Accuracy of Myocardial Perfusion Imaging (MPI) in Predicting Post Transplant Cardiovascular Events among Diabetic Kidney Transplant Candidates: A tertiary care centre experience from Saudi Arabia

Accuracy of Myocardial Perfusion Imaging (MPI) in Predicting Post Transplant Cardiovascular Events among Diabetic Kidney Transplant Candidates: A tertiary care centre experience from Saudi Arabia | 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 Accuracy of Myocardial Perfusion Imaging (MPI) in Predicting Post Transplant Cardiovascular Events among Diabetic Kidney Transplant Candidates: A tertiary care centre experience from Saudi Arabia Shahad Alaydarous, Yasir Alfi, Bilal Mohsin, Lujain Bashamakh, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6789799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular risk assessment is crucial before kidney transplantation, especially for diabetic patients, as 6.6% experience major adverse cardiovascular events (MACE) within three years. Myocardial perfusion imaging (MPI) is commonly used to evaluate this risk. Objective This study assessed MPI’s ability to predict post-transplant cardiovascular events in asymptomatic diabetic candidates by examining its diagnostic accuracy for obstructive coronary artery disease (CAD) compared to coronary angiography and analyzing MACE incidence over two years. Methods In this retrospective, single-center study, 121 diabetic patients who underwent stress MPI before kidney transplantation were followed for two years post-transplant. The primary outcome included death, acute coronary syndrome (ACS), coronary revascularization, acute decompensated heart failure (ADHF), and cerebrovascular accident (CVA). Diagnostic metrics for MPI were evaluated in candidates undergoing pre-transplant coronary angiography. Results Among 121 candidates, 76 had normal MPI, and 45 had abnormal MPI. MACE occurred in 11.8% of normal MPI patients and 8.9% of abnormal MPI patients. MPI showed sensitivity of 60%, specificity of 59.7%, NPV of 85%, and PPV of 27.9% for obstructive CAD detection. Conclusion Despite NPV of 85% for ruling out obstructive coronary artery disease, stress MPI is not a reliable predictor of post kidney transplant adverse cardiovascular events in diabetic kidney transplant candidates. Findings of normal MPI should be interpreted with caution and we recommend against over utilization of stress MPI in diabetic kidney transplant candidates to avoid post-transplant cardiovascular events. Acute coronary syndrome coronary angiography diabetes mellitus Kidney transplant myocardial perfusion imaging Figures Figure 1 Figure 2 Introduction Kidney transplant is the preferred treatment option for End Stage Kidney disease (ESKD) as it improves clinical outcomes and quality of life as compared to other kidney replacement therapy options. 1 , 2 Coronary artery disease (CAD) is the most common cause of death in ESKD patients. 1 – 3 The risk of cardiovascular disease (CVD) remains a major concern before, during and after kidney transplantation. 4 – 8 The risk of cardiovascular events remains significant after kidney transplantation, and can reach 5.0%, 6.6% and 8.1% after the 1st, 3rd and 5th years respectively, with long term cardiovascular mortality of 30–50% in kidney transplant recipients. 9 Therefore, prior to kidney transplant cardiovascular risk assessment has an important role in evaluation of patients. Coronary angiography is the gold standard to diagnose CAD but it is an invasive procedure with associated complications. 10 Therefore, non-invasive investigations are commonly used to assess CAD prior to kidney transplant especially in asymptomatic patients. 11 Myocardial perfusion imaging (MPI) is a non-invasive, sensitive and specific investigation to assess cardiac perfusion and its response to increased demand. MPI is frequently utilized to assess cardiovascular risk in kidney transplant candidates. Candidates with previous history or active symptoms of CAD or with abnormal initial cardiac assessment are usually investigated through coronary angiography. 12 There is a low threshold for invasive cardiac investigations in asymptomatic diabetic candidates undergoing pre kidney transplant workup. The role of MPI and coronary angiography has not been well studied in this group. Various screening pathways had been suggested by different healthcare institutions worldwide to assess the risks for cardiovascular events in kidney transplant candidates including candidates with history of diabetes mellitus. A standardized cardiovascular risk assessment tool still remains undetermined, non-uniformed, and conflicting globally. 13 – 15 This study aims to evaluate the predictive role of MPI in asymptomatic diabetic candidates for post-kidney transplant cardiovascular events. Methods Study Design and Setting This retrospective, single-center observational study was conducted at a large kidney transplant center in Saudi Arabia. Adult diabetic patients with end-stage kidney disease (ESKD), aged over 25 years at their initial visit and who underwent cardiac risk assessment using myocardial perfusion imaging (MPI) prior to kidney transplant between January 2017 and December 2019, were included. Patients were followed for 2 years post-kidney transplant. Patient Selection Inclusion criteria comprised adult patients with diabetes and ESKD who had undergone stress MPI as part of their pre-transplant cardiac workup and who underwent kidney transplantation with availability of follow up data for 2 years. Exclusion criteria included non-diabetic patients, patients with known coronary artery disease (CAD), those with incomplete follow up details and those who did not proceed to transplantation. As per institutional protocol, patients with type 2 diabetes for more than 10 years or type 1 diabetes for more than 25 years were recommended to undergo coronary angiography in addition to MPI. Myocardial Perfusion Imaging Protocol Stress MPI was performed using the single-photon emission computed tomography (SPECT) technique with stress induced via exercise in mobile patients and use of pharmacological agents(dipyridamole) in patients with limited mobility. For exercise stress MPI, patients were connected to ECG electrodes; together with frequent monitoring of blood pressure heart rate and any new symptoms. The patient was exercised according to the Bruce protocol with the goal to achieve 10 METS (Metabolic Equivalents), and to reach 85% of MPHR (Maximum Predicted Heart Rate). In patient who developed new onset chest pain; syncopy; ECG changes; or unexpected changes of blood pressure; the exercise stress test was aborted. The pharmacolgical stress test was conducted in patients with limited mobility while using dipyridamole ( 0.56 mg/kg dose) with monitoring for pulse, blood pressure, ECG changes and any concerning symptoms. The studied parameters including reversible or irreversible perfusion defects and Left ventricular Ejection fraction Tc-99m sestamibi was administered during rest and stress phase. Rest imaging was acquired approximately 60 minutes post-injection while stress imaging was taken during exercise or pharmacological stress phase. A dual-detector gamma camera with attenuation correction and truncation compensation was used. All imaging followed the latest recommendations from the American Society of Nuclear Cardiology (ASNC). Visual interpretation was performed using Auto SPECT software, and analysis was conducted by experienced nuclear medicine specialists. Images were reoriented into short-axis, vertical long-axis, and horizontal long-axis views and assessed across 17 myocardial segments using a 4-point scale: 0 = normal 1 = mild perfusion defect 2 = moderate perfusion defect 3 = severe perfusion defect Summed Stress Scores (SSS), Summed Rest Scores (SRS), and Summed Difference Scores (SDS = SSS − SRS) were calculated. SDS values were categorized as follows: 50% at rest considered normal. Final MPI interpretations incorporated both perfusion and functional parameters. Results were reviewed by a cardiologist who determined the need for further investigation, including coronary angiography. Normal MPI study Normal MPI study is defined as a test result indicating homogeneous and adequate myocardial blood flow during both rest and stress conditions. There is no evidence of reversible or fixed perfusion defects Abnormal MPI study: Abnormal MPI study is a test result demonstrating diminished myocardial blood flow, either transient (suggesting ischemia) or permanent (indicating infarction or scar tissue) during during rest or stress conditions Outcomes Patients were categorized into normal and abnormal MPI groups, and outcomes were compared accordingly. Primary Outcome A composite of death, acute coronary syndrome (ACS), coronary revascularization, hospitalization for acute decompensated heart failure (ADHF), and cerebrovascular accident (CVA). Secondary Outcomes Each of the primary outcome components evaluated separately. Additionally, among patients who underwent coronary angiography, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MPI in detecting obstructive CAD were calculated. Ethical Consideration The study was approved by the institutional ethical review board, and informed consent was waived due to the retrospective design. All patients had provided informed consent for pre-transplant workup and kidney transplantation. To protect patient confidentiality, a unique study code was assigned to each participant, and only authorized research team members had access to personal identifiers. Data Collection Data were extracted retrospectively from electronic medical records. Baseline demographic and clinical data—including age, gender, smoking status, comorbidities, dialysis modality prior to transplant, LVEF, and primary renal diagnosis—were collected. MPI and angiographic findings, as well as outcome data, were documented for analysis. Data Analysis Data were analyzed using SPSS version 25. Continuous variables were described using mean and standard deviation for normally distributed data and median with interquartile ranges for skewed distributions. Categorical data were summarized using frequencies and percentages. Comparisons between groups were made using the Chi-square test or Fisher’s exact test for categorical variables, and t-tests for continuous variables. A p-value of < 0.05 was considered statistically significant. Univariate analysis was conducted to identify risk factors for cardiovascular events and mortality, followed by multivariable regression for variables with significant univariate associations. Results A total of 640 patients who underwent kidney transplant in KFSHRC from January, 2017 to December, 2019 were screened and 121 patients were included in the study. 537 patient were excluded (398 patients were not diabetics, 19 patients already known to have CAD, 42 patients had no stress test done prior to transplant and 60 patients were less than 25 years old). Table 1 describe the socio-demographic and clinical characteristics of study population in both groups (normal and abnormal-MPI). In terms of gender distribution, more male patients were represented in both groups, with a median age being 53.74 years in the normal-MPI group and 53.36 years in the abnormal-MPI group. The Ejection Fraction, was 50% or more in 96.1% of patients in the normal-MPI group and 86.7% in the Abormal-MPI group. Table 1 Socio-Demographic Characteristics and Comorbidities of the Participants by Myocardial Perfusion Imaging (MPI). Characteristics Normal-MPI (n = 76) Abormal-MPI (n = 45) Total (n = 121) Gender Male 46 (60.5%) 28 (62.2%) 74 (62.2%) Female 30 (39.5%) 17 (37.8%) 47 (38.8%) Age (Median) years 27–77 (53.74) 35–70 (53.36) 27–77 (53.74) Smoking Yes 5 (6.6%) 1 (2.2%) 6 (5%) No 34 (44.7%) 23 (51.1%) 57 (47.1%) Unknown 37 (48.7%) 21 (46.7%) 58 (47.9%) Comorbidities DM type 1 13 (17.1%) 4 (8.9%) 17 (14%) DM type2 63 (82.9%) 41 (91.1%) 104 (86%) HTN 72 (94.7%) 44 (97.8%) 116 (95.9%) Moderate aortic stenosis 1 (1.3%) 1 (2.2%) 2 (1.7%) Moderate TR 3 (3.9%) 0 3 (2.5%) Severe MR 0 1 (2.2%) 1 (0.8%) Moderate MR 2 (2.6%) 0 2 (1.7%) Severe MS 0 1 (2.2%) 1 (0.8%) Ejection fraction 50% or more 73 (96.1%) 39 (86.7%) 112 (92.6%) Less than 50% 3 (3.9%) 6 (13.3%) 9 (7.4%) Primary disease responsible for kidney failure Both DM and HTN 10 (13.2%) 11 (24.4%) 21 (17.4%) DM 27 (35.5%) 18 (40%) 45 (37.2%) HTN 5 (6.6%) 4 (8.9%) 9 (7.4%) GN 4 (5.3%) 0 4 (3.3%) Obstructive uropathy 3 (3.9%) 0 3 (2.5%) Polycystic kidney disease 2 (2.6%) 1 (2.2%) 3 (2.5%) Unknown 25 (32.9%) 11 (24.4%) 36 (29.8%) Dialysis No 17 (22.4%) 8 (17.8%) 25 (20.7%) Yes 59 (77.6%) 37 (82.2%) 96 (97.3%) HD (n = 96) 57 (96.6%) 34 (91.9%) 91 (94.8%) Peritoneal (n = 96) 2 (3.4%) 3 (8.1%) 5 (5.2%) CA findings prior to kidney transplant Normal coronaries 17 (22.4%) 18 (40%) 35 (28.9%) Non-obstructive CAD 29 (38.2%) 13 (28.9%) 42 (34.7%) CAD without PCI 6 (7.9%) 3 (6.7%) 9 (7.4%) CAD with PCI 2 (2.6%) 7 (15.6%) 9 (7.4%) CAD with CABG 0 2 (4.4%) 2 (1.7%) No Coronary angiography 22 (28.9%) 2 (4.4%) 24 (19.8%) DM: Diabetes mellites, HTN: Hypertension, TR: Tricuspid regurgitation, MR: Mitral regurgitation, MS: Mitral stenosis, GN: Glomerulonephritis, HD: Hemodialysis CAD: Coronary angiography, PCI: Percutaneous intervention, CABG: Coronary artery bypass graft, ACS: Acute coronary angiography, US: Unstable Angina, NSTEMI: Non-ST-elevation myocardial infarction STEMI: ST-elevation myocardial infarction. Table 2 displays the distribution and percentages of various outcomes in association with MPI results in both groups. The primary composite outcome was observed in 11.8% (n = 9) in the normal-MPI group as compared to 8.9% (n = 4) in abnormal-MPI group, with no statistically significant difference. Hospitalizations for heart failure were observed in 1.3% in the normal-MPI group, as compared to 8.9% in the abnormal-MPI group, however the difference did not reach statistical significance (p-value 0.063). Other secondary outcomes including Death, ACS, revascularization and CVA also were not different between the 2 groups with p-values of 0.29, 0.71, 0.29 and 0.63 respectively. Three deaths were reported in the study, all of them in the normal-MPI group, with 2 of them being attributed to septic shock and 1 due to massive pulmonary embolism (PE). Table 2 Comparison of Primary and Secondary Outcomes Based on MPI Results. Study Outcomes Normal-MPI (n = 76) Abnormal-MPI (n = 45) (n) (%) (n) (%) p-value* Primary Composite Outcome 9 11.8 4 8.9 0.765 Death 3 3.9 0 0 0.293 Acute coronary syndrome ACS 4 5.2 3 6.7 0.710 Revascularization 3 3.9 0 0 0.293 Hospitalization for heart failure 1 1.3 4 8.9 0.063 Stroke 1 1.3 0 0 0.628 In our study group, 80.16% (n = 97) out of 121 candidates underwent Coronary angiography (CA) prior to kidney transplant, 54 in normal-MPI group and 43 in abnormal-MPI group (Figs. 1 and 2 ). It revealed normal coronaries in 22.4% (n = 17) of patients in normal-MPI group and 40% (n = 18) of patients with abnormal-MPI group. Non-obstructive coronary artery disease (CAD) was found in 38.2% (n = 29) of the normal-MPI group and 28.9% (n = 13) of the abnormal-MPI group. In normal-MPI group, 8(15%) patients had obstructive coronary artery disease out of whom six underwent medical management and two underwent PCI. In the abnormal-MPI group, 12 (26.6%) has obstructive coronary artery disease out of whom 3 received medical management; seven underwent PCI and two underwent CABG. The sensitivity of MPI in our study detecting angiographically significant coronary lesions was 60%, specificity was 59.7%, positive predictive and negative predictive values were 27.9% and 85%, respectively (Table 3 ). Table 3 MPI Results and Outcomes of Coronary Angiography Outcomes. Positive CA Negative CA Total Normal-MPI 8 46 54 Abnormal-MPI 12 31 43 Total 20 77 97 Sensitivity: 60%, Specificity: 59.7%, Positive Predictive Value: 27.9%, Negative Predictive Value: 85%, McNemar Chi-Square: 13.56, P-value: <0.001. Among individuals with a normal-MPI (n = 22) with no previous coronary agiography, two experienced a cardiac event (NSTEMI) out of a total of 22 cases. Both underwent coronary angiogram; one underwent PCI while the other had CABG. For those with an abnormal-MPI result who did not undergo coronary angiography (n = 2) due to low risk MPI study with normal Echocardiogram, none had a cardiac event during two years of follow up. Regarding association of all-cause mortality, ACS, and HF with patient factors, smoking had statistically significant association with development of ACS ( p -value = 0.006). There was no significant association of age, gender, BMI, EF or duration of kidney replacement therapy before transplant on development of ACS, HF and death (Table 4 ). Table 4 Statistical Significance of Patient Associated Factors with Study Outcomes ( p -value). Patient factors ACS HF Death Gender 0.2 0.1 0.5 Age 0.8 0.3 0.5 BMI 0.21 0.3 0.9 Smoking 0.006 0.1 0.5 Ejection fraction 0.3 0.5 0.9 Months of dialysis before transplant 0.4 0.8 0.4 Discussion In our study cohort of diabetic transplant candidates we observed similar incidence of major cardiovascular events in normal and abnormal MPI groups over a follow-up of two years post kidney transplant. Ives et al. 16 had reported that the incidence of early cardiovascular events in normal-MPI patients and abnormal-MPI patients was 3.0% and 4.4%, respectively ( p = 0.09). Our study shows higher overall incidence of major cardiovascular events, however similarly showed no statistically significant difference between the two groups. Huck et al. 17 had reported that the association between an abnormal-MPI and cardiovascular events remained statistically insignificant with hazard ratio of 1.39 (C.I. = 0.72–2.66, p -value = 0.33) which is in keeping with findings in our study which does not show a statistically significant association between abnormal-MBI and cardiovascular events. Overall incidence of major cardiovascular events in our study was 10.7%. According to Seoane et al., 9 the overall incidence rate of cardiovascular events is 5.0%, 6.6% and 8.1% after 1st, 3rd and 5th year post kidney transplant, respectively. This higher event rate in our results compared to other cohorts can be explained by the difference in baseline characteristics of study population as we included only diabetic patients in our study. Keldermann et al. 18 stated that MPI had a moderate diagnostic accuracy to predict cardiovascular events in patients with kidney transplant and usually had a high rate of false negative results. Keldermann et al. 18 reported sensitivity and specificity of MPI for detecting coronary lesions detected concomitantly on coronary angiography to be 41% and 96%, respectively. Our study shows higher NPV (85%) than Atkinson et al. 19 (65%), and sensitivity 60% vs 41% while specifictiy was found to be lower in our study (59.7%) as compared to Atkinson et al. 19 (96%) differences can be due to the baseline characteristics of study population and study design. In the study by Atkinson et al, 19 all 47 pre-kidney transplant candidates underwent both, MPI scan and coronary angiogram; they enrolled both diabetic and nondiabetic patients; and it followed patients who had MPI scan but did not proceed to kidney transplant (29 out of 47) while we excluded this group of subjects. The comparison of MPI results with those of coronary angiography in diabetic kidney transplant candidates provides an insight to the diagnostic performance of MPI in detecting coronary artery disease. The positive predictive value (PPV) of 27.9% implies that approximately only 1 in 4 positive MPI results correspond to actual positive findings on coronary angiography. In contrast, the negative predictive value (NPV) of 85% highlights the strength of MPI in ruling out obstructive coronary artery disease. These findings emphasize the importance of considering the strengths and limitations of each diagnostic modality and may provide insight into cardiovascular risk assessment in diabetic kidney transplant candidates. Limitations The retrospective nature of study could introduce selection bias limiting the ability to establish causal relationships between variables. It also limits detection and reporting of cardiovascular events that were managed outside our health facility. Furthermore, a single center study restricts the generalizability of the findings to other populations or settings, potentially reducing the external validity of the study. The follow up period of 2 years post kidney transplant could not capture long term outcomes in diabetic kidney transplant candidates. Conclusion Although MPI demonstrated a high negative predictive value (85%) for excluding obstructive coronary artery disease, it is not a reliable predictor of post kidney transplant adverse cardiovascular events in diabetic kidney transplant candidates. The findings in our study advice against over utilization of stress MPI as a predictor of cardiovascular events post kidney transplantation in patients with diabetes mellitus. Declarations Ethical considerations This retrospective study was conducted in accordance with the ethical standards of Institutional Review Board and adhered to the principles outlined in the Declaration of Helsinki (as revised in 2013). The study protocol was reviewed and approved by the ethics committee. Consent to participate Given the retrospective nature of the study and the use of de-identified data from ESRD patients, the requirement for informed consent was waived by the IRB Consent for publication All patient details have been anonymized to ensure confidentiality in accordance with ethical guidelines and the Declaration of Helsinki. Declaration of conflicting interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article Funding statement The authors declare that this study did not receive any financial support from any individual institute, organization, or group. Data availability Data is provided within the manuscript or supplementary information files. Writing assistance and third party submissions: We utilized Enago article services for grammatical corrections and editing the article to fit according to journal requirements . Author Contribution Dr. Y.A. and Dr. S.A. contributed to the study design. Dr. B.M., Dr. N.O., Dr. L.B., and Dr. S.A. were responsible for writing the paper and conducting data analysis. Dr. L.B., Dr. N.A., Dr. A.A., and Dr. S.A. contributed to the organization of the MS Excel sheet. Dr. B.M., Dr. L.B., and Dr. S.A. contributed to the study introduction and discussion. Data collection was carried out by Dr. L.B., Dr. N.A., Dr. A.A., Dr. N.O., and Dr. S.A. Dr. W.H., Dr. Y.A., and Dr. B.M. oversaw work supervision and coordination. Statistical analysis was performed by Dr. N.S.B. and Dr. S.A.Proofreading of the manuscript was done by Dr. B.M.Dr B.M. is the corresponding author. References Lentine KL, Costa SP, Weir MR et al (2012) Cardiac disease evaluation and management among kidney and liver transplantation candidates: a scientific statement from the American Heart Association and the American College of Cardiology Foundation: endorsed by the American Society of Transplant Surgeons, American Society of Transplantation, and National Kidney Foundation. Circulation 126:617–663 Wolfe RA, Ashby VB, Milford EL et al (1999) Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med 341:1725–1730 Hakeem A, Bhatti S, Chang SM (2014) Screening and risk stratification of coronary artery disease in end-stage renal disease. JACC