Authors

• Krešimir Gabaldo — Opća bolnica ‘’Dr. Josip Benčević’’ Slavonski Brod, Slavonski Brod, Hrvatska — ORCID: 0000-0002-0116-5929
• Domagoj Vučić — Opća bolnica ‘’Dr. Josip Benčević’’ Slavonski Brod, Slavonski Brod, Hrvatska — ORCID: 0000-0003-3169-3658
• Ivan Bitunjac — Opća bolnica ‘’Dr. Josip Benčević’’ Slavonski Brod, Slavonski Brod, Hrvatska — ORCID: 0000-0002-4396-6628
• Marijana Knežević Praveček — Opća bolnica ‘’Dr. Josip Benčević’’ Slavonski Brod, Slavonski Brod, Hrvatska — ORCID: 0000-0002-8727-7357
• Katica Cvitkušić Lukenda — Opća bolnica ‘’Dr. Josip Benčević’’ Slavonski Brod, Slavonski Brod, Hrvatska — ORCID: 0000-0001-6188-0708
• Tomislav Krčmar — Klinički bolnički centar Rijeka, Rijeka, Hrvatska — ORCID: 0000-0003-4689-1673
• Blaženka Miškić — Opća bolnica ‘’Dr. Josip Benčević’’ Slavonski Brod, Slavonski Brod, Hrvatska — ORCID: 0000-0001-6568-3306

Abstract

Diabetes and its complications causes up to 9% of total mortality worldwide. Peripheral arterial disease is, in addition to cardiovascular diseases, the most common complication of diabetes with a prevalence that increases with age and the duration of diabetes. The specificity of peripheral artery disease in diabetics is the diffuse involvement of the arterial system, especially the popliteal arteries. Consequently, diabetes is still the main cause of small and large limb amputations, which, in addition to a reduction in the quality of life, significantly affects the survival of patients. Since the developed of atherosclerotic disease involves a number of complications from the professional domain of various subspecialties, such as diabetic foot, it is necessary to organize multidisciplinary teams for the diagnostic and therapeutic purposes. For this purpose, the General Hospital “Dr. Josip Benčević” in Slavonski Brod organized a multidisciplinary team with the goal of early recognition of peripheral artery disease and application of timely treatment. Experience from everyday clinical practice indicates that proper functioning of the team requires an accurate diagnostic and therapeutic algorithm to avoid long waiting lists for imaging, which includes Color Doppler and multislice computed tomography. The diagnostic algorithm was based on the ankle-brachial index, and its value and clinical picture guided and determined the degree of urgency and the type of image processing. By integrating the algorithm into the online database registry, we were able to more easily monitor the incidence rate, treatment success, and dependence on the entered variables. We hope that this approach will result in earlier detection of symptomatic disease and thus a significant reduction in lower limb amputations and, ultimately, mortality.

Keywords

algorithm, multidisciplinary team, peripheral arterial disease, register, diabetes

