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Shi W, Lin H, Zhang X, Xu W, Lan T, Jiang W, Chen X, Lu W. The association between frailty and the risk of mortality in critically ill congestive heart failure patients: findings from the MIMIC-IV database. Front Endocrinol (Lausanne) 2024; 15:1424257. [PMID: 39161392 PMCID: PMC11330805 DOI: 10.3389/fendo.2024.1424257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 07/17/2024] [Indexed: 08/21/2024] Open
Abstract
Background Frailty is a severe, common co-morbidity associated with congestive heart failure (CHF). This retrospective cohort study assesses the association between frailty and the risk of mortality in critically ill CHF patients. Methods Eligible patients with CHF from the Medical Information Base for Intensive Care IV database were retrospectively analyzed. The frailty index based on laboratory tests (FI_Lab) index was calculated using 33 variables to assess frailty status. The primary outcomes were in-hospital mortality and one-year mortality. The secondary outcomes were the incidence of acute kidney injury (AKI) and the administration of renal replacement therapy (RRT) in patients with concurrent AKI. Survival disparities among the FI_Lab subgroups were estimated with Kaplan-Meier survival analysis. The association between the FI_Lab index and mortality was examined with Cox proportional risk modeling. Results A total of 3273 adult patients aged 18 years and older were enrolled in the study, with 1820 men and 1453 women included. The incidence rates of in-hospital mortality and one-year mortality rate were 0.96 per 1,000 person-days and 263.8 per 1,000 person-years, respectively. Multivariable regression analysis identified baseline FI_Lab > 0.45 as an independent risk factor predicting in-hospital mortality (odds ratio = 3.221, 95% CI 2.341-4.432, p < 0.001) and one-year mortality (hazard ratio=2.152, 95% CI: 1.730-2.678, p < 0.001). In terms of predicting mortality, adding FI_Lab to the six disease severity scores significantly improved the overall performance of the model (all p < 0.001). Conclusions We established a positive correlation between the baseline FI_Lab and the likelihood of adverse outcomes in critical CHF patients. Given its potential as a reliable prognostic tool for such patients, further validation of FI_Lab across multiple centers is recommended for future research.
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Affiliation(s)
- Wenhua Shi
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Hong Lin
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xinyu Zhang
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Wenjing Xu
- Department of Cardiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Taohua Lan
- Department of Cardiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Academician Chen Keji Workstation, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Wei Jiang
- Department of Cardiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xiankun Chen
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Key Unit of Methodology in Clinical Research, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Weihui Lu
- Department of Cardiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Academician Chen Keji Workstation, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Chinese Medicine Guangdong Laboratory, Hengqin, Guangdong, China
- State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
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Sun N, Li R, Deng H, Li Q, Deng J, Zhu Y, Mo W, Guan W, Hu M, Liu M, Xie X, Lin X, Zhou C. The prognostic impact of severe grade immune checkpoint inhibitor related pneumonitis in non-small cell lung cancer patients. Front Oncol 2024; 14:1372532. [PMID: 38983925 PMCID: PMC11231069 DOI: 10.3389/fonc.2024.1372532] [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: 01/18/2024] [Accepted: 05/17/2024] [Indexed: 07/11/2024] Open
Abstract
Objective To compare the prognostic differences between non-small cell lung cancer (NSCLC) patients with mild and severe checkpoint inhibitor-associated pneumonitis (CIP), and explore the causes of death and prognostic risk factors in NSCLC patients with severe CIP. Methods A retrospective study of a cohort of 116 patients with unresectable stage III or IV NSCLC with any grade CIP from April 2016 to August 2022 were conducted. To analyze the clinical characteristics of patients with different CIP grades, patients were divided into mild CIP group (grade 1-2, n=49) and severe CIP group (grade 3-5, n=67) according to the grade of CIP. To explore the OS-related risk factors in the severe CIP group, the patients were divided into a good prognosis (GP) group (≥ median OS, n=30) and a poor prognosis (PP) group (< median OS, n=37) based on whether their overall survival (OS) were greater than median OS. Baseline clinical and laboratory data were collected for analysis. Results The median OS of all NSCLC patients combined with CIP was 11.4 months (95%CI, 8.070-16.100), The median OS for mild CIP and severe CIP was 22.1 months and 4.4 months respectively (HR=3.076, 95%CI, 1.904-4.970, P<0.0001). The results showed that the most common cause of death among severe CIP patients in the PP group was CIP and the most common cause in the GP group was tumor. The univariate regression analysis showed that suspension of antitumor therapy was a risk factor for poor prognosis (OR=3.598, 95%CI, 1.307-9.905, p=0.013). The multivariate logistic regression analysis showed that suspension of anti-tumor therapy (OR=4.24, 95%CI, 1.067-16.915, p=0.040) and elevated KL-6 (OR=1.002, 95%CI, 1.001-1.002, p<0.001) were independent risk factors for poor prognosis. Conclusion In conclusion, patients with severe CIP had a poor prognosis, especially those with elevated KL-6, and the main cause of death is immune checkpoint inhibitor-associated pneumonitis complicated with infection. In addition, anti-tumor therapy for severe CIP patients should be resumed in time and should not be delayed for too long.