Cardiovasc Imaging 7:715–728 Lentine KL, Brennan DC, Schnitzler MA (2005) Incidence and predictors of myocardial infarction after kidney transplantation. J Am Soc Nephrol 16:496–506 Kasiske BL, Maclean JR, Snyder JJ (2006) Acute myocardial infarction and kidney transplantation. J Am Soc Nephrol 17:900–907 Lentine KL, Schnitzler MA, Abbott KC et al (2005) De novo congestive heart failure after kidney transplantation: a common condition with poor prognostic implications. Am J Kidney Dis 46:720–733 Lentine KL, Schnitzler MA, Abbott KC et al (2006) Incidence, predictors, and associated outcomes of atrial fibrillation after kidney transplantation. Clin J Am Soc Nephrol 1:288–296 Ramanathan V, Goral S, Tanriover B et al (2005) Screening asymptomatic diabetic patients for coronary artery disease prior to renal transplantation. Transplantation 79:1453–1458 Seoane-Pillado MT, Pita-Fernández S, Valdés-Cañedo F et al (2017) Incidence of cardiovascular events and associated risk factors in kidney transplant patients: a competing risks survival analysis. BMC Cardiovasc Disord 17:72 Gowdak LHW, de Paula FJ, César LAM et al (2013) A new risk score model to predict the presence of significant coronary artery disease in kidney transplant candidates. Transpl Res 2:18 Herzog AL, Kalogirou C, Wanner C et al (2020) Comparison of different algorithms for the assessment of cardiovascular risk after kidney transplantation by the time of entering waiting list. Clin Kidney J 13:150–158 Pilmore H (2006) Cardiac assessment for renal transplantation. Am J Transpl 6:659–665 Yilmaz KC, Akgün AN, Ciftci O et al (2019) Preoperative cardiac risk assessment in renal transplant recipients: a single-center experience. Exp Clin Transpl 17:478–482 Nasr G, Cao L, Chua F et al (2021) A novel risk prediction model for preoperative cardiac clearance in chronic kidney disease patients on hemodialysis undergoing kidney transplant evaluation. J Am Coll Cardiol 77:93–93 Wong CF, Little MA, Vinjamuri S et al (2008) Technetium myocardial perfusion scanning in prerenal transplant evaluation in the United Kingdom. Transplant Proc . ; 40: 1324–1328 Ives CW, AlJaroudi WA, Kumar V et al (2018) Prognostic value of myocardial perfusion imaging performed pre-renal transplantation: post-transplantation follow-up and outcomes. Eur J Nucl Med Mol Imaging 45:1998–2008 Huck DM, Weber B, Schreiber B et al (2024) Comparative effectiveness of PET and SPECT MPI for predicting cardiovascular events after kidney transplant. Circ Cardiovasc Imaging 17:e015858 Kelderman JR, Jolink FEJ, Benjamens S et al (2022) Diagnostic accuracy of myocardial perfusion imaging in patients evaluated for kidney transplantation: a systematic review and meta-analysis. J Nucl Cardiol 29:3405–3415 Atkinson P, Chiu DYY, Sharma R et al (2011) Predictive value of myocardial and coronary imaging in the long-term outcome of potential renal transplant recipients. Int J Cardiol Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6789799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466040968,"identity":"c39f3a29-11b7-4e71-9fd1-411ac6122d28","order_by":0,"name":"Shahad Alaydarous","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Shahad","middleName":"","lastName":"Alaydarous","suffix":""},{"id":466040969,"identity":"a66baf63-9860-4eda-ae29-db1e98e7a5f6","order_by":1,"name":"Yasir Alfi","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Yasir","middleName":"","lastName":"Alfi","suffix":""},{"id":466040970,"identity":"1b35adbd-0b4b-4736-9b56-211d2ac99acd","order_by":2,"name":"Bilal Mohsin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3QsWrDMBCA4Ts8dCpZFQx5g4LB4MTgPEkXaXEn7xlCUTBcpuzJW2QKGV0E7RLwKvCSUOjsbO1WKQ0dApbXDPonIfRxkgB8vjssCgIJR7sAlHCWdg8XlZuYk/xKcCOBGSLdBP4JQPB4IQBOMn7ARcthNxoPFH1O99nr01KZKfPsuYukJZaMQxOna7GMi0POkoMw5D0vZNfFFEpLxFYjhQUpllSGoFQuUn5fSP1GYWpJfeol9DelEhSiJbpninkLTXjUxJEWNFxRPtxpM4U73mI+Sul21oyi+uOL/VA2SOqX07GdZ53ker3bDe487vP5fL6+fgE3dmQ3KyK8JQAAAABJRU5ErkJggg==","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Bilal","middleName":"","lastName":"Mohsin","suffix":""},{"id":466040971,"identity":"85a52120-8a01-4663-9e10-ce6f0752878f","order_by":3,"name":"Lujain Bashamakh","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Lujain","middleName":"","lastName":"Bashamakh","suffix":""},{"id":466040972,"identity":"34177757-8757-479e-a41c-419ce23fa2d6","order_by":4,"name":"Nasser Odah","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Nasser","middleName":"","lastName":"Odah","suffix":""},{"id":466040973,"identity":"baa0bb52-4a4b-4d22-9502-3ac1810d7845","order_by":5,"name":"Naief Alhowaiti","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Naief","middleName":"","lastName":"Alhowaiti","suffix":""},{"id":466040974,"identity":"b1399a11-b897-462e-82f3-50e78ebc739b","order_by":6,"name":"Aseel Attar","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Aseel","middleName":"","lastName":"Attar","suffix":""},{"id":466040975,"identity":"571facae-a9d9-4412-9009-3e7502306d18","order_by":7,"name":"Nadeem Shafique Butt","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Nadeem","middleName":"Shafique","lastName":"Butt","suffix":""},{"id":466040976,"identity":"cbaa373c-b0e1-4728-9bd9-fb58e0d23548","order_by":8,"name":"Wael Habhab","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Wael","middleName":"","lastName":"Habhab","suffix":""}],"badges":[],"createdAt":"2025-05-31 08:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6789799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6789799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84206605,"identity":"ccc3a8b3-0180-4818-9f31-60e3246e5765","added_by":"auto","created_at":"2025-06-09 09:13:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47838,"visible":true,"origin":"","legend":"\u003cp\u003eCoronary angiography finding in patient who had normal nuclear scan. MPI: myocardial perfusion image. CAD: Coronary Artery disease, PCI: Primary Cutaneous Intervention.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6789799/v1/875c56958ae03c9fe4b2a470.png"},{"id":84206610,"identity":"acb7a1d2-8aa1-4c6a-8412-fb1720dd4f81","added_by":"auto","created_at":"2025-06-09 09:13:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53499,"visible":true,"origin":"","legend":"\u003cp\u003eCoronary angiography finding in patient who had abnormal nuclear scan. MPI: myocardial perfusion image. CAD: Coronary Artery disease, PCI: Primary Cutaneous Intervention, CABG: Coronary Artery Bypass Grafting\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6789799/v1/9bb482ef9cf748f4587830c6.png"},{"id":86067304,"identity":"ab0155fa-7d15-4cc9-bcee-e13c24a64f15","added_by":"auto","created_at":"2025-07-05 12:01:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":959400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6789799/v1/65619c98-4c54-4cef-bd0c-faaf26f326fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accuracy of Myocardial Perfusion Imaging (MPI) in Predicting Post Transplant Cardiovascular Events among Diabetic Kidney Transplant Candidates: A tertiary care centre experience from Saudi Arabia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney transplant is the preferred treatment option for End Stage Kidney disease (ESKD) as it improves clinical outcomes and quality of life as compared to other kidney replacement therapy options.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Coronary artery disease (CAD) is the most common cause of death in ESKD patients.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The risk of cardiovascular disease (CVD) remains a major concern before, during and after kidney transplantation.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The risk of cardiovascular events remains significant after kidney transplantation, and can reach 5.0%, 6.6% and 8.1% after the 1st, 3rd and 5th years respectively, with long term cardiovascular mortality of 30\u0026ndash;50% in kidney transplant recipients.