Association between BMI and outcomes in critically ill patients: An analysis of the MIMIC-III database

doi:10.21203/rs.3.rs-4953010/v1

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Association between BMI and outcomes in critically ill patients: An analysis of the MIMIC-III database

https://doi.org/10.21203/rs.3.rs-4953010/v1

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Background: Obesity is a significant public health challenge, yet the link between BMI and mortality in critical illness remains unclear.

Methods: This study analyzed 19,311 patients from the MIMIC-III database, categorized into underweight, healthy, overweight, and obese groups. Using Cox models and restricted cubic splines, we explored the complex relationship between BMI and mortality. Finally, multiple sensitivity analyses were performed to test the robustness of the results.

Results: The proportion of underweight patients (2.3%) was the lowest, while overweight (34.8%) and obese (34.5%) patients constituted a large proportion. Being underweight portended a greater risk, while being overweight or obese was associated with a lower risk of mortality, which is consistent with the sensitivity analysis. Moreover, we observed a typical L-shaped relationship between BMI and the risk of mortality with and without adjustment, with the lowest mortality occurring at approximately 32 kg/m². The overall pattern of the nonlinear association between BMI and mortality was similar for most subgroup analyses.

Conclusions: Underweight was associated with a greater risk of mortality, while obesity and overweight were both associated with a lower risk of mortality in critically ill patients. BMI had a typical L-shaped association with the risk of mortality.

Health sciences/Medical research/Outcomes research

Health sciences/Risk factors

BMI

ICU

critically ill

mortality

risk factor

obesity

According to a report published by the World Health Organization (WHO), excess or abnormal fat accumulation has emerged as a rapidly growing threat to the health of humans in an increasing number of countries. The latest data show that a high body mass index (BMI) caused approximately 4 million deaths globally in 2015¹. Many health problems, such as sleep apnea, type 2 diabetes, hypertension, cardiovascular diseases, dyslipidemia, and physical disabilities, are a direct consequence of increased fat mass. Moreover, the number of obese adults continues to increase from 100 million in 1975 to 671 million in 2016 and is projected to increase to 1.12 billion by 2030^2,3.

BMI [weight (kg)/height2 (m)], which forms the basis for the WHO classification, is commonly used to estimate the amount of body fat. Multiple large observational studies have suggested that BMI is closely related to all-cause mortality, and those studies revealed a U-shaped association with minimum mortality in the range from 20 to 25 kg/m²^4–7. Increasing evidence suggests that patients with similar BMIs may have different obesity-related complications depending on the distribution of excess fat, including visceral and ectopic versus subcutaneous fat^8,9. The phenomenon that obesity is a risk factor for mortality in the general population but a protective factor for mortality in patients with chronic diseases, such as chronic kidney disease, heart failure, cancer, and diabetes mellitus, is called the “obesity paradox”, which indicates a more complex and variable association between BMI and mortality^10–12. In addition, BMI is a poor marker of excess body fat in patients with either increased or low muscle mass.

As the prevalence of obesity continues to increase, it has become one of the greatest health issues affecting all age groups and is also prevalent in the intensive care unit (ICU) population^13,14. Some studies have shown that BMI may be associated with morbidity¹³ and increased resource utilization¹⁵, but the impact of obesity on ICU mortality is still debated.

In this study, we aimed to retrospectively examine the effect of BMI on mortality in ICU patients using the MIMIC-III database. The robustness of our primary outcome was tested by a series of sensitivity analyses. We performed a series of subgroup analyses, including age (≤ 60 and > 60), sex, hypertension status, diabetes status, liver disease status, CHF status and ethnicity. Our study could help ICU physicians better understand the effect of BMI on ICU patients and assist them in the management of this vulnerable population.

This study aimed to evaluate the relationship between Body Mass Index (BMI) and mortality in critically ill patients. We conducted a retrospective cohort study using data from the MIMIC-III v1.4 database, which includes detailed clinical information from the Beth Israel Deaconess Medical Center collected between 2001 and 2012. We analyzed demographic information, comorbidities, severity scores, vital signs, laboratory results, and treatments. The primary outcomes were 28-day and 60-day mortality rates. To assess the impact of BMI on these outcomes, we used Cox proportional hazard models and restricted cubic spline analysis, adjusting for potential confounders. Sensitivity analyses were conducted across various subgroups to validate the robustness of our results. This methodological approach aimed to provide insights into how BMI affects mortality in critically ill patients, aiding in risk stratification and clinical decision-making in the ICU setting.

