Predictors of lower extremity amputation in patients with diabetic foot ulcer: findings from MEDFUN, a multi‐center observational study

Ugwu, Ejiofor; Adeleye, Olufunmilayo; Gezawa, Ibrahim; Okpe, Innocent; Enamino, Marcelina; Ezeani, Ignatius

doi:10.1186/s13047-019-0345-y

Cited by 104 publications

(130 citation statements)

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“…Our risk models did not provide enough evidence of an impact of the two remaining PEDIS risk factors infection status and sensation. This stands in contrast with previous studies [42] but confirms our univariate findings. However, as the risk factor infection is a red flag, it may have facilitated close ulcer monitoring and attentive care as an amputation protective measure.…”

Section: Discussionsupporting

confidence: 78%

Predicting the amputation risk for patients with diabetic foot ulceration – a Bayesian decision support tool

Hüsers

Hafer²,

Heggemann³

et al. 2020

BMC Med Inform Decis Mak

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Background: Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge. Method: A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge. Results: This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5% were male. Concerning the outcome, 31.6% underwent any-and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen's d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen's d 0.22). Conclusions: Both of the Bayesian amputation risk models showed acceptable prognostic values, and the majoramputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.

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Section: Discussionsupporting

confidence: 78%

Predicting the amputation risk for patients with diabetic foot ulceration – a Bayesian decision support tool

Hüsers

Hafer²,

Heggemann³

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Our risk models did not provide enough evidence of an impact of the two remaining PEDIS risk factors infection status and sensation. This stands in contrast with previous studies [40] but confirms our univariate findings. However, as the risk factor infection is a red flag, it may have facilitated close ulcer monitoring and attentive care as an amputation protective measure.…”

Section: Discussion Summarysupporting

confidence: 78%

Predicting the Amputation Risk for Patients with Diabetic Foot Ulceration – A Bayesian Decision Support Tool

Hüsers

Hafer²,

Heggemann³

et al. 2020

Preprint

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Background: Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge.Method: A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge.Results: This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5 per cent were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen’s d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen’s d 0.22).Conclusions: Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.

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“…[2][3][4][5] Although up to 50% of patients with DPN are asymptomatic, some patients may manifest burning feet pain, due to progressive sensory impairment, which responds poorly to analgesics. Rarely, this sensory impairment could lead to foot ulceration and eventually to lower limb amputation; 6,7 diabetes is a leading cause of "nontraumatic" lower limb amputation. 8 There is wide agreement that DPN is a consequence of diabetes longstanding hyperglycemia which is critical for peripheral nerve damage and distal-predominant nerve fiber degeneration.…”

Section: Introductionmentioning

confidence: 99%

<p>Prevalence and Risk Factors for Diabetic Peripheral Neuropathy Among Saudi Hospitalized Diabetic Patients: A Nested Case-Control Study</p>

Al-Eidan

Ahmad

Alotaibi

et al. 2020

IJGM

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Objective: To determine the prevalence and the risk factors of diabetic peripheral neuropathy (DPN) in hospitalized adult Saudi diabetics. Methods: This is a retrospective, nested case-control study conducted at King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia. All diabetic patients admitted to the hospital between the January 1, and December 31, 2018 were considered for inclusion in the study. Patients with DPN were identified and three controls per case were randomly selected from the remaining diabetic patients without peripheral neuropathy (PN). Results: A total of 2,096 adult diabetic patients were identified during the study period. Of these, 73 patients (3.5%) were confirmed to be suffering from DPN and 219 were included as controls. When comparing diabetic with the control cases, DPN cases were significantly older (p=0.002), had a significantly higher proportion of type 2 diabetes (p=0.023), chronic kidney disease (p<0.0001), cerebral vascular stroke (p=0.027), hypertension (p=0.005), dyslipidemia (p=0.002), peripheral vascular disease (p<0.0001), osteoarthritis (p=0.034), diabetic ketoacidosis (p=0.003), foot ulcers (p=0.006), gangrene (p=0.001), lower limb ischemia (p=0.001), increased duration with diabetic disease (p=0.031), increased BMI (p=0.003), higher serum creatinine (p<0.001) and lower serum albumin levels (p=0.035). In the multivariate logistic regression, only older age {odds ratio (OR) 1.02, 95% CI 1.01-1.04, p=0.031}, chronic kidney disease (OR 2.39, 95% CI 1.23-4.64, p=0.010) and peripheral vascular disease (OR 3.14, 95% CI 1.39-7.13, p=0.006) were independently associated with DPN. Conclusion: This study identified several risk factors that contributed to the development of DPN in Saudis. These must be considered in strategies and campaigns aimed at risk reduction of cardiovascular and chronic diseases, and consequently progression of DPN.

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Predictors of lower extremity amputation in patients with diabetic foot ulcer: findings from MEDFUN, a multi‐center observational study

Cited by 104 publications

References 50 publications

Predicting the amputation risk for patients with diabetic foot ulceration – a Bayesian decision support tool

Predicting the amputation risk for patients with diabetic foot ulceration – a Bayesian decision support tool

Predicting the Amputation Risk for Patients with Diabetic Foot Ulceration – A Bayesian Decision Support Tool

<p>Prevalence and Risk Factors for Diabetic Peripheral Neuropathy Among Saudi Hospitalized Diabetic Patients: A Nested Case-Control Study</p>

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