PredyCLU: A prediction system for chronic leg ulcers based on fuzzy logic; part II—Exploring the arterial side

Abstract

Peripheral arterial disease (PAD) and its most severe form, critical limb ischaemia (CLI), are very common clinical conditions related to atherosclerosis and represent the major causes of morbidity, mortality, disability, and reduced quality of life (QoL), especially for the onset of ischaemic chronic leg ulcers (ICLUs) and the subsequent need of amputation in affected patients. Early identification of patients at risk of developing ICLUs may represent the best form of prevention and appropriate management. In this study, we used a Prediction System for Chronic Leg Ulcers (PredyCLU) based on fuzzy logic applied to patients with PAD. The patient population consisted of 80 patients with PAD, of which 40 patients (30 males [75%] and 10 females [25%]; mean age 66.18 years; median age 67.50 years) had ICLUs and represented the case group. Forty patients (100%) (27 males [67.50%] and 13 females [32.50%]; mean age 66.43 years; median age 66.50 years) did not have ICLUs and represented the control group. In patients of the case group, the higher was the risk calculated with the PredyCLU the more severe were the clinical manifestations recorded. In this study, the PredyCLU algorithm was retrospectively applied on a multicentre population of 80 patients with PAD. The PredyCLU algorithm provided a reliable risk score for the risk of ICLUs in patients with PAD.

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

“…An AI-based study by Karimi et al [ 111 ] presented a model to biomechanically characterize atherosclerotic coronary arteries. Fuzzy logic was used to prepare a risk score for the onset of ischemic chronic leg ulcers in PAD patients and natural language processing to better diagnose PAD patients [ 112 , 113 ]. AI methods have also been used among patients suffering from PAD to link them with potential limitations and symptom severity [ 114 ].…”

“…Seurat uses ML for cell classification. Also, other studies implement SPSS [ 94 , 110 ], PLINK [ 31 , 38 , 41 , 42 ], CARDIoGRAMplusC4D [ 26 , 34 , 37 ], or Ingenuity [ 20 , 23 , 112 ] that are strictly correlated with AI. The search biases are unavoidable as well and the authors are aware of this.…”

“…"R" is a free software for statistical analysis and graphics and the R method is widely used in new-style AI, involving ML. Seurat uses ML for cell Also, other studies implement SPSS [94,110], PLINK [31,38,41,42], CARDIoGRAMplusC4D [26,34,37], or Ingenuity [20,23,112] that are strictly correlated with AI. The search biases are unavoidable as well and the authors are aware of this.…”

Metalloproteinases between History, Health, Disease, and the Complex Dimension of Social Determinants of Health

“…For example, fuzzy logic is a flexible mathematical system that has been seen considered a powerful tool for decision making and pattern classification in clinical settings. In fact, this mathematical system can allow very fast computations of large sets of heterogeneous data from patients; therefore, it uses a large amount of information [133,134]. Considering heart disease, Garvin et al, in an eight-year follow-up study of a community-based, middle-aged population, showed that plasma MMP-9 levels are able to predict first-time coronary heart disease (CHD) [135].…”

One-year mortality rates after minor and major amputations of the lower limbs

“…Critical limb ischemia is the most severe form of peripheral arterial disease. It is generally associated with arteriosclerosis, leading to considerable morbidity, mortality, and the loss of quality of life due to disability, which can be aggravated by the occurrence of ischemic ulcers [ 1 , 2 ]. The anatomic and clinical severity of the disease is often heterogeneous and the choice of treatment is affected by different clinical and patient-related factors [ 3 ].…”