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Therefore, prior to kidney transplant cardiovascular risk assessment has an important role in evaluation of patients.\u003c/p\u003e \u003cp\u003eCoronary angiography is the gold standard to diagnose CAD but it is an invasive procedure with associated complications.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Therefore, non-invasive investigations are commonly used to assess CAD prior to kidney transplant especially in asymptomatic patients.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Myocardial perfusion imaging (MPI) is a non-invasive, sensitive and specific investigation to assess cardiac perfusion and its response to increased demand. MPI is frequently utilized to assess cardiovascular risk in kidney transplant candidates. Candidates with previous history or active symptoms of CAD or with abnormal initial cardiac assessment are usually investigated through coronary angiography.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThere is a low threshold for invasive cardiac investigations in asymptomatic diabetic candidates undergoing pre kidney transplant workup. The role of MPI and coronary angiography has not been well studied in this group. Various screening pathways had been suggested by different healthcare institutions worldwide to assess the risks for cardiovascular events in kidney transplant candidates including candidates with history of diabetes mellitus. A standardized cardiovascular risk assessment tool still remains undetermined, non-uniformed, and conflicting globally.\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e This study aims to evaluate the predictive role of MPI in asymptomatic diabetic candidates for post-kidney transplant cardiovascular events.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis retrospective, single-center observational study was conducted at a large kidney transplant center in Saudi Arabia. Adult diabetic patients with end-stage kidney disease (ESKD), aged over 25 years at their initial visit and who underwent cardiac risk assessment using myocardial perfusion imaging (MPI) prior to kidney transplant between January 2017 and December 2019, were included. Patients were followed for 2 years post-kidney transplant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient Selection\u003c/h3\u003e\n\u003cp\u003eInclusion criteria comprised adult patients with diabetes and ESKD who had undergone stress MPI as part of their pre-transplant cardiac workup and who underwent kidney transplantation with availability of follow up data for 2 years. Exclusion criteria included non-diabetic patients, patients with known coronary artery disease (CAD), those with incomplete follow up details and those who did not proceed to transplantation.\u003c/p\u003e \u003cp\u003eAs per institutional protocol, patients with type 2 diabetes for more than 10 years or type 1 diabetes for more than 25 years were recommended to undergo coronary angiography in addition to MPI.\u003c/p\u003e\n\u003ch3\u003eMyocardial Perfusion Imaging Protocol\u003c/h3\u003e\n\u003cp\u003eStress MPI was performed using the single-photon emission computed tomography (SPECT) technique with stress induced via exercise in mobile patients and use of pharmacological agents(dipyridamole) in patients with limited mobility.\u003c/p\u003e \u003cp\u003eFor exercise stress MPI, patients were connected to ECG electrodes; together with frequent monitoring of blood pressure heart rate and any new symptoms. The patient was exercised according to the Bruce protocol with the goal to achieve 10 METS (Metabolic Equivalents), and to reach 85% of MPHR (Maximum Predicted Heart Rate). In patient who developed new onset chest pain; syncopy; ECG changes; or unexpected changes of blood pressure; the exercise stress test was aborted.\u003c/p\u003e \u003cp\u003eThe pharmacolgical stress test was conducted in patients with limited mobility while using dipyridamole ( 0.56 mg/kg dose) with monitoring for pulse, blood pressure, ECG changes and any concerning symptoms.\u003c/p\u003e \u003cp\u003eThe studied parameters including reversible or irreversible perfusion defects and Left ventricular Ejection fraction\u003c/p\u003e \u003cp\u003eTc-99m sestamibi was administered during rest and stress phase. Rest imaging was acquired approximately 60 minutes post-injection while stress imaging was taken during exercise or pharmacological stress phase. A dual-detector gamma camera with attenuation correction and truncation compensation was used. All imaging followed the latest recommendations from the American Society of Nuclear Cardiology (ASNC).\u003c/p\u003e \u003cp\u003eVisual interpretation was performed using Auto SPECT software, and analysis was conducted by experienced nuclear medicine specialists. Images were reoriented into short-axis, vertical long-axis, and horizontal long-axis views and assessed across 17 myocardial segments using a 4-point scale:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;normal\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;mild perfusion defect\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;moderate perfusion defect\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;severe perfusion defect\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSummed Stress Scores (SSS), Summed Rest Scores (SRS), and Summed Difference Scores (SDS\u0026thinsp;=\u0026thinsp;SSS\u0026thinsp;\u0026minus;\u0026thinsp;SRS) were calculated. SDS values were categorized as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u0026thinsp;=\u0026thinsp;nonischemic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e4\u0026ndash;8\u0026thinsp;=\u0026thinsp;mild ischemia\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e8\u0026thinsp;=\u0026thinsp;moderate to severe ischemia\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eGated SPECT was used to calculate the left ventricular ejection fraction (LVEF), with LVEF\u0026thinsp;\u0026gt;\u0026thinsp;50% at rest considered normal. Final MPI interpretations incorporated both perfusion and functional parameters. Results were reviewed by a cardiologist who determined the need for further investigation, including coronary angiography.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNormal MPI study\u003c/strong\u003e \u003cp\u003eNormal MPI study is defined as a test result indicating homogeneous and adequate myocardial blood flow during both rest and stress conditions. There is no evidence of reversible or fixed perfusion defects\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAbnormal MPI study: Abnormal MPI study is a test result demonstrating diminished myocardial blood flow, either transient (suggesting ischemia) or permanent (indicating infarction or scar tissue) during during rest or stress conditions\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003ePatients were categorized into normal and abnormal MPI groups, and outcomes were compared accordingly.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrimary Outcome\u003c/strong\u003e \u003cp\u003eA composite of death, acute coronary syndrome (ACS), coronary revascularization, hospitalization for acute decompensated heart failure (ADHF), and cerebrovascular accident (CVA).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSecondary Outcomes\u003c/strong\u003e \u003cp\u003eEach of the primary outcome components evaluated separately.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAdditionally, among patients who underwent coronary angiography, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MPI in detecting obstructive CAD were calculated.