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Abbott EE, Oh W, Dai Y, Feuer C, Chan L, Carr BG, Nadkarni GN. Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis. JMIR Aging 2023; 6:e51844. [PMID: 38059569 PMCID: PMC10721134 DOI: 10.2196/51844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/28/2023] [Accepted: 10/29/2023] [Indexed: 12/08/2023] Open
Abstract
Background Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.
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Affiliation(s)
- Ethan E Abbott
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Wonsuk Oh
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Yang Dai
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Cole Feuer
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Lili Chan
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Brendan G Carr
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Girish N Nadkarni
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
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Yu Y, Rao J, Xu Q, Xiao J, Cheng P, Wang J, Xi W, Wang P, Zhang Y, Wang Z. Phenotyping cardiogenic shock that showed different clinical outcomes and responses to vasopressor use: a latent profile analysis from MIMIC-IV database. Front Med (Lausanne) 2023; 10:1186119. [PMID: 37425299 PMCID: PMC10325854 DOI: 10.3389/fmed.2023.1186119] [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: 03/14/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023] Open
Abstract
Background Cardiogenic shock (CS) is increasingly recognized as heterogeneous in its severity and response to therapies. This study aimed to identify CS phenotypes and their responses to the use of vasopressors. Method The current study included patients with CS complicating acute myocardial infarction (AMI) at the time of admission from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Laboratory and clinical variables were collected and used to conduct latent profile (LPA) analysis. Furthermore, we used a multivariable logistic regression (LR) model to explore the independent association between the use of vasopressors and endpoints. Result A total of 630 eligible patients with CS after AMI were enrolled in the study. The LPA identified three profiles of CS: profile 1 (n = 259, 37.5%) was considered as the baseline group; profile 2 (n = 261, 37.8%) was characterized by advanced age, more comorbidities, and worse renal function; and profile 3 (n = 170, 24.6%) was characterized by systemic inflammatory response syndrome (SIRS)-related indexes and acid-base balance disturbance. Profile 3 showed the highest all-cause in-hospital mortality rate (45.9%), followed by profile 2 (43.3%), and profile 1 (16.6%). The LR analyses showed that the phenotype of CS was an independent prognostic factor for outcomes, and profiles 2 and 3 were significantly associated with a higher risk of in-hospital mortality (profile 2: odds ratio [OR] 3.95, 95% confidence interval [CI] 2.61-5.97, p < 0.001; profile 3: OR 3.90, 95%CI 2.48-6.13, p < 0.001) compared with profile 1. Vasopressor use was associated with an improved risk of in-hospital mortality for profile 2 (OR: 2.03, 95% CI: 1.15-3.60, p = 0.015) and profile 3 (OR: 2.91, 95% CI: 1.02-8.32, p = 0.047), respectively. The results of vasopressor use showed no significance for profile 1. Conclusion Three phenotypes of CS were identified, which showed different outcomes and responses to vasopressor use.
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Affiliation(s)
- Yue Yu
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jin Rao
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Qiumeng Xu
- Department of Orthopaedics, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jian Xiao
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Pengchao Cheng
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Junnan Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wang Xi
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Pei Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Yufeng Zhang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhinong Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
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Ginsenoside Rb1 Improves Post-Cardiac Arrest Myocardial Stunning and Cerebral Outcomes by Regulating the Keap1/Nrf2 Pathway. Int J Mol Sci 2023; 24:ijms24055059. [PMID: 36902487 PMCID: PMC10003120 DOI: 10.3390/ijms24055059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 03/09/2023] Open
Abstract
The prognosis of cardiac arrest (CA) is dismal despite the ongoing progress in cardiopulmonary resuscitation (CPR). ginsenoside Rb1 (Gn-Rb1) has been verified to be cardioprotective in cardiac remodeling and cardiac ischemia/reperfusion (I/R) injury, but its role is less known in CA. After 15 min of potassium chloride-induced CA, male C57BL/6 mice were resuscitated. Gn-Rb1 was blindly randomized to mice after 20 s of CPR. We assessed the cardiac systolic function before CA and 3 h after CPR. Mortality rates, neurological outcome, mitochondrial homeostasis, and the levels of oxidative stress were evaluated. We found that Gn-Rb1 improved the long-term survival during the post-resuscitation period but did not affect the ROSC rate. Further mechanistic investigations revealed that Gn-Rb1 ameliorated CA/CPR-induced mitochondrial destabilization and oxidative stress, partially via the activation of Keap1/Nrf2 axis. Gn-Rb1 improved the neurological outcome after resuscitation partially by balancing the oxidative stress and suppressing apoptosis. In sum, Gn-Rb1 protects against post-CA myocardial stunning and cerebral outcomes via the induction of the Nrf2 signaling pathway, which may offer a new insight into therapeutic strategies for CA.
