Does obesity predict morbidity and mortality amongst patients undergoing transfemoral amputations?

Abstract

Background

We investigated the role of obesity on morbidity and mortality in patients undergoing above knee amputation.

Methods

Data of 4225 patients undergoing AKAs was extracted from NIS Database (2016–2019) for a retrospectively matched case-control study and were grouped into; Non-obese (N-Ob-BMI <29.9 kg/m²; n = 1413), class I/II obese (Ob-I/II-BMI: 30–39.9 kg/m²; n = 1413), and class III obese groups (Ob-IIIBMI > 40; n = 1399). Morbidity, mortality, length of stay, and hospital charges were analyzed.

Results

Blood loss anemia (OR = 1.42; 95% CI = 1.19–1.64), superficial SSI (OR = 5.10; 95% CI = 1.4717.63) and acute kidney injury (AKI- OR = 1.42; 95% CI = 1.21–1.67) were higher in Ob-III patients. Mortality was 5.8%, 4.5%, and 6.4% in N-Ob, Ob-I/II and Ob-III patients (p < 0.001; Ob-I/II vs. Ob-III), respectively. Hospital LOS was 3 days higher in Ob-III (16.1 ± 18.0), comparatively resulting in $25,481 higher inpatient-hospital charge.

Conclusion

Patients in Ob-III group were noted to have increased morbidity, higher LOS, and inpatient-hospital cost.

Keywords

transfemoral amputation AKA mortality morbidity transtibial amputation BKA obesity national inpatient sample

Introduction

Major lower extremity amputations (mLEAs) are associated with significant morbidity and mortality, the rates of which have remained high in the last several decades despite improvement in healthcare and surgical techniques.¹ mLEAs are defined as above (AKA) or below knee amputations (BKA) and are usually a late complication of peripheral artery disease (PAD).² Outcomes between these procedures are considerably variable; however, increasing age has shown to be a strong predictor of mortality.¹ It is important to identify role of obesity as a possible cause of increased morbidity and mortality in patients undergoing mLEAs. Class III obesity has shown to have worse odds of survival and increased ICU stay in trauma patients;³ however, studies comparing outcomes of different classes of obesity on patients undergoing mLEAs are limited.

Patients with obesity have different demographic and pre-existing comorbidities impacting rate of complications or mortality when compared to non-obese individuals, which poses a significant challenge in comparing outcomes of mLEAs between these groups.^4,5 For instance, obesity induces insulin resistance increasing the prevalence of diabetes, compared to non-obese individuals.⁶ This inadvertently increases the risk of wound dehiscence and surgical site infection, secondary to poor wound healing.⁵ Similarly, obesity has shown to have deleterious effects on ambulatory status after mLEA, increasing the risk of cardiac complications.⁷

This study evaluated the impact of different classes of obesity: N-Ob, Ob-I/II, and Ob-III, on patient outcomes. We collected data from a large national database and matched the patient groups by multiple variables. Our primary outcomes included post-operative complications, mortality, inpatient-hospital cost and length of hospital stay. We hypothesized that patients in Ob-III group will have worse outcomes, compared to N-Ob and Ob I/II groups undergoing AKA.

Methods

Database description

National Inpatient Sample (NIS) Database is the largest publicly available database in the US containing data of more than 7 million hospital stays. This data is verified by an independent contractor with assistance of quality assessment evaluation to maintain authenticity. The database contains patients’ demographic characteristics, comorbidities, medical and surgical interventions during the admission and associated inpatient complications. It also includes data for hospital length of stay (LOS), hospital costs, and discharge status. The database 2016 to 2019 version utilizes the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS).⁸

Data collection

In this retrospective, matched case–control study, we identified 4225 patients who underwent AKAs in the NIS database using ICD-10 procedural codes. This is a publicly available database which contains deidentified patient data; therefore, this project was exempted from institutional review board (IRB) approval.

We grouped the patients based on their BMI, according to the National Institute of Health: N-Ob was defined as BMI < 29.9 kg/m² (n: 1413), Ob-I/II as BMI 30–39.9 kg/m² (n = 1413), and Ob-III as BMI >40 kg/m² (n = 1399).⁹ Ob-I/II patients were grouped together due to small sample size. These groups were matched with respect to age, sex, diabetes, tobacco use, and emergency versus elective surgery. Post-operative outcomes including development of systemic complications such as myocardial infarction (MI), pneumonia (PNA), acute kidney injury (AKI), blood loss anemia, pulmonary embolism (PE), and deep venous thrombosis (DVT), local complications such as wound dehiscence and superficial and deep surgical site infection, rate of mortality, inpatient cost, and hospital length of stay were also collected from NIS database using ICD 10 codes.

