Paradoxical Mortality Benefit but Increased Procedural and Ischemic Risk in Prediabetic Patients with Chronic Total Occlusion: A National Inpatient Sample Analysis (2016–2022)

Paradoxical Mortality Benefit but Increased Procedural and Ischemic Risk in Prediabetic Patients with Chronic Total Occlusion: A National Inpatient Sample Analysis (2016–2022) | 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 Paradoxical Mortality Benefit but Increased Procedural and Ischemic Risk in Prediabetic Patients with Chronic Total Occlusion: A National Inpatient Sample Analysis (2016–2022) Aobo Li, Avilash Mondal, Ayobamidele S. Balogun, Pranav V. Patel, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7511907/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 16 You are reading this latest preprint version Abstract Background The impact of prediabetes on outcomes in chronic total occlusion (CTO) hospitalizations remains unclear. We evaluated the association between prediabetes and in-hospital mortality, complications, and procedural interventions using a nationally representative dataset. Methods We queried the National Inpatient Sample (NIS, 2016–2022) to identify adult hospitalizations with a diagnosis of CTO. Patients were stratified by prediabetes status, and 1:1 propensity score matching was performed to balance sociodemographic and comorbid covariates (Fig. 1). Multivariable-adjusted and matched logistic regression models were used to assess the primary outcome, which was all-cause in-hospital mortality, and secondary outcomes were ischemic stroke, major adverse cardiovascular events (MACE), and use of mechanical circulatory support (MCS). Results Among 269,475 CTO hospitalizations, 7,825 (2.9%) had prediabetes (Table 1). After matching (n = 1,494 per group), baseline characteristics were well-balanced. In the matched cohort, prediabetic patients had significantly lower odds of in-hospital mortality compared to those without prediabetes (OR 0.52, 95% CI: 0.31–0.87; p = 0.013). However, they demonstrated significantly higher odds of intra-aortic balloon pump (IABP) use (OR 2.20, 95%; p < 0.001) and coronary artery bypass grafting (CABG) (OR 2.08, 95% CI: 1.65–2.61; p < 0.001), suggesting hemodynamic instability in prediabetes patients. Ischemic stroke rates were higher (OR 1.38, 95% CI: 1.02–1.86; p = 0.037). No significant differences were observed in acute kidney injury, dialysis, or mechanical ventilation. MACE was lower in unadjusted (OR 0.81, 95% CI: 0.70–0.95; p = 0.009) but was not significant after matching (OR 1.01, 95% CI: 0.80–1.26; p = 0.955). (Table 2) Conclusions Despite higher use of advanced interventions and increased ischemic stroke risk, prediabetic patients hospitalized with CTO exhibited lower in-hospital mortality. This paradox demonstrates the complex interplay between early dysglycemic mileu, coronary pathophysiology, and supports the need for better risk stratification. Further prospective studies with longer follow-up durations are warranted to understand the long-term impact of prediabetes in advanced coronary disease. Coronary artery disease chronic total occlusion prediabetes Mortality Figures Figure 1 Introduction Chronic total occlusion (CTO) of the coronary arteries is a common and clinically important manifestation of coronary artery disease, affecting an estimated 15% to 20% of patients undergoing coronary angiography. These lesions represent complete blockage of a coronary artery for at least three months and are associated with ischemia, anginal symptoms, and impaired left ventricular function . Revascularization of CTO has historically been challenging due to the complexity of the lesions and the technical demands of intervention. Although early randomized trials such as DECISION-CTO did not demonstrate a mortality benefit, later studies, including the EURO-CTO trial , highlighted the role of CTO intervention in improving symptom burden and quality of life in selected patients. This underscores the growing clinical interest in identifying patients who are most likely to benefit from intervention. Metabolic dysfunction is increasingly recognized as an important factor in cardiovascular disease progression. Among metabolic risk states, prediabetes has emerged as a potential contributor to atherosclerosis, endothelial dysfunction, and systemic inflammation . Large cohort studies such as the Framingham Heart Study have shown that individuals with impaired fasting glucose or impaired glucose tolerance are at elevated risk for future cardiovascular events compared to those with normoglycemia . In addition, studies have documented that patients with prediabetes often share several clinical and biochemical features with those who have established diabetes . Despite this, the role of prediabetes in influencing in-hospital outcomes remains underexplored, especially in high-risk populations such as those with CTO. To date, few studies have specifically evaluated how prediabetes impacts acute outcomes in patients hospitalized with CTO. Most existing literature in this field has focused on diabetes as a binary risk factor , without examining the nuanced effects of glycemic status below the diabetic threshold. Given the systemic and vascular changes associated with early dysglycemia, it is plausible that prediabetes may influence both the severity of clinical presentation and the type of intervention received. At the same time, prediabetic patients may retain physiologic advantages that distinguish them from patients with overt diabetes. This makes the study of this population particularly relevant in the context of complex coronary disease. In our study, we evaluate the relationship between prediabetes and in-hospital outcomes among patients hospitalized with CTO using a large national database. We aim to explore the association between prediabetes and in-hospital mortality, complications, and procedural interventions using a nationally representative dataset. Method This study was a retrospective, observational cohort analysis. We used data from the National Inpatient Sample (NIS) covering the years 2016 through 2022 to identify adult hospitalizations, aged 18 years or older, with a diagnosis of chronic total occlusion (CTO). We excluded patients who had a concurrent diagnosis of type 1 or type 2 diabetes mellitus. Prediabetes was defined using ICD-10-CM codes in accordance with the diagnostic criteria of the American Diabetes Association. Classification was further guided by the Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software. Patients were divided into two groups based on euglycemia or prediabetes. The data selection process involved 1:1 propensity score matching to balance sociodemographic and comorbid covariates (Figure 1). Covariates used for matching included age, sex, race, insurance type, income quartile by ZIP code, and hospital characteristics such as location and geographic region. Covariate balance after matching was evaluated using standardized mean differences. A value less than 0.1 was considered acceptable. All-cause mortality (ACM) was the primary outcome of interest. Ischemic stroke, procedural intervention patterns such as intra-aortic balloon pump and coronary artery bypass grafting (CABG), and major adverse cardiovascular events (MACE, including cardiovascular death, myocardial infarction, cardiac arrest, and ischemic stroke) were secondary outcomes. To account for baseline differences, we used multivariable-adjusted and matched logistic regression models to ensure robust comparisons. To adjust for residual confounding, we performed both multivariable-adjusted and propensity score-matched logistic regression analyses. We reported odds ratios (ORs) with corresponding 95 percent confidence intervals (CIs) for each outcome. Statistical significance was defined as a two-sided p-value less than 0.05. All statistical analyses were performed using StataMP version 19 (StataCorp, College Station, TX). Result Out of a total of 269,475 chronic total occlusion (CTO) hospitalizations, 7,825 (2.9%) had prediabetes. After matching (n =1,494 per group), baseline characteristics were well-balanced. Patients in the prediabetes group had a higher prevalence of obesity (29.9% vs. 15.66%; p <0.001), dyslipidemia (83.45% vs. 71.61%; p <0.001), and hypertension (85.56% vs 83.42%; p =0.025). However, the prediabetes group has a lower prevalence of congestive heart failure (49.78% vs 55.32%; p =0.025), prior percutaneous coronary intervention (PCI) (23.9% vs. 26.33%; p =0.037), prior CABG (12.84% vs 18.9%; p <0.001), and prior transient ischemic attack (TIA) /stroke (6.45% vs. 8.6%; p =0.002). (Table 1) In the matched cohort, prediabetic patients had significantly lower odds of in-hospital mortality compared to those with euglycemia (OR 0.52, 95% CI: 0.31–0.87; p=0.013). However, they demonstrated significantly higher odds of IABP use (OR 2.20, 