Sensitivity and Predictors of False-Negative SPECT Myocardial Perfusion Imaging in a High-Burden Coronary Artery Disease Population: A Retrospective Analysis Using Revascularization as the Reference Standard

Sensitivity and Predictors of False-Negative SPECT Myocardial Perfusion Imaging in a High-Burden Coronary Artery Disease Population: A Retrospective Analysis Using Revascularization as the Reference Standard | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sensitivity and Predictors of False-Negative SPECT Myocardial Perfusion Imaging in a High-Burden Coronary Artery Disease Population: A Retrospective Analysis Using Revascularization as the Reference Standard Lauren Wright, Jay Hamze, Tarun R. Nagrani, Nikki Arnold, Raquel McGlone, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9246863/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 18 You are reading this latest preprint version Abstract Background Coronary artery disease (CAD) places a disproportionate burden on rural Appalachian communities. Scioto County, Ohio has among the highest rates of CAD-related hospitalizations in the state, approaching double the national average. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging remains a widely used non-invasive screening modality, yet its diagnostic performance in high-burden populations with significant comorbidities has not been well characterized. We aimed to evaluate the diagnostic performance of SPECT for predicting subsequent revascularization and identify predictors of false-negative results in our patient population. Methods A retrospective analysis was conducted using prospectively collected data from the American College of Cardiology CathPCI registry at a single community hospital. All cardiac catheterizations performed between Q3 2013 and Q1 2018 were included. Sensitivity for predicting subsequent revascularization was calculated using catheterization with subsequent intervention (percutaneous coronary intervention or coronary artery bypass grafting) as the reference standard. Logistic regression models were constructed to identify predictors of false-negative SPECT results. This study was reported in accordance with the STROBE guidelines for observational studies. Results Over the study period, 7,312 cardiac catheterizations were performed, with 3,116 (42.6%) requiring intervention. Among 3,189 patients with SPECT data, sensitivity for predicting subsequent revascularization was 82.0%. Among the subset of patients with negative SPECT who nevertheless proceeded to catheterization, 32.8% were found to require intervention, a rate reflecting the high-risk, selected nature of this subgroup rather than the population-level false-negative rate. Sensitivity was highest for left main and ramus lesions (91%) and lowest for mid-distal left anterior descending (LAD) lesions (82%). In multivariable analysis, false-negative results were independently associated with family history of CAD (OR 1.73, 95% CI 1.25–2.40, p < 0.001) and were less likely with critical circumflex (OR 0.60, 95% CI 0.44–0.81, p < 0.001), right coronary artery (OR 0.54, 95% CI 0.40–0.73, p < 0.001), and prior myocardial infarction (OR 0.60, 95% CI 0.41–0.88, p < 0.01). Conclusions SPECT sensitivity for predicting revascularization in this high-burden CAD population was broadly consistent with contemporary meta-analytic benchmarks, though direct comparison is limited by differing reference standards. Family history of CAD was independently associated with false-negative SPECT results, a finding not previously reported in the SPECT literature. In patients with a strong family history of CAD, particularly in settings where SPECT remains the primary cardiac imaging modality, these findings suggest that a lower threshold for further evaluation may be warranted when SPECT results are negative. SPECT myocardial perfusion imaging false negative coronary artery disease Appalachia cardiac catheterization revascularization diagnostic yield Figures Figure 1 Figure 2 Introduction Coronary artery disease (CAD) remains a leading cause of morbidity and mortality in the United States, with chronic heart disease prevalence of 14.2% in rural areas compared to 9.9% in urban areas [ 1 ]. Rural Americans are 40% more likely to develop heart disease, with a 30% higher stroke risk and a 3-year life expectancy gap compared to urban counterparts [ 2 ]. The rural-urban cardiovascular mortality gap has widened since 2010, driven by a striking rise among younger rural adults [ 3 ]. Certain regions bear a disproportionate burden: national rates of hospitalization related to CAD are 72.4 per 1,000 Medicare beneficiaries, while the state of Ohio exceeds this at 88.4 per 1,000. Scioto County, located in the Appalachian region of southern Ohio, has among the highest rates in the state at 152.0 per 1,000, approaching double the national rate [ 4 ]. Central Appalachia has a heart disease mortality rate of 249 per 100,000, nearly 1.5 times the national average, compounded by elevated rates of obesity, smoking, and physical inactivity [ 5 ]. This disease burden underscores the critical importance of accurate non-invasive diagnostic testing in such populations. The gold standard for diagnosis of coronary heart disease remains invasive coronary angiography. However, given its invasive nature, non-invasive testing modalities are employed as first-line tools for detection of myocardial ischemia. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging is the most widely utilized of these modalities, valued for its broad availability and tolerability among elderly patients and those with physical limitations [ 6 ]. The long-cited SPECT sensitivity benchmark of 88% was established by the Jaarsma et al. 2012 meta-analysis [ 7 ] and the 2003 ACC/AHA/ASNC guidelines [ 8 ]; however, both sources explicitly noted these figures were uncorrected for verification bias. The most recent comprehensive meta-analysis by Xu et al. (2021), pooling 134 SPECT studies, reported a downward-revised sensitivity of 83% (95% CI 79–87%) [ 9 ], suggesting earlier benchmarks were inflated. Despite this, false-positive and false-negative results remain a significant clinical concern, as the Xu et al. meta-analysis reported specificity of only 77% (95% CI 73–81%) [ 9 ]. The consequences of false-negative results are clinically significant. A normal pharmacologic SPECT carries an annualized mortality of 1.78% due to myocardial infarction or cardiac death, extrapolating to nearly 18% over 10 years [ 10 ]. False-negative results may lead to premature discharge of patients with significant coronary disease, placing them at substantial risk. Only one prior study has specifically examined predictors of false-negative SPECT in patients with normal imaging who subsequently underwent angiography; Nakanishi et al. (2016) identified elevated pretest probability, residual sub-threshold perfusion defects, and abnormal transient ischemic dilation as predictors, but did not examine family history of CAD [ 11 ]. Since the period of data collection for this study, the diagnostic landscape has evolved considerably. The 2021 AHA/ACC chest pain guideline elevated coronary CTA to a Class 1 recommendation for intermediate-risk patients without known CAD and expressed preference for PET over SPECT when nuclear imaging is selected [ 12 ]. Nevertheless, 2022 Medicare billing data demonstrate that SPECT remains the most commonly performed cardiac imaging modality in the United States by a factor of six, with approximately 1.34 million examinations compared to 212,000 PET and 119,000 CCTA studies [ 13 ]. Access barriers including equipment cost, trained personnel shortages, PET radiotracer logistics, and volume thresholds for reader expertise explain the continued predominance of SPECT in community and rural hospital settings [ 6 ]. Understanding the diagnostic performance and limitations of SPECT in these settings therefore remains directly relevant to clinical practice. Notably, nearly all published SPECT diagnostic accuracy data originate from academic medical centers with expert readers and optimized protocols. SPECT diagnostic accuracy data from community and non-academic hospital settings are conspicuously absent from the literature [ 9 ]. The purpose of this study was to evaluate the diagnostic performance of SPECT, using subsequent revascularization as the reference standard, in a community hospital serving a population with among the highest burden of CAD in the United States, and to identify patient and lesion characteristics independently associated with false-negative SPECT results. Methods Study Design and Setting This was a retrospective analysis of prospectively collected data from the American College of Cardiology National Cardiovascular Data Registry (NCDR) CathPCI registry at Southern Ohio Medical Center (SOMC), a community hospital serving Scioto County in the Appalachian region of southern Ohio. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 26 ]. Local institutional review board approval was obtained prior to the study. Study Population All patients who underwent cardiac catheterization at SOMC over a five-year period from Q3 2013 to Q1 2018 were included. Patients are prospectively entered into the CathPCI registry at the time of catheterization. The CathPCI registry is a national quality improvement registry that captures detailed demographic, clinical, procedural, and outcome data for cardiac catheterization procedures performed at participating institutions. Variables and Definitions Baseline demographics included age, sex, smoking status, height, and weight. CAD presentation type and angina class were extracted. Comorbidities recorded included use of antianginal medication, chronic lung disease, current dialysis, diabetes, dyslipidemia, family history of coronary artery disease, hypertension, prior cerebrovascular disease, prior heart failure, prior myocardial infarction (MI), prior peripheral artery disease, and prior percutaneous coronary intervention (PCI). Whether SPECT was performed as part of the diagnostic workup was recorded in the registry and used to define the study group. SPECT results were classified as positive, negative, indeterminate, or unavailable. For the primary analysis, indeterminate and unavailable results were grouped with negative results; a sensitivity analysis excluding these categories was also performed. The primary outcome was whether catheterization led to intervention, defined as PCI or coronary artery bypass grafting (CABG). A true positive was defined as a positive SPECT followed by catheterization requiring intervention. A false negative was defined as a negative SPECT followed by catheterization requiring intervention. Importantly, because our cohort was derived from the CathPCI registry, only patients who underwent catheterization were included; patients with negative SPECT who did not proceed to catheterization were not captured. Native coronary lesion severity was categorized by vessel territory (left main, ramus, proximal left anterior descending [LAD], mid-distal LAD, circumflex, and right coronary artery [RCA]). Critical stenosis was defined as native lesion greater than 70%. Statistical Analysis Categorical variables were reported as counts and percentages. Continuous variables were reported as mean with standard deviation or median with interquartile range as appropriate. Sensitivity for predicting subsequent revascularization was calculated overall and stratified by critical lesion location. A logistic regression model was used to compute univariate and multivariable odds ratios for a false-negative outcome based on available comorbidities and lesion location. Variables for the multivariable model were selected using forward stepwise selection based on the Akaike Information Criterion (AIC). As a robustness check, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was also performed. