Calf rEF: Impact of Calf Muscle Pump Dysfunction With Reduced Ejection Fraction on All-Cause Mortality.

OBJECTIVE: To evaluate mortality outcomes by varying degrees of reduced calf muscle pump (CMP) ejection fraction (EF). PATIENTS AND METHODS: Consecutive adult patients who underwent venous air plethysmography testing at the Mayo Clinic Gonda Vascular Laboratory (January 1, 2012, through December 31, 2022) were divided into groups based on CMP EF for the assessment of all-cause mortality. Other venous physiology included measures of valvular incompetence and clinical venous disease (CEAP [clinical presentation, etiology, anatomy, and pathophysiology] score). Mortality rates were calculated using the Kaplan-Meier method. RESULTS: During the study, 5913 patients met the inclusion criteria. During 2.84-year median follow-up, there were 431 deaths. Mortality rates increased with decreasing CMP EF. Compared with EF of 50% or higher, the hazard ratios (95% CIs) for mortality were as follows: EF of 40% to 49%, 1.4 (1.0 to 2.0); EF of 30% to 39%, 1.6 (1.2 to 2.4); EF of 20% to 29%, 1.7 (1.2 to 2.4); EF of 10% to 19%, 2.4 (1.7 to 3.3) (log-rank P≤.001). Although measures of venous valvular incompetence did not independently predict outcomes, venous disease severity assessed by CEAP score was predictive. After adjusting for several clinical covariates, both CMP EF and clinical venous disease severity assessed by CEAP score remained independent predictors of mortality. CONCLUSION: Mortality rates are higher in patients with reduced CMP EF and seem to increase with each 10% decrement in CMP EF. The mortality mechanism does not seem to be impacted by venous valvular incompetence and may represent variables intrinsic to muscular physiology.

Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus.

OBJECTIVE: Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events. METHODS: Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score. RESULTS: Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27). CONCLUSIONS: An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.

BACKGROUND: Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. METHODS AND RESULTS: Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years. CONCLUSIONS: An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Bridging the gap in BASCULE syndrome: A retrospective case series of a recently described clinical entity.

BACKGROUND: Bier anemic spots, cyanosis with urticaria-like eruption (BASCULE) syndrome is a recently described entity with episodic urticarial lesions and white anemic halos on a background of erythrocyanosis, commonly affecting the lower extremities. Possible association with autonomic dysfunction remains poorly understood. Existing publications are limited, but the condition is suggested as highly underrecognized. OBJECTIVE: To further characterize clinical and epidemiologic data for BASCULE syndrome. METHODS: We performed an IRB-approved retrospective chart review on patients with BASCULE syndrome evaluated at Mayo Clinic from April 2021 to November 2022. RESULTS: A total of 17 patients were identified (13 female, 4 male). Median age of onset was 12 years (range 9-17). Lower extremities were involved in all patients (17). Most patients were symptomatic with pruritus (8) or burning pain (8); three were asymptomatic. Triggers were standing (11), hot showers or hot environments (7), or no clear trigger (4). Autonomic dysfunction was present in 10 patients. Treatment responses were observed from propranolol (3) and high-dose cetirizine (1). CONCLUSION: Novel epidemiologic data from 17 pediatric and young adult patients with BASCULE syndrome further supports an association with autonomic dysfunction and suggests a higher prevalence than previously acknowledged.

Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

BACKGROUND: Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. OBJECTIVE: This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. METHODS: The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. RESULTS: A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). CONCLUSIONS: Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA. .

Erythromelalgia: A Review of Medical Management Options and Our Approach to Management.

