Blood samples for DNA extraction were obtained from 455 patients with a sepsis diagnosis according to the Sepsis-III criteria and from 73 control subjects. A SNP TaqMan assay was used to detect single-nucleotide polymorphisms (SNPs) in the TSC22D3 gene. Statistical and graphical analyses were performed using the SPSS Statistics and GraphPad Prism software. C-allele carriers of rs3747406 have a 2.07-fold higher mortality rate when the sequential organ failure assessment (SOFA) score is higher than eight. In a multivariate COX regression model, the SNP rs3747406 with a SOFA score ≥ 8 was found to be an independent risk factor for 30-day survival in sepsis. The HR was calculated to be 2.12, with a p-value of 0.011. The wild-type allele was present in four out of six SNPs in our cohort. The promoter of TSC22D3 was found to be highly conserved. However, we discovered that the C-allele of rs3747406 poses a risk for sepsis mortality for SOFA Scores higher than 6.
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Affiliation(s)
- Stefan Rusev
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Patrick Thon
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Tim Rahmel
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Dominik Ziehe
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Britta Marko
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Hartmuth Nowak
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
- Center for Artificial Intelligence, Medical Informatics and Data Science, University Hospital Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
| | - Björn Ellger
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Klinikum Westfalen, 44309 Dortmund, Germany;
| | - Ulrich Limper
- Department of Anesthesiology and Operative Intensive Care Medicine, Cologne Merheim Medical School, University of Witten/Herdecke, 51109 Cologne, Germany;
| | - Elke Schwier
- Department of Anesthesiology, Surgical Intensive Care, Emergency and Pain Medicine, Ruhr-University Bochum, Klinikum Herford, 32049 Herford, Germany; (E.S.); (D.H.)
| | - Dietrich Henzler
- Department of Anesthesiology, Surgical Intensive Care, Emergency and Pain Medicine, Ruhr-University Bochum, Klinikum Herford, 32049 Herford, Germany; (E.S.); (D.H.)
| | - Stefan Felix Ehrentraut
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, 53127 Bonn, Germany;
| | - Lars Bergmann
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Matthias Unterberg
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Michael Adamzik
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Björn Koos
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
| | - Katharina Rump
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany; (S.R.); (P.T.); (T.R.); (D.Z.); (B.M.); (H.N.); (L.B.); (M.U.); (M.A.); (B.K.)
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Gao L, Chang Y, Lu S, Liu X, Yao X, Zhang W, Sun E. A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study. Heliyon 2024; 10:e27416. [PMID: 38509924 PMCID: PMC10951500 DOI: 10.1016/j.heliyon.2024.e27416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/15/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
Objective This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU). Method We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility. Results The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822-0.886) for the training cohort and 0.865 (95% CI: 0.817-0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit. Conclusion We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.
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Affiliation(s)
- Liqin Gao
- Department of Neurosurgical Intensive Care Unit, Affiliated People's Hospital of Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
| | - Yafen Chang
- Department of Neurosurgical Intensive Care Unit, Affiliated People's Hospital of Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
| | - Siyuan Lu
- Department of Radiology, Affiliated People's Hospital of Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
| | - Xiyang Liu
- Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
| | - Xiang Yao
- Department of Orthopaedics, Affiliated People's Hospital of Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
| | - Wei Zhang
- Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
| | - Eryi Sun
- Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, ZhenJiang, Jiangsu Province, 212002, China
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Yang J, Chen J, Zhang M, Zhou Q, Yan B. Prognostic impacts of repeated sepsis in intensive care unit on autoimmune disease patients: a retrospective cohort study. BMC Infect Dis 2024; 24:197. [PMID: 38350868 PMCID: PMC10863122 DOI: 10.1186/s12879-024-09072-y] [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: 07/24/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Autoimmune diseases (ADs) may be complicated by sepsis when intensive care unit (ICU) admission. But repeated sepsis among AD patients has not been studied yet. The aim of this study is to investigate the impact of repeated in-ICU sepsis on the 1-year overall-cause mortality, septic shock and in-ICU death of AD patients. METHODS Data of AD patients with sepsis retrieved from Medical Information Mart for Intensive Care IV (MIMIC-IV) database were divided into the single group and the repeated group according to the frequency of in-ICU sepsis. Propensity score matching was used to balance inter-group bias. Cox proportional hazard regression and sensitivity analysis were utilized to assess the variables on mortality. RESULTS The incidence of repeated in-ICU sepsis in baseline was 19.8%. The repeated in-ICU sepsis was a risk factor for 1-year overall-cause mortality among AD patients (adjusted hazard ratio [HR] = 1.50, 95% CI: 1.16-1.93, P = 0.002), with robust adjusted HRs by the adjustment for confounders in the sensitivity analysis (all P < 0.01). Maximum Sequential Organ Failure Assessment (Max SOFA), Charlson comorbidity index (CCI) and Simplified Acute Physiology Score-II (SAPS-II) were risk factors for 1-year overall-cause mortality among AD with repeated sepsis (Max SOFA: HR = 1.09, P = 0.002; CCI: HR = 1.08, P = 0.039; SAPS-II: HR = 1.03, P < 0.001). CONCLUSIONS Compared to single hit, repeated in-ICU sepsis was independently related to a higher risk of 1-year overall-cause mortality among AD patients. Assessment tools (Higher SOFA, CCI and SAPS-II scores) were closely linked to poor prognosis of AD with repeated sepsis and helped to reflect ill physical conditions for the patients.