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC, or C-statistic). Model calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit test. All statistical analyses were performed using R version 4.5.1 (R Core Team, Vienna, Austria) with the glmnet, pROC, and ResourceSelection packages. Results Study Population A total of 7,312 cardiac catheterizations were performed during the study period (Fig. 1 ). Overall, 3,116 (42.6%) required intervention. Baseline demographics are presented in Table 1 . Patients requiring intervention were older (median age 65 vs 63, p < 0.001), more commonly male (63.1% vs 49.2%, p < 0.001), and more likely to present with some form of MI rather than angina (p < 0.001). Among comorbidities, patients requiring intervention had higher rates of diabetes (48.9% vs 40.0%, p < 0.001), dyslipidemia (76.9% vs 70.7%, p < 0.001), prior MI (26.0% vs 19.0%, p < 0.001), and prior PCI (47.6% vs 33.0%, p < 0.001). Table 1 Baseline Demographics All patients who underwent cardiac catheterization at Southern Ohio Medical Center, Q3 2013 – Q1 2018, stratified by catheterization requiring intervention. Intervention defined as PCI or CABG. No Intervention includes medical therapy/counseling, none, or other therapy. An additional 723 patients (9.9%) had no treatment outcome recorded in the registry and are included in the Overall column but excluded from the stratified columns. Variable Overall (N = 7,312) Intervention (n = 3,116) No Intervention (n = 3,473) p Age, median [IQR] 64 [54, 73] 65 [56, 74] 63 [53, 72] < 0.001 Smoker 2,395 1,056 (33.9%) 1,156 (33.3%) 0.628 Female 3,207 1,150 (36.9%) 1,763 (50.8%) < 0.001 Male 4,069 1,966 (63.1%) 1,710 (49.2%) Height, mean (SD) 169.6 (14.2) 170.2 (13.2) 169.4 (12.5) 0.007 Weight, mean (SD) 92.1 (26.0) 90.6 (24.0) 93.6 (27.1) < 0.001 **CAD Presentation** < 0.001 No Symptom, No Angina 79 18 (0.6%) 53 (1.5%) Non-STEMI 1,929 1,043 (33.5%) 763 (22.0%) Stable Angina 332 73 (2.3%) 205 (5.9%) STEMI or Equivalent 312 285 (9.1%) 17 (0.5%) Symptom Unlikely Ischemic 255 11 (0.4%) 195 (5.6%) Unstable Angina 4,295 1,686 (54.1%) 2,240 (64.5%) **Anginal Class** < 0.001 Class 1 59 5 (0.2%) 38 (1.1%) Class 2 908 111 (3.6%) 657 (18.9%) Class 3 3,473 1,426 (45.8%) 1,799 (51.8%) Class 4 2,590 1,540 (49.4%) 876 (25.2%) No Angina 150 34 (1.1%) 103 (3.0%) **Comorbidities** Antianginal medication 5,922 2,488 (79.8%) 2,896 (83.4%) < 0.001 Chronic lung disease 2,571 1,022 (32.8%) 1,288 (37.1%) < 0.001 Current dialysis 156 85 (2.7%) 52 (1.5%) 0.001 Diabetes 3,216 1,523 (48.9%) 1,390 (40.0%) < 0.001 Dyslipidemia 5,345 2,397 (76.9%) 2,455 (70.7%) < 0.001 Family history of CAD 1,955 831 (26.7%) 894 (25.7%) 0.408 Hypertension 6,447 2,800 (89.9%) 3,054 (87.9%) 0.015 Prior CVD 984 443 (14.2%) 450 (13.0%) 0.146 Prior heart failure 1,223 510 (16.4%) 602 (17.3%) 0.311 Prior MI 1,661 809 (26.0%) 661 (19.0%) < 0.001 Prior PAD 940 448 (14.4%) 418 (12.0%) 0.006 Prior PCI 2,944 1,484 (47.6%) 1,146 (33.0%) < 0.001 SPECT Utilization and Sensitivity Among all catheterization patients, 3,476 (47.5%) had stress imaging performed as part of their workup. Of these, 2,501 had positive SPECT results, 574 had negative results, 59 had indeterminate results, and 55 had unavailable results; 287 patients had stress imaging recorded but no SPECT result captured. Throughout the study period, the program demonstrated continued growth with increasing catheterization volumes and increasing utilization of SPECT (Fig. 2 ). The overall 2×2 diagnostic performance table is presented in Table 2 . Among 3,189 patients with SPECT data (including indeterminate and unavailable results grouped as negative), sensitivity for predicting subsequent revascularization was 82.0%. Notably, among the subset of patients with negative SPECT who nevertheless proceeded to catheterization (a selected, high-risk group), nearly one in three (32.8%, 226/688) were found to require intervention, underscoring the clinical significance of false-negative results in this cohort (Table 2 ). This proportion reflects the high pretest probability of patients referred onward despite a negative SPECT and should not be interpreted as a population-level false-negative rate. Specificity and negative predictive value could not be meaningfully calculated due to verification bias inherent in the study design (see Limitations). Table 2 SPECT Diagnostic Performance (Revascularization as Reference Standard) Cath Positive (Intervention) Cath Negative Total SPECT Negative* 226 (FN) 462 (TN) 688 SPECT Positive 1,031 (TP) 1,470 (FP) 2,501 **Total** **1,257** **1,932** **3,189** *Includes indeterminate (n = 59) and unavailable (n = 55) results grouped with negative. When stratified by critical native coronary lesion location (> 70% stenosis), sensitivity for predicting subsequent revascularization was highest for left main and ramus lesions (91%) and lowest for mid-distal LAD lesions (82%) (Table 3 ). Table 3 SPECT Sensitivity for Predicting Revascularization by Critical Lesion Location (Native Lesion > 70%) Vessel Territory SPECT Negative SPECT Positive Sensitivity Left Main 12 122 91% Ramus 8 83 91% Circumflex 112 741 87% Right Coronary Artery 133 858 87% Proximal LAD 89 467 84% Mid-Distal LAD 183 843 82% Sensitivity Analysis When indeterminate (n = 59) and unavailable (n = 55) SPECT results were excluded, restricting the analysis to patients with definitive positive or negative results (n = 3,075), sensitivity for predicting subsequent revascularization improved to 84.6% (1,031/1,218), and the proportion of negative SPECT results that were false negatives decreased to 32.6% (187/574). Predictors of False-Negative SPECT A comparison of patients with false-negative versus true-positive SPECT results is presented in Table 4 . False-negative patients were more commonly female (45.6% vs 35.6%, p = 0.006) and more commonly had a family history of CAD (34.5% vs 24.1%, p = 0.002). False negatives were less likely in patients with critical circumflex or RCA lesions (p < 0.001) and those with prior MI (p = 0.017). Unstable angina was the predominant presentation in both groups (81.0% of false negatives vs 78.8% of true positives), reflecting the overall population character of this high-burden cohort. Table 4 Comparison of False-Negative vs True-Positive SPECT All patients who required intervention, stratified by SPECT finding. Variable False Negative (n = 226) True Positive (n = 1,031) p Age, median [IQR] 65 [56, 75] 66 [58, 74] 0.188 Smoker 65 (28.8%) 294 (28.5%) 0.944 Female 103 (45.6%) 367 (35.6%) 0.006 Male 123 (54.4%) 664 (64.4%) Height, mean (SD) 168.4 (12.3) 170.6 (12.3) 0.014 Weight, mean (SD) 90.5 (24.7) 92.8 (24.7) 0.221 **CAD Presentation** 0.333 No Symptom, No Angina 4 (1.8%) 8 (0.8%) Non-STEMI 29 (12.8%) 154 (14.9%) Stable Angina 6 (2.7%) 46 (4.5%) STEMI or Equivalent 2 (0.9%) 8 (0.8%) Symptom Unlikely Ischemic 2 (0.9%) 3 (0.3%) Unstable Angina 183 (81.0%) 812 (78.8%) **Anginal Class** 0.033 Class 1 0 (0.0%) 3 (0.3%) Class 2 13 (5.8%) 47 (4.6%) Class 3 123 (54.4%) 654 (63.4%) Class 4 83 (36.7%) 315 (30.6%) No Angina 7 (3.1%) 12 (1.2%) **Comorbidities** Antianginal medication 210 (92.9%) 926 (89.8%) 0.191 Chronic lung disease 78 (34.5%) 345 (33.5%) 0.822 Current dialysis 1 (0.4%) 30 (2.9%) 0.054 Diabetes 104 (46.0%) 526 (51.0%) 0.198 Dyslipidemia 193 (85.4%) 839 (81.4%) 0.183 Family history of CAD 78 (34.5%) 248 (24.1%) 0.002 Hypertension 210 (92.9%) 954 (92.5%) 0.951 Prior CVD 33 (14.6%) 147 (14.3%) 0.977 Prior heart failure 30 (13.3%) 183 (17.7%) 0.127 Prior MI 44 (19.5%) 283 (27.4%) 0.017 Prior PAD 29 (12.8%) 174 (16.9%) 0.162 Prior PCI 117 (51.8%) 523 (50.7%) 0.833 **Critical Lesion (> 70%)** Circumflex 80 (42.1%) 530 (58.6%) < 0.001 Left Main 8 (9.8%) 86 (18.0%) 0.094 Mid-Distal LAD 137 (68.5%) 608 (68.1%) 0.976 Proximal LAD 66 (44.9%) 333 (48.4%) 0.496 Ramus 8 (38.1%) 65 (51.2%) 0.381 RCA 95 (45.7%) 611 (64.7%) < 0.001 Univariate and multivariable logistic regression results are presented in Table 5 . Forward stepwise selection identified eight variables for the multivariable model. After adjustment, false-negative SPECT results were independently associated with family history of CAD (OR 1.73, 95% CI 1.25–2.40, p < 0.001). False-negative results were less likely with critical circumflex lesions (OR 0.60, 95% CI 0.44–0.81, p < 0.001), critical RCA lesions (OR 0.54, 95% CI 0.40–0.73, p < 0.001), and prior MI (OR 0.60, 95% CI 0.41–0.88, p < 0.01). Critical left main stenosis (OR 0.49, 95% CI 0.23–1.04, p = 0.064), dyslipidemia (OR 1.51, 95% CI 0.99–2.28, p = 0.055), current dialysis (OR 0.19, 95% CI 0.03–1.45, p = 0.109), and height (OR 0.99, 95% CI 0.98–1.00, p = 0.081) were included in the model but did not reach statistical significance. Table 5 Logistic Regression Model for False-Negative SPECT Univariate and multivariable odds ratios. Multivariable model constructed by forward stepwise selection (AIC). Variable Univariate OR 95% CI p Multivariable OR 95% CI p Age 0.99 0.98–1.01 0.256 - - - Male sex 0.66 0.49–0.88 0.005 - - - Smoker 1.01 0.74–1.39 0.941 - - - Height 0.99 0.98–1.00 0.016 0.99 0.98–1.00 0.081 Weight 1.00 0.99–1.00 0.221 - - - Antianginal medication 1.49 0.86–2.57 0.154 - - - Chronic lung disease 1.05 0.77–1.42 0.762 - - - Current dialysis 0.15 0.02–1.09 0.061 0.19 0.03–1.45 0.109 Diabetes 0.82 0.61–1.09 0.174 - - - Dyslipidemia 1.34 0.90–2.00 0.154 1.51 0.99–2.28 0.055 **Family history of CAD** **1.66** **1.22–2.27** **0.001** **1.73** **1.25–2.40** **<0.001** Hypertension 1.06 0.61–1.85 0.840 - - - Prior CVD 1.03 0.68–1.55 0.894 - - - Prior heart failure 0.71 0.47–1.08 0.106 - - - **Prior MI** **0.64** **0.45–0.91** **0.014** **0.60** **0.41–0.88** **<0.01** Prior PAD 0.73 0.48–1.11 0.136 - - - Prior PCI 1.04 0.78–1.39 0.777 - - - **Critical circumflex** **0.52** **0.38–0.70** **<0.001** **0.60** **0.44–0.81** **<0.001** Critical left main 0.40 0.19–0.84 0.016 0.49 0.23–1.04 0.064 Critical mid-distal LAD 1.07 0.80–1.44 0.648 - - - Critical proximal LAD 0.87 0.63–1.18 0.366 - - - Critical ramus 0.55 0.26–1.15 0.113 - - - **Critical RCA** **0.50** **0.37–0.67** **<0.001** **0.54** **0.40–0.73** **<0.001** Model performance: AUC 0.678 (95% CI 0.642–0.715); Hosmer-Lemeshow p = 0.50. Model Performance The multivariable model demonstrated modest discrimination with an AUC of 0.678 (95% CI 0.642–0.715). The Hosmer-Lemeshow goodness-of-fit test indicated adequate calibration (chi-squared = 7.0, df = 8, p = 0.50). LASSO Robustness Check LASSO regression with 10-fold cross-validation was performed as a robustness check. At the most parsimonious regularization (lambda.1se), only critical circumflex stenosis and critical RCA stenosis were retained. At lambda.min, the retained variables included family history of CAD, prior MI, circumflex stenosis, RCA stenosis, left main stenosis, current dialysis, dyslipidemia, sex, height, and ramus stenosis, broadly consistent with the forward selection model. The convergence of both methods on circumflex and RCA stenosis as the strongest predictors, and the inclusion of family history at lambda.min, supports the stability of the primary model findings. Discussion This study examined the diagnostic performance of SPECT myocardial perfusion imaging in a population with one of the highest burdens of CAD in the United States. In 7,312 cardiac catheterizations performed at a single community hospital in Appalachian Ohio over five years, SPECT demonstrated 82% sensitivity for predicting subsequent revascularization, and family history of CAD was independently associated with false-negative results. To our knowledge, this is among the first studies to report SPECT diagnostic performance from a community hospital setting and among the first to identify family history of CAD as an independent predictor of false-negative SPECT. SPECT Sensitivity in Context The observed sensitivity of 82% for predicting revascularization is numerically close to the most recent meta-analytic benchmark of 83% (95% CI 79–87%) reported by Xu et al. in their pooling of 134 SPECT studies [ 9 ], though direct comparison requires caution because the meta-analytic benchmark used angiographic disease definitions rather than revascularization decisions as the reference standard. This represents a meaningful downward revision from the long-cited 88% benchmark established by Jaarsma et al. [ 7 ] and the 2003 ACC/AHA/ASNC guidelines [ 8 ], both of which acknowledged that their figures were uncorrected for verification bias. Cecil et al. demonstrated the magnitude of this bias: in their series of 2,688 SPECT studies, uncorrected sensitivity was 98%, but after Begg-Greenes correction, sensitivity dropped to 82% ± 6% [ 14 ]. Our finding of 82% in a verification-biased cohort using a revascularization-based reference standard is therefore broadly consistent with contemporary evidence and may reflect the real-world performance of SPECT in community practice. This finding is notable given the significant comorbidity burden in our population, including high rates of diabetes (48.9% among those requiring intervention), chronic lung disease (32.8%), and hypertension (89.9%). That SPECT performs comparably in this high-risk, high-prevalence population is reassuring, though the absolute number of missed diagnoses carries greater clinical consequence when disease prevalence is high. Among the selected subset of patients with negative SPECT who