DOI

Full Text

## Introduction Peripheral artery disease (PAD) is an atherosclerotic occlusive disease of the lower extremities. Diabetes and smoking are the main (strong) risk factors for developing PAD. Age, duration of diabetes, and peripheral neuropathy are closely related to the risk of PAD. The prevalence of PAD is 20% in patients with diabetes over 40 years of age, while the prevalence is around 30% in patients over 50 years of age (1). Clinically, it manifests as an asymptomatic disease or symptomatic disease by type of intermittent claudication or chronic limb ischemia (CLI), as well as particular entities such as the diabetic foot. Unfortunately, a large number of patients presents in the advanced stage of the disease, with irreversible ischemic damage leading to a large number of small and large amputations of the lower extremities. The frequency of lower limb amputations in the diabetic population ranges from 78 to 704 per 100,000 individuals per year, and the relative risk between diabetics and non-diabetics ranges between 7.4 and 41.3 (2). Of the total number of amputated patients in Western Europe, 66% are diabetics with a very high one-year mortality rate, which, according to some studies, has risen up to 50% (3, 4). The specificity of PAD in patients with diabetes is the diffuse involvement of atherosclerotic disease, with more pronounced involvement of the tibial arteries and the absence of symptoms even in the stage of critical ischemia due to neuropathic damage characteristic for diabetic foot. This also results in reduced success of revascularization treatment and a rate of amputations that is still high (5). Early detection of the presence of PAD and timely diagnosis can significantly contribute to a reduction in the number of amputations and overall mortality. Since the treatment of these patients is carried out under the domains of multiple subspecialties, it is necessary to organize multidisciplinary teams. ## Amputations Once PAD is diagnosed, the guidelines recommend the introduction of drug therapy (antiplatelet drugs, statins), exercise, and control of risk factors. Despite the measures, a large number of patients remain symptomatic, requiring some form of revascularization treatment (surgical or endovascular). As a final treatment measure, amputations of the lower extremities is reserved for cases when there is no possibility of revascularization, in case of significant tissue loss, and when revascularization and optimal drug therapy do not give positive results. Lower limb amputations are considered the last therapeutic choice due to increased mortality and treatment costs. Amputations of the lower extremities, with, can be divided into large and small amputations regard to localization and in accordance with the recommendations of the relevant societies; NICE (The National Institute for Health and Clinical Excellence) and PARC (The Peripheral Academic Research Consortium) (6, 7). Large or high amputations can be defined as amputations at or above the level of the ankle, and depending on the knee joint they can be above-knee (transfemoral) or below-knee (transtibial) amputations. Small or low amputations refer to amputations below the level of the ankle, including the foot and one or more toes. Statistics of patients undergoing major amputations indicate an almost twofold increase in the mortality rate compared with patients in whom amputation was not performed (13% vs. 6.9%) (8). Current statistics indicate the need for better prevention of PAD development and standardization of the algorithm for its diagnosis and treatment and its complications. Overall, among patients requiring lower limb amputation, 81.8% of patients are diabetics, while 75.6% are patients with diabetes and PAD (9). Statistical data in some national studies have shown a decrease in the overall rate of amputations from 27.5 to 25.0 / 10,000 in patients with diabetes, but without a significant change for large and small amputations (small amputations 15.7-14.9, large amputations 11.8-10.2, p=0.66 and p=0.29) (10). The number of hospitalizations for non-traumatic lower limb amputation in diabetics has decreased significantly in the last few decades, from 11.2 to 3.9 / 1000 patients (paankle-brachial index; bchronic critical limb ischemia; cintermittent claudication; dmultslice computed tomography; eduplex ultrasound. In order to enroll and monitor patients who have PAD in addition to diabetes, the Register of Patients with PAD was established in January 2019. It is designed as a database that, in addition to its basic purpose which is achieving clinical benefit for patients, will also facilitate research on this disease. The first version of the Registry was uploaded to Google’s server in January 2019. Patients who underwent ABI at a day diabetic hospital were admitted. In addition to the variables obtained by measuring ABI, basic demographic data, comorbidities, drugs, and laboratory values (HbA1c and lipid panel) were entered, which are a part of regular diabetological examinations. Because the Registry is on a remote server, it can be accessed from any site, allowing multidisciplinary input of a large number of variables. The main role of the Registry in clinical application is the detection of patients with reduced ABI and timely referral to additional targeted diagnostic processing and treatment. ## Results A total of 310 patients were enrolled in the period from January 2019 to March 2020. The variables used in the Registry are shown in **Table 1**. ### TABLE 1: Variables used in the Registry. | **Patient’s personal and demographic data** | Name and surname Date of birth Sex Height Weight BMI | | --- | --- | | **Comorbidity data** | Arterial hypertension Dyslipidemia Coronary artery disease Atherosclerotic carotid disease | | **Current check-in / arrival details** | Examination date