Body Mass Index and 1-Year Unplanned Readmission in Chinese Patients with Acute Myocardial Infarction: A Retrospective Cohort Study

Background Evidence regarding the relationship between body mass index (BMI) and 1-year unplanned readmission was limited. Therefore, the objective of this research is to investigate whether BMI was independently related to 1-year unplanned readmission in Chinese patients with acute myocardial infarction (AMI) after percutaneous transluminal coronary intervention (PCI) after adjusting for other covariates. Methods The present study was a cohort study. A total of 214 participants with AMI after PCI were involved in a hospital in China from 1st January 2017 to 1st January 2018. The target independent variable and the dependent variable were BMI measured at baseline and 1-year unplanned readmission, respectively. Covariates involved in this study included age, gender, TC, triglyceride, HDL-C, LDL-C, PT, APTT, INR, creatinine, HGB, LVEF, discharge medication, marital status, educational level, COPD, diabetes mellitus, heart failure, history of ischemic stroke, history of hemorrhagic stroke, arrhythmia, and hypertension. Results The average age of 172 selected participants was 60.2 ± 10.8 years old, and about 68.6% of them was male. The rate of readmission in patients with AMI was 26.14%. The result of fully adjusted binary logistic regression showed BMI was negatively associated with risk of readmission after adjusting confounders (hazard ratio (HR) = 1.1, 95% CI 0.93–1.29). Nonlinear relationship was detected between BMI and 1-year unplanned readmission, whose point was 29.3. The effect sizes and the confidence intervals of the left and right sides of inflection point were 0.9 (0.7–1.2, P for nonlinearity = 0.530) and 2.8 (1.3–5.8, P for nonlinearity = 0.530) and 2.8 (1.3–5.8, Conclusion BMI has a nonlinear relationship with 1-year unplanned readmission in patients with myocardial infarction. The 1-year unplanned readmission rate of overweight patients (BMI > 29.3 kg/m2) has increased significantly. Obesity paradox does not exist in terms of readmission of Chinese patients with myocardial infarction after PCI.

erefore, a study on the risk factors for readmission of AMI patients after PCI is needed. BMI is one of the indicators for measuring obesity [12]. Its association with a variety of diseases has been confirmed, including coronary heart disease, diabetes, sudden death, stroke, and metabolic syndrome [13][14][15][16][17][18]. Although there are some studies about the association between BMI and readmission of AMI in western countries, they yield conflict results [19][20][21]. Because of the ethnic differences, Asians and Westerners have different physiques. In addition, there is currently limited evidence of a link between BMI and readmission of AMI in the Chinese population. erefore, this study set out to investigate whether BMI was independently related to 1-year unplanned readmission in Chinese patients with AMI after PCI.

Study Design.
In the present study, a retrospective cohort study was performed to address the relationship between BMI and readmission of AMI patients. e target independent variable is BMI obtained at baseline. e dependent variable is 1-year unplanned readmission (dichotomous variable: 1 � readmission after PCI; 0 � nonreadmission).

Study Population.
e data of participants of Chinese patients with newly-diagnosed AMI were nonselectively and consecutively collected from the Department of cardiology, Affiliated Hospital of Jining Medical University, Jining City, Shandong province, China. Our data did not include identifiable participants data for the purpose of safeguarding patient privacy. Data were compiled from the hospital electronic medical record system. Participants informed consent is not required in this study because of the nature of the retrospective cohort study. e hospital institutional review board approved this study. e study was initially collected a total of 214 participants. Participants' entry time and deadline for inclusion were 1 January 2017 and 1 January 2018, respectively. e clinical diagnosis and treatment process of each participant is completely in accordance with the ESC Guidelines on STsegment elevation myocardial infarction, 2017. Inclusion criteria were as follows: 1, patients who were diagnosed with AMI in the emergency department; 2, patients who underwent PCI operation through emergency green channel. Exclusion criteria were as follows: 1, patients who changed their phone number; 2, patients who were not connected to the phone; 3, refused to answer the question; 4, patients of complicated vascular disease with coronary artery bypass surgery.