Erythromelalgia (EM) is a rare disorder characterized by episodic, burning pain associated with erythema and warmth of the extremities. The feet and hands are most commonly affected. The pain can be so severe that patients may engage in behaviors, sometimes extreme, to cool the affected areas and change their lifestyle to avoid precipitating factors, such as exercise and increased ambient heat. A literature search was performed with PubMed and MEDLINE with the search term erythromelalgia. Inclusion criteria were studies on EM published after 1985 until January 1, 2022, in the English language and studies that provided information on medical treatment of EM. Studies were excluded if they were duplicates or did not include treatment data. No guidelines exist for the treatment of this complex disorder. Lifestyle modifications and pharmacologic treatments (topical and systemic) are discussed in this article, which provides a comprehensive review of published medical management options for erythromelalgia and a proposed approach to management.

Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index.

BACKGROUND: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. METHODS: Consecutive patients (4/8/2015 - 12/31/2020) undergoing rest and postexercise ankle-brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 - 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. RESULTS: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92-0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91-0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). CONCLUSION: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.

Major adverse events associated with inducible cardiac ischemia during treadmill exercise testing for peripheral artery disease.

BACKGROUND: The coexistence of coronary artery disease and peripheral artery disease (PAD) is well-established. Whether myocardial ischemia by electrocardiography during treadmill testing to evaluate PAD severity is associated with adverse cardiac and limb events has not been established. The aim of the current study is to assess the risk of major adverse cardiac events (MACE), major adverse limb events (MALE), and all-cause mortality in patients with evidence of myocardial ischemia on ECG compared with those without ischemia in patients undergoing treadmill testing for PAD evaluation. METHODS: Patients undergoing treadmill exercise ankle-brachial index (ABI) evaluation (January 1, 2003, to December 31, 2006) were identified using the Mayo Clinic Gonda Vascular Laboratory database. Patients with ischemia by electrocardiogram (ECG) were age and sex matched to patients without ischemia. Outcomes were compared by ECG category. RESULTS: Of 4128 patients who underwent treadmill exercise, 170 (4.1%) had inducible myocardial ischemia by ECG. These were matched with 340 patients without ischemia. The positive ECG group had a higher percentage of diabetes mellitus (31.2% vs 21.8%; P = .02), carotid artery disease (22.4% vs 13.2%; P = .009), exercise-induced angina (14.1% vs 2.9%; P < .0001), and dyspnea (60.6% vs 35.6%; P < .0001). While the resting ABI was similar, the postexercise ABI was lower in the positive ECG group (0.5 vs 0.7; P = .04). After a median follow-up of 8 years, MACE were significantly greater in the positive ECG group (62.4% vs 46.5%; P < .001). MALE were significantly less frequent (17.1% vs 23.2%; P = .02), without an increased risk of amputation. In multivariable analysis, inducible ischemia was associated with higher incidence of MACE (hazard ratio, 1.65; 95% confidence interval, 1.25-2.16; P < .001) and lower incidence of MALE (hazard ratio, 0.51; 95% confidence interval, 0.31-0.84; P < .05). CONCLUSIONS: ECG monitoring during vascular treadmill testing identified a subset of patients with more frequent MACE but less MALE.

Usability of a Digital Registry to Promote Secondary Prevention for Peripheral Artery Disease Patients.

OBJECTIVE: To evaluate usability of a quality improvement tool that promotes guideline-based care for patients with peripheral arterial disease (PAD). PATIENTS AND METHODS: The study was conducted from July 19, 2018, to August 21, 2019. We compared the usability of a PAD cohort knowledge solution (CKS) with standard management supported by an electronic health record (EHR). Two scenarios were developed for usability evaluation; the first for the PAD-CKS while the second evaluated standard EHR workflow. Providers were asked to provide opinions about the PAD-CKS tool and to generate a System Usability Scale (SUS) score. Metrics analyzed included time required, number of mouse clicks, and number of keystrokes. RESULTS: Usability evaluations were completed by 11 providers. SUS for the PAD-CKS was excellent at 89.6. Time required to complete 21 tasks in the CKS was 4 minutes compared with 12 minutes for standard EHR workflow (median, P = .002). Completion of CKS tasks required 34 clicks compared with 148 clicks for the EHR (median, P = .002). Keystrokes for CKS task completion was 8 compared with 72 for EHR (median, P = .004). Providers indicated that overall they found the tool easy to use and the PAD mortality risk score useful. CONCLUSIONS: Usability evaluation of the PAD-CKS tool demonstrated time savings, a high SUS score, and a reduction of mouse clicks and keystrokes for task completion compared to standard workflow using the EHR. Provider feedback regarding the strengths and weaknesses also created opportunities for iterative improvement of the PAD-CKS tool.