95%; p<0.001) and CABG (OR 2.08, 95% CI: 1.65–2.61; p<0.001), suggesting hemodynamic instability in prediabetes patients. Ischemic stroke rates were higher (OR 1.38, 95% CI: 1.02-1.86; p=0.037). No significant differences were observed in acute kidney injury, dialysis, or mechanical ventilation. MACE was lower in unadjusted (OR 0.81, 95% CI: 0.70-0.95; p=0.009) but was not significant after matching (OR 1.01, 95% CI: 0.80-1.26; p=0.955). (Table 2) Discussion In this nationwide analysis from 2016 to 2022 of patients hospitalized with chronic total occlusion (CTO), we observed a paradoxical relationship between prediabetes and in-hospital outcomes. Patients with prediabetes experienced lower in-hospital mortality despite having higher rates of ischemic stroke and more frequent use of invasive procedures such as IABP and CABG. This unexpected pattern highlights the complex relationship between early dysglycemia and cardiovascular pathophysiology, clinical decision-making, and outcomes. Our findings underscore the need to better understand the mechanisms driving this apparent paradox. The reduced mortality in prediabetic patients is consistent with prior studies that characterize prediabetes as an intermediate-risk metabolic state. These patients may retain more physiologic reserve than those with overt diabetes, especially in acute settings 8 . They also tend to receive earlier cardiovascular screening and more proactive care, which may improve short-term outcomes. The concept of “metabolic reserve” may also help explain this paradox. Short-term hyperglycemia increases blood levels of free fatty acids (FFA) , which increases peroxisome proliferator-activated receptor alpha (PPAR-α) , supporting the muscle’s metabolic needs and avoiding the accumulation of metabolic byproducts that could be harmful to the muscle. Prior meta-analyses reported a U-shaped relationship between HbA1c and mortality, with the lowest risk clustering in the prediabetic range 8 . However, the observed mortality benefit should be interpreted with caution. While physiologic reserve and early intervention may play a role, this association may also reflect selection bias or residual confounding. Clinicians may perceive prediabetic patients as healthier or more salvageable, making them more likely to receive aggressive interventions. Additionally, in-hospital mortality does not capture long-term cardiovascular risk. Several studies have shown that the cardiovascular event risk in prediabetes rises significantly over time, often approaching that of diabetes . Therefore, the apparent short-term advantage should not obscure the need for ongoing surveillance and intervention in this population. Our study also found a significantly higher odds of IABP use and CABG in the prediabetes group. These findings suggest that patients with prediabetes may present with greater hemodynamic instability or more complex coronary disease . One plausible explanation is that clinicians may view prediabetic patients as better procedural candidates, leading to more frequent use of advanced support strategies or revascularization . In this context, the short-term survival advantage may reflect favorable patient selection and effective intervention rather than intrinsic protection from prediabetes itself. The association between prediabetes and ischemic stroke only became significant after adjustment and matching. This suggests that the raw comparison was affected by confounding variables. Prediabetic patients may also have subtle vascular stiffness and endothelial activation , which increase vulnerability to periprocedural embolic events, particularly in the context of complex revascularization strategies such as IABP and CABG. Given that CTO is associated with extensive atherosclerosis and impaired collateral perfusion, the added burden of invasive intervention may further elevate stroke risk . These findings highlight the value of adjusted models and the need for stroke prevention strategies in CTO patients with early metabolic dysfunction. Limitations Some limitations are worth mentioning. First, the analysis was based on administrative data using ICD-10 codes, which may be subject to coding inaccuracies. Second, the NIS lacks clinical granularity, including laboratory values, angiographic features, and medication use, which limits detailed risk stratification. Third, outcomes were limited to the index hospitalization, and no follow-up data were available to assess long-term risks. Fourth, although we used multivariable adjustment and propensity score matching, residual confounding from unmeasured variables may remain. Finally, because the cohort included only hospitalized patients with CTO, the findings may not be generalizable to patients managed in outpatient settings. Despite these limitations, our study provides valuable insight into the complex relationship between early metabolic risk and in-hospital outcomes in patients with advanced coronary artery disease. Future Directions This study suggests that prediabetes is not without harm in the setting of chronic coronary disease. Instead, it may represent a critical transition point where early recognition and timely intervention can influence outcomes. The lower in-hospital mortality observed in patients with prediabetes may reflect this pattern of proactive care. However, the higher rates of invasive procedures and ischemic stroke also point to ongoing vascular risk in this group. While our findings offer valuable insight into short-term in-hospital outcomes, they do not capture what occurs beyond discharge. The long-term consequences of prediabetes in patients with CTO remain unclear. To address this gap, future research should validate our observations in prospective cohorts with detailed clinical phenotyping. A particularly meaningful approach would involve a multicenter registry of CTO patients with serial glycemic profiling, angiographic characterization, and extended follow-up for cardiovascular events. Such data would help determine whether the early survival benefit observed in prediabetic patients represents a durable protective effect or a transient phase that precedes elevated long-term risk. Further prospective studies with longer follow-up durations are essential to clarify the true prognostic impact of prediabetes in the setting of advanced coronary artery disease. Conclusion Our findings highlight the importance of not dismissing prediabetes as benign in patients with advanced coronary artery disease. While short-term outcomes may appear favorable, the elevated procedural burden and increased stroke risk suggest that vascular fragility remains a concern. Early identification of prediabetic patients with chronic total occlusion should prompt both aggressive risk factor control and thoughtful procedural planning. Future prospective studies are essential to determine whether metabolic reserve translates into long-term benefit or if it merely delays progression to adverse outcomes. In either case, a more nuanced approach to risk stratification in CTO patients with early dysglycemia is warranted. Abbreviations Chronic total occlusion (CTO), National Inpatient Sample (NIS), All-cause mortality (ACM), major adverse cardiovascular events (MACE), mechanical circulatory support (MCS), intra-aortic balloon pump (IABP), percutaneous coronary intervention (PCI) coronary artery bypass grafting (CABG), Healthcare Cost and Utilization Project (HCUP), odds ratios (ORs), confidence intervals (CIs), transient ischemic attack (TIA), free fatty acids (FFA), peroxisome proliferator-activated receptor alpha (PPAR-α) Declarations Human Ethics and Consent to Participate declarations: Not applicable. IRB PDF is attached. We utilized the National Inpatient Sample (NIS) database for this study. As the NIS is a large, publicly available, de-identified dataset, the analysis represents secondary use of existing data. Therefore, individual patient consent was not required, as no personal identifiers are accessible and the study posed no direct risk to patients. The institutional Review Board (IRB) at Inspira Health also provides the exemption for this study. Funding Declaration: None Please provide a Consent to Participate declaration in the manuscript. Every human participant should provide their consent. We utilized the National Inpatient Sample (NIS) database for this study. As the NIS is a large, publicly available, de-identified dataset, the analysis represents secondary use of existing data. Therefore, individual patient consent was not required, as no personal identifiers are accessible and the study posed no direct risk to patients. If your study is a clinical trial, please provide the necessary registration details (registry, trial registration number, and data of registration). If not applicable, please state following in the manuscript: ‘Clinical trial number: not applicable.’ Clinical trial number: not applicable. Human Ethics and Consent to Participate declarations missing. Please ensure that all the necessary declarations are listed in the manuscript. Please refer to the submission guidelines for more information. If not applicable, please provide the following declaration in the manuscript: ‘Human Ethics and Consent to Participate declarations: not applicable’. We utilized the National Inpatient Sample (NIS) database for this study. As the NIS is a large, publicly available, de-identified dataset, the analysis represents secondary use of existing data. ‘Human Ethics and Consent to Participate declarations: not applicable’ The study was exempted by Inspira Health Network Institutional Review Board. Ethics approval and consent to participate: Not applicable. IRB documentation PDF attached. Consent for publication: Not applicable Availability of data and materials Data from the Healthcare Cost and Utilization Project (HCUP) National (Nationwide) Inpatient Sample (NIS), Agency for Healthcare Research and Quality, were used in this study (NIS 2016–2022). Under the HCUP Data Use Agreement, the raw NIS data cannot be made publicly available. Qualified researchers may obtain the NIS via the HCUP Central Distributor after completing the HCUP DUA training and signing the Nationwide DUA (see access instructions at the HCUP Central Distributor). Competing Interests No, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding No, this research did not receive funding. Authors' contributions: Conceptualization: A.L. Methodology: A.L., AM, L.Z., AS.B., PV.P., KW.K.. Formal Analysis: A.L., A.M.. Writing – Original Draft: A.L. Writing – All authors reviewed the manuscript Supervision: L.Z., KW. K., A.F. Acknowledgements: Not applicable References Brilakis ES, Mashayekhi K, Tsuchikane E, et al. Guiding Principles for Chronic Total Occlusion Percutaneous Coronary Intervention: A Global Expert Consensus Document. 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Sociodemographic differences in CTO patients with prediabetes vs euglycemia: NIS 2016-2022 Variable Euglycemia (n=261,650) Prediabetes (n=7,825) P-value Age (mean) 68.28 66.55 <0.001 Age Group (18-45) 3.47 3.45 <0.001 Age Group (46-65) 33.85 39.74 65) 62.68 56.81 <0.001 Female 25.33 21.73 0.002 Race (White) 79.44 70.40 <0.001 Race (Black) 9.46 12.39 <0.001 Race (Hispanic) 5.71 7.91 <0.001 Race (API) 2.16 4.81 <0.001 Race (NA) 0.43 0.59 <0.001 Race (Others) 2.80 3.89 <0.001 Income Quartile (1) 28.01 22.90 <0.001 Income Quartile (2) 26.73 25.55 <0.001 Income Quartile (3) 24.54 24.77 <0.001 Income Quartile (4) 20.72 26.78 <0.001 Payer (Medicare) 61.75 52.88 <0.001 Payer (Medicaid) 8.89 10.9 <0.001 Payer (Private) 22.47 28.33 <0.001 Payer (Self-pay) 3.74 4.94 <0.001 Payer (No charge) 0.34 0.26 <0.001 Payer (Other) 2.79 2.69 <0.001 Hospital Location (Rural) 5.05 3.64 <0.001 Hospital Location (Urban Non-teaching) 19.96 12.72 <0.001 Hospital Location (Urban Teaching) 74.98 83.64 <0.001 Hospital Region (Northeast) 17.18 18.85 <0.001 Hospital Region (Midwest) 25.74 25.18 <0.001 Hospital Region (South) 39.76 29.14 <0.001 Hospital Region (West) 17.32 26.84 <0.001 Comorbid Conditions Congestive Heart Failure 55.32 49.78 <0.001 Cardiac Arrhythmias 49.57 47.03 0.048 Valvular Disease 23.22 19.87 0.002 Pulmonary Circulation Disorders 9.75 7.86 0.011 Peripheral Vascular Disorders 24.39 22.24 0.061 Paralysis 1.04 0.96 0.748 Chronic Pulmonary Disease 28.17 22.56 <0.001 Hypothyroidism 11.37 10.29 0.180 Renal Failure 22.63 20.38 0.038 Liver Disease 4.36 5.18 0.117 Peptic Ulcer Disease 0.68 0.51 0.419 Metastatic Cancer 1.11 0.51 0.025 Rheumatoid Arthritis/Collagen Vascular 2.97 1.85 0.013 Coagulopathy 8.64 8.05 0.422 Obesity 15.66 29.90 <0.001 Fluid and Electrolyte Disorders 27.5 24.47 0.008 Deficiency Anemia 3.95 3.77 0.714 Alcohol Abuse 4.71 4.28 0.437 Drug Abuse 4.40 4.98 0.279 Psychoses 0.72 0.58 0.517 Depression 9.65 9.90 0.745 Dyslipidemia 71.61 83.45 <0.001 Hypertension 83.42 85.56 0.025 Smoking 30.09 31.44 0.257 Pulmonary Hypertension 9.04 7.48 0.031 Prior AMI 29.74 29.97 0.845 Prior PCI 26.33 23.9 0.037 Prior CABG 18.9 12.84 <0.001 Prior OHT 0.02 0 0.579 Prior VTE 4.38 3.71 0.190 TIA/Stroke Without Neuro Deficit 8.6 6.45 0.002 Elixhauser Group (0) 2.34 2.36 0.0003 Elixhauser Group (1-5) 71.13 76.04 0.0003 Elixhauser Group (6-13) 26.52 21.60 0.0003 Elixhauser Group (≥14) 0 0 0.0003 CHA2DS2Vasc (mean) 3.65 3.4 <0.001 In-Hospital Outcomes(%) All-Cause Mortality 3.74 1.41 <0.001 MACE 14.14 11.82 0.009 Stroke 1.90 2.11 0.556 Ischemic Stroke 5.96 6.84 0.152 Cardiac Arrest 3.78 2.94 0.089 Ventricular Fibrillation 4.51 3.26 0.020 New Atrial Fibrillation 8.01 6.58 0.040 ACS 41.28 44.60 0.010 AKI 19.72 19.11 0.542 Index Hemodialysis 1.9 1.21 0.061 Index PCI 37.8 39.81 0.107 Pressor Use 2.21 3.19 0.016 Invasive Mechanical Ventilation 6.43 4.98 0.019 LOS (mean) 5.21 5.20 0.899 Total Charges (mean) 111544.9 123774.3 <0.001 Abbreviation AMI(Acute myocardial infarction ), PCI(Percutaneous coronary intervention), CABG(Coronary artery bypass surgery), OHT(Orthotopic heart transplantation), VTE(Venous thromboembolism), TIA(Transient ischemic attack), MACE(Major Adverse Cardiovascular Events), MACCE (Major Adverse Cardiac and Cerebrovascular Events), ACS(Acute coronary syndrome), AKI(Acute kidney injury), LOS(Long of stay) Table 2. Outcome Unadjusted OR (95% CI) Unadjusted P-value Adjusted OR (95% CI) Adjusted P-value Matched OR (95% CI) Matched p-value AKI 0.96 (0.85-1.09) 0.542 1.13 (0.97-1.31) 0.109 1.12 (0.93-1.35) 0.222 Index Hemodialysis 0.63 (0.39-1.02) 0.064 0.80 (0.48-1.35) 0.404 0.57 (0.31-1.03) 0.065 Index IABP 1.49 (1.19-1.88) 0.001 1.51 (1.17-1.94) 0.001 2.20 (1.47-3.29) <0.001 Index LVAD 0.84 (0.56-1.26) 0.398 0.95 (0.63-1.45) 0.823 1.00 (0.56-1.77) 1 Index ECMO 0.77 (0.24-2.42) 0.649 1.01 (0.30-3.38) 0.989 0.60 (0.14-2.52) 0.484 Index CABG 2.15 (1.86-2.47) <0.001 1.78 (1.52-2.09) <0.001 2.08 (1.65-2.61) <0.001 ACS 1.14 (1.03-1.27) 0.010 1.09 (0.98-1.23) 0.125 1.20 (1.03-1.39) 0.017 Index PCI 1.09 (0.98-1.21) 0.107 0.96 (0.85-1.07) 0.434 0.93 (0.80-1.08) 0.336 Pressor use 1.46 (1.07-2.00) 0.017 1.45 (1.04-2.03) 0.030 2.22 (1.30-3.80) 0.004 Mechanical ventilation 0.76 (0.61-0.96) 0.020 0.83 (0.64-1.08) 0.167 0.81 (0.59-1.11) 0.186 Blood transfusion 1.01 (0.79-1.29) 0.931 1.14 (0.88-1.47) 0.329 1.14 (0.80-1.62) 0.47 New Afib 0.81 (0.66-0.99) 0.040 0.88 (0.71-1.09) 0.242 0.76 (0.58-1.00) 0.047 All-cause mortality 0.37 (0.24-0.56) <0.001 0.49 (0.32-0.76) 0.001 0.52 (0.31-0.87) 0.013 MACE 0.81 (0.70-0.95) 0.009 0.87 (0.73-1.03) 0.100 1.01 (0.80-1.26) 0.955 Stroke 1.11 (0.78-1.57) 0.557 1.24 (0.83-1.85) 0.291 1.34 (0.79-2.29) 0.282 Ischemic stroke 1.16 (0.95-1.41) 0.152 1.36 (1.10-1.68) 0.005 1.38 (1.02-1.86) 0.037 Cardiac arrest 0.77 (0.57-1.04) 0.090 0.77 (0.55-1.07) 0.119 1.11 (0.70-1.75) 0.649 Ventricular fibrillation 0.71 (0.54-0.95) 0.020 0.63 (0.46-0.87) 0.005 0.86 (0.57-1.30) 0.475 Cardiogenic shock 0.95 (0.77-1.17) 0.633 1.04 (0.83-1.31) 0.712 1.30 (0.95-1.78) 0.104 Abbreviation AKI(Acute kidney injury), IABP (Intra-aortic balloon pump), LVAD(left ventricular assist device), ECMO(Extracorporeal membrane oxygenation), CABG(Coronary artery bypass surgery), ACS(Acute coronary syndrome), PCI(Percutaneous coronary intervention), Afib (Atrial Fibrillation), MACE(Major Adverse Cardiovascular Events). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 08 Oct, 2025 Editor invited by journal 30 Sep, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 22 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Balogun","email":"","orcid":"","institution":"Inspira Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ayobamidele","middleName":"S.","lastName":"Balogun","suffix":""},{"id":533047063,"identity":"76ab17f1-a4d6-4b0b-b5e5-406a2214c896","order_by":3,"name":"Pranav V. Patel","email":"","orcid":"","institution":"Inspira Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Pranav","middleName":"V.","lastName":"Patel","suffix":""},{"id":533047064,"identity":"f5a7dbee-b6c8-47fd-b795-b6c01db128e8","order_by":4,"name":"Kurt W. Kaulback","email":"","orcid":"","institution":"Inspira Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kurt","middleName":"W.","lastName":"Kaulback","suffix":""},{"id":533047065,"identity":"007a3864-a100-4316-8670-73828069f882","order_by":5,"name":"Alka Farmer","email":"","orcid":"","institution":"Inspira Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Alka","middleName":"","lastName":"Farmer","suffix":""},{"id":533047067,"identity":"b6faebe3-50c1-40f7-8e05-9390d79e3ddd","order_by":6,"name":"Zheng Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIUlEQVRIie2RP0vDQBTAXwjE5SxOciFBv8KTQGshmK8SKcTloK5OCoVMfoCKX0K3ul0JJMu5RxxMF10cWijSyfZdtE4JdHS4Hwf3Dt6P9+cADIZ/SDSur1MGe6OqDhHAktYNRbJZsX4UzoDluFVATndQ6IiD3RTbG+WzJXC/535a1WISDnvUYbaYwFGnjBsVx88vAp8a698L++ROJf2n2xzlVEHgtiiMi67nfnOGr8Lx9tMMsYxJSeH8oUXhfPjlcaqCL0ora8S3j7lWrtsU5MJx51op2SEpkqqwukqMrUrS9fSSUSUBzTJAVOJSPqecHlWjEo0H7+4KwgiLbEYbO0MsisfqKg2PO0VzFY3NtlH96b9DtqbXiasGxWAwGAx/bAB07WSqsDav+QAAAABJRU5ErkJggg==","orcid":"","institution":"Inspira Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-09-01 