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Affiliation(s)
- Jinming Yang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, No.37 Guoxue Alley, 610041, Chengdu, China
| | - Jie Chen
- Department of Rheumatology, People's Hospital of Leshan, Leshan, China
| | - Min Zhang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, No.37 Guoxue Alley, 610041, Chengdu, China
| | - Qingsa Zhou
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, No.37 Guoxue Alley, 610041, Chengdu, China
| | - Bing Yan
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, No.37 Guoxue Alley, 610041, Chengdu, China.
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9
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Zhou L, Li Y, Ni Y, Liu C. Analysis of postoperative pulmonary complications after gastrectomy for gastric cancer: development and validation of a nomogram. Front Surg 2023; 10:1308591. [PMID: 38186389 PMCID: PMC10768169 DOI: 10.3389/fsurg.2023.1308591] [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: 10/17/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
Background Postoperative pulmonary complications (PPCs) are common in gastric cancer patients after gastrectomy. The aim of our study was to investigate the perioperative risk factors and to develop a nomogram to identify patients who are at significant risk of PPCs. Methods The clinical data of gastric cancer patients who underwent elective gastrectomy in the First Affiliated Hospital of Nanjing Medical University from 2017 to 2021 were retrospectively collected. All patients were randomly divided into a training and a validation cohort at a ratio of 7:3. Univariate and multivariate analysis were applied to identify the independent risk factors that might predict PPCs, and a nomogram was constructed. Both discrimination and calibration abilities were estimated by the area under a receiver operating characteristic curve (AUC) and calibration curves. The clinical effectiveness of the nomogram was further quantified with the decision curve analysis (DCA). Results Of 2,124 included patients, one hundred and fifty patients (7.1%) developed PPCs. Binary logistic analysis showed that age > 65 years, higher total cholesterol level, longer duration of surgery, total gastrectomy, and the dose of oxycodone > 5.5 mg were independent risk factors for the occurrence of PPCs, which were contained in the nomogram. The predictive nomogram showed good discrimination and calibration [an AUC of 0.735 (95% CI: 0.687-0.783) in a training cohort and 0.781 (95% CI: 0.715-0.847) in a validation cohort]. The calibration curve and decision curve analysis showed a good agreement between nomogram predictions and actual observations. Conclusion We developed a nomogram model based on age, total cholesterol, extent of resection, duration of surgery, and the dose of oxycodone to predict the risk of PPCs in gastric cancer patients after elective gastrectomy.
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Affiliation(s)
| | | | | | - Cunming Liu
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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10
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Liu X, Shen X, Wang H, Wang J, Ren Y, Zhang M, Li S, Guo L, Li J, Wang Y. Mollugin prevents CLP-induced sepsis in mice by inhibiting TAK1-NF-κB/MAPKs pathways and activating Keap1-Nrf2 pathway in macrophages. Int Immunopharmacol 2023; 125:111079. [PMID: 38149576 DOI: 10.1016/j.intimp.2023.111079] [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: 06/11/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 12/28/2023]
Abstract
Sepsis is a life-threatening organ dysfunction associated with macrophage overactivation. Targeted therapy against macrophages is considered a promising strategy for sepsis treatment. Mollugin (MLG), a compound extracted from traditional Chinese medicine Rubia cordifolia L., possesses anti-tumor and anti-inflammatory activities. This study aimed to investigate the anti-inflammatory effects and mechanisms of MLG in macrophages and its therapeutic role in CLP-induced sepsis in mice. The results demonstrated that MLG downregulated the inflammatory response induced by LPS or tumor necrosis factor α (TNF-α) in macrophages. Mechanistically, MLG suppressed the phosphorylation of TAK1, the upstream modulator of IKKα/β and MAPKs, thereby inhibiting the pro-inflammatory signaling transduction of NF-κB and MAPKs. Additionally, MLG also activated the Nrf2 antioxidant pathway, reducing intracellular reactive oxygen species. CETSA and molecular docking analyses revealed that MLG could effectively bind to TAK1 and Keap1, which may be involved in the inhibition of TAK1- NF-κB/MAPKs and activation of Nrf2 mediated by MLG. Animal study demonstrated that MLG ameliorated inflammatory injury of lung and liver in CLP-induced sepsis mice probably by reducing the levels of pro-inflammatory cytokines. Therefore, our study suggests that bi-directional roles of MLG in improving sepsis via blocking the TAK1-NF-κB/MAPKs and activating Nrf2 pathways, indicating its potential as a promising candidate drug for sepsis treatment.