Calf muscle pump function as a predictor of all-cause mortality.

Calf muscle pump (CMP) promotes venous return from the lower extremity and contributes to preload and cardiac output. Impaired CMP function may reflect a measure of frailty or cumulative disease burden or may impede cardiac function. The study objective was to test the hypothesis that impaired CMP negatively impacts survival. Consecutive adult patients who underwent venous strain gauge plethysmography at the Mayo Clinic Gonda Vascular Laboratory (January 1, 1998 - December 31, 2011) were assessed for overall survival. Patients with venous incompetence, venous obstruction or unilateral calf pump dysfunction were excluded. Risk of mortality was assessed with Cox proportional hazard ratios and after adjusting for Charlson Comorbidity Index variables. Over the study period, 2728 patients were included in the analysis. Compared to patients with normal CMP, those with impaired CMP were older (p < 0.001), predominantly female (p = 0.01) and had higher mean Charlson scores (p < 0.001). Patients with impaired CMP had a higher mortality rate at 5 (8.9% vs 2.4%), 10 (17.5% vs 5.9%), and 15 years (22.8% vs 8.3%) compared to those with normal CMP (p < 0.001 for each comparison). Of patients with heart failure, those with impaired CMP had worse survival at each 5-year increment compared to those with normal CMP (p < 0.05 at each increment). In conclusion, impaired CMP appears to be an independent predictor of poor outcomes after adjusting for variables within the Charlson Comorbidity Index. The association between impaired CMP, heart failure, and mortality may represent a negative impact on circulatory function or a surrogate measure of frailty.

Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease.

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P<0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and ß estimates. Big data infrastructure enabled deployment of the real-time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real-time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real-time risk calculator deployed at the point of care.

Burden of hospitalization in clinically diagnosed peripheral artery disease: A community-based study.

The burden and predictors of hospitalization over time in community-based patients with peripheral artery disease (PAD) have not been established. This study evaluates the frequency, reasons and predictors of hospitalization over time in community-based patients with PAD. We assembled an inception cohort of 1798 PAD cases from Olmsted County, MN, USA (mean age 71.2 years, 44% female) from 1 January 1998 through 31 December 2011 who were followed until 2014. Two age- and sex-matched controls ( n = 3596) were identified for each case. ICD-9 codes were used to ascertain the primary reasons for hospitalization. Patients were censored at death or last follow-up. The most frequent reasons for hospitalization were non-cardiovascular: 68% of 8706 hospitalizations in cases and 78% of 8005 hospitalizations in controls. A total of 1533 (85%) cases and 2286 (64%) controls ( p < 0.001) were hospitalized at least once; 1262 (70%) cases and 1588 (44%) controls ( p < 0.001) ≥ two times. In adjusted models, age, prior hospitalization and comorbid conditions were independently associated with increased risk of recurrent hospitalizations in both groups. In cases, severe PAD (ankle-brachial index < 0.5) (HR: 1.25; 95% CI: 1.15, 1.36) and poorly compressible arteries (HR: 1.26; 95% CI: 1.16, 1.38) were each associated with increased risk for recurrent hospitalization. We demonstrate an increased rate of hospitalization in community-based patients with PAD and identify predictors of recurrent hospitalizations. These observations may inform strategies to reduce the burden of hospitalization of PAD patients.

Association of Ankle-Brachial Indices With Limb Revascularization or Amputation in Patients With Peripheral Artery Disease.