Variables.
We obtained BMI at baseline and recorded as continuous variable. e detailed process is described as follows: BMI, which was defined as weight in kilograms divided by height in meters squared (kg/m 2 ). e height and weight of the patient were measured by the nurse at the time of the first admission.
According to published guideline and researches, we obtained the final outcome variable (dichotomous variable). We extracted the data of patients who are rehospitalized with the same ID number from the electronic case data system of the Affiliated Hospital of Jining Medical University. If no information was found about the patient's unplanned readmission, the subjects were followed up by telephone because the possibility of changing the ID number or going to another hospital was not ruled out.
In this study, we included the following covariates that can be summarized as follows: (1) demographic data; (2) variables that can affect BMI or 1-year unplanned readmission reported by the previous literature; (3) based on our clinical experiences. erefore, the following variables were used to construct the fully-adjusted model: (1) continuous variable: age, total cholesterol (TC), triglyceride, highdensity lipoprotein C (HDL-C), low-density lipoprotein C (LDL-C), prothrombin time (PT), activated part of prothrombin time (APTT), international normalized ratio (INR), creatinine, hemoglobin (HGB), and left ventricular ejection fraction (LVEF) (obtained at baseline); (2) categorical variables: gender, discharge medications, marital status, educational level, chronic obstructive pulmonary disease (COPD), diabetes mellitus, heart failure, history of ischemic stroke, history of hemorrhagic stroke, arrhythmia, and hypertension (obtained at baseline).

Treatment Protocol.
Percutaneous coronary intervention was implemented in each of our participants. Each patient is given personalized medication by the doctor.

Follow-Up Procedure.
We performed the follow-up through the telephone inquiry. e cutoff date for participants follow-up was 31 st , January, 2019. Follow-up data were managed by the first author. Follow-up data were stored in the Hospital electronic medical record system. Follow-up interval was 1 year. Monitoring indicators at each follow-up included the patients' readmission information.
1.1.6. Statistical Analysis. We presented continuous variables by two forms. In the first form, we expressed continuous variables with normal distribution as mean ± standard deviation. In the second form, we presented continuous variables with Skewed distribution as medium (min, max). Categorical variables were expressed in frequency or as a percentage. We used χ 2 (categorical variables), one-way ANOVA test (normal distribution), or Kruskal-Wallis H test (skewed distribution) to test for differences among different BMI groups (Tertial). e data analysis process of this study was based on three criteria: (1) what is the relationship between BMI and readmission of AMI patients (linear or nonlinear); (2) which factors modify or interfere with the relationship between BMI and readmission of AMI patients; and (3) adjust the interference factors or after the stratified analysis, what is the true relationship between BMI and readmission of AMI patients? erefore, data analysis can be summarized in two steps.
Step 1: univariate and multivariate binary logistic regression was employed. We constructed three models: model 1, no covariates were adjusted; model 2, only adjusted for sociodemographic data; model 3, model 2 and other covariates presented in Table 1.
Step 2: to address for nonlinearity of BMI and 1-year unplanned readmission, a Cox proportional hazards regression model with cubic spline functions and smooth curve fitting (penalized spline method) were conducted. If nonlinearity was detected, we first calculated the inflection point using recursive algorithm and then constructed a two-piecewise binary logistic regression on  25

Baseline Characteristics of Selected Participants.
A total of 172 participants were selected for the final data analysis after screening by inclusion and exclusion criteria (see Figure 1 for a flow chart). We showed baseline characteristics of these selected participants in Table 1

Results of Unadjusted and Adjusted Binary Logistic
Regression. In this study, we constructed three models to analyze the independent effects of BMI on 1-year unplanned readmission (univariate and multivariate binary logistic regression). e effect sizes (hazards ratio (HR)) and 95% confidence intervals are listed in