\u003c/p\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003e The study was approved by the institutional ethical review board, and informed consent was waived due to the retrospective design. All patients had provided informed consent for pre-transplant workup and kidney transplantation. To protect patient confidentiality, a unique study code was assigned to each participant, and only authorized research team members had access to personal identifiers.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eData were extracted retrospectively from electronic medical records. Baseline demographic and clinical data\u0026mdash;including age, gender, smoking status, comorbidities, dialysis modality prior to transplant, LVEF, and primary renal diagnosis\u0026mdash;were collected. MPI and angiographic findings, as well as outcome data, were documented for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 25. Continuous variables were described using mean and standard deviation for normally distributed data and median with interquartile ranges for skewed distributions. Categorical data were summarized using frequencies and percentages. Comparisons between groups were made using the Chi-square test or Fisher\u0026rsquo;s exact test for categorical variables, and t-tests for continuous variables. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Univariate analysis was conducted to identify risk factors for cardiovascular events and mortality, followed by multivariable regression for variables with significant univariate associations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 640 patients who underwent kidney transplant in KFSHRC from January, 2017 to December, 2019 were screened and 121 patients were included in the study. 537 patient were excluded (398 patients were not diabetics, 19 patients already known to have CAD, 42 patients had no stress test done prior to transplant and 60 patients were less than 25 years old). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describe the socio-demographic and clinical characteristics of study population in both groups (normal and abnormal-MPI). In terms of gender distribution, more male patients were represented in both groups, with a median age being 53.74 years in the normal-MPI group and 53.36 years in the abnormal-MPI group. The Ejection Fraction, was 50% or more in 96.1% of patients in the normal-MPI group and 86.7% in the Abormal-MPI group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-Demographic Characteristics and Comorbidities of the Participants by Myocardial Perfusion Imaging (MPI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal-MPI (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbormal-MPI (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;121)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (62.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (62.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Median) years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u0026ndash;77 (53.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;70 (53.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u0026ndash;77 (53.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (47.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM type 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM type2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (94.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (95.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate aortic stenosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate TR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEjection fraction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (96.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (92.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary disease responsible for kidney failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth DM and HTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive uropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolycystic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDialysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (77.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (82.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (97.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (91.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (94.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeritoneal (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCA findings prior to kidney transplant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal coronaries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-obstructive CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD without PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD with PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD with CABG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Coronary angiography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDM: Diabetes mellites, HTN: Hypertension, TR: Tricuspid regurgitation, MR: Mitral regurgitation, MS: Mitral stenosis, GN: Glomerulonephritis, HD: Hemodialysis CAD: Coronary angiography, PCI: Percutaneous intervention, CABG: Coronary artery bypass graft, ACS: Acute coronary angiography, US: Unstable Angina, NSTEMI: Non-ST-elevation myocardial infarction STEMI: ST-elevation myocardial infarction.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the distribution and percentages of various outcomes in association with MPI results in both groups. The primary composite outcome was observed in 11.8% (n\u0026thinsp;=\u0026thinsp;9) in the normal-MPI group as compared to 8.9% (n\u0026thinsp;=\u0026thinsp;4) in abnormal-MPI group, with no statistically significant difference. Hospitalizations for heart failure were observed in 1.3% in the normal-MPI group, as compared to 8.9% in the abnormal-MPI group, however the difference did not reach statistical significance (p-value 0.063). Other secondary outcomes including Death, ACS, revascularization and CVA also were not different between the 2 groups with p-values of 0.29, 0.71, 0.29 and 0.63 respectively. Three deaths were reported in the study, all of them in the normal-MPI group, with 2 of them being attributed to septic shock and 1 due to massive pulmonary embolism (PE).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Primary and Secondary Outcomes Based on MPI Results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNormal-MPI (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAbnormal-MPI (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Composite Outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute coronary syndrome ACS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization for heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn our study group, 80.16% (n\u0026thinsp;=\u0026thinsp;97) out of 121 candidates underwent Coronary angiography (CA) prior to kidney transplant, 54 in normal-MPI group and 43 in abnormal-MPI group (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt revealed normal coronaries in 22.4% (n\u0026thinsp;=\u0026thinsp;17) of patients in normal-MPI group and 40% (n\u0026thinsp;=\u0026thinsp;18) of patients with abnormal-MPI group. Non-obstructive coronary artery disease (CAD) was found in 38.2% (n\u0026thinsp;=\u0026thinsp;29) of the normal-MPI group and 28.9% (n\u0026thinsp;=\u0026thinsp;13) of the abnormal-MPI group. In normal-MPI group, 8(15%) patients had obstructive coronary artery disease out of whom six underwent medical management and two underwent PCI. In the abnormal-MPI group, 12 (26.6%) has obstructive coronary artery disease out of whom 3 received medical management; seven underwent PCI and two underwent CABG.