Importance: The prevalence and morbidity of peripheral artery disease (PAD) are high, with limb outcomes including revascularization and amputation. In community-dwelling patients with PAD, the role of noninvasive evaluation for risk assessment and rates of limb outcomes have not been established to date. Objective: To evaluate whether ankle-brachial indices are associated with limb outcomes in community-dwelling patients with PAD. Design, Setting, and Participants: A population-based, observational, test-based cohort study of patients was performed from January 1, 1998, to December 31, 2014. Data analysis was conducted from July 15 to December 15, 2017. Participants included a community-based cohort of 1413 patients with PAD from Olmsted County, Minnesota, identified by validated algorithms deployed to electronic health records. Automated algorithms identified limb outcomes used to build Cox proportional hazards regression models. Ankle-brachial indices and presence of poorly compressible arteries were electronically identified from digital data sets. Guideline-recommended management strategies within 6 months of diagnosis were also electronically retrieved, including therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers, and smoking abstention. Main Outcomes and Measures: Ankle-brachial index (index ≤0.9 indicates PAD; <.05, severe PAD; and ≥1.40, poorly compressible arteries) and limb revascularization or amputation. Results: Of 1413 patients, 633 (44.8%) were women; mean (SD) age was 70.8 (13.3) years. A total of 283 patients (20.0%) had severe PAD (ankle-brachial indices <0.5) and 350 (24.8%) had poorly compressible arteries (ankle-brachial indices ≥1.4); 780 (55.2%) individuals with less than severe disease formed the reference group. Only 32 of 283 patients (11.3%) with severe disease and 68 of 350 patients (19.4%) with poorly compressible arteries were receiving 4 guideline-recommended management strategies. In the severe disease subgroup, the 1-year event rate for revascularization was 32.4% (90 events); in individuals with poorly compressible arteries, the 1-year amputation rate was 13.9% (47 events). In models adjusted for age, sex, and critical limb ischemia, poorly compressible arteries were associated with amputation (hazard ratio [HR], 3.12; 95% CI, 2.16-4.50; P < .001) but not revascularization (HR, 0.91; 95% CI, 0.69-1.20; P = .49). In contrast, severe disease was associated with revascularization (HR, 2.69; 95% CI, 2.15-3.37; P < .001) but not amputation (HR, 1.30; 95% CI, 0.82-2.07; P = .27). Conclusions and Relevance: Community-dwelling patients with severe PAD or poorly compressible arteries have high rates of revascularization or limb loss, respectively. Guideline-recommended management strategies for secondary risk prevention are underused in the community.

Discrepancies in Prevalence of Peripheral Arterial Disease between Lower Extremities at Rest and Postexercise.

Background Lower extremity peripheral arterial disease (PAD) is a common medical condition causing substantial morbidity. Limited data exist on whether discrepancies in PAD prevalence exist between the lower extremities using resting ankle-brachial indices (ABIs) and postexercise pressures. Objective We predicted the prevalence of PAD between the lower extremities. Methods and Results Consecutive patients who had undergone a noninvasive arterial lower extremity study at Mayo Clinic, Rochester, MN, between January 1996 and December 2012 with suspected PAD were retrospectively reviewed. We identified 12,312 consecutive patients who underwent an arterial lower extremity and an exercise treadmill study. Prevalence of PAD was assessed at rest and after exercise using two criteria: a resting ABI ≤ 0.90 and a postexercise pressure decrease of > 30 mm Hg. Mean age was 67 ± 12 years and there were 4,780 (39%) women studied. At rest, we found a higher prevalence of PAD on the left extremity (27.4%) compared with the right (24.6%) ( p < 0.0001). After exercise, we found a higher prevalence of PAD on the right extremity (25.1%) compared with the left (19.0%) ( p < 0.0001). These discrepancies between the prevalence of PAD at rest and after exercise were present in women and men. Conclusion Using validated criteria of a resting ABI of ≤ 0.90 and postexercise ankle pressure decrease > 30 mm Hg, our results suggest that there is a significantly higher prevalence of PAD in the left lower extremity at rest, in contrast to a greater prevalence of abnormal postexercise testing in the right lower extremity. The reason(s) of these discrepancies remain to be studied.