Method of referral Doctor from the team Nurse from the team | | **Data from current status and anamnesis** | ABI Smoking and cigarette quantity Diabetes therapy HbA1c Lipid profile Renal function* | [†] BMI = body mass index (based on the ratio of body weight to square of a person’s height); ABI = ankle-brachial index; HbA1c = glycated hemoglobin. *assessment of renal function based on creatinine clearance (value obtained using the Cockcroft-Gault formula). The number of patients diagnosed with diabetes was 265, of whom 196 had associated arterial hypertension, while 61 were smokers. The mean HbA1c was 8%, while the average LDL was 3.3 mmol/L. Decreased ABI 1.4 were verified in 58 patients who were further processed according to the algorithm and referred for assessment of revascularization options. Percutaneous transluminal angioplasty (PTA) was performed in 26 patients, while 17 patients were treated with surgical revascularization. Revascularization treatment was not possible in 15 patients, who continued conservative treatment. In the group of patients with a pathological ABI index, 62% had hypertension while 19% were also smokers. The average HbA1c in this group of patients was 8.3%, while the average LDL was 3.4 mmol/L (**Table 2**). ### TABLE 2: Cross-sectional indicators of selected variables from the Registry for the total number of diabetic patients vs. abnormal ankle-brachial index. | | **Total number of diabetic patients** | **ABI 1.4** | | --- | --- | --- | | Number of patients | 265 | 58 | | The ration of women to men % | 48: 52 | 49:51 | | Average BMI | 32 | 31 | | Average HbA1c | 8.0% | 8.3% | | Average LDL (mmol/L) | 3.3 | 3.3 | | Proportion of smokers | 23% | 19% | | Proportion of patients with arterial hypertension | 74% | 62% | | Proportion of patients with coronary heart disease | 5.0% | 6.3% | | Proportion of patients on insulin therapy | 41% | 38% | [†] ABI = ankle-brachial index; BMI = body mass index (based on the ratio of body weight to the square of a person’s height); HbA1c = glycated hemoglobin; LDL = low-density lipoprotein. ## Discussion The diagnosis of PAD is based on duplex ultrasound and MSCT imaging methods. However, the extremely long waiting list for these tests prevents timely diagnosis and delays the required revascularization procedure. The potential of ABI in the assessment of PAD, as well as the overall cardiovascular risk, remained untapped (14). For the same reason, we devised a diagnostic algorithm based on ABI, and its value as well as the clinical picture are used to guide and determine the degree of urgency and the type of image processing. By integrating the algorithm into the database registry, we obtained data on the prevalence and influence of risk factors in the examined group of patients. ABI 1.4 were recorded in 22% of subjects (n = 58), which corresponds to an estimated prevalence of PAD of 20-30%. Namely, back in 1992, Walters et al. found the prevalence of PAD in diabetic patients to be 23.5%, while Elhad et al. reported a prevalence of 33% (15, 16). According to the recommendations, 74% (n = 43) of patients underwent one of the forms of revascularization treatment (17). Among the included subjects, the main comorbidity with diabetes (of which 41% of patients were on insulin therapy) was arterial hypertension (in 74% of patients), while 24% of the subjects were smokers. In the subanalysis of patients with ABI 1.4 (n = 58), no significant difference was found in comorbidities, with a verified slightly smaller comorbidity prevalence (hypertension 62%, smoking 19%). The mean value of LDL cholesterol in the subgroup with pathological ABI was not significantly different, 3.4 vs. 3.3 mmol/L, while HbA1c was slightly higher, 8.3 vs. 8.0%. According to the obtained data, as expected, unregulated diabetes was the main cause of the development of PAD, while arterial hypertension in patients with diabetes and PAD is a “stronger” additional factor compared with smoking. Associated coronary artery disease was found in only 6.3% of patients with ABI <0.9. Namely, it is a known fact that ABI is an independent risk factor for cardiovascular and cerebrovascular morbidity and mortality, and, according to Weitz et al., 20% of patients with PAD develop a non-fatal coronary incident over a 5-year period (18-20). The main technical limitation of the current version of the Registry is that it is not relationally related to the Coronary Patient Registry, which is also kept in the same institution. Therefore, one of the main goals for a future version of the Registry is to create a unique relational database for PAD and coronary artery disease which will allow more objective data on the association between these two diseases. ## Conclusion Multidisciplinary teams can significantly contribute to the improvement of diagnosis and treatment of patients with diabetes and peripheral artery disease. However, proper functioning of the team requires development of clear diagnostic-therapeutic algorithms. The diagnostic algorithm based on ABI enables timely diagnosis of PAD by determining the degree of urgency and the type of image processing that should be applied. By integrating the algorithm into the online Registry, better monitoring of the diagnostic and therapeutic protocol is achieved for each individual patient with the possibility of monitoring and better control of glycemia and other risk factors. Access to the Registry from a remote server enables interactive participation of all members of the multidisciplinary team in both diagnostic and therapeutic procedures. It also enables retrospective and prospective analysis of all necessary data, which can greatly contribute to improving treatment and reducing the negative outcomes of the disease, which primarily refers to reducing the rate of amputations.

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