Comparison of mortality and amputation after lower extremity bypass versus peripheral vascular intervention in patients with chronic limb-threatening ischemia and comorbid chronic kidney disease.

OBJECTIVE: Comorbid chronic kidney disease (CKD) is associated with worse outcomes for patients with chronic limb-threatening ischemia (CLTI). However, comparative effectiveness data are limited for lower extremity bypass (LEB) vs peripheral vascular intervention (PVI) in patients with CLTI and CKD. We aimed to evaluate (1) 30-day all-cause mortality and amputation and (2) 5-year all-cause mortality and amputation for LEB vs PVI in patients with comorbid CKD. METHODS: Individuals who underwent LEB and PVI were queried from the Vascular Quality Initiative with Medicare claims-linked outcomes data. Propensity scores were calculated using 13 variables, and a 1:1 matching method was used. The mortality risk at 30 days and 5 years in LEB vs PVI by CKD was assessed using Kaplan-Meier and Cox proportional hazards models, with interaction terms added for CKD. For amputation, cumulative incidence functions and Fine-Gray models were used to account for the competing risk of death, with interaction terms for CKD added. RESULTS: Of 4084 patients (2042 per group), the mean age was 71.0 ± 10.8 years, and 69.0% were male. Irrespective of CKD status, 30-day mortality (hazard ratio [HR]: 0.94, 95% confidence interval [CI]: 0.63-1.42, P = .78) was similar for LEB vs PVI, but LEB was associated with a lower risk of 30-day amputation (sub-HR [sHR]: 0.66, 95% CI: 0.44-0.97, P = .04). CKD status, however, did not modify these results. Similarly, LEB vs PVI was associated with a lower risk of 5-year mortality (HR: 0.79, 95% CI: 0.71-0.88, P < .001) but no difference in 5-year amputation (sHR: 1.03, 95% CI: 0.89-1.20, P = .67). CKD status did not modify these results. CONCLUSIONS: Regardless of CKD status, patients had a lower risk of 5-year all-cause mortality and 30-day amputation with LEB vs PVI. Results may help inform preference-sensitive treatment decisions on LEB vs PVI for patients with CLTI and CKD, who may commonly be deemed too high risk for surgery.

Evaluation of short-term mortality in patients with Medicare undergoing endovascular interventions for chronic limb-threatening ischemia.

INTRODUCTION: Patients with chronic limb-threatening ischemia (CLTI) have high mortality rates after revascularization. Risk stratification for short-term outcomes is challenging. We aimed to develop machine-learning models to rank predictive variables for 30-day and 90-day all-cause mortality after peripheral vascular intervention (PVI). METHODS: Patients undergoing PVI for CLTI in the Medicare-linked Vascular Quality Initiative were included. Sixty-six preprocedural variables were included. Random survival forest (RSF) models were constructed for 30-day and 90-day all-cause mortality in the training sample and evaluated in the testing sample. Predictive variables were ranked based on the frequency that they caused branch splitting nearest the root node by importance-weighted relative importance plots. Model performance was assessed by the Brier score, continuous ranked probability score, out-of-bag error rate, and Harrell's C-index. RESULTS: A total of 10,114 patients were included. The crude mortality rate was 4.4% at 30 days and 10.6% at 90 days. RSF models commonly identified stage 5 chronic kidney disease (CKD), dementia, congestive heart failure (CHF), age, urgent procedures, and need for assisted care as the most predictive variables. For both models, eight of the top 10 variables were either medical comorbidities or functional status variables. Models showed good discrimination (C-statistic 0.72 and 0.73) and calibration (Brier score 0.03 and 0.10). CONCLUSION: RSF models for 30-day and 90-day all-cause mortality commonly identified CKD, dementia, CHF, need for assisted care at home, urgent procedures, and age as the most predictive variables as critical factors in CLTI. Results may help guide individualized risk-benefit treatment conversations regarding PVI.

Long-Term Mortality Predictors Using a Machine-Learning Approach in Patients With Chronic Limb-Threatening Ischemia After Peripheral Vascular Intervention.

BACKGROUND: Patients with chronic limb-threatening ischemia (CLTI) face a high long-term mortality risk. Identifying novel mortality predictors and risk profiles would enable individual health care plan design and improved survival. We aimed to leverage a random survival forest machine-learning algorithm to identify long-term all-cause mortality predictors in patients with CLTI undergoing peripheral vascular intervention. METHODS AND RESULTS: Patients with CLTI undergoing peripheral vascular intervention from 2017 to 2018 were derived from the Medicare-linked VQI (Vascular Quality Initiative) registry. We constructed a random survival forest to rank 66 preprocedural variables according to their relative importance and mean minimal depth for 3-year all-cause mortality. A random survival forest of 2000 trees was built using a training sample (80% of the cohort). Accuracy was assessed in a testing sample (20%) using continuous ranked probability score, Harrell C-index, and out-of-bag error rate. A total of 10 114 patients were included (mean±SD age, 72.0±11.0 years; 59% men). The 3-year mortality rate was 39.1%, with a median survival of 1.4 years (interquartile range, 0.7-2.0 years). The most predictive variables were chronic kidney disease, age, congestive heart failure, dementia, arrhythmias, requiring assisted care, living at home, and body mass index. A total of 41 variables spanning all domains of the biopsychosocial model were ranked as mortality predictors. The accuracy of the model was excellent (continuous ranked probability score, 0.172; Harrell C-index, 0.70; out-of-bag error rate, 29.7%). CONCLUSIONS: Our random survival forest accurately predicts long-term CLTI mortality, which is driven by demographic, functional, behavioral, and medical comorbidities. Broadening frameworks of risk and refining health care plans to include multidimensional risk factors could improve individualized care for CLTI.

COVID-19-Associated Portal Vein Thrombosis Post-Cholecystitis.

This case study describes a 45-year-old Caucasian male with a past medical history of obesity, hypertension, and non-insulin-dependent diabetes mellitus, who in the setting of coronavirus disease 2019 (COVID-19) pneumonia, developed portal vein thrombosis (PVT) presenting as an acute abdomen after hospital discharge from a cholecystitis episode. PVT is a very infrequent thromboembolic condition, classically occurring in patients with systemic conditions such as cirrhosis, malignancy, pancreatitis, diverticulitis, autoimmunity, and thrombophilia. PVT can cause serious complications, such as intestinal infarction, or even death, if not promptly treated. Due to the limited number of reports in the literature describing PVT in the COVID-19 setting, its prevalence, natural history, mechanism, and precise clinical features remain unknown. Therefore, clinical suspicion should be high for PVT, in any COVID-19 patient who presents with abdominal pain or associated signs and symptoms. To the best of our knowledge, this is the first report of COVID-19-associated PVT causing extensive thrombosis in the portal vein and its right branch, occurring in the setting of early-stage cirrhosis after a preceding episode of cholecystitis.

Diabetes Distal Peripheral Neuropathy: Subtypes and Diagnostic and Screening Technologies.

Diabetes distal symmetrical peripheral neuropathy (DSPN) is the most prevalent form of neuropathy in industrialized countries, substantially increasing risk for morbidity and pre-mature mortality. DSPN may manifest with small-fiber disease, large-fiber disease, or a combination of both. This review summarizes: (1) DSPN subtypes (small- and large-fiber disease) with attention to clinical signs and patient symptoms; and (2) technological diagnosis and screening for large- and small-fiber disease with inclusion of a comprehensive literature review of published studies from 2015-present (N = 66). Review findings, informed by the most up-to-date research, advance critical understanding of DSPN large- and small-fiber screening technologies, including those designed for point-of-care use in primary care and endocrinology practices.