nevertheless proceeded to catheterization, 32.8% required intervention, a proportion that reflects the high pretest probability driving continued workup in these patients and that underscores the need for clinical vigilance when interpreting negative SPECT results in high-burden populations. The total proportion of patients undergoing SPECT prior to catheterization at SOMC (47.5%) was comparable to the 54.5% rate of appropriate SPECT use reported by Doukky et al. [ 10 ]. Family History as a Predictor of False-Negative SPECT The finding that family history of CAD was independently associated with false-negative SPECT results (OR 1.73, 95% CI 1.25–2.40) represents the most clinically noteworthy finding of this study. To our knowledge, this association has not been previously reported in the SPECT literature. Nakanishi et al. examined predictors of high-risk CAD in patients with normal SPECT but did not assess family history [ 11 ]. Our finding fills this gap and is supported by a convergent mechanistic framework through three pathways. First, family history is associated with more extensive multivessel disease. Hindieh et al. demonstrated that among patients with premature acute coronary syndromes, those with family history had significantly more multivessel disease (49.7% vs 37.9%, p < 0.01) and were more likely to have three-vessel disease (OR 2.26, 95% CI 1.29–3.95) [ 15 ]. Because SPECT relies on relative perfusion differences between myocardial territories, balanced three-vessel disease can produce globally reduced but homogeneous perfusion that appears "normal," a phenomenon demonstrated by Aernoudse et al. using invasive coronary pressure measurement [ 16 ]. Second, family history is associated with a diffuse, predominantly noncalcified plaque phenotype. In the GeneSTAR study, Kral et al. found that 45% of asymptomatic individuals from families with early-onset CAD had coronary plaque on CTA, with the majority being noncalcified and distributed across multiple territories [ 17 ]. Sunman et al. similarly found that family history independently predicted noncalcified plaque (OR 3.32, 95% CI 1.74–6.34) [ 18 ]. This diffuse plaque pattern impairs flow reserve without producing focal high-grade stenosis, reducing the likelihood of a regional perfusion defect on SPECT. Third, family history is associated with coronary microvascular dysfunction. Schächinger et al. demonstrated that family history independently predicted impaired endothelium-dependent coronary blood flow regulation (p = 0.008 on multivariate analysis), even in patients with angiographically normal or minimally diseased vessels [ 19 ]. This global microvascular dysfunction reduces coronary flow reserve homogeneously across all territories, producing apparently normal relative perfusion on SPECT. Notably, family history remained significant in our model even after controlling for sex, lesion location, prior MI, and other comorbidities, suggesting it captures an independent risk dimension not accounted for by traditional clinical variables. These findings suggest that, pending prospective validation, clinicians evaluating patients with a strong family history of CAD may wish to maintain a lower threshold for further evaluation when SPECT results are negative. Sensitivity by Coronary Territory Sensitivity for predicting subsequent revascularization varied by the location of the critical coronary lesion, a finding consistent with known limitations of the modality [ 6 ]. Sensitivity was highest for left main and ramus lesions (91%) and lowest for mid-distal LAD lesions (82%). In the multivariable model, critical circumflex (OR 0.60) and RCA (OR 0.54) lesions were protective against false-negative results, suggesting that perfusion defects in these territories are more readily detected by SPECT. This is consistent with the typical perfusion distribution patterns of these territories and has been reported in prior studies of SPECT diagnostic performance [ 9 ]. The lower sensitivity in mid-distal LAD lesions may reflect the smaller territory at risk or the challenge of distinguishing apical thinning from true perfusion defects in this region. These findings reinforce the importance of correlating SPECT results with the clinical probability of disease in specific coronary territories. Evolving Diagnostic Landscape The data for this study were collected between 2013 and 2018, prior to the publication of the 2021 AHA/ACC chest pain guideline that elevated CCTA to a Class 1 recommendation and expressed preference for PET over SPECT [ 12 ]. The DISCHARGE trial (2022) demonstrated non-inferior outcomes with a CT-first strategy in stable chest pain with fewer procedural complications [ 20 ]. CT-derived fractional flow reserve has shown overall diagnostic accuracy of 82.2% in meta-analysis [ 21 ], and artificial intelligence-assisted SPECT interpretation has achieved AUC of 0.83 compared to 0.71 for expert readers in multicenter validation [ 22 ]. However, 2022 Medicare data confirm that SPECT remains the dominant cardiac imaging modality, with volumes exceeding CCTA by a factor of eleven and PET by a factor of six [ 13 ]. Equipment costs, trained personnel shortages, PET radiotracer logistics, and volume thresholds for reader expertise present significant barriers to adoption of advanced modalities in community and rural settings [ 6 ]. Our findings therefore remain directly relevant to the majority of clinical settings where SPECT continues to serve as the primary non-invasive cardiac imaging tool. Notably, AI-enhanced SPECT interpretation may offer a pathway to improve diagnostic accuracy at community hospitals without requiring capital investment in new imaging equipment [ 22 ]. Limitations Several limitations should be considered when interpreting these results. First, this study is subject to verification bias (also termed workup bias), a well-described limitation of diagnostic accuracy studies in cardiac imaging [ 14 , 23 , 24 ]. Because our cohort was derived from the CathPCI registry, only patients who proceeded to cardiac catheterization were included. Patients with negative SPECT who were managed conservatively and never underwent catheterization are not captured. Miller et al. demonstrated the magnitude of this bias at Mayo Clinic, where only 13% of SPECT patients underwent angiography within 3 months, and apparent sensitivity of 98% fell to approximately 67% after adjustment [ 25 ]. Our design precludes calculation of specificity, negative predictive value, or the true false-negative rate in the broader SPECT population. Paradoxically, however, the unusually high catheterization referral rate in this high-burden population may attenuate the magnitude of verification bias compared to lower-risk cohorts. Second, indeterminate and unavailable SPECT results (n = 114) were grouped with negative results in the primary analysis. While our sensitivity analysis excluding these patients showed improved sensitivity (84.6% vs 82.0%), this methodological choice may have modestly overestimated the false-negative proportion. Third, the multivariable model was constructed using forward stepwise selection based on AIC, a method known to be potentially unstable. However, LASSO regression as a robustness check produced broadly concordant results, with the core predictors (circumflex stenosis, RCA stenosis) retained even at the most parsimonious regularization and family history retained at lambda.min. Fourth, the model demonstrated modest discrimination (AUC 0.678), indicating that the identified predictors explain only a portion of the variance in false-negative outcomes. This likely reflects the multifactorial nature of SPECT interpretation and the influence of unmeasured variables such as SPECT protocol (exercise vs pharmacologic stress), radiotracer used, camera type, body habitus, and interpreter experience. Fifth, the positive catheterization outcome was defined as requiring intervention (PCI or CABG) rather than by angiographic stenosis severity alone. This revascularization-based reference standard conflates clinical decision-making with anatomic disease severity and may not capture all patients with significant coronary disease who were managed medically. Comparisons between our sensitivity estimate and meta-analytic benchmarks derived from angiographic disease definitions should therefore be interpreted with this distinction in mind. Sixth, this is a single-center, retrospective study, and findings may not generalize to other populations or practice settings. However, the large sample size and the unique characteristics of this high-burden population provide data from a clinical context that is systematically underrepresented in the diagnostic accuracy literature. Finally, the data were collected between 2013 and 2018. Practice patterns, SPECT technology, and clinical guidelines have evolved since the study period [ 12 ]. These findings should be interpreted as reflecting diagnostic performance during this historical period, though the continued dominance of SPECT in community practice [ 13 ] supports their ongoing relevance. Conclusions In a large retrospective cohort from one of the highest-burden CAD populations in the United States, SPECT myocardial perfusion imaging demonstrated 82% sensitivity for predicting subsequent revascularization, numerically close to the most recent meta-analytic benchmark of 83%, though direct comparison is limited by differing reference standards. Family history of CAD was independently associated with false-negative results, a finding not previously reported in the SPECT literature, supported by convergent mechanistic evidence through balanced ischemia, diffuse plaque phenotypes, and coronary microvascular dysfunction. Sensitivity varied by coronary territory, with lowest sensitivity observed in mid-distal LAD lesions. In patients with a strong family history of coronary artery disease, particularly in high-prevalence populations where SPECT remains the primary advanced cardiac imaging modality, these findings suggest that a lower threshold for further evaluation may be appropriate when SPECT results are negative. Prospective validation in independent cohorts is needed before firm clinical recommendations can be made. Declarations Ethics Approval and Consent to Participate: This study was conducted in accordance with the Declaration of Helsinki and approved by the Southern Ohio Medical Center Institutional Review Board (approved September 1, 2018). The requirement for informed consent was waived due to the retrospective nature of the study using de-identified registry data. Clinical Trial Number: Not applicable. Consent for Publication: Not applicable. Competing Interests: The authors declare no competing interests. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contributions: LW conceived the study, performed chart review and supplemental data collection, and drafted the original manuscript. JM designed the study, performed the statistical analysis, coordinated the project, and revised the manuscript. NA and RM managed the CathPCI registry data. JH and TRN provided clinical expertise in interventional cardiology and reviewed the manuscript. All authors reviewed and approved the final manuscript. Availability of Data and Materials: The dataset supporting the conclusions of this article is derived from the NCDR CathPCI registry. Restrictions apply to the availability of these data, which were used under a data use agreement for the current study and are not publicly available due to patient privacy restrictions. Data are available from the corresponding author on reasonable request and with permission of the NCDR. Acknowledgments: Not applicable. References Martin SS, Aday AW, Almarzooq ZI, et al. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. *Circulation*. 2024;149(8):e347–e913. DOI: 10.1161/CIR.0000000000001209. Harrington RA, Califf RM, Balamurugan A, et al. Call to action: rural health: a presidential advisory from the American Heart Association and American Stroke Association. *Circulation*. 2020;141(10):e615–e644. DOI: 10.1161/CIR.0000000000000753. Marinacci LX, Zheng Z, Mein S, Wadhera RK. Rural-urban differences in cardiovascular mortality in the United States, 2010-2022. *Journal of the American College of Cardiology*. 2025;85(1):93–97. DOI: 10.1016/j.jacc.2024.09.1215. Centers for Disease Control and Prevention. Interactive Atlas of Heart Disease and Stroke. Available at: https://www.cdc.gov/dhdsp/maps/atlas/index.htm. Accessed March 7, 2026. Appalachian Regional Commission. *Health Disparities in Appalachia*. Washington, DC: PDA, Inc. and Cecil G. Sheps Center for Health Services Research; 2017. Available at: https://www.arc.gov/report/health-disparities-in-appalachia/. Accessed March 7, 2026. Dorbala S, Ananthasubramaniam K, Armstrong IS, et al. Single photon emission computed tomography (SPECT) myocardial perfusion imaging guidelines: instrumentation, acquisition, processing, and interpretation. *Journal of Nuclear Cardiology*. 