Chronic Limb-Threatening Ischemia does not Enclose a Homogenous Population: Time for a More Detailed Classification.
Schraepen Cédricvan der Laan LijckleNick SmetMeulenbroek AnneFourneau IngePublished in: The International journal of angiology : official publication of the International College of Angiology, Inc (2023)
Objective Chronic limb-threatening ischemia (CLTI) is associated with high morbidity and mortality. Classification methods differentiate into patients with rest pain or with ischemic ulcers. No distinction is made between the presence or absence of rest pain in patients with ischemic ulcers. Our aim is to determine any differences in outcome between these subdivisions so we can improve preoperative counseling and risk assessment. Materials and Methods This multicenter retrospective cohort study included all patients revascularized for a first episode of CLTI between 2013 and 2018. The cohort was divided in three groups: patients with solely rest pain (RP), solely ischemic ulcers (IU), and patients with both rest pain and ischemic ulcers (RP + IU). Baseline characteristics, morbidity, and mortality were analyzed. Results A total of 624 limbs in 599 patients were included: 225 (36.1%) in the rest pain group, 169 (27.1%) in the ischemic ulcers group, and 230 (36.2%) in combined group. Amputation rates were higher in the combined group at 6 months. Mortality rates were significantly higher in the ischemic ulcers group and the combined group at 6 months and 1 year. Conclusion Patients with solely rest pain have significantly lower mortality rates in comparison to patients with ischemic ulcers. Rest pain did not affect mortality rates in patients with ulcers. There was a higher amputation rate in patients with combined rest pain and ischemic ulcers because the presence of rest pain CLTI patients had a significant negative effect on amputation risk. A separate subdivision for patients with combined ulcers and rest pain is indicated.
Keyphrases
- chronic pain
- pain management
- neuropathic pain
- risk assessment
- end stage renal disease
- newly diagnosed
- ischemia reperfusion injury
- ejection fraction
- machine learning
- chronic kidney disease
- type diabetes
- prognostic factors
- wound healing
- cardiovascular events
- cardiovascular disease
- spinal cord injury
- oxidative stress
- patients undergoing
- clinical trial
- spinal cord
- coronary artery disease
- deep learning
- heavy metals
- cross sectional
- human immunodeficiency virus
- brain injury
- blood brain barrier
- smoking cessation