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19:21:50","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90391,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7511907/v1/c30a9aa211bd88f38bde6c79.html"},{"id":94138578,"identity":"483f2732-5f63-4859-a4bf-e91028638da2","added_by":"auto","created_at":"2025-10-22 19:29:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80809,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Diagram of the study cohort\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7511907/v1/1b653f4bd6dcbd4313da8cf9.png"},{"id":98244109,"identity":"e1892da9-2d36-4cd6-b070-f1ae2da9c67d","added_by":"auto","created_at":"2025-12-15 16:13:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7511907/v1/e4314d74-e3a0-481f-952d-a04df6296e5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Paradoxical Mortality Benefit but Increased Procedural and Ischemic Risk in Prediabetic Patients with Chronic Total Occlusion: A National Inpatient Sample Analysis (2016–2022)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic total occlusion (CTO) of the coronary arteries is a common and clinically important manifestation of coronary artery disease, affecting an estimated 15% to 20% of patients undergoing coronary angiography. These lesions represent complete blockage of a coronary artery for at least three months and are associated with ischemia, anginal symptoms, and impaired left ventricular function\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e. Revascularization of CTO has historically been challenging due to the complexity of the lesions and the technical demands of intervention. Although early randomized trials such as DECISION-CTO\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e did not demonstrate a mortality benefit, later studies, including the EURO-CTO trial\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e, highlighted the role of CTO intervention in improving symptom burden and quality of life in selected patients. This underscores the growing clinical interest in identifying patients who are most likely to benefit from intervention.\u003c/p\u003e\u003cp\u003eMetabolic dysfunction is increasingly recognized as an important factor in cardiovascular disease progression. Among metabolic risk states, prediabetes has emerged as a potential contributor to atherosclerosis, endothelial dysfunction, and systemic inflammation\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e. Large cohort studies such as the Framingham Heart Study have shown that individuals with impaired fasting glucose or impaired glucose tolerance are at elevated risk for future cardiovascular events compared to those with normoglycemia\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e. In addition, studies have documented that patients with prediabetes often share several clinical and biochemical features with those who have established diabetes\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e. Despite this, the role of prediabetes in influencing in-hospital outcomes remains underexplored, especially in high-risk populations such as those with CTO.\u003c/p\u003e\u003cp\u003eTo date, few studies have specifically evaluated how prediabetes impacts acute outcomes in patients hospitalized with CTO. Most existing literature in this field has focused on diabetes as a binary risk factor\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e, without examining the nuanced effects of glycemic status below the diabetic threshold. Given the systemic and vascular changes associated with early dysglycemia, it is plausible that prediabetes may influence both the severity of clinical presentation and the type of intervention received. At the same time, prediabetic patients may retain physiologic advantages that distinguish them from patients with overt diabetes. This makes the study of this population particularly relevant in the context of complex coronary disease.\u003c/p\u003e\u003cp\u003eIn our study, we evaluate the relationship between prediabetes and in-hospital outcomes among patients hospitalized with CTO using a large national database. We aim to explore the association between prediabetes and in-hospital mortality, complications, and procedural interventions using a nationally representative dataset.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis study was a retrospective, observational cohort analysis. We used data from the National Inpatient Sample (NIS) covering the years 2016 through 2022 to identify adult hospitalizations, aged 18 years or older, with a diagnosis of chronic total occlusion (CTO). We excluded patients who had a concurrent diagnosis of type 1 or type 2 diabetes mellitus. Prediabetes was defined using ICD-10-CM codes in accordance with the diagnostic criteria of the American Diabetes Association. Classification was further guided by the Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software. Patients were divided into two groups based on euglycemia or prediabetes.\u003c/p\u003e\n\u003cp\u003eThe data selection process involved 1:1 propensity score matching to balance sociodemographic and comorbid covariates (Figure 1). Covariates used for matching included age, sex, race, insurance type, income quartile by ZIP code, and hospital characteristics such as location and geographic region. Covariate balance after matching was evaluated using standardized mean differences. A value less than 0.1 was considered acceptable.\u003c/p\u003e\n\u003cp\u003eAll-cause mortality (ACM) was the primary outcome of interest. Ischemic stroke, procedural intervention patterns such as intra-aortic balloon pump and coronary artery bypass grafting (CABG), and major adverse cardiovascular events (MACE, including cardiovascular death, myocardial infarction, cardiac arrest, and ischemic stroke) were secondary outcomes. To account for baseline differences, we used multivariable-adjusted and matched logistic regression models to ensure robust comparisons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo adjust for residual confounding, we performed both multivariable-adjusted and propensity score-matched logistic regression analyses. We reported odds ratios (ORs) with corresponding 95 percent confidence intervals (CIs) for each outcome. Statistical significance was defined as a two-sided p-value less than 0.05. All statistical analyses were performed using StataMP version 19 (StataCorp, College Station, TX).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eOut of a total of 269,475 chronic total occlusion (CTO) hospitalizations, 7,825 (2.9%) had prediabetes. After matching (n =1,494 per group), baseline characteristics were well-balanced. Patients in the prediabetes group had a higher prevalence of obesity (29.9% vs. 15.66%; p \u0026lt;0.001), dyslipidemia (83.45% vs. 71.61%; p \u0026lt;0.001), and hypertension (85.56% vs 83.42%; p =0.025). However, the prediabetes group has a lower prevalence of congestive heart failure (49.78% vs 55.32%; p\u0026nbsp;=0.025), prior percutaneous coronary intervention (PCI) (23.9% vs. 26.33%; p =0.037), prior CABG (12.84% vs 18.9%; p \u0026lt;0.001), and prior transient ischemic attack (TIA) /stroke (6.45% vs. 8.6%; p =0.002). (Table 1)\u003c/p\u003e\n\u003cp\u003eIn the matched cohort, prediabetic patients had significantly lower odds of in-hospital mortality compared to those with euglycemia (OR 0.52, 95% CI: 0.31–0.87; p=0.013). However, they demonstrated significantly higher odds of IABP use (OR 2.20, 95%; p\u0026lt;0.001) and CABG (OR 2.08, 95% CI: 1.65–2.61; p\u0026lt;0.001), suggesting hemodynamic instability in prediabetes patients. Ischemic stroke rates were higher (OR 1.38, 95% CI: 1.02-1.86; p=0.037). No significant differences were observed in acute kidney injury, dialysis, or mechanical ventilation. MACE was lower in unadjusted (OR 0.81, 95% CI: 0.70-0.95; p=0.009) but was not significant after matching (OR 1.01, 95% CI: 0.80-1.26; p=0.955). (Table 2)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide analysis from 2016 to 2022 of patients hospitalized with chronic total occlusion (CTO), we observed a paradoxical relationship between prediabetes and in-hospital outcomes. Patients with prediabetes experienced lower in-hospital mortality despite having higher rates of ischemic stroke and more frequent use of invasive procedures such as IABP and CABG. This unexpected pattern highlights the complex relationship between early dysglycemia and cardiovascular pathophysiology, clinical decision-making, and outcomes. Our findings underscore the need to better understand the mechanisms driving this apparent paradox.