2018;25(5):1784–1846. DOI: 10.1007/s12350-018-1283-y. Jaarsma C, Leiner T, Bekkers SC, et al. Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease: a meta-analysis. *Journal of the American College of Cardiology*. 2012;59(19):1719–1728. DOI: 10.1016/j.jacc.2011.12.040. Klocke FJ, Baird MG, Lorell BH, et al. ACC/AHA/ASNC guidelines for the clinical use of cardiac radionuclide imaging: executive summary. *Journal of the American College of Cardiology*. 2003;42(7):1318–1333. DOI: 10.1016/j.jacc.2003.08.011. Xu J, Cai F, Geng C, Wang Z, Tang X. Diagnostic performance of CMR, SPECT, and PET imaging for the identification of coronary artery disease: a meta-analysis. *Frontiers in Cardiovascular Medicine*. 2021;8:621389. DOI: 10.3389/fcvm.2021.621389. Doukky R, Hayes K, Frogge N, et al. Impact of appropriate use on the prognostic value of single-photon emission computed tomography myocardial perfusion imaging. *Circulation*. 2013;128:1634–1643. DOI: 10.1161/CIRCULATIONAHA.113.002744. Nakanishi R, Gransar H, Slomka P, et al. Predictors of high-risk coronary artery disease in subjects with normal SPECT myocardial perfusion imaging. *Journal of Nuclear Cardiology*. 2016;23(3):530–541. DOI: 10.1007/s12350-015-0150-3. Gulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain. *Circulation*. 2021;144(22):e368–e454. DOI: 10.1161/CIR.0000000000001029. Al-Mallah M, Alwan M, Al Rifai M, Sayed A. Cardiac positron emission tomography and other modalities for coronary artery disease assessment: a snapshot from the Medicare data. *Journal of Nuclear Cardiology*. 2024;41:102030. DOI: 10.1016/j.nuclcard.2024.102030. Cecil MP, Kosinski AS, Jones MT, et al. The importance of work-up (verification) bias correction in assessing the accuracy of SPECT thallium-201 testing for the diagnosis of coronary artery disease. *Journal of Clinical Epidemiology*. 1996;49(7):735–742. DOI: 10.1016/0895-4356(96)00014-5. Hindieh W, Pilote L, Cheema A, et al. Association between family history, a genetic risk score, and severity of coronary artery disease in patients with premature acute coronary syndromes. *Arteriosclerosis, Thrombosis, and Vascular Biology*. 2016;36(6):1286–1292. DOI: 10.1161/ATVBAHA.115.306944. Aernoudse WH, Botman KJ, Pijls NH. False-negative myocardial scintigraphy in balanced three-vessel disease, revealed by coronary pressure measurement. *International Journal of Cardiovascular Interventions*. 2003;5(2):67–71. DOI: 10.1080/14628840310003244. Kral BG, Becker LC, Vaidya D, et al. Noncalcified coronary plaque volumes in healthy people with a family history of early onset coronary artery disease. *Circulation: Cardiovascular Imaging*. 2014;7(3):446–453. DOI: 10.1161/CIRCIMAGING.113.000980. Sunman H, Yorgun H, Canpolat U, et al. Association between family history of premature coronary artery disease and coronary atherosclerotic plaques shown by multidetector computed tomography coronary angiography. *International Journal of Cardiology*. 2013;164(3):355–358. DOI: 10.1016/j.ijcard.2011.07.043. Schächinger V, Britten MB, Elsner M, et al. A positive family history of premature coronary artery disease is associated with impaired endothelium-dependent coronary blood flow regulation. *Circulation*. 1999;100(14):1502–1508. DOI: 10.1161/01.CIR.100.14.1502. DISCHARGE Trial Group; Maurovich-Horvat P, Bosserdt M, Kofoed KF, et al. CT or invasive coronary angiography in stable chest pain. *New England Journal of Medicine*. 2022;386(17):1591–1602. DOI: 10.1056/NEJMoa2200963. Faulder TI, Prematunga K, Moloi SB, et al. Agreement of fractional flow reserve estimated by computed tomography with invasively measured fractional flow reserve: a systematic review and meta-analysis. *Journal of the American Heart Association*. 2024;13(10):e034552. DOI: 10.1161/JAHA.124.034552. Otaki Y, Singh A, Kavanagh P, et al. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease. *JACC: Cardiovascular Imaging*. 2022;15(6):1091–1102. DOI: 10.1016/j.jcmg.2021.04.030. Begg CB, Greenes RA. Assessment of diagnostic tests when disease verification is subject to selection bias. *Biometrics*. 1983;39(1):207–215. DOI: 10.2307/2530820. de Groot JAH, Bossuyt PMM, Reitsma JB, et al. Verification problems in diagnostic accuracy studies: consequences and solutions. *BMJ*. 2011;343:d4770. DOI: 10.1136/bmj.d4770. Miller TD, Hodge DO, Christian TF, et al. Effects of adjustment for referral bias on the sensitivity and specificity of single photon emission computed tomography for the diagnosis of coronary artery disease. *American Journal of Medicine*. 2002;112(4):290–297. DOI: 10.1016/S0002-9343(01)01111-1. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. *Journal of Clinical Epidemiology*. 2008;61(4):344–349. DOI: 10.1016/j.jclinepi.2007.11.008. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9246863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622071857,"identity":"b652f967-5fa6-4d76-a7b5-458b107efe72","order_by":0,"name":"Lauren Wright","email":"","orcid":"","institution":"Southern Ohio Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"","lastName":"Wright","suffix":""},{"id":622071858,"identity":"66fd41cc-2a8d-424e-99c3-96c130fe4dfa","order_by":1,"name":"Jay Hamze","email":"","orcid":"","institution":"Southern Ohio Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jay","middleName":"","lastName":"Hamze","suffix":""},{"id":622071861,"identity":"790d8e3f-aab6-4ce0-ae2f-4c9a68802f70","order_by":2,"name":"Tarun R. Nagrani","email":"","orcid":"","institution":"Southern Ohio Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tarun","middleName":"R.","lastName":"Nagrani","suffix":""},{"id":622071862,"identity":"48846d17-e668-4d84-a76f-6c4e73080d4a","order_by":3,"name":"Nikki Arnold","email":"","orcid":"","institution":"Southern Ohio Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Nikki","middleName":"","lastName":"Arnold","suffix":""},{"id":622071863,"identity":"eb7d76e5-099a-4ea0-a59d-d78cc47c9210","order_by":4,"name":"Raquel McGlone","email":"","orcid":"","institution":"Southern Ohio Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"McGlone","suffix":""},{"id":622071866,"identity":"202af6bc-4748-4bf5-9424-6b8b6687a8ca","order_by":5,"name":"Jeremiah Martin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYLCCBAY2Bn5SNDA2ALVISDaQpAVISBgcIFa9bvvZ5w8e7uCrMz5/+OiGHww2+fIOBLSYnUk3bEg8wyZhdiMt7WYPQ5rlRkLWmR1IY2xIbANp4TG7zcBw2MCwgZCW888gWoz7zxCr5QbUFgOGHIgWeQI6gFqeMc4AapGcAfaLQZqBAUEt59MYPv5sO8bP33/42I0fFTYG8oQcBgXHoDTQCmIjqAbBJNaWUTAKRsEoGDkAAILIQGd6uKtDAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Ohio Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Jeremiah","middleName":"","lastName":"Martin","suffix":""}],"badges":[],"createdAt":"2026-03-27 16:25:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9246863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9246863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106948226,"identity":"a680f22e-0cd7-41bb-b45e-f2a07cf2a780","added_by":"auto","created_at":"2026-04-15 07:00:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient Flow Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9246863/v1/ced2de9769548bd7ab51d918.png"},{"id":106961950,"identity":"1775e072-97ed-4857-8c6b-ccff5e064783","added_by":"auto","created_at":"2026-04-15 09:28:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCardiac Catheterization Volume and SPECT Utilization. \u003c/strong\u003e\u003cem\u003eSouthern Ohio Medical Center, Q3 2013 – Q1 2018. Blue bars represent quarterly cardiac catheterization volume. Red line represents SPECT utilization as a percentage of catheterization patients.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9246863/v1/488a0fcb64f751c39051c686.png"},{"id":106963530,"identity":"8f004e6f-8871-4530-aa55-f41f53ca5cc6","added_by":"auto","created_at":"2026-04-15 09:45:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1372060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9246863/v1/c8a21eff-803e-4a8f-9a87-31f23e441fd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sensitivity and Predictors of False-Negative SPECT Myocardial Perfusion Imaging in a High-Burden Coronary Artery Disease Population: A Retrospective Analysis Using Revascularization as the Reference Standard","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) remains a leading cause of morbidity and mortality in the United States, with chronic heart disease prevalence of 14.2% in rural areas compared to 9.9% in urban areas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Rural Americans are 40% more likely to develop heart disease, with a 30% higher stroke risk and a 3-year life expectancy gap compared to urban counterparts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The rural-urban cardiovascular mortality gap has widened since 2010, driven by a striking rise among younger rural adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Certain regions bear a disproportionate burden: national rates of hospitalization related to CAD are 72.4 per 1,000 Medicare beneficiaries, while the state of Ohio exceeds this at 88.4 per 1,000. Scioto County, located in the Appalachian region of southern Ohio, has among the highest rates in the state at 152.0 per 1,000, approaching double the national rate [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Central Appalachia has a heart disease mortality rate of 249 per 100,000, nearly 1.5 times the national average, compounded by elevated rates of obesity, smoking, and physical inactivity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This disease burden underscores the critical importance of accurate non-invasive diagnostic testing in such populations.\u003c/p\u003e \u003cp\u003eThe gold standard for diagnosis of coronary heart disease remains invasive coronary angiography. However, given its invasive nature, non-invasive testing modalities are employed as first-line tools for detection of myocardial ischemia. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging is the most widely utilized of these modalities, valued for its broad availability and tolerability among elderly patients and those with physical limitations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The long-cited SPECT sensitivity benchmark of 88% was established by the Jaarsma et al. 2012 meta-analysis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the 2003 ACC/AHA/ASNC guidelines [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; however, both sources explicitly noted these figures were uncorrected for verification bias. The most recent comprehensive meta-analysis by Xu et al. (2021), pooling 134 SPECT studies, reported a downward-revised sensitivity of 83% (95% CI 79\u0026ndash;87%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], suggesting earlier benchmarks were inflated. Despite this, false-positive and false-negative results remain a significant clinical concern, as the Xu et al. meta-analysis reported specificity of only 77% (95% CI 73\u0026ndash;81%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe consequences of false-negative results are clinically significant. A normal pharmacologic SPECT carries an annualized mortality of 1.78% due to myocardial infarction or cardiac death, extrapolating to nearly 18% over 10 years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. False-negative results may lead to premature discharge of patients with significant coronary disease, placing them at substantial risk. Only one prior study has specifically examined predictors of false-negative SPECT in patients with normal imaging who subsequently underwent angiography; Nakanishi et al. (2016) identified elevated pretest probability, residual sub-threshold perfusion defects, and abnormal transient ischemic dilation as predictors, but did not examine family history of CAD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the period of data collection for this study, the diagnostic landscape has evolved considerably. The 2021 AHA/ACC chest pain guideline elevated coronary CTA to a Class 1 recommendation for intermediate-risk patients without known CAD and expressed preference for PET over SPECT when nuclear imaging is selected [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, 2022 Medicare billing data demonstrate that SPECT remains the most commonly performed cardiac imaging modality in the United States by a factor of six, with approximately 1.34\u0026nbsp;million examinations compared to 212,000 PET and 119,000 CCTA studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Access barriers including equipment cost, trained personnel shortages, PET radiotracer logistics, and volume thresholds for reader expertise explain the continued predominance of SPECT in community and rural hospital settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Understanding the diagnostic performance and limitations of SPECT in these settings therefore remains directly relevant to clinical practice.