Peripheral Artery Disease Mortality: Systematic Review with ☸️SAIMSARA.

• Generated: 2026-01-30 21:36:38 CET
• Plan: Pro (expanded craft tokens; source: Europe PMC)
• Source: Europe PMC
• Scope: Titles/Abstracts (tiab)
• Keyword Gate: Fuzzy (≥60% of required terms, minimum 2 terms matched in title/abstract)
• Total Abstracts/Papers: 4394
• Downloaded Abstracts/Papers: 4394
• Included original Abstracts/Papers: 2149
• Total study participants (naïve ΣN): 15723713

• Bias: Qualitatively inferred from study design fields. The included studies primarily consist of cohort designs, with a mix of prospective and retrospective approaches, and some randomized controlled trials (RCTs). This introduces a risk of selection bias and confounding, particularly in observational studies. Case series and mixed-design studies further contribute to heterogeneity in evidence strength.

• Comorbidities as Mortality Predictors: Coexisting conditions significantly elevate mortality risk. Chronic kidney disease (CKD) or end-stage renal disease (ESRD) with PAD is associated with substantially elevated risks of all-cause and cardio-cerebrovascular disease (CCD) mortality (adjusted HR 3.25 for all-cause, 4.76 for CCD) [4], and higher in-hospital mortality [1244, 186, 238, 366]. Type 2 diabetes (T2D) with PAD increases all-cause and cardiovascular mortality (adjusted HR 1.12 and 1.13, respectively) [16, 208, 325, 1328, 1652, 1773, 2092, 2096, 1016, 700, 721], particularly if uncontrolled (HbA1c >10.0%) [1326]. Depression in PAD patients is linked to increased all-cause mortality (HR 1.24) [91, 1455, 1467] and highest mortality risk when combined with T2D (HR 2.209) [22]. Coronary artery disease (CAD) is a strong predictor of mortality in PAD patients [192, 270, 754, 931, 1931, 1934, 1968, 1975, 1988, 1999, 1819, 1920, 1897, 2080, 2056, 2069, 2123], with PAD independently associated with worse outcomes after acute myocardial infarction (AMI) (adjusted OR 1.25 for in-hospital mortality) [93, 113, 352, 721, 868, 899, 1173, 1820, 1648]. Heart failure (HF) and atrial fibrillation (AF) also increase mortality risk in PAD patients [1338, 454, 517, 689, 790, 662, 1593, 1142, 1606, 1608, 1732, 365]. Chronic obstructive pulmonary disease (COPD) is associated with higher in-hospital mortality (OR 1.16) [1161, 360, 1787]. Frailty and malnutrition are significant predictors of mortality (HR 2.11 for frailty, HR 4.02 for low LBMI) [143, 290, 202, 733, 186].
• Biomarkers for Risk Stratification: Several biomarkers show promise for predicting mortality. Leucine-rich α-2 glycoprotein 1 (LRG1) is an independent predictor of 10-year all-cause (HR 3.12) and cardiovascular (HR 2.58) mortality in PAD patients [2]. Elevated cardiac troponins on admission are associated with increased all-cause mortality (HR 2.85) [100, 191, 328]. Inflammatory markers like hs-CRP and C-reactive protein-to-albumin ratio (CAR) are linked to increased all-cause mortality (HR 3.11 for high CAR) [48, 49, 740, 542, 887]. FGF-23 (HR 1.35) [107] and urinary Galectin-3 (uGal-3/cr) (HR 1.71) [175] predict all-cause mortality. Elevated plasma trimethylamine N-oxide (TMAO) (HR 2.06) [356] and YKL-40 (HR 1.66) [241] are also associated with increased mortality.
• Impact of Interventions and Therapies: Guideline-directed medical therapy (GDMT) is associated with lower mortality (adjusted HR 0.329) [25]. Statin therapy reduces all-cause mortality (HR 0.87 for women) [102, 318, 743, 1266, 1830, 2003, 917] and cardiovascular death [318, 917]. Dual pathway inhibition with low-dose rivaroxaban plus aspirin reduces overall (HR 0.82) and cardiovascular (HR 0.78) mortality [156, 413, 498, 520, 769, 1077, 1214, 1218]. GLP-1 receptor agonists are beneficial in PAD, reducing revascularization and all-cause mortality (OR 0.55) [125, 150, 174, 258]. Surgical revascularization for lower extremity artery disease (LEAD) is associated with decreased adjusted mortality rates compared to percutaneous transluminal angioplasty (PTA) [344, 885]. However, some studies show no significant difference in mortality between endovascular therapy and vein bypass [31, 110] or between paclitaxel-coated and uncoated devices [18, 155, 180, 209, 333, 843, 1034, 1144, 736, 476, 398, 245]. Exercise-based rehabilitation is fundamental for reducing high cardiovascular mortality rates [66, 540, 1509].
• Prognostic Tools and Scores: The CHA2DS2-VASc score is independently associated with adverse long-term outcomes and mortality in PAD patients [148, 1117]. Machine learning models can predict in-hospital mortality in PAD patients with adequate predictability [82, 668, 870], and also predict future mortality in general PAD populations [544]. The Hospital Frailty Risk Score (HFRS) predicts mortality in ESRD patients with PAD [186].
• Demographic and Geographic Disparities: PAD mortality is projected to increase in Brazil by 10.99% by 2040, with a notable rise among women [12]. Sub-Saharan Africa (SSA) has seen sharp increases in PAD burden and mortality, contrasting with global declines [87, 508]. Globally, while the burden remains substantial, age-standardized mortality rates are projected to decline, yet disparities persist across regions [130, 89]. Women with PAD often face higher mortality risks in specific contexts, such as after open abdominal aortic aneurysm repair (OR 3.96) [78], lower extremity peripheral vascular intervention (OR 1.21) [486, 723, 741], or CABG [2145, 1627]. Older age is consistently identified as a risk factor for mortality [97, 191, 1058, 1067, 1118, 1205, 1341, 1488, 1849, 1993, 2133, 1771].
• Specific PAD Manifestations and Severity: Chronic limb-threatening ischemia (CLTI), the most severe form of PAD, is associated with significant amputation and mortality risk [72, 672, 680, 835, 1530, 1642, 511, 492, 204]. A higher number of leg arteries with PAD is associated with a progressively higher risk of mortality [190]. High ankle-brachial pressure index (ABI >1.4) is associated with elevated cardiovascular and all-cause mortality, similar to PAD [21]. Lower ankle systolic blood pressure (SBP) quartiles predict higher mortality [20].
• Perioperative and Procedural Risks: Concurrent abdominal stoma is associated with increased postoperative mortality after open lower extremity revascularization (LER) [23]. Acute limb ischemia (ALI) after percutaneous coronary intervention (PCI) is associated with a 1.6 times greater risk of in-hospital mortality, with PAD being the strongest predictor of post-PCI ALI [30, 352]. PAD is independently associated with increased mortality in patients undergoing transcatheter aortic valve replacement (TAVR) [6, 42, 69, 205, 275, 712, 1028, 1366, 1428, 1788, 1803, 1826, 549, 923]. Major amputations are associated with high mortality rates [36, 231, 300, 52, 546]. Postoperative delirium is associated with increased mortality after lower extremity bypass (HR 2.0) [877]. Pre-admission opioid use is linked to higher all-cause mortality after revascularization [181]. High inflammation-related biomarkers (e.g., NLR) are associated with increased mortality after PCI for PAD [188, 437, 348].