\u003c/p\u003e \u003cp\u003eThe sensitivity of MPI in our study detecting angiographically significant coronary lesions was 60%, specificity was 59.7%, positive predictive and negative predictive values were 27.9% and 85%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMPI Results and Outcomes of Coronary Angiography Outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive CA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative CA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal-MPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbnormal-MPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSensitivity: 60%, Specificity: 59.7%, Positive Predictive Value: 27.9%, Negative Predictive Value: 85%, McNemar Chi-Square: 13.56, P-value: \u0026lt;0.001.\u003c/p\u003e \u003cp\u003eAmong individuals with a normal-MPI (n\u0026thinsp;=\u0026thinsp;22) with no previous coronary agiography, two experienced a cardiac event (NSTEMI) out of a total of 22 cases. Both underwent coronary angiogram; one underwent PCI while the other had CABG. For those with an abnormal-MPI result who did not undergo coronary angiography (n\u0026thinsp;=\u0026thinsp;2) due to low risk MPI study with normal Echocardiogram, none had a cardiac event during two years of follow up.\u003c/p\u003e \u003cp\u003eRegarding association of all-cause mortality, ACS, and HF with patient factors, smoking had statistically significant association with development of ACS (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.006). There was no significant association of age, gender, BMI, EF or duration of kidney replacement therapy before transplant on development of ACS, HF and death (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Significance of Patient Associated Factors with Study Outcomes (\u003cem\u003ep\u003c/em\u003e-value).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEjection fraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonths of dialysis before transplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study cohort of diabetic transplant candidates we observed similar incidence of major cardiovascular events in normal and abnormal MPI groups over a follow-up of two years post kidney transplant. Ives et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e had reported that the incidence of early cardiovascular events in normal-MPI patients and abnormal-MPI patients was 3.0% and 4.4%, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09). Our study shows higher overall incidence of major cardiovascular events, however similarly showed no statistically significant difference between the two groups. Huck et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e had reported that the association between an abnormal-MPI and cardiovascular events remained statistically insignificant with hazard ratio of 1.39 (C.I. = 0.72\u0026ndash;2.66, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.33) which is in keeping with findings in our study which does not show a statistically significant association between abnormal-MBI and cardiovascular events.\u003c/p\u003e \u003cp\u003eOverall incidence of major cardiovascular events in our study was 10.7%. According to Seoane et al.,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e the overall incidence rate of cardiovascular events is 5.0%, 6.6% and 8.1% after 1st, 3rd and 5th year post kidney transplant, respectively. This higher event rate in our results compared to other cohorts can be explained by the difference in baseline characteristics of study population as we included only diabetic patients in our study.\u003c/p\u003e \u003cp\u003eKeldermann et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e stated that MPI had a moderate diagnostic accuracy to predict cardiovascular events in patients with kidney transplant and usually had a high rate of false negative results. Keldermann et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e reported sensitivity and specificity of MPI for detecting coronary lesions detected concomitantly on coronary angiography to be 41% and 96%, respectively. Our study shows higher NPV (85%) than Atkinson et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (65%), and sensitivity 60% vs 41% while specifictiy was found to be lower in our study (59.7%) as compared to Atkinson et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (96%) differences can be due to the baseline characteristics of study population and study design. In the study by Atkinson et al,\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e all 47 pre-kidney transplant candidates underwent both, MPI scan and coronary angiogram; they enrolled both diabetic and nondiabetic patients; and it followed patients who had MPI scan but did not proceed to kidney transplant (29 out of 47) while we excluded this group of subjects.\u003c/p\u003e \u003cp\u003eThe comparison of MPI results with those of coronary angiography in diabetic kidney transplant candidates provides an insight to the diagnostic performance of MPI in detecting coronary artery disease. The positive predictive value (PPV) of 27.9% implies that approximately only 1 in 4 positive MPI results correspond to actual positive findings on coronary angiography. In contrast, the negative predictive value (NPV) of 85% highlights the strength of MPI in ruling out obstructive coronary artery disease. These findings emphasize the importance of considering the strengths and limitations of each diagnostic modality and may provide insight into cardiovascular risk assessment in diabetic kidney transplant candidates.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe retrospective nature of study could introduce selection bias limiting the ability to establish causal relationships between variables. It also limits detection and reporting of cardiovascular events that were managed outside our health facility. Furthermore, a single center study restricts the generalizability of the findings to other populations or settings, potentially reducing the external validity of the study. The follow up period of 2 years post kidney transplant could not capture long term outcomes in diabetic kidney transplant candidates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAlthough MPI demonstrated a high negative predictive value (85%) for excluding obstructive coronary artery disease, it is not a reliable predictor of post kidney transplant adverse cardiovascular events in diabetic kidney transplant candidates. The findings in our study advice against over utilization of stress MPI as a predictor of cardiovascular events post kidney transplantation in patients with diabetes mellitus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the ethical standards of Institutional Review Board and adhered to the principles outlined in the Declaration of Helsinki (as revised in 2013). The study protocol was reviewed and approved by the ethics committee.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the retrospective nature of the study and the use of de-identified data from ESRD patients, the requirement for informed consent was waived by the IRB\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patient details have been anonymized to ensure confidentiality in accordance with ethical guidelines and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study did not receive any financial support from any individual institute, organization, or group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting assistance and third party submissions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized Enago article services for grammatical corrections and editing the article to fit according to journal requirements\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Y.A. and Dr. S.A. contributed to the study design. Dr. B.M., Dr. N.O., Dr. L.B., and Dr. S.A. were responsible for writing the paper and conducting data analysis. Dr. L.B., Dr. N.A., Dr. A.A., and Dr. S.A. contributed to the organization of the MS Excel sheet. Dr. B.M., Dr. L.B., and Dr. S.A. contributed to the study introduction and discussion. Data collection was