\u003c/p\u003e \u003cp\u003eNotably, nearly all published SPECT diagnostic accuracy data originate from academic medical centers with expert readers and optimized protocols. SPECT diagnostic accuracy data from community and non-academic hospital settings are conspicuously absent from the literature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The purpose of this study was to evaluate the diagnostic performance of SPECT, using subsequent revascularization as the reference standard, in a community hospital serving a population with among the highest burden of CAD in the United States, and to identify patient and lesion characteristics independently associated with false-negative SPECT results.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis was a retrospective analysis of prospectively collected data from the American College of Cardiology National Cardiovascular Data Registry (NCDR) CathPCI registry at Southern Ohio Medical Center (SOMC), a community hospital serving Scioto County in the Appalachian region of southern Ohio. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Local institutional review board approval was obtained prior to the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eAll patients who underwent cardiac catheterization at SOMC over a five-year period from Q3 2013 to Q1 2018 were included. Patients are prospectively entered into the CathPCI registry at the time of catheterization. The CathPCI registry is a national quality improvement registry that captures detailed demographic, clinical, procedural, and outcome data for cardiac catheterization procedures performed at participating institutions.\u003c/p\u003e\n\u003ch3\u003eVariables and Definitions\u003c/h3\u003e\n\u003cp\u003eBaseline demographics included age, sex, smoking status, height, and weight. CAD presentation type and angina class were extracted. Comorbidities recorded included use of antianginal medication, chronic lung disease, current dialysis, diabetes, dyslipidemia, family history of coronary artery disease, hypertension, prior cerebrovascular disease, prior heart failure, prior myocardial infarction (MI), prior peripheral artery disease, and prior percutaneous coronary intervention (PCI).\u003c/p\u003e \u003cp\u003eWhether SPECT was performed as part of the diagnostic workup was recorded in the registry and used to define the study group. SPECT results were classified as positive, negative, indeterminate, or unavailable. For the primary analysis, indeterminate and unavailable results were grouped with negative results; a sensitivity analysis excluding these categories was also performed.\u003c/p\u003e \u003cp\u003eThe primary outcome was whether catheterization led to intervention, defined as PCI or coronary artery bypass grafting (CABG). A true positive was defined as a positive SPECT followed by catheterization requiring intervention. A false negative was defined as a negative SPECT followed by catheterization requiring intervention. Importantly, because our cohort was derived from the CathPCI registry, only patients who underwent catheterization were included; patients with negative SPECT who did not proceed to catheterization were not captured.\u003c/p\u003e \u003cp\u003eNative coronary lesion severity was categorized by vessel territory (left main, ramus, proximal left anterior descending [LAD], mid-distal LAD, circumflex, and right coronary artery [RCA]). Critical stenosis was defined as native lesion greater than 70%.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were reported as counts and percentages. Continuous variables were reported as mean with standard deviation or median with interquartile range as appropriate. Sensitivity for predicting subsequent revascularization was calculated overall and stratified by critical lesion location.\u003c/p\u003e \u003cp\u003eA logistic regression model was used to compute univariate and multivariable odds ratios for a false-negative outcome based on available comorbidities and lesion location. Variables for the multivariable model were selected using forward stepwise selection based on the Akaike Information Criterion (AIC). As a robustness check, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was also performed.\u003c/p\u003e \u003cp\u003eModel discrimination was assessed using the area under the receiver operating characteristic curve (AUC, or C-statistic). Model calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit test.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.5.1 (R Core Team, Vienna, Austria) with the glmnet, pROC, and ResourceSelection packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA total of 7,312 cardiac catheterizations were performed during the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, 3,116 (42.6%) required intervention. Baseline demographics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients requiring intervention were older (median age 65 vs 63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more commonly male (63.1% vs 49.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more likely to present with some form of MI rather than angina (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among comorbidities, patients requiring intervention had higher rates of diabetes (48.9% vs 40.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), dyslipidemia (76.9% vs 70.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prior MI (26.0% vs 19.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and prior PCI (47.6% vs 33.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline Demographics\u003c/b\u003e \u003cem\u003eAll patients who underwent cardiac catheterization at Southern Ohio Medical Center, Q3 2013 \u0026ndash; Q1 2018, stratified by catheterization requiring intervention. Intervention defined as PCI or CABG. No Intervention includes medical therapy/counseling, none, or other therapy. An additional 723 patients (9.9%) had no treatment outcome recorded in the registry and are included in the Overall column but excluded from the stratified columns.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;7,312)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention (n\u0026thinsp;=\u0026thinsp;3,116)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Intervention (n\u0026thinsp;=\u0026thinsp;3,473)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 [54, 73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 [56, 74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 [53, 72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,056 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,156 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,150 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,763 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,966 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,710 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.6 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.2 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169.4 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.1 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.6 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.6 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**CAD Presentation**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Symptom, No Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-STEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,043 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e763 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTEMI or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom Unlikely Ischemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstable Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,686 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,240 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Anginal Class**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,426 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,799 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,540 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e876 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Comorbidities**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntianginal medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,488 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,896 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,022 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,288 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent dialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,523 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,390 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,397 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,455 (70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e831 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e894 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,800 (89.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,054 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e510 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e809 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e661 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e418 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,484 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,146 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSPECT Utilization and Sensitivity\u003c/h3\u003e\n\u003cp\u003eAmong all catheterization patients, 3,476 (47.5%) had stress imaging performed as part of their workup. Of these, 2,501 had positive SPECT results, 574 had negative results, 59 had indeterminate results, and 55 had unavailable results; 287 patients had stress imaging recorded but no SPECT result captured. Throughout the study period, the program demonstrated continued growth with increasing catheterization volumes and increasing utilization of SPECT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall 2\u0026times;2 diagnostic performance table is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among 3,189 patients with SPECT data (including indeterminate and unavailable results grouped as negative), sensitivity for predicting subsequent revascularization was 82.0%. Notably, among the subset of patients with negative SPECT who nevertheless proceeded to catheterization (a selected, high-risk group), nearly one in three (32.8%, 226/688) were found to require intervention, underscoring the clinical significance of false-negative results in this cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This proportion reflects the high pretest probability of patients referred onward despite a negative SPECT and should not be interpreted as a population-level false-negative rate. Specificity and negative predictive value could not be meaningfully calculated due to verification bias inherent in the study design (see Limitations).