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Jiang X, Zhang W, Pan Y, Cheng X. Identification of subphenotypes in critically ill thrombocytopenic patients with different responses to therapeutic interventions: a retrospective study. Front Med (Lausanne) 2023; 10:1166896. [PMID: 37181358 PMCID: PMC10174319 DOI: 10.3389/fmed.2023.1166896] [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: 02/15/2023] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction The causes of thrombocytopenia (TP) in critically ill patients are numerous and heterogeneous. Currently, subphenotype identification is a popular approach to address this problem. Therefore, this study aimed to identify subphenotypes that respond differently to therapeutic interventions in patients with TP using routine clinical data and to improve individualized management of TP. Methods This retrospective study included patients with TP admitted to the intensive care unit (ICU) of Dongyang People's Hospital during 2010-2020. Subphenotypes were identified using latent profile analysis of 15 clinical variables. The Kaplan-Meier method was used to assess the risk of 30-day mortality for different subphenotypes. Multifactorial Cox regression analysis was used to analyze the relationship between therapeutic interventions and in-hospital mortality for different subphenotypes. Results This study included a total of 1,666 participants. Four subphenotypes were identified by latent profile analysis, with subphenotype 1 being the most abundant and having a low mortality rate. Subphenotype 2 was characterized by respiratory dysfunction, subphenotype 3 by renal insufficiency, and subphenotype 4 by shock-like features. Kaplan-Meier analysis revealed that the four subphenotypes had different in-30-day mortality rates. The multivariate Cox regression analysis indicated a significant interaction between platelet transfusion and subphenotype, with more platelet transfusion associated with a decreased risk of in-hospital mortality in subphenotype 3 [hazard ratio (HR): 0.66, 95% confidence interval (CI): 0.46-0.94]. In addition, there was a significant interaction between fluid intake and subphenotype, with a higher fluid intake being associated with a decreased risk of in-hospital mortality for subphenotype 3 (HR: 0.94, 95% CI: 0.89-0.99 per 1 l increase in fluid intake) and an increased risk of in-hospital mortality for high fluid intake in subphenotypes 1 (HR: 1.10, 95% CI: 1.03-1.18 per 1 l increase in fluid intake) and 2 (HR: 1.19, 95% CI: 1.08-1.32 per 1 l increase in fluid intake). Conclusion Four subphenotypes of TP in critically ill patients with different clinical characteristics and outcomes and differential responses to therapeutic interventions were identified using routine clinical data. These findings can help improve the identification of different subphenotypes in patients with TP for better individualized treatment of patients in the ICU.
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Xing L, Yao M, Goyal H, Hong Y, Zhang Z. Latent transition analysis of cardiac arrest patients treated in the intensive care unit. PLoS One 2021; 16:e0252318. [PMID: 34043699 PMCID: PMC8158944 DOI: 10.1371/journal.pone.0252318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/13/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Post-cardiac arrest (CA) syndrome is heterogenous in their clinical presentations and outcomes. This study aimed to explore the transition and stability of subphenotypes (profiles) of CA treated in the intensive care unit (ICU). PATIENTS AND METHODS Clinical features of CA patients on day 1 and 3 after ICU admission were modeled by latent transition analysis (LTA) to explore the transition between subphenotypes over time. The association between different transition patterns and mortality outcome was explored using multivariable logistic regression. RESULTS We identified 848 eligible patients from the database. The LPA identified three distinct subphenotypes: Profile 1 accounted for the largest proportion (73%) and was considered as the baseline subphenotype. Profile 2 (13%) was characterized by brain injury and profile 3 (14%) was characterized by multiple organ dysfunctions. The same three subphenotypes were identified on day 3. The LTA showed consistent subphenotypes. A majority of patients in profile 2 (72%) and 3 (82%) on day 1 switched to profile 1 on day 3. In the logistic regression model, patients in profile 1 on day 1 transitioned to profile 3 had worse survival outcome than those continue to remain in profile 1 (OR: 20.64; 95% CI: 6.01 to 70.94; p < 0.001) and transitioned to profile 2 (OR: 8.42; 95% CI: 2.22 to 31.97; p = 0.002) on day 3. CONCLUSION The study identified three subphenotypes of CA, which was consistent on day 1 and 3 after ICU admission. Patients who transitioned to profile 3 on day 3 had significantly worse survival outcome than those remained in profile 1 or 2.
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Affiliation(s)
- Lifeng Xing
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Yao
- Department of Surgery, Wound Care Clinical Research Program, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, United States of America
| | - Hemant Goyal
- Department of Internal Medicine, Mercer University School of Medicine, Macon, Georgia, United States of America
| | - Yucai Hong
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- * E-mail: (ZZ); (YH)
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Emergency and Trauma, Ministry of Education, College of Emergency and Trauma, Hainan Medical University, Haikou, China
- * E-mail: (ZZ); (YH)
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