Trajectories of Circulating Monocyte Subsets After ST-Elevation Myocardial Infarction During Hospitalization: Latent Class Growth Modeling for High-Risk Patient Identification

Zeng, Shan; Yan, Lifang; Luo, Yanwei; Liu, Xinlin; Liu, Junxiang; Guo, Zhao-Zeng; Xu, Zhongwei; Li, Yuming; Ji, Wenjie; Zhou, Xin

doi:10.1007/s12265-017-9782-9

Cited by 12 publications

(14 citation statements)

References 40 publications

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“…23 Monocyte subsets were implicated in the pathophysiology of acute myocardial infarction and correlated with outcome and left ventricular function in those patients. [33][34][35] The detailed mechanisms are not understood; therefore, one could speculate that the observed monocyte subset changes in our study might be due to myocardial injury.…”

Section: Discussionmentioning

confidence: 85%

Monocyte subsets predict mortality after cardiac arrest

Krychtiuk

Lenz

Richter

et al. 2020

Journal of Leukocyte Biology

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After successful cardiopulmonary resuscitation (CPR), many patients show signs of an overactive immune activation. Monocytes are a heterogeneous cell population that can be distinguished into 3 subsets by flow cytometry (classical monocytes [CM: CD14 ++ CD16-], intermediate monocytes [IM: CD14 ++ CD16 + CCR2 + ] and non-classical monocytes [NCM: CD14 + CD16 ++ CCR2-]). Fiftythree patients admitted to the medical intensive care unit (ICU) after cardiac arrest were included. Blood was taken on admission and after 72 h. The primary endpoint of this study was survival at 6 months and the secondary endpoint was neurological outcome as determined by cerebral performance category (CPC)-score at 6 months. Median age was 64.5 (49.8-74.3) years and 75.5% were male. Six-month mortality was 50.9% and survival with good neurological outcome was 37.7%. Monocyte subset distribution upon admission to the ICU did not differ according to survival. Seventy-two hours after admission, patients who died within 6 months showed a higher percentage of the pro-inflammatory subset of IM (8.3% [3.8-14.6]% vs. 4.1% [1.5-8.2]%; P = 0.025), and a lower percentage of CM (87.5% [79.9-89.0]% vs. 90.8% [85.9-92.7]%; P = 0.036) as compared to survivors. In addition, IM were predictive of outcome independent of time to ROSC and witnessed cardiac arrest, and correlated with CPC-score at 6 months (R = 0.32; P = 0.043). These findings suggest a possible role of the innate immune system in the pathophysiology of post cardiac arrest syndrome.

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Section: Discussionmentioning

confidence: 85%

Monocyte subsets predict mortality after cardiac arrest

Krychtiuk

Lenz

Richter

et al. 2020

Journal of Leukocyte Biology

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“…It should be emphasized that our cohort is an ambulatory chronic cohort, with much less inflammation activation than seen in acute HF, but we also found such prognostic relationship. Other studies have indicated that the intermediate subset proved to be risk factor for post-ST elevation myocardial infarction adverse outcomes and cardiovascular events [ 29 , 30 ]. Also, the amount of intermediate monocytes was found to correlate with worse cardiac function and predicted the possibility to reach an improvement in NYHA functional class at 3 months after transcatheter aortic valve replacement [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Circulating monocyte subsets and heart failure prognosis

et al. 2018

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Monocytes are a heterogeneous population of effector cells with key roles in tissue integrity restoration and maintenance. Here, we explore the association of monocyte subsets and prognosis in patients with ambulatory heart failure (HF). Monocyte subsets were classified as classical (CD14++/CD16–), intermediate (CD14++/CD16+), or non-classical (CD14+/CD16++). Percentage distribution and absolute cell count were assessed in each subset, and multivariable Cox regression analyses were performed with all-cause death, HF-related hospitalization, and the composite end-point of both as dependent variables. 400 patients were consecutively included (72.8% male, age 69.4±12.2 years, 45.5% from ischemic aetiology, left ventricle ejection fraction (LVEF) 41.6% ±14.5, New York Heart Association (NYHA) class II 62.8% and III 30.8%). During a mean follow-up of 2.6±0.9 years, 107 patients died, 99 had a HF-related hospitalization and 160 suffered the composite end-point of all-cause death or HF-related hospitalization. Monocyte subsets assessed in percentages were not independently associated to any of the end-points. When considering number of cells/μL, intermediate subset was independently associated with an increase of all-cause death (HR 1.25 [95% CI 1,02–1.52], p = 0.03), and the composite end-point HR 1.20 [95% CI 1,03–1.40], p = 0.02). The presented findings show that absolute cell count of monocyte subsets was preferred over monocyte percentage for prognosis stratification for outpatients with HF. The intermediate monocyte subset provides information on increased risk of all-cause death and the composite end-point.

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“…Parameters and/or estimates from this/these model(s) are then included as covariates in a survival model. The Cox PH model was used in most studies (n = 26, 89.7%) [ 57 , 58 , 60 – 63 , 65 – 73 , 75 – 78 , 80 – 85 , 88 ]. A weakness of the two-stage approach is that uncertainty in the longitudinal data summaries produced in the first stage is ignored.…”

Section: Resultsmentioning

confidence: 99%

“…The second most frequently used method (n = 17, 58.6%) was group-based trajectory models (GBTMs) to model the trajectory of the longitudinal variable [ 58 , 60 , 61 , 65 – 70 , 72 , 73 , 75 – 77 , 81 , 84 , 88 ]. Wang et al identified four separate trajectories of sleep duration and used these to predict risk of cardiovascular events or mortality [ 69 ].…”

Section: Resultsmentioning

confidence: 99%

Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review

Stevens

Lane

Harrison

et al. 2021

BMC Med Res Methodol

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Objective The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories. Methods We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms covered three areas: data type, modelling type and disease area including search terms such as “longitudinal”, “trajector*” and “cardiovasc*” respectively. Studies were filtered to meet the following inclusion criteria: longitudinal individual patient data in adult patients with ≥3 time-points and a CVD or mortality outcome. Studies were screened and analyzed by one author. Any queries were discussed with the other authors. Comparisons were made between the methods identified looking at assumptions, flexibility and software availability. Results From the initial 2601 studies returned by the searches 80 studies were included. Four statistical approaches were identified for modelling the longitudinal data: 3 (4%) studies compared time points with simple statistical tests, 40 (50%) used single-stage approaches, such as including single time points or summary measures in survival models, 29 (36%) used two-stage approaches including an estimated longitudinal parameter in survival models, and 8 (10%) used joint models which modelled the longitudinal and survival data together. The proportion of CVD risk prediction models created using longitudinal data using two-stage and joint models increased over time. Conclusions Single stage models are still heavily utilized by many CVD risk prediction studies for modelling longitudinal data. Future studies should fully utilize available longitudinal data when analyzing CVD risk by employing two-stage and joint approaches which can often better utilize the available data.

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Trajectories of Circulating Monocyte Subsets After ST-Elevation Myocardial Infarction During Hospitalization: Latent Class Growth Modeling for High-Risk Patient Identification

Cited by 12 publications

References 40 publications

Monocyte subsets predict mortality after cardiac arrest

Monocyte subsets predict mortality after cardiac arrest

Circulating monocyte subsets and heart failure prognosis

Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review

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