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSPECT Diagnostic Performance (Revascularization as Reference Standard)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCath Positive (Intervention)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCath Negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPECT Negative*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (FN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462 (TN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPECT Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,031 (TP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,470 (FP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Total**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e**1,257**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**1,932**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**3,189**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Includes indeterminate (n\u0026thinsp;=\u0026thinsp;59) and unavailable (n\u0026thinsp;=\u0026thinsp;55) results grouped with negative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen stratified by critical native coronary lesion location (\u0026gt;\u0026thinsp;70% stenosis), sensitivity for predicting subsequent revascularization was highest for left main and ramus lesions (91%) and lowest for mid-distal LAD lesions (82%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSPECT Sensitivity for Predicting Revascularization by Critical Lesion Location (Native Lesion\u0026thinsp;\u0026gt;\u0026thinsp;70%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVessel Territory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPECT Negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPECT Positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Main\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumflex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Coronary Artery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximal LAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMid-Distal LAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eWhen indeterminate (n\u0026thinsp;=\u0026thinsp;59) and unavailable (n\u0026thinsp;=\u0026thinsp;55) SPECT results were excluded, restricting the analysis to patients with definitive positive or negative results (n\u0026thinsp;=\u0026thinsp;3,075), sensitivity for predicting subsequent revascularization improved to 84.6% (1,031/1,218), and the proportion of negative SPECT results that were false negatives decreased to 32.6% (187/574).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of False-Negative SPECT\u003c/h2\u003e \u003cp\u003eA comparison of patients with false-negative versus true-positive SPECT results is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. False-negative patients were more commonly female (45.6% vs 35.6%, p\u0026thinsp;=\u0026thinsp;0.006) and more commonly had a family history of CAD (34.5% vs 24.1%, p\u0026thinsp;=\u0026thinsp;0.002). False negatives were less likely in patients with critical circumflex or RCA lesions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those with prior MI (p\u0026thinsp;=\u0026thinsp;0.017). Unstable angina was the predominant presentation in both groups (81.0% of false negatives vs 78.8% of true positives), reflecting the overall population character of this high-burden cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison of False-Negative vs True-Positive SPECT\u003c/b\u003e \u003cem\u003eAll patients who required intervention, stratified by SPECT finding.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalse Negative (n\u0026thinsp;=\u0026thinsp;226)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrue Positive (n\u0026thinsp;=\u0026thinsp;1,031)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 [56, 75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 [58, 74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e367 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664 (64.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168.4 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.6 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.5 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.8 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**CAD Presentation**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Symptom, No Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-STEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTEMI or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom Unlikely Ischemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstable Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e812 (78.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Anginal Class**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e654 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Comorbidities**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntianginal medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e926 (89.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e345 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent dialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e526 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e839 (81.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e954 (92.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e523 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Critical Lesion (\u0026gt;\u0026thinsp;70%)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumflex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e530 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Main\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMid-Distal LAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e608 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximal LAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e611 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate and multivariable logistic regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Forward stepwise selection identified eight variables for the multivariable model. After adjustment, false-negative SPECT results were independently associated with family history of CAD (OR 1.73, 95% CI 1.25\u0026ndash;2.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). False-negative results were less likely with critical circumflex lesions (OR 0.60, 95% CI 0.44\u0026ndash;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), critical RCA lesions (OR 0.54, 95% CI 0.40\u0026ndash;0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and prior MI (OR 0.60, 95% CI 0.41\u0026ndash;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Critical left main stenosis (OR 0.49, 95% CI 0.23\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.064), dyslipidemia (OR 1.51, 95% CI 0.99\u0026ndash;2.28, p\u0026thinsp;=\u0026thinsp;0.055), current dialysis (OR 0.19, 95% CI 0.03\u0026ndash;1.45, p\u0026thinsp;=\u0026thinsp;0.109), and height (OR 0.99, 95% CI 0.98\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.081) were included in the model but did not reach statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eLogistic Regression Model for False-Negative SPECT\u003c/b\u003e \u003cem\u003eUnivariate and multivariable odds ratios. Multivariable model constructed by forward stepwise selection (AIC).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntianginal medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent dialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026ndash;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Family history of CAD**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e**1.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e**1.22\u0026ndash;2.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e**0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**1.73**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**1.25\u0026ndash;2.40**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u0026lt;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Prior MI**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e**0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e**0.45\u0026ndash;0.91**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e**0.014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**0.60**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**0.41\u0026ndash;0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u0026lt;0.01**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Critical circumflex**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e**0.52**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e**0.38\u0026ndash;0.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e**\u0026lt;0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**0.60**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**0.44\u0026ndash;0.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u0026lt;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical left main\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical mid-distal LAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical proximal LAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical ramus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e**Critical RCA**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e**0.50**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e**0.37\u0026ndash;0.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e**\u0026lt;0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**0.40\u0026ndash;0.73**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u0026lt;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel performance: AUC 0.678 (95% CI 0.642\u0026ndash;0.715); Hosmer-Lemeshow p\u0026thinsp;=\u0026thinsp;0.50.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance\u003c/h2\u003e \u003cp\u003eThe multivariable model demonstrated modest discrimination with an AUC of 0.678 (95% CI 0.642\u0026ndash;0.715). The Hosmer-Lemeshow goodness-of-fit test indicated adequate calibration (chi-squared\u0026thinsp;=\u0026thinsp;7.0, df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;=\u0026thinsp;0.50).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLASSO Robustness Check\u003c/h2\u003e \u003cp\u003eLASSO regression with 10-fold cross-validation was performed as a robustness check. At the most parsimonious regularization (lambda.1se), only critical circumflex stenosis and critical RCA stenosis were retained. At lambda.min, the retained variables included family history of CAD, prior MI, circumflex stenosis, RCA stenosis, left main stenosis, current dialysis, dyslipidemia, sex, height, and ramus stenosis, broadly consistent with the forward selection model. The convergence of both methods on circumflex and RCA stenosis as the strongest predictors, and the inclusion of family history at lambda.min, supports the stability of the primary model findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the diagnostic performance of SPECT myocardial perfusion imaging in a population with one of the highest burdens of CAD in the United States. In 7,312 cardiac catheterizations performed at a single community hospital in Appalachian Ohio over five years, SPECT demonstrated 82% sensitivity for predicting subsequent revascularization, and family history of CAD was independently associated with false-negative results. To our knowledge, this is among the first studies to report SPECT diagnostic performance from a community hospital setting and among the first to identify family history of CAD as an independent predictor of false-negative SPECT.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSPECT Sensitivity in Context\u003c/h2\u003e \u003cp\u003eThe observed sensitivity of 82% for predicting revascularization is numerically close to the most recent meta-analytic benchmark of 83% (95% CI 79\u0026ndash;87%) reported by Xu et al. in their pooling of 134 SPECT studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], though direct comparison requires caution because the meta-analytic benchmark used angiographic disease definitions rather than revascularization decisions as the reference standard. This represents a meaningful downward revision from the long-cited 88% benchmark established by Jaarsma et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the 2003 ACC/AHA/ASNC guidelines [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], both of which acknowledged that their figures were uncorrected for verification bias. Cecil et al. demonstrated the magnitude of this bias: in their series of 2,688 SPECT studies, uncorrected sensitivity was 98%, but after Begg-Greenes correction, sensitivity dropped to 82% \u0026plusmn; 6% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our finding of 82% in a verification-biased cohort using a revascularization-based reference standard is therefore broadly consistent with contemporary evidence and may reflect the real-world performance of SPECT in community practice.\u003c/p\u003e \u003cp\u003eThis finding is notable given the significant comorbidity burden in our population, including high rates of diabetes (48.9% among those requiring intervention), chronic lung disease (32.8%), and hypertension (89.9%). That SPECT performs comparably in this high-risk, high-prevalence population is reassuring, though the absolute number of missed diagnoses carries greater clinical consequence when disease prevalence is high. Among the selected subset of patients with negative SPECT who nevertheless proceeded to catheterization, 32.8% required intervention, a proportion that reflects the high pretest probability driving continued workup in these patients and that underscores the need for clinical vigilance when interpreting negative SPECT results in high-burden populations. The total proportion of patients undergoing SPECT prior to catheterization at SOMC (47.5%) was comparable to the 54.5% rate of appropriate SPECT use reported by Doukky et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFamily History as a Predictor of False-Negative SPECT\u003c/h2\u003e \u003cp\u003eThe finding that family history of CAD was independently associated with false-negative SPECT results (OR 1.73, 95% CI 1.25\u0026ndash;2.40) represents the most clinically noteworthy finding of this study. To our knowledge, this association has not been previously reported in the SPECT literature. Nakanishi et al. examined predictors of high-risk CAD in patients with normal SPECT but did not assess family history [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our finding fills this gap and is supported by a convergent mechanistic framework through three pathways.