\u003c/p\u003e\u003cp\u003eThe reduced mortality in prediabetic patients is consistent with prior studies that characterize prediabetes as an intermediate-risk metabolic state. These patients may retain more physiologic reserve than those with overt diabetes, especially in acute settings\u003csup\u003e8\u003c/sup\u003e. They also tend to receive earlier cardiovascular screening and more proactive care, which may improve short-term outcomes. The concept of \u0026ldquo;metabolic reserve\u0026rdquo;\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e may also help explain this paradox. Short-term hyperglycemia increases blood levels of free fatty acids (FFA)\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e, which increases peroxisome proliferator-activated receptor alpha (PPAR-α)\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e, supporting the muscle\u0026rsquo;s metabolic needs and avoiding the accumulation of metabolic byproducts that could be harmful to the muscle. Prior meta-analyses reported a U-shaped relationship between HbA1c and mortality, with the lowest risk clustering in the prediabetic range\u003csup\u003e8\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn13\" id=\"#FNLinkFn13\"\u003e\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eHowever, the observed mortality benefit should be interpreted with caution. While physiologic reserve and early intervention may play a role, this association may also reflect selection bias or residual confounding. Clinicians may perceive prediabetic patients as healthier or more salvageable, making them more likely to receive aggressive interventions. Additionally, in-hospital mortality does not capture long-term cardiovascular risk. Several studies have shown that the cardiovascular event risk in prediabetes rises significantly over time, often approaching that of diabetes\u003ca class=\"FNLink\" href=\"#Fn14\" id=\"#FNLinkFn14\"\u003e\u003c/a\u003e\u003ca class=\"FNLink\" href=\"#Fn15\" id=\"#FNLinkFn15\"\u003e\u003c/a\u003e. Therefore, the apparent short-term advantage should not obscure the need for ongoing surveillance and intervention in this population.\u003c/p\u003e\u003cp\u003eOur study also found a significantly higher odds of IABP use and CABG in the prediabetes group. These findings suggest that patients with prediabetes may present with greater hemodynamic instability or more complex coronary disease\u003ca class=\"FNLink\" href=\"#Fn16\" id=\"#FNLinkFn16\"\u003e\u003c/a\u003e. One plausible explanation is that clinicians may view prediabetic patients as better procedural candidates, leading to more frequent use of advanced support strategies or revascularization\u003ca class=\"FNLink\" href=\"#Fn17\" id=\"#FNLinkFn17\"\u003e\u003c/a\u003e. In this context, the short-term survival advantage may reflect favorable patient selection and effective intervention rather than intrinsic protection from prediabetes itself.\u003c/p\u003e\u003cp\u003eThe association between prediabetes and ischemic stroke only became significant after adjustment and matching. This suggests that the raw comparison was affected by confounding variables. Prediabetic patients may also have subtle vascular stiffness and endothelial activation\u003ca class=\"FNLink\" href=\"#Fn18\" id=\"#FNLinkFn18\"\u003e\u003c/a\u003e, which increase vulnerability to periprocedural embolic events, particularly in the context of complex revascularization strategies such as IABP and CABG. Given that CTO is associated with extensive atherosclerosis and impaired collateral perfusion, the added burden of invasive intervention may further elevate stroke risk\u003ca class=\"FNLink\" href=\"#Fn19\" id=\"#FNLinkFn19\"\u003e\u003c/a\u003e. These findings highlight the value of adjusted models and the need for stroke prevention strategies in CTO patients with early metabolic dysfunction.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eSome limitations are worth mentioning. First, the analysis was based on administrative data using ICD-10 codes, which may be subject to coding inaccuracies. Second, the NIS lacks clinical granularity, including laboratory values, angiographic features, and medication use, which limits detailed risk stratification. Third, outcomes were limited to the index hospitalization, and no follow-up data were available to assess long-term risks. Fourth, although we used multivariable adjustment and propensity score matching, residual confounding from unmeasured variables may remain. Finally, because the cohort included only hospitalized patients with CTO, the findings may not be generalizable to patients managed in outpatient settings. Despite these limitations, our study provides valuable insight into the complex relationship between early metabolic risk and in-hospital outcomes in patients with advanced coronary artery disease.\u003c/p\u003e\n\u003ch3\u003eFuture Directions\u003c/h3\u003e\n\u003cp\u003eThis study suggests that prediabetes is not without harm in the setting of chronic coronary disease. Instead, it may represent a critical transition point where early recognition and timely intervention can influence outcomes. The lower in-hospital mortality observed in patients with prediabetes may reflect this pattern of proactive care. However, the higher rates of invasive procedures and ischemic stroke also point to ongoing vascular risk in this group.\u003c/p\u003e\u003cp\u003eWhile our findings offer valuable insight into short-term in-hospital outcomes, they do not capture what occurs beyond discharge. The long-term consequences of prediabetes in patients with CTO remain unclear. To address this gap, future research should validate our observations in prospective cohorts with detailed clinical phenotyping. A particularly meaningful approach would involve a multicenter registry of CTO patients with serial glycemic profiling, angiographic characterization, and extended follow-up for cardiovascular events. Such data would help determine whether the early survival benefit observed in prediabetic patients represents a durable protective effect or a transient phase that precedes elevated long-term risk. Further prospective studies with longer follow-up durations are essential to clarify the true prognostic impact of prediabetes in the setting of advanced coronary artery disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings highlight the importance of not dismissing prediabetes as benign in patients with advanced coronary artery disease. While short-term outcomes may appear favorable, the elevated procedural burden and increased stroke risk suggest that vascular fragility remains a concern. Early identification of prediabetic patients with chronic total occlusion should prompt both aggressive risk factor control and thoughtful procedural planning. Future prospective studies are essential to determine whether metabolic reserve translates into long-term benefit or if it merely delays progression to adverse outcomes. In either case, a more nuanced approach to risk stratification in CTO patients with early dysglycemia is warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eChronic total occlusion (CTO), National Inpatient Sample (NIS), All-cause mortality (ACM), \u0026nbsp;major adverse cardiovascular events (MACE), mechanical circulatory support (MCS), intra-aortic balloon pump (IABP), percutaneous coronary intervention (PCI) coronary artery bypass grafting (CABG), Healthcare Cost and Utilization Project (HCUP), odds ratios (ORs), confidence intervals (CIs), transient ischemic attack (TIA), free fatty acids (FFA), peroxisome proliferator-activated receptor alpha (PPAR-\u0026alpha;)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable. IRB PDF is attached.\u003c/p\u003e\n\u003cp\u003eWe utilized the National Inpatient Sample (NIS) database for this study. As the NIS is a large, publicly available, de-identified dataset, the analysis represents secondary use of existing data. Therefore, individual patient consent was not required, as no personal identifiers are accessible and the study posed no direct risk to patients. The institutional Review Board (IRB) at Inspira Health also provides the exemption for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration: None\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePlease provide a Consent to Participate declaration in the manuscript. Every human participant should provide their consent.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe utilized the National Inpatient Sample (NIS) database for this study. As the NIS is a large, publicly available, de-identified dataset, the analysis represents secondary use of existing data. Therefore, individual patient consent was not required, as no personal identifiers are accessible and the study posed no direct risk to patients.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u0026nbsp;If your study is a clinical trial, please provide the necessary registration details (registry, trial registration number, and data of registration). If not applicable, please state following in the manuscript: \u0026lsquo;Clinical trial number: not applicable.