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Affiliation(s)
- Xiaojun Liu
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Xiaofei Shen
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Han Wang
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Jiayi Wang
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Yanlin Ren
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Min Zhang
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Sixu Li
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Lijuan Guo
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Jingyu Li
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China
| | - Yi Wang
- Department of Pathophysiology, West China College of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041 Chengdu, China.
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Wu Z, Liu C, Ma Z, Li Z, Wang S, Chen Y, Han M, Huang S, Zhou Q, Zhang C, Hou B. A hierarchical prognostic model for Co-diabetes pancreatic adenocarcinoma. Heliyon 2023; 9:e21642. [PMID: 38027595 PMCID: PMC10663840 DOI: 10.1016/j.heliyon.2023.e21642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Co-diabetes pancreatic adenocarcinoma has a poorer prognosis than pancreatic adenocarcinoma without diabetes. This study aimed to develop a reliable prognostic model for patients with co-diabetes pancreatic adenocarcinoma. Method Overall, 169 patients with co-diabetes pancreatic adenocarcinoma were included in our study. First, the independent risk factors affecting the prognosis of patients with co-diabetes pancreatic adenocarcinoma were determined by univariate and multivariate Cox regression analyses. Based on these identified risk factors, we developed a nomogram and evaluated its predictive ability using the concordance index, receiver operating characteristic curve, calibration plot, decision curve, and net reclassification index. Results In this study, prealbumin, transferrin, carcinoembryonic antigen, distant metastasis, tumor differentiation neutrophil count, lymphocyte count and fasting blood glucose were confirmed as significant prognostic factors. Based on these predictors, a new nomogram was developed. Compared with the American Joint Committee on Cancer 8 staging system and other models, the nomogram achieved a higher concordance index in the training (0.795) and validation (0.729) queues. The area under the nomogram's curve for predicting patient survival at 0.5, 1, and 1.5 years in the training queue was >0.8. Patients were risk-stratified using the nomogram, and Kaplan-Meier survival curves of subgroups were plotted. The Kaplan-Meier curve also showed better separation than the American Joint Committee on Cancer 8 staging system, indicating that our model has a better risk hierarchical ability. Conclusions Compared to the American Joint Committee on Cancer 8 staging system and other predictive models, our model showed better predictive ability for patients with co-diabetes pancreatic adenocarcinoma. Our model will help in patients' risk stratification and improves their prognosis.
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Affiliation(s)
- Zelong Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Chunsheng Liu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Zuyi Ma
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100005, China
| | - Zhenchong Li
- German Cancer Research Center (DKFZ), Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, Heidelberg, Germany
| | - Shujie Wang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Yubin Chen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| | - Mingqian Han
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Shanzhou Huang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| | - Qi Zhou
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
- Department of General Surgery, Hui Ya Hospital of the First Affiliated Hospital, Sun Yat-sen University, Huizhou, Guangdong 516081, China
| | - Chuanzhao Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| | - Baohua Hou
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
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12
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Yuan J, Liu X, Liu Y, Li W, Chen X, Chen Q, Xiao C, Wan Y, Li S, Li Q, Li L, He J, Chen L, Shen F. Association between base excess and 28-day mortality in sepsis patients: A secondary analysis based on the MIMIC- IV database. Heliyon 2023; 9:e15990. [PMID: 37215834 PMCID: PMC10199177 DOI: 10.1016/j.heliyon.2023.e15990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/24/2023] Open
Abstract
Objective The relationship between base excess (BE) and 28-day death in sepsis patients remains to be elucidated. The aim of our clinical study is to explore the association of BE with 28-day mortality in patients with sepsis by using a large sample, multicenter Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Methods We extracted the data of 35,010 patients with sepsis from the MIMIC-IV database, in which we used BE as an exposure variable and the 28-day mortality as an outcome variable, respectively, so as to explore the impact of BE on the 28-day mortality of patients with sepsis after adjusting for covariates. Results BE and the 28-day mortality of patients with sepsis appeared to have a U-shaped relationship. The calculated inflection points were -2.5 mEq/L and 1.9 mEq/L, respectively. Our data demonstrated that BE was negatively associated with 28-day mortality in the range of -41.0 mEq/L to -2.5 mEq/L (odds ratio: 0.95; 95% confidence intervals (95%CI): 0.93 to 0.96), p < 0.0001. When BE was in the range of 1.9 mEq/L to 55.5 mEq/L, however, a positive association existed between BE and 28-day mortality of patients with sepsis (odds ratio: 1.03; 95% CI: 1.00 to 1.05; p < 0.05). Conclusion The BE levels have a U-shaped relationship with the 28-day mortality in patients with sepsis, in which the mortality of patients will gradually decrease with a BE value from -41.0 mEq/L to -2.5 mEq/L, while the mortality will increase with a BE value from 1.9 mEq/L to 55.5 mEq/L.