Discordant Diagnosis of Lower Extremity Peripheral Artery Disease Using American Heart Association Postexercise Guidelines.

To determine whether postexercise criteria for peripheral artery disease (PAD) diagnosis recommended by the American Heart Association (AHA) identifies the same group of PAD patients.Diagnosis of PAD is performed using ankle-brachial index at rest (resting-ABI). When resting-ABI is not contributive, an AHA scientific statement recommend to use 1 of 2 following criteria: a postexercise ABI decrease of greater than 20% or a postexercise ankle pressure decrease of greater than 30 mm Hg.Between 1996 and 2012, 31,663 consecutive patients underwent lower-extremity arterial study at Mayo Clinic. Among them, only unique patients who had exercise treadmill testing were analyzed. In this retrospective analysis, resting-ABI, postexercise ABI, and postexercise decrease of ankle pressure measured at 1-minute were measured in each patient. We conducted an analysis of agreement between postexercise criteria expressing the agreement separately for the positive and the negative ratings. Twelve thousand three hundred twelve consecutive patients were studied with a mean age of 67 ±â€Š12 years, 61% male. According to resting-ABI, 4317 (35%) patients had PAD. In the whole population, if a clinician diagnoses "PAD" with 1 postexercise criterion, the probability that other clinicians would also diagnose "PAD" is 74.3%. If a clinician diagnoses "no PAD", the probability that other clinicians would also diagnose "no PAD" is 82.4%. In the patients to be of potential benefit from treadmill test when the resting-ABI > 0.90, if a clinician diagnoses "PAD" with 1 postexercise criterion, the probability that other clinicians would also diagnose "PAD" is 58.4% whereas if a clinician diagnoses "no PAD," the probability that other clinicians would also diagnose "no PAD" is 87.5%.Postexercise criteria do not identify the same group of PAD patients. In our opinion, postexercise criteria to define PAD deserve additional study.

Digital obstructive arterial disease can be detected by laser Doppler measurements with high sensitivity and specificity.

OBJECTIVE: This study was conducted to determine the sensitivity and specificity of laser Doppler flowmetry (LDF) measurements for digital obstructive arterial disease (DOAD) using angiography as the reference standard and to compare the accuracy of different classical tests used to assess DOAD. Diagnosis of vascular abnormalities at the digital level is challenging. Angiography is the gold standard for assessment of DOAD but is invasive and expensive to perform. METHODS: We performed a retrospective analysis of consecutive patients referred at Mayo Clinic (Rochester, Minn) for upper extremity arterial assessment during a 27-month period. Finger-brachial index, skin blood flow (in arbitrary units [a.u.]), and skin temperature (in degrees Celsius) were recorded in each digit on the pulp at baseline and after a thermal challenge test (hand placed in a thermal box at 47.0°C for 15 minutes). Angiogram analysis was blinded and performed by a radiologist using a vascularization scale ranging from 0 (no vessel) to 4 (normal). The receiver operating characteristic curve was used to define a specific cutoff point to detect DOAD. Twenty-two patients had LDF measurements and complete angiograms. RESULTS: A total of 185 digits were analyzed because some patients had only analysis of one hand. The best area under the curve (AUC) was 0.98 (range, 0.94-0.99) for postwarming skin blood flow, with a cutoff point of ≤206 a.u. This AUC was statistically different from AUCs of all the other tests (P < .01). Sensitivity and specificity were 93% (95% confidence interval, 85%-97%) and 96% (95% confidence interval, 90%-99%), respectively. CONCLUSIONS: LDF combined with a thermal challenge is highly accurate, safe, and noninvasive means to detect DOAD.