\u0026rsquo;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u0026nbsp;Human Ethics and Consent to Participate declarations missing. Please ensure that all the necessary declarations are listed in the manuscript. Please refer to the submission guidelines for more information. If not applicable, please provide the following declaration in the manuscript: \u0026lsquo;Human Ethics and Consent to Participate declarations: not applicable\u0026rsquo;.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe utilized the National Inpatient Sample (NIS) database for this study. As the NIS is a large, publicly available, de-identified dataset, the analysis represents secondary use of existing data. \u0026lsquo;Human Ethics and Consent to Participate declarations: not applicable\u0026rsquo; The study was exempted by Inspira Health Network Institutional Review Board.\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eEthics approval and consent to participate: Not applicable. IRB documentation PDF attached.\u003c/li\u003e\n \u003cli\u003eConsent for publication: Not applicable\u003c/li\u003e\n \u003cli\u003eAvailability of data and materials\u003cbr\u003e\u0026nbsp;Data from the Healthcare Cost and Utilization Project (HCUP) National (Nationwide) Inpatient Sample (NIS), Agency for Healthcare Research and Quality, were used in this study (NIS 2016\u0026ndash;2022). Under the HCUP Data Use Agreement, the raw NIS data cannot be made publicly available. Qualified researchers may obtain the NIS via the HCUP Central Distributor after completing the HCUP DUA training and signing the Nationwide DUA (see access instructions at the HCUP Central Distributor).\u003c/li\u003e\n \u003cli\u003eCompeting Interests\u003cbr\u003e\u0026nbsp;No, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/li\u003e\n \u003cli\u003eFunding\u003cbr\u003e\u0026nbsp;No, this research did not receive funding.\u003c/li\u003e\n \u003cli\u003eAuthors\u0026apos; contributions:\u003cbr\u003e\u0026nbsp;Conceptualization: A.L.\u003cbr\u003e\u0026nbsp;Methodology: A.L., AM, L.Z., \u0026nbsp;AS.B., PV.P., KW.K..\u003cbr\u003e\u0026nbsp;Formal Analysis: A.L., A.M..\u0026nbsp;\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; Original Draft: A.L.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; All authors reviewed the manuscript\u003cbr\u003e\u0026nbsp;Supervision: L.Z., KW. K., A.F.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Acknowledgements: Not applicable\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrilakis ES, Mashayekhi K, Tsuchikane E, et al. Guiding Principles for Chronic Total Occlusion Percutaneous Coronary Intervention: A Global Expert Consensus Document. Circulation. 2019;140:420\u0026ndash;433. DOI: 10.1161/CIRCULATIONAHA.119.039797\u003c/li\u003e\n \u003cli\u003eLee S-W, Lee PH, et al. Randomized Trial Evaluating Percutaneous Coronary Intervention for the Treatment of Chronic Total Occlusion: The DECISION-CTO Trial. Circulation. 2019;139:1674-1683.\u003c/li\u003e\n \u003cli\u003eWerner GS, Martin-Yuste V, et al. A randomized multicentre trial to compare revascularization with optimal medical therapy for the treatment of chronic total coronary occlusions. European Heart Journal. 2018;39:2484\u0026ndash;2493. DOI: 10.1093/eurheartj/ehy220.\u003c/li\u003e\n \u003cli\u003eGurgoglione FL, Pitocco D, et al. Microvascular Complications Are Associated With Coronary Collateralization in Type 2 Diabetes and Chronic Occlusion. The Journal of Clinical Endocrinology \u0026amp; Metabolism. 2023;109:237\u0026ndash;244.DOI: 10.1210/clinem/dgad396\u003c/li\u003e\n \u003cli\u003eAhsan MJ, Latif A, et al. Outcomes of Prediabetes Compared with Normoglycaemia and Diabetes Mellitus in Patients Undergoing Percutaneous Coronary Intervention: A Systematic Review and Meta-analysis. Heart International. 2023;17:45. DOI: https://doi.org/10.17925/HI.2023.17.1.45\u003c/li\u003e\n \u003cli\u003eD\u0026rsquo;Agostino RB, Vasan RS, et al. General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation. 2008;117:743\u0026ndash;753.DOI: 10.1161/CIRCULATIONAHA.107.699579\u003c/li\u003e\n \u003cli\u003eKim YH, Her A-Y , Jeong MH, et al. Outcomes in prediabetes vs. diabetes in patients with non-ST-\u0026shy; segment elevation myocardial infarction undergoing percutaneous intervention. Coron Artery Dis. 2021;32:211\u0026ndash;23. DOI: 10.1097/MCA.0000000000000969.\u003c/li\u003e\n \u003cli\u003eZhong GC, Ye MX, Cheng JH, et al. HbA1c and risks of all-cause and cause-specific death in subjects without known diabetes:A dose-response meta-analysis of prospective cohort studies.Sci Rep. 2016;6:24071. DOI: 10.1038/srep24071\u003c/li\u003e\n \u003cli\u003eZhu Y, Meng S, et al. Long-term prognosis of chronic total occlusion treated by successful percutaneous coronary intervention in patients with or without diabetes mellitus: a systematic review and meta-analysis. Cardiovasc Diabetol. 2021;20:29.DOI:10.1186/s12933-021-01223-8\u003c/li\u003e\n \u003cli\u003eKassiotis C, Rajabi M, Taegtmeyer H. Metabolic reserve of the heart: the forgotten link between contraction and coronary flow. Prog Cardiovasc Dis. 2008;51(1):74-88. doi:10.1016/j.pcad.2007.11.005.\u003c/li\u003e\n \u003cli\u003eBrandt JM, Djouadi F, Kelly DP. Fatty acids activate transcription of the muscle carnitine palmitoyltransferase I gene in cardiac myocytes via the peroxisome proliferator-activated receptor alpha. J Biol Chem. 1998;273:23786\u0026ndash;23792. doi: 10.1074/jbc.273.37.23786.\u003c/li\u003e\n \u003cli\u003eFinck BN, Kelly DP. Peroxisome proliferator-activated receptor gamma coactivator-1 (PGC-1) regulatory cascade in cardiac physiology and disease. Circulation. 2007;115:2540\u0026ndash;2548. doi: 10.1161/CIRCULATIONAHA.107.670588.\u003c/li\u003e\n \u003cli\u003eKomatsu T, Yaguchi I, Komatsu S, et al. Impact of insulin resistance on neointimal tissue proliferation after 2nd-generation drug-eluting stent implantation. Tex Heart Inst J. 2015;42:327\u0026ndash;32. DOI: 10.14503/THIJ-14-4393\u003c/li\u003e\n \u003cli\u003eXu R, Wang C, et el. Prediabetes is Associated with Worse Long-Term Outcomes in Young Patients with Acute Coronary Syndrome. Diabetes Metab Syndr Obes. 2023;16:3213-3222.DOI: 10.2147/DMSO.S433112\u003c/li\u003e\n \u003cli\u003eChoi W gil, Rha S-W, et al. The Impact of Prediabetes on Two-Year Clinical Outcomes in Patients Undergoing Elective Percutaneous Coronary Intervention. Yonsei Med J. 2018;59:489.DOI: 10.3349/ymj.2018.59.4.489.\u003c/li\u003e\n \u003cli\u003eCalligaris SD, Lecanda M, et al. Mice Long-Term High-Fat Diet Feeding Recapitulates Human Cardiovascular Alterations: An Animal Model to Study the Early Phases of Diabetic Cardiomyopathy. PLoS ONE. 2013;8:e60931. doi: 10.1371/journal.pone.0060931. Print 2013.\u003c/li\u003e\n \u003cli\u003eAn X, Zhang Y, et al. Early effective intervention can significantly reduce all-cause mortality in prediabetic patients: a systematic review and meta-analysis based on high-quality clinical studies. Front Endocrinol. 2024;15:1294819. DOI: 10.3389/fendo.2024.1294819\u003c/li\u003e\n \u003cli\u003ePrenner SB, Chirinos JA. Arterial stiffness in diabetes mellitus. Atherosclerosis. 2015;238:370\u0026ndash;379. DOI: 10.1016/j.atherosclerosis.2014.12.023\u003c/li\u003e\n \u003cli\u003eMares A, Mukherjee D. Management of Chronic Total Occlusion of Coronary Artery. Int J Angiol. 2021;30:048\u0026ndash;052. DOI: 10.1055/s-0040-1721478\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Sociodemographic differences in CTO patients with prediabetes vs euglycemia: NIS 2016-2022\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEuglycemia (n=261,650)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrediabetes (n=7,825)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAge (mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e68.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e66.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAge Group (18-45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAge Group (46-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e33.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e39.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAge Group (\u0026gt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e62.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e56.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e25.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e21.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRace (White)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e79.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e70.