Results of Nonlinearity of BMI and 1-Year Unplanned
Readmission. In the present study, we analyzed the nonlinear relationship between BMI and 1-year unplanned readmission ( Figure 2). Smooth curve and the result of the Cox proportional hazards regression model with cubic spline functions showed that the relationship between BMI, and BMI was nonlinear after adjusting for age, gender, TC, triglyceride, HDL-C, LDL-C, PT, APTT, INR, creatinine, HGB, discharge medications, marital status, educational level, COPD, diabetes mellitus, heart failure, history of ischemic stroke, history of hemorrhagic stroke, arrhythmia, and hypertension. We used both binary logistic regression and two-piecewise binary logistic regression to fit the association and select the best fit model based on P for the log likelihood ratio test. Because the P for the log likelihood ratio test was less than 0.05, we chose two-piecewise binary logistic regression for fitting the association between BMI and 1-year unplanned readmission because it can accurately represent the relationship. By two-piecewise binary logistic regression and recursive algorithm, we calculated the inflection point was 29.3. On the left side of inflection point, the effect size and 95% CI were 0.9 and 0.7-1.2, respectively. On the right side of inflection point, the effect size and 95% CI were 2.8 and 1.3-5.8, respectively (Table 4).

Discussion
Our findings indicate BMI is negatively associated with 1year unplanned readmission after adjusting other covariates. Besides, we also find the trend of the effect sizes on the left and right sides of the inflection point is not consistent (left 0.9 (95% CI 0.7-1.2); right 2.8 (95% CI 1.3-5.8)). is result suggests a threshold effect on the independent association between BMI and 1-year unplanned readmission.
Wang et al. [22] suggested that the risk of restenosis was lowest among underweight or normal weight patients and highest among severely obese patients in meta-analysis. However, there are also some other studies that are inconsistent with our findings. Paratz et al. [23] reported that obesity is not necessarily related with readmission in patients undergoing PCI. Akin et al. [24] showed that patients suffering from cardiogenic shock showed no impact of BMI on clinical endpoints. However, there are also some studies that showed that a higher BMI was not associated with worse outcomes of AMI patients after PCI, which was called obesity paradox [25,26]. Whether the obesity paradox exists in Asian or Chinese patients still remains controversial [27]. We analyzed these studies that are inconsistent with our results, and  Figure 2: Association between BMI and readmission of AMI. A threshold, nonlinear association between BMI and readmission of AMI was found (P � 0.022) in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit, all adjusted for age, gender, TC, triglyceride, HDL-C, LDL-C, PT, APTT, INR, creatinine, HGB, LVEF, discharge medications, marital status, educational level, COPD, diabetes mellitus, heart failure, history of ischemic stroke, history of hemorrhagic stroke, arrhythmia, and hypertension. we speculate that the reasons for the different results may be caused by the following factors: (1) the research population is different; these studies, which were inconsistent with our findings, were targeted at the USA; (2) these different conclusions do not clarify the nonlinear relationship; (3) compared with our work, these studies did not take into account the effect of triglyceride, HDL-C, PT, APTT, INR, creatinine, and HGB on the BMI and 1-year unplanned readmission relationships when adjusting covariates. However, the previous studies have confirmed that these variables are related to BMI or 1-year unplanned readmission [28− 30]; (4) as with many other observational studies, reverse causality or residual confounding may potentially explain some findings. e clinical value of this study is as follows: (1) to our best knowledge, it is the first time to observe the independent association between BMI and 1-year unplanned readmission in Chinese patients with AMI after PCI; (2) the findings of this study should be helpful for future research on the establishment of diagnostic or predictive models of 1-year unplanned readmission.
Our study has some strengths. (1) we address the nonlinearity in the present study and further explore this; (2) this study is an observational study and therefore susceptible to potential confounding; we used strict statistical adjustment to minimize residual confounders; (3) we handled target independent variable as both continuous variable and categorical variable. Such an approach can reduce the contingency in the data analysis and enhance the robustness of results.
ere is some limitation in the present study including the following: (1) in this study, our research subjects are Chinese patients with AMI after PCI. erefore, there is a certain deficiency in the universality and extrapolation of research. (2) Because we exclude patients of complicated vascular disease with coronary artery bypass surgery, the findings of this study cannot be used for these people.

Conclusions
In this retrospective study of Chinese patients with AMI after PCI, we found overweight patients (BMI > 29.3 kg/m 2 ) were associated with increased odds of readmission. It is important for doctors to recommend their obese patients to lose weight. Prospective studies are needed to further examine the relationship between BMI and readmission to help guide management of Chinese AMI patients.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that they have no conflicts of interest.