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Affiliation(s)
- Jia Yuan
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Xu Liu
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Ying Liu
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Wei Li
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Xianjun Chen
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Qiming Chen
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Chuan Xiao
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Ying Wan
- Department of Good Clinical Practice, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Shuwen Li
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Qing Li
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Lu Li
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Juan He
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Lu Chen
- Department of Good Clinical Practice, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
| | - Feng Shen
- Department of Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550001, Guizhou Province, China
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13
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Jian X, Du S, Zhou X, Xu Z, Wang K, Dong X, Hu J, Wang H. Development and validation of nomograms for predicting the risk probability of carbapenem resistance and 28-day all-cause mortality in gram-negative bacteremia among patients with hematological diseases. Front Cell Infect Microbiol 2023; 12:969117. [PMID: 36683699 PMCID: PMC9849754 DOI: 10.3389/fcimb.2022.969117] [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: 06/14/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023] Open
Abstract
Objectives Gram-negative bacteria (GNB) bloodstream infections (BSIs) are the most widespread and serious complications in hospitalized patients with hematological diseases. The emergence and prevalence of carbapenem-resistant (CR) pathogens has developed into a considerable challenge in clinical practice. Currently, nomograms have been extensively applied in the field of medicine to facilitate clinical diagnosis and treatment. The purpose of this study was to explore risk indicators predicting mortality and carbapenem resistance in hematological (HM) patients with GNB BSI and to construct two nomograms to achieve personalized prediction. Methods A single-center retrospective case-control study enrolled 244 hospitalized HM patients with GNB-BSI from January 2015 to December 2019. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were conducted to select potential characteristic predictors of plotting nomograms. Subsequently, to evaluate the prediction performance of the models, the prediction models were internally validated using the bootstrap approach (resampling = 1000) and 10-fold cross validation. Results Of all 244 eligible patients with BSI attributed to GNB in this study, 77 (31.6%) were resistant to carbapenems. The rate of carbapenem resistance exhibited a growing tendency year by year, from 20.4% in 2015 to 42.6% in 2019 (p = 0.004). The carbapenem resistance nomogram constructed with the parameters of hypoproteinemia, duration of neutropenia ≥ 6 days, previous exposure to carbapenems, and previous exposure to cephalosporin/β-lactamase inhibitors indicated a favorable discrimination ability with a modified concordance index (C-index) of 0.788 and 0.781 in both the bootstrapping and 10-fold cross validation procedures. The 28-day all-cause mortality was 28.3% (68/240). The prognosis nomogram plotted with the variables of hypoproteinemia, septic shock, isolation of CR-GNB, and the incomplete remission status of underlying diseases showed a superior discriminative ability of poorer clinical prognosis. The modified C-index of the prognosis nomogram was 0.873 with bootstrapping and 0.887 with 10-fold cross validation. The decision curve analysis (DCA) for two nomogram models both demonstrated better clinical practicality. Conclusions For clinicians, nomogram models were effective individualized risk prediction tools to facilitate the early identification of HM patients with GNB BSI at high risk of mortality and carbapenem resistance.
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Affiliation(s)
- Xing Jian
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuaixian Du
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Zhou
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziwei Xu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kejing Wang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Dong
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junbin Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Junbin Hu, ; Huafang Wang,
| | - Huafang Wang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Junbin Hu, ; Huafang Wang,
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