2.1. Data Sources

All data in our study were extracted from the MIMIC-III v 1.4, a large, single-center, open and free critical care database that contains comprehensive clinical data of patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts between June 2001 and October 2012¹⁶. The database was released on 2nd September 2016, in which comprehensive and deidentified in-hospital information, including vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, and survival data of 53,423 distinct ICU admissions, was stored. The final diagnosis was recorded according to the International Classification of Disease 9th Edition code when patients were discharged. Ethical clearance and informed consent were not required because the MIMIC-III is an anonymized database that is available to the public. The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) Statement was applied in the present study.

2.2. Study Cohort

We included all individuals with BMI data and excluded those who met the following criteria: (1) age < 18 years OR > 85 years; (2) length of stay < 24 hours; (3) pregnancy; and (4) missing body weight or height data. In the case of more than one hospital admission, each patient contributed only their first admission to the analysis. Altogether, 19,311 ICU patients were included in the present study.

The World Health Organization Consultation published a report on obesity that defined BMI-based categories of underweight, normal weight, preobesity, and obesity in 1997¹⁷. The same cutoff BMI values were adopted by the National Heart, Lung, and Blood Institute in 1998¹⁸. In this study, all participants were divided into four groups, namely, underweight (BMI < 18.5 kg/m²), healthy weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (BMI ≥ 30 kg/m²), based on the WHO categories.

This study was conducted in accordance with the Helsinki Declaration (WMA Declaration of Helsinki, 2013).

2.3. Covariates and Outcomes

We utilized Structured Query Language to extract a large amount of clinical information for each enrolled patient from MIMIC-III, including (1) demographic data (age, sex, BMI and ethnicity); (2) etiology at admission; (3) comorbidities (the Elixhauser Comorbidity Index, hypertension, cardiac arrhythmias, congestive heart failure, peptic ulcer, liver disease, diabetes, chronic obstructive pulmonary disease, coagulopathy, and solid tumor); (4) severity scores, including the Sequential Organ Failure Assessment (SOFA) score and the Simplified Acute Physiology Score (SAPS II)); (5) mean value of vital signs within 24 h after ICU admission (heart rate, respiratory rate, temperature, mean arterial pressure, pO2, pCO2 and pH); (6) the poorest laboratory test value during the first day after ICU admission (white blood cell count), platelet count, hemoglobin, glucose, creatinine, chloride, sodium, potassium, blood urea nitrogen (BUN), base excess, international normalized ratio (INR), prothrombin time (PT), partial thromboplastin time (PTT), and bicarbonate); and (7) the treatment received (mechanical ventilation; renal replacement therapy).

The primary outcomes of the study were 28-day mortality and 60-day mortality. Patient mortality information for discharged patients was gathered from the US Social Security Death Index. Secondary outcomes included the number of days of mechanical ventilation, renal replacement therapy, and length of stay.

2.4. Statistical analysis

The raw data were extracted from the MIMIC-III database using SQL (structure query language) with Navicat Premium version 15.0.26. Further statistical analyses were performed with the statistical software R v4.2.0. We first divided all patients into four groups according to BMI. We used the Shapiro‒Wilk test to check for normality, and normally distributed continuous variables are presented as the mean ± standard deviation, nonnormally distributed continuous variables as the median (interquartile range), and categorical variables as frequencies (percentages). Continuous variables were compared by ANOVA (analysis of variance), and categorical variables were compared by the chi-square test or Fisher’s exact test, with Bonferroni correction for multiple pairwise comparisons. Then, we used two main methods to analyze the relationship between BMI and mortality in critically ill patients. First, five Cox proportional hazards regression models were used to evaluate the independent associations of BMI with survival. Model 1 was adjusted for age and ethnicity. Model 2 was adjusted for Model 1 plus PCO2, pH, heart rate and temperature. Model 3 was adjusted for Model 2 plus glucose, hemoglobin, WBC, platelet count, and creatinine. Model 4 was adjusted for all covariates except the SOFA score and SAPS score. Model 5 was adjusted for all covariates. Second, to evaluate the possible nonlinear impact of BMI as a continuous variable on survival, a multivariable Cox model with restricted cubic splines (RCSs) was built.