\u003c/p\u003e \u003cp\u003eFirst, family history is associated with more extensive multivessel disease. Hindieh et al. demonstrated that among patients with premature acute coronary syndromes, those with family history had significantly more multivessel disease (49.7% vs 37.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and were more likely to have three-vessel disease (OR 2.26, 95% CI 1.29\u0026ndash;3.95) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Because SPECT relies on relative perfusion differences between myocardial territories, balanced three-vessel disease can produce globally reduced but homogeneous perfusion that appears \"normal,\" a phenomenon demonstrated by Aernoudse et al. using invasive coronary pressure measurement [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, family history is associated with a diffuse, predominantly noncalcified plaque phenotype. In the GeneSTAR study, Kral et al. found that 45% of asymptomatic individuals from families with early-onset CAD had coronary plaque on CTA, with the majority being noncalcified and distributed across multiple territories [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Sunman et al. similarly found that family history independently predicted noncalcified plaque (OR 3.32, 95% CI 1.74\u0026ndash;6.34) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This diffuse plaque pattern impairs flow reserve without producing focal high-grade stenosis, reducing the likelihood of a regional perfusion defect on SPECT.\u003c/p\u003e \u003cp\u003eThird, family history is associated with coronary microvascular dysfunction. Sch\u0026auml;chinger et al. demonstrated that family history independently predicted impaired endothelium-dependent coronary blood flow regulation (p\u0026thinsp;=\u0026thinsp;0.008 on multivariate analysis), even in patients with angiographically normal or minimally diseased vessels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This global microvascular dysfunction reduces coronary flow reserve homogeneously across all territories, producing apparently normal relative perfusion on SPECT.\u003c/p\u003e \u003cp\u003eNotably, family history remained significant in our model even after controlling for sex, lesion location, prior MI, and other comorbidities, suggesting it captures an independent risk dimension not accounted for by traditional clinical variables. These findings suggest that, pending prospective validation, clinicians evaluating patients with a strong family history of CAD may wish to maintain a lower threshold for further evaluation when SPECT results are negative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity by Coronary Territory\u003c/h2\u003e \u003cp\u003eSensitivity for predicting subsequent revascularization varied by the location of the critical coronary lesion, a finding consistent with known limitations of the modality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Sensitivity was highest for left main and ramus lesions (91%) and lowest for mid-distal LAD lesions (82%). In the multivariable model, critical circumflex (OR 0.60) and RCA (OR 0.54) lesions were protective against false-negative results, suggesting that perfusion defects in these territories are more readily detected by SPECT. This is consistent with the typical perfusion distribution patterns of these territories and has been reported in prior studies of SPECT diagnostic performance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lower sensitivity in mid-distal LAD lesions may reflect the smaller territory at risk or the challenge of distinguishing apical thinning from true perfusion defects in this region. These findings reinforce the importance of correlating SPECT results with the clinical probability of disease in specific coronary territories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEvolving Diagnostic Landscape\u003c/h2\u003e \u003cp\u003eThe data for this study were collected between 2013 and 2018, prior to the publication of the 2021 AHA/ACC chest pain guideline that elevated CCTA to a Class 1 recommendation and expressed preference for PET over SPECT [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The DISCHARGE trial (2022) demonstrated non-inferior outcomes with a CT-first strategy in stable chest pain with fewer procedural complications [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. CT-derived fractional flow reserve has shown overall diagnostic accuracy of 82.2% in meta-analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and artificial intelligence-assisted SPECT interpretation has achieved AUC of 0.83 compared to 0.71 for expert readers in multicenter validation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, 2022 Medicare data confirm that SPECT remains the dominant cardiac imaging modality, with volumes exceeding CCTA by a factor of eleven and PET by a factor of six [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Equipment costs, trained personnel shortages, PET radiotracer logistics, and volume thresholds for reader expertise present significant barriers to adoption of advanced modalities in community and rural settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our findings therefore remain directly relevant to the majority of clinical settings where SPECT continues to serve as the primary non-invasive cardiac imaging tool. Notably, AI-enhanced SPECT interpretation may offer a pathway to improve diagnostic accuracy at community hospitals without requiring capital investment in new imaging equipment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these results.\u003c/p\u003e \u003cp\u003eFirst, this study is subject to verification bias (also termed workup bias), a well-described limitation of diagnostic accuracy studies in cardiac imaging [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Because our cohort was derived from the CathPCI registry, only patients who proceeded to cardiac catheterization were included. Patients with negative SPECT who were managed conservatively and never underwent catheterization are not captured. Miller et al. demonstrated the magnitude of this bias at Mayo Clinic, where only 13% of SPECT patients underwent angiography within 3 months, and apparent sensitivity of 98% fell to approximately 67% after adjustment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our design precludes calculation of specificity, negative predictive value, or the true false-negative rate in the broader SPECT population. Paradoxically, however, the unusually high catheterization referral rate in this high-burden population may attenuate the magnitude of verification bias compared to lower-risk cohorts.\u003c/p\u003e \u003cp\u003eSecond, indeterminate and unavailable SPECT results (n\u0026thinsp;=\u0026thinsp;114) were grouped with negative results in the primary analysis. While our sensitivity analysis excluding these patients showed improved sensitivity (84.6% vs 82.0%), this methodological choice may have modestly overestimated the false-negative proportion.\u003c/p\u003e \u003cp\u003eThird, the multivariable model was constructed using forward stepwise selection based on AIC, a method known to be potentially unstable. However, LASSO regression as a robustness check produced broadly concordant results, with the core predictors (circumflex stenosis, RCA stenosis) retained even at the most parsimonious regularization and family history retained at lambda.min.\u003c/p\u003e \u003cp\u003eFourth, the model demonstrated modest discrimination (AUC 0.678), indicating that the identified predictors explain only a portion of the variance in false-negative outcomes. This likely reflects the multifactorial nature of SPECT interpretation and the influence of unmeasured variables such as SPECT protocol (exercise vs pharmacologic stress), radiotracer used, camera type, body habitus, and interpreter experience.\u003c/p\u003e \u003cp\u003eFifth, the positive catheterization outcome was defined as requiring intervention (PCI or CABG) rather than by angiographic stenosis severity alone. This revascularization-based reference standard conflates clinical decision-making with anatomic disease severity and may not capture all patients with significant coronary disease who were managed medically. Comparisons between our sensitivity estimate and meta-analytic benchmarks derived from angiographic disease definitions should therefore be interpreted with this distinction in mind.\u003c/p\u003e \u003cp\u003eSixth, this is a single-center, retrospective study, and findings may not generalize to other populations or practice settings. However, the large sample size and the unique characteristics of this high-burden population provide data from a clinical context that is systematically underrepresented in the diagnostic accuracy literature.\u003c/p\u003e \u003cp\u003eFinally, the data were collected between 2013 and 2018. Practice patterns, SPECT technology, and clinical guidelines have evolved since the study period [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings should be interpreted as reflecting diagnostic performance during this historical period, though the continued dominance of SPECT in community practice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] supports their ongoing relevance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn a large retrospective cohort from one of the highest-burden CAD populations in the United States, SPECT myocardial perfusion imaging demonstrated 82% sensitivity for predicting subsequent revascularization, numerically close to the most recent meta-analytic benchmark of 83%, though direct comparison is limited by differing reference standards. Family history of CAD was independently associated with false-negative results, a finding not previously reported in the SPECT literature, supported by convergent mechanistic evidence through balanced ischemia, diffuse plaque phenotypes, and coronary microvascular dysfunction. Sensitivity varied by coronary territory, with lowest sensitivity observed in mid-distal LAD lesions. In patients with a strong family history of coronary artery disease, particularly in high-prevalence populations where SPECT remains the primary advanced cardiac imaging modality, these findings suggest that a lower threshold for further evaluation may be appropriate when SPECT results are negative. Prospective validation in independent cohorts is needed before firm clinical recommendations can be made.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Southern Ohio Medical Center Institutional Review Board (approved September 1, 2018). The requirement for informed consent was waived due to the retrospective nature of the study using de-identified registry data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e LW conceived the study, performed chart review and supplemental data collection, and drafted the original manuscript. JM designed the study, performed the statistical analysis, coordinated the project, and revised the manuscript. NA and RM managed the CathPCI registry data. JH and TRN provided clinical expertise in interventional cardiology and reviewed the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e The dataset supporting the conclusions of this article is derived from the NCDR CathPCI registry. Restrictions apply to the availability of these data, which were used under a data use agreement for the current study and are not publicly available due to patient privacy restrictions. Data are available from the corresponding author on reasonable request and with permission of the NCDR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMartin SS, Aday AW, Almarzooq ZI, et al. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. *Circulation*. 2024;149(8):e347\u0026ndash;e913. DOI: 10.1161/CIR.0000000000001209.