Proteinuria and Risk of Lower-Extremity Amputation in Patients With Peripheral Artery Disease

Grams, Morgan E.; Coresh, Josef; Chang, Alex R.; Matsushita, Kunihiro

doi:10.2337/dc19-0764

Cited by 7 publications

(6 citation statements)

References 5 publications

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“… 54 Significant proteinuria has also been shown to be associated with the risk of lower limb amputation in patients with PAD. 55 Kapukaya et al 56 found that PLT was not significantly different between amputation and no amputation group ( p = 0.064), which is similar to our study. However, it is supported that PLT could be a biomarker to predict amputation in DF monitoring, coincidentally, RSF identified PLT as a predictor for amputation.…”

Section: Discussionsupporting

confidence: 91%

“…PRO was found to be the most important predictor of amputation; a possible explanation is that high PRO is a sign of diabetic nephropathy, 52,53 which increases the risk of amputation 54 . Significant proteinuria has also been shown to be associated with the risk of lower limb amputation in patients with PAD 55 . Kapukaya et al 56 found that PLT was not significantly different between amputation and no amputation group ( p = 0.064), which is similar to our study.…”

Section: Discussionsupporting

confidence: 89%

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Multicomponent prediction of 2‐year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors

Li,

Tang,

Lao

et al. 2023

International Wound Journal

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The current methods for the prediction of mortality and amputation for inpatients with diabetic foot (DF) use only conventional, simple variables, which limits their performance. Here, we used a random survival forest (RSF) model and multicomponent variables to improve the prediction of mortality and amputation for these patients. We performed a retrospective cohort study of 175 inpatients with DF who were recruited between 2014 and 2021. Thirty‐one predictors in six categories were considered as potential covariates. Seventy percent (n = 122) of the participants were randomly selected to constitute a training set, and 30% (n = 53) were assigned to a testing set. The RSF model was used to screen appropriate variables for their value as predictors of 2‐year all‐cause mortality and amputation, and a multicomponent prediction model was established. Model performance was evaluated using the area under the curve (AUC) and the Hosmer–Lemeshow test. The AUCs were compared using the Delong test. Seventeen variables were selected to predict mortality and 23 were selected to predict amputation. Uric acid and alanine transaminase were the top two most useful variables for the prediction of mortality, whereas urine protein and platelet were the top variables for the prediction of amputation. The AUCs were 0.913 and 0.851 for the prediction of mortality for the training and testing sets, respectively; and the equivalent AUCs were 0.963 and 0.893 for the prediction of amputation. There were no significant differences between the AUCs for the training and testing sets for both the mortality and amputation models. These models showed a good degree of fit. Thus, the RSF model can predict mortality and amputation in inpatients with DF. This multicomponent prediction model could help clinicians consider predictors of different dimensions to effectively prevent DF from clinical outcomes .

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 89%

Multicomponent prediction of 2‐year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors

Li,

Tang,

Lao

et al. 2023

International Wound Journal

View full text Add to dashboard Cite

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“…The clinical factors associated with LEA were selected based on previous studies. 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 Stages of CKD were determined using the mean level of outpatient estimated glomerular filtration rate (eGFR) in that year 40 : eGFR greater than or equal to 60 mL/min/1.73 m 2 was defined as no CKD, eGFR between 45 and 59 mL/min/1.73 m 2 was defined as CKD 3A, eGFR between 30 and 44 mL/min/1.73 m 2 was defined as CKD 3B, and eGFR between 15 and 29 mL/min/1.73 m 2 was defined as CKD 4. CKD 5 was defined as eGFR less than 15 mL/min/1.73 m 2 with no history of kidney transplantation or dialysis.…”