Finally, we conducted several sensitivity analyses to test the robustness of our primary findings and how our conclusions can be affected in different situations. First, we used Cox proportional hazards models and restricted cubic spline analysis to determine the impact of various subgroups on our findings. Second, we performed a restricted cubic spline analysis to evaluate the impact of BMI on the risk of needing mechanical ventilation and RRT.

Missing values were handled by multiple imputation methods based on the “Mice” package of R. However, these variables were omitted when more than 25% of the values were missing. A two-tailed P value of < 0.05 was considered to indicate statistical significance.

3.1. Baseline Characteristics of the Participants

Of the 38,597 individuals from the MIMIC-III database, 19,311 were included in the study. The flow diagram of patient selection is presented in Fig. 1. Among them, 5,485 were healthy-weight individuals, 448 were underweight individuals, 6,712 were overweight individuals, and 6,666 were obese individuals (Supplementary Table 1). More males than females were included in this study (male: 61.3%; female: 38.7%). Women (55.8%) composed the majority of the underweight group, while men made up the majority of the other groups. A positive association was observed between glucose and BMI (median glucose of 123 mg/dl in underweight individuals, 125 mg/dl in healthy-weight individuals, 128 mg/dl in overweight individuals, and 134 mg/dl in obese individuals), consistent with expected trends¹⁹. The greater the BMI is, the greater the proportion of patients with hypertension. Healthy-weight patients had the highest PO2 and pH. The lowest proportion of underweight patients had hypertension. A negative association was observed between mortality and BMI as well as between the SAPS II score and BMI. No significant differences were observed in the mean blood pressure, PTT, INR, or prevalence of liver disease or peptic ulcers among the different groups.

3.2. Relationship between BMI and Outcomes

We applied Cox proportional hazards models to evaluate the association between BMI and mortality in critical patients. Univariate Cox regression analysis for the hazard ratio of survival indicated that a lower BMI was associated with a greater risk of 28-day mortality (underweight: HR, 1.44; overweight: HR, 0.75; obese: HR, 0.74) and 60-day mortality (underweight: HR, 1.56; overweight: HR, 0.74; obese: HR, 0.71). After further adjustment for a series of covariates in the other four models, the relationship between BMI and mortality was largely consistent (Table 1). Patients with a higher BMI had a greater likelihood of receiving mechanical ventilation therapy and RRT (Supplementary Table 1). K‒M survival curves indicated that underweight patients had the worst survival among all groups, while obese patients and overweight patients had better survival than healthy-weight patients did (Fig. 2).

Table 1

Associations between BMI group and the outcomes of critically ill patients
Models		28-day mortality		60-day mortality
		HR (95% CI)	p	HR (95% CI)	p
Model 1	Underweight	1.5(1.16–1.93)	< 0.001	1.61(1.3-2)	< 0.001
	Overweight	0.74(0.66–0.84)	< 0.001	0.73(0.66–0.81)	< 0.001
	Obese	0.77(0.68–0.87)	< 0.001	0.75(0.68–0.83)	< 0.001
Model 2	Underweight	1.32(1.02–1.7)	0.03	1.45(1.17–1.79)	< 0.001
	Overweight	0.79(0.7–0.89)	< 0.001	0.77(0.7–0.85)	< 0.001
	Obese	0.81(0.72–0.91)	< 0.001	0.78(0.71–0.87)	< 0.001
Model 3	Underweight	1.42(1.1–1.83)	0.01	1.53(1.23–1.89)	< 0.001
	Overweight	0.78(0.7–0.88)	< 0.001	0.77(0.7–0.86)	< 0.001
	Obese	0.72(0.64–0.82)	< 0.001	0.71(0.64–0.79)	< 0.001
Model 4	Underweight	1.32(1.02–1.7)	0.04	1.42(1.14–1.76)	< 0.001
	Overweight	0.82(0.72–0.92)	< 0.001	0.8(0.72–0.89)	< 0.001
	Obese	0.76(0.67–0.86)	< 0.001	0.74(0.67–0.83)	< 0.001
Model 5	Underweight	1.22(0.94–1.58)	0.14	1.28(1.03–1.59)	0.03
	Overweight	0.84(0.74–0.94)	< 0.001	0.82(0.74–0.91)	< 0.001
	Obese	0.77(0.68–0.87)	< 0.001	0.75(0.67–0.83)	< 0.001

* HRs and 95% CIs for 28-day mortality and 60-day mortality in different groups categorized by BMI from univariate and multivariate Cox proportional hazards regression models. Underweight was significantly associated with a higher 28-day mortality rate and 60-day mortality rate relative to normal MAP patients, while overweight and obese patients were not. Model 1 was adjusted for age and ethnicity. Model 2 was adjusted for Model 1 plus PCO2, pH, heart rate and temperature. Model 3 was adjusted for Model 2 plus glucose, platelet count, creatinine and BUN. Model 4 was adjusted for all covariates except the SOFA score and SAPS. Model 5 was adjusted for all covariates.