\u003c/li\u003e\n\u003cli\u003eHarrington RA, Califf RM, Balamurugan A, et al. Call to action: rural health: a presidential advisory from the American Heart Association and American Stroke Association. *Circulation*. 2020;141(10):e615\u0026ndash;e644. DOI: 10.1161/CIR.0000000000000753.\u003c/li\u003e\n\u003cli\u003eMarinacci LX, Zheng Z, Mein S, Wadhera RK. Rural-urban differences in cardiovascular mortality in the United States, 2010-2022. *Journal of the American College of Cardiology*. 2025;85(1):93\u0026ndash;97. DOI: 10.1016/j.jacc.2024.09.1215.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Interactive Atlas of Heart Disease and Stroke. Available at: https://www.cdc.gov/dhdsp/maps/atlas/index.htm. Accessed March 7, 2026.\u003c/li\u003e\n\u003cli\u003eAppalachian Regional Commission. *Health Disparities in Appalachia*. Washington, DC: PDA, Inc. and Cecil G. Sheps Center for Health Services Research; 2017. Available at: https://www.arc.gov/report/health-disparities-in-appalachia/. Accessed March 7, 2026.\u003c/li\u003e\n\u003cli\u003eDorbala S, Ananthasubramaniam K, Armstrong IS, et al. Single photon emission computed tomography (SPECT) myocardial perfusion imaging guidelines: instrumentation, acquisition, processing, and interpretation. *Journal of Nuclear Cardiology*. 2018;25(5):1784\u0026ndash;1846. DOI: 10.1007/s12350-018-1283-y.\u003c/li\u003e\n\u003cli\u003eJaarsma C, Leiner T, Bekkers SC, et al. Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease: a meta-analysis. *Journal of the American College of Cardiology*. 2012;59(19):1719\u0026ndash;1728. DOI: 10.1016/j.jacc.2011.12.040.\u003c/li\u003e\n\u003cli\u003eKlocke FJ, Baird MG, Lorell BH, et al. ACC/AHA/ASNC guidelines for the clinical use of cardiac radionuclide imaging: executive summary. *Journal of the American College of Cardiology*. 2003;42(7):1318\u0026ndash;1333. DOI: 10.1016/j.jacc.2003.08.011.\u003c/li\u003e\n\u003cli\u003eXu J, Cai F, Geng C, Wang Z, Tang X. Diagnostic performance of CMR, SPECT, and PET imaging for the identification of coronary artery disease: a meta-analysis. *Frontiers in Cardiovascular Medicine*. 2021;8:621389. DOI: 10.3389/fcvm.2021.621389.\u003c/li\u003e\n\u003cli\u003eDoukky R, Hayes K, Frogge N, et al. Impact of appropriate use on the prognostic value of single-photon emission computed tomography myocardial perfusion imaging. *Circulation*. 2013;128:1634\u0026ndash;1643. DOI: 10.1161/CIRCULATIONAHA.113.002744.\u003c/li\u003e\n\u003cli\u003eNakanishi R, Gransar H, Slomka P, et al. Predictors of high-risk coronary artery disease in subjects with normal SPECT myocardial perfusion imaging. *Journal of Nuclear Cardiology*. 2016;23(3):530\u0026ndash;541. DOI: 10.1007/s12350-015-0150-3.\u003c/li\u003e\n\u003cli\u003eGulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain. *Circulation*. 2021;144(22):e368\u0026ndash;e454. DOI: 10.1161/CIR.0000000000001029.\u003c/li\u003e\n\u003cli\u003eAl-Mallah M, Alwan M, Al Rifai M, Sayed A. Cardiac positron emission tomography and other modalities for coronary artery disease assessment: a snapshot from the Medicare data. *Journal of Nuclear Cardiology*. 2024;41:102030. DOI: 10.1016/j.nuclcard.2024.102030.\u003c/li\u003e\n\u003cli\u003eCecil MP, Kosinski AS, Jones MT, et al. The importance of work-up (verification) bias correction in assessing the accuracy of SPECT thallium-201 testing for the diagnosis of coronary artery disease. *Journal of Clinical Epidemiology*. 1996;49(7):735\u0026ndash;742. DOI: 10.1016/0895-4356(96)00014-5.\u003c/li\u003e\n\u003cli\u003eHindieh W, Pilote L, Cheema A, et al. Association between family history, a genetic risk score, and severity of coronary artery disease in patients with premature acute coronary syndromes. *Arteriosclerosis, Thrombosis, and Vascular Biology*. 2016;36(6):1286\u0026ndash;1292. DOI: 10.1161/ATVBAHA.115.306944.\u003c/li\u003e\n\u003cli\u003eAernoudse WH, Botman KJ, Pijls NH. False-negative myocardial scintigraphy in balanced three-vessel disease, revealed by coronary pressure measurement. *International Journal of Cardiovascular Interventions*. 2003;5(2):67\u0026ndash;71. DOI: 10.1080/14628840310003244.\u003c/li\u003e\n\u003cli\u003eKral BG, Becker LC, Vaidya D, et al. Noncalcified coronary plaque volumes in healthy people with a family history of early onset coronary artery disease. *Circulation: Cardiovascular Imaging*. 2014;7(3):446\u0026ndash;453. DOI: 10.1161/CIRCIMAGING.113.000980.\u003c/li\u003e\n\u003cli\u003eSunman H, Yorgun H, Canpolat U, et al. Association between family history of premature coronary artery disease and coronary atherosclerotic plaques shown by multidetector computed tomography coronary angiography. *International Journal of Cardiology*. 2013;164(3):355\u0026ndash;358. DOI: 10.1016/j.ijcard.2011.07.043.\u003c/li\u003e\n\u003cli\u003eSch\u0026auml;chinger V, Britten MB, Elsner M, et al. A positive family history of premature coronary artery disease is associated with impaired endothelium-dependent coronary blood flow regulation. *Circulation*. 1999;100(14):1502\u0026ndash;1508. DOI: 10.1161/01.CIR.100.14.1502.\u003c/li\u003e\n\u003cli\u003eDISCHARGE Trial Group; Maurovich-Horvat P, Bosserdt M, Kofoed KF, et al. CT or invasive coronary angiography in stable chest pain. *New England Journal of Medicine*. 2022;386(17):1591\u0026ndash;1602. DOI: 10.1056/NEJMoa2200963.\u003c/li\u003e\n\u003cli\u003eFaulder TI, Prematunga K, Moloi SB, et al. Agreement of fractional flow reserve estimated by computed tomography with invasively measured fractional flow reserve: a systematic review and meta-analysis. *Journal of the American Heart Association*. 2024;13(10):e034552. DOI: 10.1161/JAHA.124.034552.\u003c/li\u003e\n\u003cli\u003eOtaki Y, Singh A, Kavanagh P, et al. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease. *JACC: Cardiovascular Imaging*. 2022;15(6):1091\u0026ndash;1102. DOI: 10.1016/j.jcmg.2021.04.030.\u003c/li\u003e\n\u003cli\u003eBegg CB, Greenes RA. Assessment of diagnostic tests when disease verification is subject to selection bias. *Biometrics*. 1983;39(1):207\u0026ndash;215. DOI: 10.2307/2530820.\u003c/li\u003e\n\u003cli\u003ede Groot JAH, Bossuyt PMM, Reitsma JB, et al. Verification problems in diagnostic accuracy studies: consequences and solutions. *BMJ*. 2011;343:d4770. DOI: 10.1136/bmj.d4770.\u003c/li\u003e\n\u003cli\u003eMiller TD, Hodge DO, Christian TF, et al. Effects of adjustment for referral bias on the sensitivity and specificity of single photon emission computed tomography for the diagnosis of coronary artery disease. *American Journal of Medicine*. 2002;112(4):290\u0026ndash;297. DOI: 10.1016/S0002-9343(01)01111-1.\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. *Journal of Clinical Epidemiology*. 2008;61(4):344\u0026ndash;349. DOI: 10.1016/j.jclinepi.2007.11.008.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SPECT, myocardial perfusion imaging, false negative, coronary artery disease, Appalachia, cardiac catheterization, revascularization, diagnostic yield","lastPublishedDoi":"10.21203/rs.3.rs-9246863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9246863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary artery disease (CAD) places a disproportionate burden on rural Appalachian communities. Scioto County, Ohio has among the highest rates of CAD-related hospitalizations in the state, approaching double the national average. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging remains a widely used non-invasive screening modality, yet its diagnostic performance in high-burden populations with significant comorbidities has not been well characterized. We aimed to evaluate the diagnostic performance of SPECT for predicting subsequent revascularization and identify predictors of false-negative results in our patient population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted using prospectively collected data from the American College of Cardiology CathPCI registry at a single community hospital. All cardiac catheterizations performed between Q3 2013 and Q1 2018 were included. Sensitivity for predicting subsequent revascularization was calculated using catheterization with subsequent intervention (percutaneous coronary intervention or coronary artery bypass grafting) as the reference standard. Logistic regression models were constructed to identify predictors of false-negative SPECT results. This study was reported in accordance with the STROBE guidelines for observational studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver the study period, 7,312 cardiac catheterizations were performed, with 3,116 (42.6%) requiring intervention. Among 3,189 patients with SPECT data, sensitivity for predicting subsequent revascularization was 82.0%. Among the subset of patients with negative SPECT who nevertheless proceeded to catheterization, 32.8% were found to require intervention, a rate reflecting the high-risk, selected nature of this subgroup rather than the population-level false-negative rate. Sensitivity was highest for left main and ramus lesions (91%) and lowest for mid-distal left anterior descending (LAD) lesions (82%). In multivariable analysis, false-negative results were independently associated with family history of CAD (OR 1.73, 95% CI 1.25\u0026ndash;2.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and were less likely with critical circumflex (OR 0.60, 95% CI 0.44\u0026ndash;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), right coronary artery (OR 0.54, 95% CI 0.40\u0026ndash;0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and prior myocardial infarction (OR 0.60, 95% CI 0.41\u0026ndash;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSPECT sensitivity for predicting revascularization in this high-burden CAD population was broadly consistent with contemporary meta-analytic benchmarks, though direct comparison is limited by differing reference standards. Family history of CAD was independently associated with false-negative SPECT results, a finding not previously reported in the SPECT literature. In patients with a strong family history of CAD, particularly in settings where SPECT remains the primary cardiac imaging modality, these findings suggest that a lower threshold for further evaluation may be warranted when SPECT results are negative.\u003c/p\u003e","manuscriptTitle":"Sensitivity and Predictors of False-Negative SPECT Myocardial Perfusion Imaging in a High-Burden Coronary Artery Disease Population: A Retrospective Analysis Using Revascularization as the Reference Standard","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 06:59:57","doi":"10.21203/rs.3.rs-9246863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T07:24:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:24:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:56:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T07:25:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T06:07:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T20:25:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165193664874758713248621611928959450476","date":"2026-04-13T08:08:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T02:31:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293012144490499020391568516693763033489","date":"2026-04-13T02:22:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90191844758716195068145612632021712967","date":"2026-04-12T17:52:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47092705840975970452337831727596813394","date":"2026-04-11T15:19:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287055596651930458674218370801130483202","date":"2026-04-10T02:55:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185425422455147644547829122132924129018","date":"2026-04-07T23:40:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T17:50:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T15:02:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T09:06:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T09:06:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-27T16:11:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9c3b89a-6dee-41c5-ae46-568728ebf47b","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T07:24:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:24:34+00:00","index":90,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T11:56:23+00:00","index":89,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T07:41:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 06:59:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9246863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9246863","identity":"rs-9246863","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}