carried out by Dr. L.B., Dr. N.A., Dr. A.A., Dr. N.O., and Dr. S.A. Dr. W.H., Dr. Y.A., and Dr. B.M. oversaw work supervision and coordination. Statistical analysis was performed by Dr. N.S.B. and Dr. S.A.Proofreading of the manuscript was done by Dr. B.M.Dr B.M. is the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLentine KL, Costa SP, Weir MR et al (2012) Cardiac disease evaluation and management among kidney and liver transplantation candidates: a scientific statement from the American Heart Association and the American College of Cardiology Foundation: endorsed by the American Society of Transplant Surgeons, American Society of Transplantation, and National Kidney Foundation. Circulation 126:617\u0026ndash;663\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolfe RA, Ashby VB, Milford EL et al (1999) Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med 341:1725\u0026ndash;1730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHakeem A, Bhatti S, Chang SM (2014) Screening and risk stratification of coronary artery disease in end-stage renal disease. JACC Cardiovasc Imaging 7:715\u0026ndash;728\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLentine KL, Brennan DC, Schnitzler MA (2005) Incidence and predictors of myocardial infarction after kidney transplantation. J Am Soc Nephrol 16:496\u0026ndash;506\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasiske BL, Maclean JR, Snyder JJ (2006) Acute myocardial infarction and kidney transplantation. J Am Soc Nephrol 17:900\u0026ndash;907\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLentine KL, Schnitzler MA, Abbott KC et al (2005) De novo congestive heart failure after kidney transplantation: a common condition with poor prognostic implications. Am J Kidney Dis 46:720\u0026ndash;733\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLentine KL, Schnitzler MA, Abbott KC et al (2006) Incidence, predictors, and associated outcomes of atrial fibrillation after kidney transplantation. Clin J Am Soc Nephrol 1:288\u0026ndash;296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamanathan V, Goral S, Tanriover B et al (2005) Screening asymptomatic diabetic patients for coronary artery disease prior to renal transplantation. Transplantation 79:1453\u0026ndash;1458\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeoane-Pillado MT, Pita-Fern\u0026aacute;ndez S, Vald\u0026eacute;s-Ca\u0026ntilde;edo F et al (2017) Incidence of cardiovascular events and associated risk factors in kidney transplant patients: a competing risks survival analysis. BMC Cardiovasc Disord 17:72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGowdak LHW, de Paula FJ, C\u0026eacute;sar LAM et al (2013) A new risk score model to predict the presence of significant coronary artery disease in kidney transplant candidates. Transpl Res 2:18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerzog AL, Kalogirou C, Wanner C et al (2020) Comparison of different algorithms for the assessment of cardiovascular risk after kidney transplantation by the time of entering waiting list. Clin Kidney J 13:150\u0026ndash;158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePilmore H (2006) Cardiac assessment for renal transplantation. Am J Transpl 6:659\u0026ndash;665\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz KC, Akg\u0026uuml;n AN, Ciftci O et al (2019) Preoperative cardiac risk assessment in renal transplant recipients: a single-center experience. Exp Clin Transpl 17:478\u0026ndash;482\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasr G, Cao L, Chua F et al (2021) A novel risk prediction model for preoperative cardiac clearance in chronic kidney disease patients on hemodialysis undergoing kidney transplant evaluation. J Am Coll Cardiol 77:93\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong CF, Little MA, Vinjamuri S et al (2008) Technetium myocardial perfusion scanning in prerenal transplant evaluation in the United Kingdom. \u003cem\u003eTransplant Proc\u003c/em\u003e. ; 40: 1324\u0026ndash;1328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIves CW, AlJaroudi WA, Kumar V et al (2018) Prognostic value of myocardial perfusion imaging performed pre-renal transplantation: post-transplantation follow-up and outcomes. Eur J Nucl Med Mol Imaging 45:1998\u0026ndash;2008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuck DM, Weber B, Schreiber B et al (2024) Comparative effectiveness of PET and SPECT MPI for predicting cardiovascular events after kidney transplant. Circ Cardiovasc Imaging 17:e015858\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelderman JR, Jolink FEJ, Benjamens S et al (2022) Diagnostic accuracy of myocardial perfusion imaging in patients evaluated for kidney transplantation: a systematic review and meta-analysis. J Nucl Cardiol 29:3405\u0026ndash;3415\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkinson P, Chiu DYY, Sharma R et al (2011) Predictive value of myocardial and coronary imaging in the long-term outcome of potential renal transplant recipients. Int J Cardiol\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute coronary syndrome, coronary angiography, diabetes mellitus, Kidney transplant, myocardial perfusion imaging","lastPublishedDoi":"10.21203/rs.3.rs-6789799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6789799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular risk assessment is crucial before kidney transplantation, especially for diabetic patients, as 6.6% experience major adverse cardiovascular events (MACE) within three years. Myocardial perfusion imaging (MPI) is commonly used to evaluate this risk.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study assessed MPI\u0026rsquo;s ability to predict post-transplant cardiovascular events in asymptomatic diabetic candidates by examining its diagnostic accuracy for obstructive coronary artery disease (CAD) compared to coronary angiography and analyzing MACE incidence over two years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective, single-center study, 121 diabetic patients who underwent stress MPI before kidney transplantation were followed for two years post-transplant. The primary outcome included death, acute coronary syndrome (ACS), coronary revascularization, acute decompensated heart failure (ADHF), and cerebrovascular accident (CVA). Diagnostic metrics for MPI were evaluated in candidates undergoing pre-transplant coronary angiography.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 121 candidates, 76 had normal MPI, and 45 had abnormal MPI. MACE occurred in 11.8% of normal MPI patients and 8.9% of abnormal MPI patients. MPI showed sensitivity of 60%, specificity of 59.7%, NPV of 85%, and PPV of 27.9% for obstructive CAD detection.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDespite NPV of 85% for ruling out obstructive coronary artery disease, stress MPI is not a reliable predictor of post kidney transplant adverse cardiovascular events in diabetic kidney transplant candidates. Findings of normal MPI should be interpreted with caution and we recommend against over utilization of stress MPI in diabetic kidney transplant candidates to avoid post-transplant cardiovascular events.\u003c/p\u003e","manuscriptTitle":"Accuracy of Myocardial Perfusion Imaging (MPI) in Predicting Post Transplant Cardiovascular Events among Diabetic Kidney Transplant Candidates: A tertiary care centre experience from Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 09:13:23","doi":"10.21203/rs.3.rs-6789799/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5353064c-db35-498c-af1a-bc15eb1d742f","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 09:13:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6789799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6789799","identity":"rs-6789799","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}