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRace (Black)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRace (Hispanic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRace (API)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRace (NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRace (Others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIncome Quartile (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e28.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIncome Quartile (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e26.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIncome Quartile (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e24.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e24.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIncome Quartile (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e20.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e26.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePayer (Medicare)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e61.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e52.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePayer (Medicaid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePayer (Private)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e28.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePayer (Self-pay)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePayer (No charge)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePayer (Other)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Location (Rural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Location (Urban Non-teaching)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e19.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Location (Urban Teaching)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e74.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e83.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Region (Northeast)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e17.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Region (Midwest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e25.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Region (South)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e39.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHospital Region (West)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e17.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e26.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbid Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCongestive Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e55.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e49.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCardiac Arrhythmias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e49.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e47.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eValvular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e23.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e19.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePulmonary Circulation Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePeripheral Vascular Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e24.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eParalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eChronic Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e28.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRenal Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eLiver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePeptic Ulcer Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eMetastatic Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eRheumatoid Arthritis/Collagen Vascular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCoagulopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e15.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eFluid and Electrolyte Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e24.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eDeficiency Anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAlcohol Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eDrug Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePsychoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e9.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e71.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e83.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e83.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e85.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e30.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e31.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePulmonary Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePrior AMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e29.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePrior PCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e26.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePrior CABG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePrior OHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePrior VTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eTIA/Stroke Without Neuro Deficit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eElixhauser Group (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eElixhauser Group (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e71.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e76.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eElixhauser Group (6-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e26.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e21.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eElixhauser Group (\u0026ge;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCHA2DS2Vasc (mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-Hospital Outcomes(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAll-Cause Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIschemic Stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCardiac Arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eVentricular Fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eNew Atrial Fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e41.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e44.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eAKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e19.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e19.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIndex Hemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eIndex PCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e39.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePressor Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eInvasive Mechanical Ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eLOS (mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eTotal Charges (mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e111544.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e123774.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cem\u003eAbbreviation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 378px;\"\u003e\n \u003cp\u003eAMI(Acute myocardial infarction ), PCI(Percutaneous coronary intervention), CABG(Coronary artery bypass surgery), \u0026nbsp;OHT(Orthotopic heart transplantation), VTE(Venous thromboembolism), TIA(Transient ischemic attack), MACE(Major Adverse Cardiovascular Events), MACCE (Major Adverse Cardiac and Cerebrovascular Events), \u0026nbsp;ACS(Acute coronary syndrome), AKI(Acute kidney injury), LOS(Long of stay)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatched OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatched p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.96 (0.85-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.13 (0.97-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.12 (0.93-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex Hemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.63 (0.39-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.80 (0.48-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.57 (0.31-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex IABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.49 (1.19-1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.51 (1.17-1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.20 (1.47-3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex LVAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.84 (0.56-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.95 (0.63-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00 (0.56-1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex ECMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.77 (0.24-2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.01 (0.30-3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.60 (0.14-2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex CABG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2.15 (1.86-2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.78 (1.52-2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.08 (1.65-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.14 (1.03-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.09 (0.98-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.20 (1.03-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIndex PCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.09 (0.98-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.96 (0.85-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.93 (0.80-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ePressor use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.46 (1.07-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.45 (1.04-2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.22 (1.30-3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.76 (0.61-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.83 (0.64-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.81 (0.59-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eBlood transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.01 (0.79-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.14 (0.88-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.14 (0.80-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eNew Afib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.81 (0.66-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.88 (0.71-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.76 (0.58-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.37 (0.24-0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.49 (0.32-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.52 (0.31-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.81 (0.70-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.87 (0.73-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.01 (0.80-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.11 (0.78-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.24 (0.83-1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.34 (0.79-2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eIschemic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.16 (0.95-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.36 (1.10-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.38 (1.02-1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eCardiac arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.77 (0.57-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.77 (0.55-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.11 (0.70-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eVentricular fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.71 (0.54-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.63 (0.46-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.86 (0.57-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eCardiogenic shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.95 (0.77-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.04 (0.83-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.30 (0.95-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cem\u003eAbbreviation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 508px;\"\u003e\n \u003cp\u003eAKI(Acute kidney injury), IABP (Intra-aortic balloon pump), LVAD(left ventricular assist device), ECMO(Extracorporeal membrane oxygenation), CABG(Coronary artery bypass surgery), ACS(Acute coronary syndrome), PCI(Percutaneous coronary intervention), Afib (Atrial Fibrillation), MACE(Major Adverse Cardiovascular Events).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Coronary artery disease, chronic total occlusion, prediabetes, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-7511907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7511907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe impact of prediabetes on outcomes in chronic total occlusion (CTO) hospitalizations remains unclear. We evaluated the association between prediabetes and in-hospital mortality, complications, and procedural interventions using a nationally representative dataset.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe queried the National Inpatient Sample (NIS, 2016\u0026ndash;2022) to identify adult hospitalizations with a diagnosis of CTO. Patients were stratified by prediabetes status, and 1:1 propensity score matching was performed to balance sociodemographic and comorbid covariates (Fig.\u0026nbsp;1). Multivariable-adjusted and matched logistic regression models were used to assess the primary outcome, which was all-cause in-hospital mortality, and secondary outcomes were ischemic stroke, major adverse cardiovascular events (MACE), and use of mechanical circulatory support (MCS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 269,475 CTO hospitalizations, 7,825 (2.9%) had prediabetes (Table\u0026nbsp;1). After matching (n\u0026thinsp;=\u0026thinsp;1,494 per group), baseline characteristics were well-balanced. In the matched cohort, prediabetic patients had significantly lower odds of in-hospital mortality compared to those without prediabetes (OR 0.52, 95% CI: 0.31\u0026ndash;0.87; p\u0026thinsp;=\u0026thinsp;0.013). However, they demonstrated significantly higher odds of intra-aortic balloon pump (IABP) use (OR 2.20, 95%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and coronary artery bypass grafting (CABG) (OR 2.08, 95% CI: 1.65\u0026ndash;2.61; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting hemodynamic instability in prediabetes patients. Ischemic stroke rates were higher (OR 1.38, 95% CI: 1.02\u0026ndash;1.86; p\u0026thinsp;=\u0026thinsp;0.037). No significant differences were observed in acute kidney injury, dialysis, or mechanical ventilation. MACE was lower in unadjusted (OR 0.81, 95% CI: 0.70\u0026ndash;0.95; p\u0026thinsp;=\u0026thinsp;0.009) but was not significant after matching (OR 1.01, 95% CI: 0.80\u0026ndash;1.26; p\u0026thinsp;=\u0026thinsp;0.955). (Table\u0026nbsp;2)\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDespite higher use of advanced interventions and increased ischemic stroke risk, prediabetic patients hospitalized with CTO exhibited lower in-hospital mortality. This paradox demonstrates the complex interplay between early dysglycemic mileu, coronary pathophysiology, and supports the need for better risk stratification. Further prospective studies with longer follow-up durations are warranted to understand the long-term impact of prediabetes in advanced coronary disease.\u003c/p\u003e","manuscriptTitle":"Paradoxical Mortality Benefit but Increased Procedural and Ischemic Risk in Prediabetic Patients with Chronic Total Occlusion: A National Inpatient Sample Analysis (2016–2022)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:21:45","doi":"10.21203/rs.3.rs-7511907/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T10:08:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T13:19:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T18:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75873865690281529278070024320256814923","date":"2025-10-27T13:55:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T10:55:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11823432026755532912294731382484112075","date":"2025-10-23T19:40:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318181078788866163220211868375191526526","date":"2025-10-22T10:08:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42098007361068029197683781633840402678","date":"2025-10-21T22:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174060380032072181877463666744678303011","date":"2025-10-21T18:45:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120983368877722326502225317392021394641","date":"2025-10-21T14:34:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59238919664687272901048042865955767504","date":"2025-10-11T12:26:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T03:30:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-09T03:29:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-30T10:09:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T07:44:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-09-22T23:28:25+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":"8c9ce5bb-6d26-44e1-bf63-91b144c6bea9","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:05:53+00:00","versionOfRecord":{"articleIdentity":"rs-7511907","link":"https://doi.org/10.1186/s12872-025-05421-0","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-12-11 15:59:22","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-10-22 19:21:45","video":"","vorDoi":"10.1186/s12872-025-05421-0","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05421-0","workflowStages":[]},"version":"v1","identity":"rs-7511907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7511907","identity":"rs-7511907","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}