Statistical analysis

We analyzed the data through SPSS version 27.0 (IBM; Armonk, NY, USA). Patient demographic details were aggregated using descriptive statistics. Groups were matched using chi-square test and propensity score was calculated using SPSS software. Furthermore, Ob I and Ob II classes were grouped together and compared with N-Ob and Ob-III groups using T-tests when analyzing numerical variables and chi-square test when analyzing binomial variables. Level of significance was defined as p-value < 0.05. Odds ratio for patient outcomes and complications were measured as a ratio of incidence in the case group and incidence in the control group, with a confidence interval of 95%.

Results

Patient demographics

The study cohort included 4225 patients who underwent AKA between 2016 and 2019. Patients were divided into three groups: N-Ob, Ob-I/II, and OB-III, and were subsequently matched for certain variables as depicted in Table 1. Average age for our patients was approximately 64 years (64.2 for N-Ob; 64.3 for Ob-I/II and 63.6 for Ob-III).

Table 1.

Patient demographics in patients undergoing above the knee amputations.

	Non-obese (N = 1413)	Obesity I/II (N = 1413)	Obesity III (N = 1399)
Age [y.o. ± (SD)]	64.2 (13.3)	64.3 (12.7)	63.6 (12.7)
Age category	Matched	Matched	Matched
Female [N (%)]	600 (42.5)	600 (42.5)	657 (47.0)
Diabetes [N (%)]	56 (4.0)	56 (4.0)	49 (3.5)
Tobacco use [N (%)]	56 (4.0)	56 (4.0)	46 (3.3)
Elective versus emergent [N (%)]	400 (28.3)	400 (28.3)	389 (27.8)

N-Ob = Non-Obese (BMI <29.9 Kg/m²); Ob-I/II = Class I and II obesity (BMI 30–39.9 Kg/m²), and Ob-III = Class III obesity (BMI >40 Kg/m²).

Morbidity and mortality in patients undergoing AKA

Ob-III was associated with higher rates of blood loss anemia (OR = 1.42; 95% CI = 1.19–1.64), superficial surgical site infections [SSI] (OR = 5.10; 95% CI = 1.47–17.63) and AKI (OR = 1.42; 95% CI = 1.21–1.67) compared to N-Ob and Ob-I/II patients. Mortality was 5.8%, 4.5%, and 6.4% in N-Ob, Ob-I/II, and Ob-III cohorts, respectively. However, this difference was only significant (p < 0.001) in Ob-I/II versus Ob-III groups. No significant associations were noted in the incidence of myocardial infarction (MI), pneumonia, deep venous thrombosis (DVT) and pulmonary embolism (PE) between these groups. However, hospital LOS was almost 2-day higher in Ob-III patients, accounting for an increased average inpatient charge per patient by $25,481 compared to N-Ob and Ob-I/II groups. These results are summarized in Table 2.

Table 2.

Morbidity and mortality of Non-obese, Obesity I/II, and Obesity III patients undergoing above the knee amputations. The average total cost per patient per hospitalization is also included.

	Non-obese (n = 1413)	Obesity I/II (n = 1413)		Obesity III (n = 1399)
	N (%)	N (%)	Odds ratio (95% CI)	N (%)	Odds ratio (95% CI)
Blood loss anemia	364 (25.8)	402 (25.8)	1.14 (0.97–1.35)	501 (35.8)	1.42 (1.19–1.64)
Blood transfusion	281 (19.9)	263 (18.6)	0.92 (0.76–1.11)	259 (18.5)	0.99 (0.82–1.20)
Wound dehiscence	41 (2.9)	50 (3.5)	1.23 (0.81–1.87)	57 (4.1)	1.16 (0.79–1.71)
Superficial SSI	**(0.2)	**(0.2)	1.00 (0.20–5.00)	15 (1.1)	5.10 (1.47–17.63)
Deep SSI	**(0.4)	**(0.3)	0.799 (0.21–2.98)	15 (1.1)	2.28 (0.70–7.42)
DVT	43 (3.0)	44 (3.1)	1.02 (0.67–1.57)	52 (3.7)	1.20 (0.80–1.81)
PE	19 (1.3)	18 (1.3)	0.95 (0.50–1.81)	20 (1.4)	1.12 (0.59–2.13)
PNA	68 (4.8)	58 (4.1)	0.85 (0.59–1.21)	55 (3.9)	0.96 (0.66–1.39)
MI	16 (1.1)	12 (0.8)	1.34 (0.63–2.83)	20 (1.4)	1.27 (0.65–2.45)
AKI	343 (24.3)	366 (25.9)	1.09 (0.92–1.29)	464 (33.2)	1.42 (1.21–1.67)
LOS (Days ± SD)	13.1 ± 13.6	13.5 ± 11.8	0.42	16.1 ± 18.0	<0.001
Mortality	82 (5.8)	64 (4.5)	0.77 (0.55–1.08)	89 (6.4)	1.42 (1.03–1.99)
Charges (USD)	160,096.96	158,102.95	p = 0.85	183,583.59	p < 0.001

SSI: surgical site infection; DVT: deep venous thrombosis; PNA: pneumonia; MI: myocardial infarction; AKI: acute renal failure; LOS: hospital length of stay; SD: standard deviation; USD: US dollars.

