The importance of socio-economic determinants of health in the care of patients with peripheral artery disease: A narrative review from VAS

Farkas, Katalin; Karahan, Oğuz; Golledge, Jonathan; Schernthaner, Gerit‐Holger; Karplus, Thomas E.; Bernardo, Jonathan James; Marschang, Sascha; Abola, Maria Teresa B.; Heinzmann, Mónica; Edmonds, Michael; Catalano, M.

doi:10.1177/1358863x231169316

Cited by 13 publications

(7 citation statements)

References 122 publications

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“…In summary, our study reported more on the sociodemographic context, considering the person, place, and social risk factors implicated as obvious determinants of health [43,118]. This was in addition to observing the challenges to managing diabetes which revolve around diabetes knowledge, medication adherence, and other context barriers, as the literature documents [44,119].…”

Section: Discussionmentioning

confidence: 70%

Self-Management of Diabetes and Associated Factors among Patients Seeking Chronic Care in Tshwane, South Africa: A Facility-Based Study

Zwane,

Modjadji,

Madiba

et al. 2023

IJERPH

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The burden of diabetes continues to increase in South Africa and a significant number of diabetes patients present at public primary healthcare facilities with uncontrolled glucose. We conducted a facility-based cross-sectional study to determine the diabetes self-management practices and associated factors among out-patients in Tshwane, South Africa. An adapted validated questionnaire was used to collect data on sociodemography, diabetes knowledge, and summaries of diabetes self-management activities measured in the previous seven days, and over the last eight weeks. Data were analysed using STATA 17. A final sample of 402 diabetes out-patients was obtained (mean age: 43 ± 12 years) and over half of them were living in poor households. The mean total diabetes self-management of score was 41.5 ± 8.2, with a range of 21 to 71. Almost two thirds of patients had average self-management of diabetes, and 55% had average diabetes knowledge. Twenty-two percent of patients had uncontrolled glucose, hypertension (24%) was the common comorbidity, and diabetic neuropathy (22%) was the most common complication. Sex [male: AOR = 0.55, 95% CI: 0.34–0.90], race [Coloured: AOR = 2.84, 95% CI: 1.69–4.77 and White: AOR = 3.84, 95% CI: 1.46–10.1], marital status [divorced: AOR = 3.41, 95% CI: 1.13–10.29], social support [average: AOR = 2.51, 95% CI: 1.05–6.00 and good: AOR = 4.49, 95% CI: 1.61–7.57], body mass index [obesity: AOR = 0.31, 95% CI: 0.10–0.95], diabetes knowledge [average: AOR = 0.58, 95% CI: 0.33–0.10 and good: AOR = 1.86, 95% CI: 0.71–4.91], and uncontrolled glucose [AOR = 2.97, 95% CI: 1.47–5.98] were factors independently predictive of diabetes self-management. This study emphasizes that the self-management of diabetes was mostly on average among patients and was associated with the aforementioned factors. Innovative approaches are perhaps needed to make diabetes education more effective. Face-to-face sessions delivered generally during clinic visits should be better tailored to the individual circumstances of diabetes patients. Considerations should be given to the options of leveraging information technology to ensure the continuity of diabetes education beyond clinic visits. Additional effort is also needed to meet the self-care needs of all patients.

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Section: Discussionmentioning

confidence: 70%

Self-Management of Diabetes and Associated Factors among Patients Seeking Chronic Care in Tshwane, South Africa: A Facility-Based Study

Zwane,

Modjadji,

Madiba

et al. 2023

IJERPH

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“…Patients at high risk of long-term complications should receive early support from allied health professionals to optimize safe discharge planning with consideration of early follow-up . Given the important role of social determinants of health in postoperative outcomes, it is critical to effectively follow-up with high-risk patients postoperatively and implement strategies for proactive and multidisciplinary care, including physiotherapy, interdisciplinary treatment facilities, and regular follow-up phone calls to mitigate adverse events Figure 4. illustrates a proposed clinical workflow for our ML tool to support clinical decision-making at the preoperative, intraoperative, and postoperative stages.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

Li,

Warren,

Eisenberg

et al. 2024

JAMA Netw Open

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ImportanceEndovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited.ObjectiveTo develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD.Design, Setting, and ParticipantsThis prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets.ExposuresA total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified.Main Outcomes and MeasuresUsing 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data.ResultsOverall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively.Conclusions and RelevanceIn this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

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“…PAD has long been underdiagnosed and it is estimated that up to half of all people with PAD are undetected [ 4 ]. Many systematic reviews have focused on this disease, including understanding patients’ beliefs about their illness [ 41 ], addressing the importance of socioeconomic determinants of health in the care of PAD [ 42 ], and evaluating what the public and healthcare practitioners and trainees know about PAD [ 8 ]. Although these are all important research questions, to initiate timely treatment, those living with PAD must first recognize their symptoms and seek medical attention.…”

Section: Discussionmentioning

confidence: 99%

A Scoping Review of Measurement Tools Evaluating Awareness and Disease-Related Knowledge in Peripheral Arterial Disease Patients

Felix,

Pereira,

Pakosh

et al. 2023

JCM

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Background: Peripheral arterial disease (PAD) is the third leading cause of atherosclerotic cardiovascular morbidity worldwide, with high prevalence and associated complications, and is often overlooked and undertreated. Research has shown that there is a profound lack of PAD-related knowledge and awareness; additionally, information sources are not often reliable and accessible. The objective of this scoping review was: (1) to identify and critically appraise instruments that measure patients’ disease-related knowledge/awareness about PAD, and (2) to characterize the current state of knowledge/awareness levels among these patients. Methods: This systematic review was conducted and reported in accordance with the PRISMA statement. Six databases (APA PsycInfo, CINAHL Ultimate, Embase, Emcare Nursing, Medline ALL and Web of Science Core Collection) were searched, and search strategies were developed utilizing the PICO framework. Potential studies of any methodological design were considered for inclusion through a snowball hand search. Data from the included articles were extracted by a reviewer, and the extraction accuracy was independently cross-checked by another author. Results: The initial database search yielded 9832 records, of which sixteen studies (thirteen quantitative and three qualitative) were included. Only three questionnaires had their psychometric properties assessed. Questionnaire items focused on the following topics: definition/characteristics, risk factors/causes, treatment, complications, and personal issues regarding the perception/management of the disease. Overall, knowledge/awareness about PAD was low among patients. Conclusions: This study identified major gaps in PAD education, including the lack of availability of a validated measurement tool addressing all educational topics relevant to care and low knowledge/awareness of patients about their condition.

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The importance of socio-economic determinants of health in the care of patients with peripheral artery disease: A narrative review from VAS

Cited by 13 publications

References 122 publications

Self-Management of Diabetes and Associated Factors among Patients Seeking Chronic Care in Tshwane, South Africa: A Facility-Based Study

Self-Management of Diabetes and Associated Factors among Patients Seeking Chronic Care in Tshwane, South Africa: A Facility-Based Study

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

A Scoping Review of Measurement Tools Evaluating Awareness and Disease-Related Knowledge in Peripheral Arterial Disease Patients

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