• Aggressive Comorbidity Management: Clinicians should prioritize aggressive screening and management of comorbidities like CKD, T2D, CAD, and depression in PAD patients, given their strong association with increased mortality [4, 16, 22, 91, 93, 113, 1338].
• Biomarker-Guided Risk Stratification: Incorporating emerging biomarkers such as LRG1, cardiac troponins, and inflammatory markers (hs-CRP, CAR) can enhance early risk stratification and guide intensified interventions for high-risk PAD patients [2, 100, 49, 191, 328, 740].
• Optimized Therapeutic Strategies: Adherence to guideline-directed medical therapy (GDMT), including statins and dual antiplatelet therapy (DAPT), and considering GLP-1 receptor agonists, is crucial for reducing mortality in PAD patients [25, 102, 125, 150, 156, 318, 891].
• Enhanced Perioperative Surveillance: Patients undergoing PAD-related surgical or endovascular interventions, especially those with additional risk factors like frailty, malnutrition, or hostile vascular access, require heightened perioperative monitoring for complications that directly impact mortality [23, 143, 202, 205, 290].
• Addressing Health Disparities: Awareness of demographic and geographic disparities in PAD burden and mortality, such as higher rates in women and specific regions, should inform targeted screening and intervention programs [12, 78, 87, 486, 723, 741].

• Standardized Mortality Reporting: Develop standardized definitions and reporting timepoints for PAD-related mortality across studies to enable more robust meta-analyses and comparisons [e.g., 30-day, 1-year, 5-year all-cause and cardiovascular mortality].
• Prospective Comparative Effectiveness: Conduct large-scale prospective comparative effectiveness research comparing surgical and endovascular revascularization strategies in diverse PAD populations, with long-term mortality as a primary endpoint [e.g., 31, 110, 344, 885].
• Biomarker Integration Algorithms: Develop and validate clinical algorithms that integrate novel biomarkers (e.g., LRG1, uGal-3, TMAO, YKL-40, TFF3, GDF15) with established risk factors to improve mortality prediction in PAD patients [e.g., 2, 107, 175, 356, 241, 337, 447].
• Intervention for Depression in PAD: Investigate the impact of targeted depression screening and treatment interventions on all-cause and cardiovascular mortality in PAD patients through randomized controlled trials [e.g., 22, 91, 1455].
• Impact of Socioeconomic Factors: Explore the causal pathways through which socioeconomic factors and health literacy contribute to PAD mortality disparities, informing public health interventions [e.g., 428, 538, 605, 606, 807].

• Heterogeneous Study Designs — The varied study designs (cohort, RCT, mixed) and methodologies limit direct comparability and the strength of aggregated conclusions.
• Inconsistent Outcome Definitions — Mortality endpoints, follow-up durations, and reported statistics are not uniform, making quantitative synthesis challenging.
• Observational Data Reliance — A significant portion of the evidence is observational, which inherently carries risks of confounding and bias, impacting causal inference.
• Geographic and Demographic Specificity — Many findings are specific to certain populations or regions, limiting generalizability to a global or broader PAD patient cohort.
• Missing Data and Unspecified Factors — Several summaries lack details on specific study characteristics, such as directionality, or comprehensive reporting of all relevant covariates, potentially obscuring important associations.

• Standardize Outcome Reporting: Implement core outcome sets for PAD mortality studies.
• Conduct Large RCTs: Evaluate interventions with long-term mortality endpoints.
• Validate Novel Biomarkers: Test new biomarkers in diverse PAD cohorts.
• Develop Integrated Risk Models: Combine clinical, imaging, and biomarker data.
• Investigate Health Equity: Research interventions to reduce disparities.