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Affiliation(s)
- Ni Sun
- Guangzhou Medical University, Guangzhou, Guangdong, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Ru Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Henan University, Kaifeng, Henan, China
| | - Haiyi Deng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Qingyang Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jiaxi Deng
- Guangzhou Medical University, Guangzhou, Guangdong, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yue Zhu
- Guangzhou Medical University, Guangzhou, Guangdong, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wenwei Mo
- Guangzhou Medical University, Guangzhou, Guangdong, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wenhui Guan
- Guangzhou Medical University, Guangzhou, Guangdong, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Minjuan Hu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Ming Liu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaohong Xie
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xinqing Lin
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Chengzhi Zhou
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine-Section 5, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
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Wang L, Liu T, Zhu Z, Wang B, Lu Z, Pan Y, Sun L. Associations between static and dynamic changes of platelet counts and in-hospital mortality in critical patients with acute heart failure. Sci Rep 2024; 14:9147. [PMID: 38644461 PMCID: PMC11033279 DOI: 10.1038/s41598-024-59892-w] [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: 08/28/2023] [Accepted: 04/16/2024] [Indexed: 04/23/2024] Open
Abstract
To investigate the predictive value of baseline platelet count and its short-term dynamic changes in the prognosis of patients with acute heart failure (AHF) in the intensive care unit. Patients diagnosed with AHF in the medical information mart for intensive care III and their clinical data were retrospectively filtered. Patients were divided into survivor and non-survivor groups based on their prognosis during hospitalization, and differences in baseline data between groups were compared. Logistic regression models and restricted cubic spline (RCS) plots were performed to evaluate the relationship between baseline platelet counts and in-hospital mortality. Changes and trends in platelet counts were compared between the survivor and non-survivor groups after adjusting for confounders with the generalized additive mixing model (GAMM). A total of 2930 critical patients with acute heart failure were included, of which 2720 were survivors and 210 were non-survivors. Multiple logistic regression models revealed that baseline platelet count was an independent factor in hospital mortality (OR 0.997, 95% CI 0.994-0.999, P-value = 0.018). The RCS plot demonstrated a U-shaped dose-response relationship between baseline platelet count and in-hospital mortality. GAMM analysis suggested that the platelet counts decreased and then increased in the survivor group and gradually decreased in the non-survivor group, with a gradual increase of difference between two groups. After adjusting for confounders, the mean daily increase was -6.014 (95% CI -7.076-4.953, P-value < 0.001). Baseline platelet demonstrated a U-shaped dose-response relationship with adverse outcomes in critical patients with AHF. Early elevation of platelet was correlated with higher in-hospital mortality, indicating that tracking early changes in platelet might help determine the short-term prognosis of critical patients with AHF.
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Affiliation(s)
- Lili Wang
- Department of Cardiology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Tao Liu
- Department of Cardiology, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhijian Zhu
- Department of Cardiology, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Bing Wang
- Department of Cardiology, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhigang Lu
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yesheng Pan
- Department of Cardiology, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Lifang Sun
- Department of Cardiology, Jinshan District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, China.
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Li M, Liu F, Yang Y, Lao J, Yin C, Wu Y, Yuan Z, Wei Y, Tang F. Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome. Respir Res 2024; 25:8. [PMID: 38178157 PMCID: PMC10765902 DOI: 10.1186/s12931-023-02643-8] [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: 11/14/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored. METHODS Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test. RESULTS A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020). CONCLUSIONS For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.
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Affiliation(s)
- Mingzhuo Li
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Fen Liu
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
| | - Yang Yang
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Jiahui Lao
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Chaonan Yin
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Yafei Wu
- Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Fang Tang
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China.
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
- Shandong Data Open Innovative Application Laboratory, Jinan, China.
- Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Chen J, Tang J, Nie M, Li Y, Wurfel MM, Meyer NJ, Wei Y, Zhao Y, Frank AJ, Thompson BT, Christiani DC, Chen F, Zhang R. WNT9A Affects Late-Onset Acute Respiratory Distress Syndrome and 28-Day Survival: Evidence from a Three-Step Multiomics Study. Am J Respir Cell Mol Biol 2023; 69:220-229. [PMID: 37094100 PMCID: PMC10399141 DOI: 10.1165/rcmb.2022-0416oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 04/24/2023] [Indexed: 04/26/2023] Open
Abstract
Late-onset (more than 48 h after ICU admission) acute respiratory distress syndrome (ARDS) is associated with shorter survival time and higher mortality; however, the underlying molecular targets remain unclear. As the WNT gene family is known to drive inflammation, immunity, and tissue fibrosis, all of which are closely related to the pathogenesis and prognosis of ARDS, we aim to investigate the associations of the WNT family with late-onset ARDS and 28-day survival. Genetic (n = 380), epigenetic (n = 185), transcriptional (n = 160), and protein (n = 300) data of patients with ARDS were extracted from the MEARDS (Molecular Epidemiology of ARDS) cohort. We used sure independence screening to identify late onset-related genetic biomarkers and constructed a genetic score on the basis of eight SNPs, which was associated with risk for late-onset ARDS (odds ratio [OR], 2.72; P = 3.81 × 10-14) and survival (hazard ratio [HR], 1.28; P = 0.008). The associations were further externally validated in the iSPAAR (Identification of SNPs Predisposing to Altered Acute Lung Injury Risk) (ORlate onset, 2.49 [P = 0.006]; HRsurvival, 1.87 [P = 0.045]) and MESSI (Molecular Epidemiology of Severe Sepsis in the ICU) (ORlate onset, 4.12 [P = 0.026]; HRsurvival, 1.45 [P = 0.036]) cohorts. Furthermore, we functionally interrogated the six mapped genes of eight SNPs in the multiomics data and noted associations of WNT9A (WNT family member 9A) in epigenetic (ORlate onset, 2.95 [P = 9.91 × 10-4]; HRsurvival, 1.53 [P = 0.011]) and protein (ORlate onset, 1.42 [P = 0.035]; HRsurvival, 1.38 [P = 0.011]) data. The mediation analysis indicated that the effects of WNT9A on ARDS survival were mediated by late onset (HRindirect, 1.12 [P = 0.014] for genetic data; HRindirect, 1.05 [P = 0.030] for protein data). The essential roles of WNT9A in immunity and fibrosis may explain the different trajectories of recovery and dysfunction between early- and late-onset ARDS, providing clues for ARDS treatment.
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Affiliation(s)
- Jiajin Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, and
| | - Jiaqi Tang
- Department of Biostatistics, Center for Global Health, School of Public Health, and
| | - Mengli Nie
- Department of Biostatistics, Center for Global Health, School of Public Health, and
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Mark M. Wurfel
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, Washington
| | - Nuala J. Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, and
- China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, and
- China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Angela J. Frank
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - B. Taylor Thompson
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - David C. Christiani
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts; and
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, and
- China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, and
- China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts; and
- Changzhou Medical Center, Nanjing Medical University, Changzhou, China
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Bai W, Hao B, Xu L, Qin J, Xu W, Qin L. Frailty index based on laboratory tests improves prediction of short-and long-term mortality in patients with critical acute myocardial infarction. Front Med (Lausanne) 2022; 9:1070951. [PMID: 36561712 PMCID: PMC9763273 DOI: 10.3389/fmed.2022.1070951] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Background Previous studies have shown that the frailty index based on laboratory tests (FI-Lab) can identify older adults at increased risk of adverse health outcomes. This study aimed to determine whether the FI-Lab is associated with mortality risk and can provide incremental improvements in risk stratification of patients with critical acute myocardial infarction (AMI). Materials and methods We conducted a secondary analysis of data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. A 33-item FI-Lab was constructed. Outcomes of interest were in-hospital and 1-year mortality. Logistic regression models were used to investigate the association between the FI-Lab and outcomes. For the assessment of the incremental predictive value, the FI-Lab was added to several risk stratification scoring systems for critically ill patients, and the following indices were calculated: Δ C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results Out of 2,159 patients, 477 died in hospital (22.1%), and 898 died during the 1-year follow-up period. After adjustment for confounders, the FI-Lab was associated with increased in-hospital mortality [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.05-1.07] and 1-year mortality (OR = 1.05, 95% CI: 1.04-1.06) when assessed as a continuous variable (per 0.01-score increase). When assessed as a categorical variable, the FI-Lab was associated with in-hospital mortality (2nd Quartile: OR = 1.89, 95% CI: 1.18-3.03; 3rd Quartile: OR = 3.46, 95% CI: 2.20-5.46; and 4th Quartile: OR = 5.79, 95% CI: 3.61-9.28 compared to 1st Quartile) as well as 1-year mortality (2nd Quartile: OR = 1.66, 95% CI: 1.23-2.24; 3rd Quartile: OR = 2.40, 95% CI: 1.76-3.26; and 4th Quartile: OR = 3.76, 95% CI: 2.66-5.30 compared to 1st Quartile) after adjustment for confounders. The addition of the FI-Lab to all disease severity scores improved discrimination and significantly reclassified in-hospital and 1-year mortality risk. Conclusion The FI-Lab was a strong predictor of short- and long-term mortality in patients with critical AMI. The FI-Lab improved the ability to predict mortality in patients with critical AMI and therefore might be useful in the clinical decision-making process.
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Affiliation(s)
- Weimin Bai
- Department of Emergency, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, China
| | - Benchuan Hao
- Medical School of Chinese PLA, Beijing, China,Department of Cardiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Lijun Xu
- Department of Emergency, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, China
| | - Ji Qin
- Medical School of Chinese PLA, Beijing, China,Department of Cardiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Weihao Xu
- Haikou Cadre’s Sanitarium of Hainan Military Region, Haikou, China,*Correspondence: Weihao Xu,
| | - Lijie Qin
- Department of Emergency, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, China,Lijie Qin,
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