Bold font p ≤ 0.05. **—Number between 1 and 10 not reported as per HCUP data use agreement.

Discussion

The obesity epidemic has continued to be on a rise which has undeniably intensified the underlying disease burden of hypertension,¹⁰ diabetes, as well as cardiovascular disease (CVD),^11,12 increasing the risk of all cause-mortality.¹³ However, there have been several reports conferring a survival advantage of overweight (BMI: 25–29.9 mg/kg²) and class 1 obesity (BMI 30–34.9 mg/kg²), particularly in patients with CVD, describing the phenomenon as the “obesity paradox.”¹⁴ Similarly, a moderate increase in BMI has also shown a protective effect in patients with heart failure,^15,16 as well as individuals undergoing cardiac surgery.¹⁷ Recently, reports have emerged with evidence of the “obesity paradox” in other chronic conditions including renal disease and chronic obstructive pulmonary disease (COPD).¹⁴ Physiologically, this phenomenon has been explained by decreased impact of oxidative stress and inflammation in individuals with excess fat stores,¹⁸ and improved ability to tolerate catabolic states, such as surgery, due to higher metabolic reserves.¹⁹

The “obesity paradox” has also been studied in patients undergoing vascular surgery and has shown contrasting results. For major reconstructive vascular surgery, obese patients were found to have higher rates of wound and chest infections compared to non-obese, however, no significant differences were noted in operative mortality or other major complications.²⁰ Similarly, obesity was identified as a risk factor for poor outcomes in patients with carotid artery stenting.²¹ In another study, obesity did not worsen morbidity or mortality for patients undergoing lower extremity revascularization.²² These studies are consistent with the intuitive argument that obesity causes a higher risk of morbidity in patients submitting to vascular operations.

However, the “obesity paradox” was further explored by Devenport et al.⁴ who extracted data for 7543 patients undergoing vascular surgery procedures showing decreased 30-days mortality in class I obese patients, compared to non-obese patients. Morbidity demonstrated a ‘U-shaped’ distribution, with underweight and class III obese patients at highest risk of developing complications. Among patients with diabetes, recent data shows decreased amputation risk with higher BMI consistent with the “obesity paradox” observations.²³

The association between obesity and development of complications in patients undergoing AKAs is not currently well known. We used NIS database, which is the largest publicly available inpatient database, and extracted data for patients undergoing AKAs between 2016 and 2019. The selected patients were corrected for variables that could potentially confound the association between obesity and study outcomes. These included age, gender, history of diabetes, tobacco use, and acuity of surgery: elective versus emergent. This study endeavors to identify role of obesity in a high-risk operation and ascertain the effect it has on hospital costs.

Obesity did not provide any survival benefit or prove to be protective for adverse outcomes in our analysis. Ob-III was associated with higher rates of blood loss anemia (OR = 1.42; 95% CI = 1.19–1.64), superficial SSI (OR = 5.10; 95% CI = 1.47–17.63), and AKI (OR = 1.42; 95% CI = 1.21–1.67) compared to N-Ob and Ob-I/II patients. Ob-III patients also had the highest mortality (6.4%) compared to N-Ob (5.8%) and Ob I/II (4.5%). No significant associations were noted in the incidence of MI, pneumonia, DVT, and PE between these groups. However, hospital LOS was almost 3-day higher in Ob-III patients, accounting for an increased average inpatient charge per patient by $25,481 compared to N-Ob and Ob-I/II groups. The results are summarized in Table 2.

Our results are consistent with the extensive literature showing that obesity, particularly Ob-III, leads to worse outcomes, and does not support ‘obesity paradox’ phenomenon in patients undergoing AKAs. These results suggest that strategies directed towards decreasing prevalence of obesity might yield reduction in morbidity and mortality in patients undergoing AKAs.

This study has several limitations. We assessed the short-term complications associated with AKA in the inpatient setting; however, the effects of obesity in the long-term still need to be explored. Similarly, while we matched the patients for several potential confounders, our approach does not consider the cause for amputation. Misclassification bias cannot be completely ruled out in this study, considering that the data was collected through NIS database for health records. Our analysis is an observational study; hence, a causal link cannot be established between obesity and morbidity and mortality associated with AKAs.

Conclusion

In conclusion, our data shows that class-III obesity is associated with an increased risk of morbidity and mortality in patients undergoing AKAs. After adjusting for variables, Ob-III patients were found to be at a higher risk of developing blood loss anemia, superficial SSI and AKI, which explains the increase in hospital LOS and inpatient costs in this group.

Footnotes

Author contributions

All authors contributed significantly to this manuscript according to the ICMJE criteria.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institution and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

ORCID iD

Syed M MA Bokhari

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