3.3. Nonlinear Association between BMI and Mortality

Restricted cubic spline models were used to visualize the shape of the association between BMI and outcomes in critical patients. With this analysis, we observed a typical L-shaped relationship between BMI and the outcomes of critical patients with and without adjustment, with the lowest mortality occurring at approximately 32 kg/m² (Fig. 3). The results from the cubic spline model suggested that critical patients had a marked increase in risk at low BMIs but only a minimal elevation in risk at higher BMIs. The nadir for both 28-day mortality and 60-day mortality risk among critically ill patients was estimated to be at a BMI of 32 kg/m² from the RCS curve. Higher BMIs were likely protective factors for critical patients compared with healthy weight patients under the influence of covariates (Fig. 4).

3.4. Sensitivity Analysis

To further verify the robustness of the reported findings, sensitivity analyses were conducted. Primary covariates of interest, which included age, sex, hypertension status, CHF status, liver disease status, diabetes status, and ethnicity, were included in the sensitivity analyses. Compared with those of normal-weight patients, the risk of 28-day mortality and 60-day mortality was lower for both overweight and obese patients in the following subgroups: those with hypertension, without diabetes, those with and without CHF, those with liver disease, those who were male, and those aged older than 60 years (Fig. 5). The overall pattern of the nonlinear association between BMI and mortality was similar for most subgroup analyses (Fig. 6). All of the sensitivity analyses confirmed our main analyses.

In this retrospective cohort study, 19,311 critically ill patients were included from the MIMIC-III database and then classified into four BMI-based categories: underweight, healthy weight, overweight, and obese. Notably, the proportion of underweight patients (2.3%) was the lowest, while overweight (34.8%) and obese (34.5%) patients constituted a much larger proportion of critically ill patients. The prevalence of obesity in our study was much greater than that in recent reports, in which the proportion was approximately 20%¹⁴. A positive association was observed between glucose and BMI as well as between the frequency of hypertension and BMI, which is consistent with previous studies^20–22. Briefly, we found that underweight was associated with a significantly greater risk for 28-day mortality and 60-day mortality, while overweight was associated with a lower risk for 28-day mortality and 60-day mortality than healthy weight in critically ill patients. Additionally, we observed a typical L-shaped association between BMI and mortality, with the lowest mortality occurring at approximately 32 kg/m². Moreover, patients with a higher BMI had a greater likelihood of receiving mechanical ventilation therapy and RRT. A large amount of research has shown that BMI has J-shaped associations with overall mortality and most specific causes of death²³. However, a small amount of research has estimated a lower risk among overweight patients than among healthy weight patients²⁴. The discrepant results between different studies might be explained by reverse causality and residual confounding. Most studies have shown clear heterogeneity in the associations between BMI and different causes of death, which means that the association of all-cause mortality with BMI will be affected by cause-of-death distributions in the source population. Currently, the impact of obesity on ICU mortality is still debated. Although obesity seems to be associated with morbidity, the relationship between the two is not yet clear ^13,14. Thus, this study provides evidence for the possibility of using BMI to predict the prognosis of critically ill patients, highlighting the possibility of employing BMI as a novel, easily obtained risk marker.