Section: Methodsmentioning

confidence: 99%

Temporal Trends in Incidence Rates of Lower Extremity Amputation and Associated Risk Factors Among Patients Using Veterans Health Administration Services From 2008 to 2018

et al. 2021

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IMPORTANCE Lower extremity amputation (LEA) is associated with significant morbidity and mortality. However, national temporal trends of LEA incidence rates among US veterans and associated factors have not been well characterized. OBJECTIVE To describe the temporal trends of LEA, characterize associated risk factors, and decompose the associations of these risk factors with changes in temporal trends of LEA among US veterans using Department of Veteran Affairs (VA) services between 2008 and 2018. DESIGN, SETTING, AND PARTICIPANTS This cohort study used VA data from 2008 to 2018 to estimate incidence rates of LEA among veterans using VA services. Cox regression models were used to identify risk factors associated with LEA. Decomposition analyses estimated the associations of changes in prevalence of risk factors with changes in LEA rates. Data were analyzed from October 1, 2007, to September 30, 2018. MAIN OUTCOMES AND MEASURES Toe, transmetatarsal, below-knee, or above-knee LEA. RESULTS A total of 6 493 141 veterans were included (median [interquartile range] age, 64 [54-76] years; 6 060 390 [93.4%] men). Veterans were studied for a median (interquartile range) of 10.9

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“…The clinical risk factors selected to compare for LEA were based on prior studies. [16][17][18][19][20][21] Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Patients' weight status was then further classified according to the WHO classification system using BMI as follows: non-obese (<30 kg/m 2 ), obesity class I (30-34.9 kg/m 2 ), obesity class II (35-39.9 kg/m 2 ), and obesity class III (≥40 kg/m 2 ).…”

Section: Clinical Risk Factorsmentioning

confidence: 99%

The relationship between lower extremity amputation and body mass index

et al. 2022

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Objectives Non-traumatic lower extremity amputation (LEA) is associated with significant morbidity and mortality. Diabetes mellitus (DM) and peripheral vascular disease (PVD) are associated with increased risk for LEA. As such, DM and PVD account for 54% of all LEA’s, performed in the United States annually. As obesity is highly associated with both DM and PVD, our study sought to explore the relationship between LEA and obesity defined by BMI. Methods Using the National Inpatient Sample (NIS) database, a retrospective review of patients who underwent non-traumatic LEA (LEA) between 2008 and 2014 was performed. The International Classification of Diseases 9th edition (ICD-9) codes were utilized to determine the diagnoses, comorbidities, and procedures. Patient BMIs were classified as follows: Non-obese [BMI <30], Obesity class I [BMI 30–34.9], Obesity class II [BMI 35–39.9], and Obesity class III [BMI ≥40]. Predictors for LEA were compared between groups using chi-square test and binary logistic regression to identify possible underlying factors associated with LEA. We also conducted a multivariate analysis to measure the effect of multiple variables on LEA. Results We identified 16,259 patients with non-traumatic LEA and a mean age of 59.9 years. Rate of amputation in females was lower than males at 0.35% vs 0.87% respectively ( p < 0.001). Of patients that underwent amputation there was a V-shape trend based on BMI, with 30.4% in non-obese patients, 18.2% in obesity class I, 17.3% in obesity class II, and 34.1% in obesity class III. The incidence of diabetes increased with obesity class, while the incidence of PVD decreased. Interestingly, of those with DM there was an inverse relationship between amputation rate and BMI class, with LEA rates in non-obese versus obesity class III patients were 1.63% vs 0.98% respectively ( p < 0.001). Similarly, patients who had both diabetes and PVD showed a downward trend in LEA rate as obesity class increased; non-obese patients had a LEA rate of 8.01%, while obesity class III had 4.65% ( p < 0.001). Patients in higher income bracket have lower odds of LEA (OR 0.77, p < 0.001) compared to the lowest income patients. Also, patients with comorbidities such as PVD (OR 10.78), diabetes (OR 5.02), renal failure (OR 1.41), and hypertension (OR 1.36) had higher odds to get an LEA ( p < 0.001). Individuals with obesity class III are almost at half the odds (OR 0.52) to get an LEA compared to non-obese ( p < 0.001). Conclusions Higher BMI and female gender are protective factors against lower extremity amputation. Factors that predisposing to LEA include lower household income and certain comorbidities such as PVD, diabetes, renal failure, and hypertension. These findings warrant further research to identify patients at high risk for LEA and help develop management guidelines for targeted populations.

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Proteinuria and Risk of Lower-Extremity Amputation in Patients With Peripheral Artery Disease

Cited by 7 publications

References 5 publications

Multicomponent prediction of 2‐year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors

Multicomponent prediction of 2‐year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors

Temporal Trends in Incidence Rates of Lower Extremity Amputation and Associated Risk Factors Among Patients Using Veterans Health Administration Services From 2008 to 2018

The relationship between lower extremity amputation and body mass index

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