The prevalence of obesity has tripled worldwide and has been increasing since 1975. It has become a nonnegligible health issue that affects all age groups, populations, and countries of all income levels. Identifying the complex relationship between BMI and mortality is challenging. This challenge becomes increasingly difficult in complex, dynamic environments such as the ICU. In general, innovations that have been found to be beneficial in specific disease populations may be applied to other populations in which those findings have not been adequately analyzed, potentially exposing patients to harm without commensurate benefit ²⁵. Large cohort studies in the general population demonstrated that BMI had J-shaped associations with overall mortality and most specific causes of death, except for mental and behavioral, neurological, and external causes, which means that a high BMI was associated with an elevated risk of mortality for most patients ^23,26. More recent data in hospitalized patients or patients with chronic illnesses have shown that overweight and moderate obesity are associated with lower mortality than a normal BMI or more severe obesity ^14,18. Such a scenario has been observed not only in multiple chronic diseases, such as heart failure ²⁷, end-stage kidney disease ²⁸ and coronary artery disease ²⁹ but also in acute conditions, such as pneumonia ³⁰, acute respiratory distress syndrome ³¹, sepsis ³² or critical illness in general ³³. In addition, increasing BMI was not associated with increased mortality in patients with COVID-19 admitted to intensive care ³⁴. The phenomenon known as the “obesity paradox” is that obesity increases the risk of obesity-related disease but is paradoxically associated with increased survival in patients with these conditions. Our results are derived from an analysis of a real-world database and provide additional evidence for understanding the relationship between BMI and mortality in critically ill patients.

Although a large number of studies have revealed the obesity survival paradox, the actual protective effect of adipose tissue has been challenged. The reason might be that the protective effect of adipose tissue could be influenced by factors that are difficult to control. First, some young and healthy obese patients may be admitted to the ICU at a low threshold to avoid complications, thereby leading to selection bias. Second, the obesity survival paradox may be affected by therapeutic factors. Obese patients prefer to receive a lower weight-based dosage of fluids and vasopressors, which potentially reduces the side effects of these therapies ³⁵. Finally, there are increasing criticisms of the use of BMI as a measure of obesity.

In this study, we systematically analyzed the relationship between outcomes and BMI based on data from a large sample size extracted from a real-world database. We employed a novel methodology to address continuous covariates and reverse causality and conducted a wide range of sensitivity analyses.

However, our research has several limitations. First, approximately 50% of individuals who were otherwise eligible were excluded, mostly because they had no BMI records available. This would induce selection bias, which is difficult to avoid. Second, the lack of relevant information about therapy may limit the generalizability of these results. However, sensitivity analyses for subgroups with diabetes or liver disease who might receive a nutritional approach reached similar conclusions. Third, the MIMIC-III population is predominantly white, so these findings may not be generalizable to other populations.

Compared with normal weight, underweight was associated with a significantly greater risk of mortality, while obesity and overweight were both associated with a significantly lower risk of mortality in critically ill patients. BMI had a typical L-shaped association with the risk of mortality, and a BMI above the healthy range was associated with an elevated risk of mechanical ventilation, RRT, and prolonged length of stay. Based on our results, we recommend BMI as an easily obtained prognostic marker for critically ill patients. More research is needed to determine the optimal BMI and potential role in risk stratification to more precisely guide therapeutic decisions.

Ethical Approval: This study used the Medical Information Mart for Intensive Care (MIMIC) database, which is a freely accessible critical care database. The use of the MIMIC database is approved by the Institutional Review Boards (IRB) of the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC). The database does not contain protected health information, and all patient data are de-identified to ensure patient privacy and confidentiality. Consequently, the requirement for ethical consent was waived in accordance with local legislation and institutional guidelines. The principal investigator, Wenbo Yu, has completed the necessary ethics training and obtained certification for using the MIMIC database.

Consent for publication: not applicable.

Data availability statement: Both original data and any secondary data presented in this study are available upon request from the corresponding author.

Competing interests: The authors declare no conflicts of interest.

Funding: This research received no external funding.

Authors contributions: WY, WJ and JY were responsible for the methodology and writing of the manuscript and contributed equally. TF, HX and LS were responsible for medical information collection, data extraction and verification. YZ, WL and SW designed and guided this study. WY, WJ and JY contributed equally and share first authorship. All authors read and approved the final manuscript.

Acknowledgements: This work was supported by the Innovative Research Team of High-Level Local University in Shanghai. We thank all patients and authors involved in this study.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

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No competing interests reported.

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31 Aug, 2024
First submitted to journal
21 Aug, 2024

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Association between BMI and outcomes in critically ill patients: An analysis of the MIMIC-III database

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Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1. Data Sources

2.2. Study Cohort

2.3. Covariates and Outcomes

2.4. Statistical analysis

3. Results

3.1. Baseline Characteristics of the Participants

3.2. Relationship between BMI and Outcomes

3.3. Nonlinear Association between BMI and Mortality

3.4. Sensitivity Analysis

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1