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437 results on '"Shah, Anoop S V"'

1. Prognostic significance of troponin in patients with malignancy (NIHR Health Informatics Collaborative TROP-MALIGNANCY study)

Author

Samuel, Nathan A., Roddick, Alistair, Glampson, Ben, Mulla, Abdulrahim, Davies, Jim, Papadimitriou, Dimitri, Panoulas, Vasileios, Mayer, Erik, Woods, Kerrie, Shah, Anoop D., Gautama, Sanjay, Elliott, Paul, Hemmingway, Harry, Williams, Bryan, Asselbergs, Folkert W., Melikian, Narbeh, Kharbanda, Rajesh, Shah, Ajay M., Perera, Divaka, Patel, Riyaz S., Channon, Keith M., Mayet, Jamil, Shah, Anoop S. V., and Kaura, Amit

Published
2024
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2. The burden and natural history of cardiac pathology at TB diagnosis in a high-HIV prevalence district in Zambia: protocol for the TB-Heart study

Author

Scopazzini, Marcello S., Chansa, Pamela, Majonga, Edith D., Bual, Nina, Schaap, Albertus, Mateyo, Kondwelani J., Musukuma, Remmy, Mweemba, Veronica, Cheeba, Maina, Mwila, Chipili C., Sigande, Lucheka, Banda, Isabel, Ngulube, Joseph, Shanaube, Kwame, Zenner, Dominik, Ayles, Helen, and Shah, Anoop S. V.

Published
2024
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3. Trajectories of cardiac troponin in the decades before cardiovascular death: a longitudinal cohort study

Author

Kimenai, Dorien M., Anand, Atul, de Bakker, Marie, Shipley, Martin, Fujisawa, Takeshi, Lyngbakken, Magnus N., Hveem, Kristian, Omland, Torbjørn, Valencia-Hernández, Carlos A., Lindbohm, Joni V., Kivimaki, Mika, Singh-Manoux, Archana, Strachan, Fiona E., Shah, Anoop S. V., Kardys, Isabella, Boersma, Eric, Brunner, Eric J., and Mills, Nicholas L.

Published
2023
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4. Comparison of prehospital and in‐hospital HEART scores in patients with possible myocardial infarction

Author

Cooper, Jamie G., primary, Donaldson, Lorna A., additional, Coutts, Amanda J., additional, Black, Kim M. M., additional, Ferguson, James, additional, Livock, Kate J., additional, Horrill, Judith L., additional, Davidson, Elaine M., additional, Scott, Neil W., additional, Lee, Amanda J., additional, Fujisawa, Takeshi, additional, Lee, Kuan Ken, additional, Anand, Atul, additional, Shah, Anoop S. V., additional, and Mills, Nicholas L., additional

Published
2024
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6. Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis

Author

Mills, Nicholas L, Strachan, Fiona E, Tuck, Christopher, Shah, Anoop SV, Anand, Atul, Chapman, Andrew R, Ferry, Amy V, Lee, Kuan Ken, Doudesis, Dimitrios, Bularga, Anda, Wereski, Ryan, Taggart, Caelan, Lowry, Matthew TH, Mendusic, Filip, Kimenai, Dorien M, Sandeman, Dennis, Adamson, Philip D, Stables, Catherine L, Vallejos, Catalina A, Tsanas, Athanasios, Marshall, Lucy, Stewart, Stacey D, Fujisawa, Takeshi, Hautvast, Mischa, McPherson, Jean, McKinlay, Lynn, Ford, Ian, Newby, David E, Fox, Keith AA, Berry, Colin, Walker, Simon, Weir, Christopher J, Gray, Alasdair, Collinson, Paul O, Apple, Fred S, Reid, Alan, Cruikshank, Anne, Findlay, Iain, Amoils, Shannon, McAllister, David A, Maguire, Donogh, Stevens, Jennifer, Norrie, John, Andrews, Jack PM, Moss, Alastair, Anwar, Mohamed S, Hung, John, Malo, Jonathan, Fischbacher, Colin, Croal, Bernard L, Leslie, Stephen J, Keerie, Catriona, Parker, Richard A, Walker, Allan, Harkess, Ronnie, Wackett, Tony, Armstrong, Roma, Stirling, Laura, MacDonald, Claire, Sadat, Imran, Finlay, Frank, Charles, Heather, Linksted, Pamela, Young, Stephen, Alexander, Bill, Duncan, Chris, Yang, Jason, Shah, Anoop S V, Pickering, John W, and Than, Martin P

Published
2022
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11. Application of the Universal Definition of Myocardial Infarction in Clinical Practice in Scotland and Sweden

Author

Taggart, Caelan, Roos, Andreas, Kadesjö, Erik, Anand, Atul, Li, Ziwen, Doudesis, Dimitrios, Lee, Kuan Ken, Bularga, Anda, Wereski, Ryan, Lowry, Matthew T. H., Chapman, Andrew R., Ferry, Amy V., Shah, Anoop S. V., Gard, Anton, Lindahl, Bertil, Edgren, Gustaf, Mills, Nicholas L., Kimenai, Dorien M., Taggart, Caelan, Roos, Andreas, Kadesjö, Erik, Anand, Atul, Li, Ziwen, Doudesis, Dimitrios, Lee, Kuan Ken, Bularga, Anda, Wereski, Ryan, Lowry, Matthew T. H., Chapman, Andrew R., Ferry, Amy V., Shah, Anoop S. V., Gard, Anton, Lindahl, Bertil, Edgren, Gustaf, Mills, Nicholas L., and Kimenai, Dorien M.

Abstract

Importance: Whether the diagnostic classifications proposed by the universal definition of myocardial infarction (MI) to identify type 1 MI due to atherothrombosis and type 2 MI due to myocardial oxygen supply-demand imbalance have been applied consistently in clinical practice is unknown. Objective: To evaluate the application of the universal definition of MI in consecutive patients with possible MI across 2 health care systems. Design, Setting, and Participants: This cohort study used data from 2 prospective cohorts enrolling consecutive patients with possible MI in Scotland (2013-2016) and Sweden (2011-2014) to assess accuracy of clinical diagnosis of MI recorded in hospital records for patients with an adjudicated diagnosis of type 1 or type 2 MI. Data were analyzed from August 2022 to February 2023. Main Outcomes and Measures: The main outcome was the proportion of patients with a clinical diagnosis of MI recorded in the hospital records who had type 1 or type 2 MI, adjudicated by an independent panel according to the universal definition. Characteristics and risk of subsequent MI or cardiovascular death at 1 year were compared. Results: A total of 50 356 patients were assessed. The cohort from Scotland included 28 783 (15 562 men [54%]; mean [SD] age, 60 [17] years), and the cohort from Sweden included 21 573 (11 110 men [51%]; mean [SD] age, 56 [17] years) patients. In Scotland, a clinical diagnosis of MI was recorded in 2506 of 3187 patients with an adjudicated diagnosis of type 1 MI (79%) and 122 of 716 patients with an adjudicated diagnosis of type 2 MI (17%). Similar findings were observed in Sweden, with 970 of 1111 patients with adjudicated diagnosis of type 1 MI (87%) and 57 of 251 patients with adjudicated diagnosis of type 2 MI (23%) receiving a clinical diagnosis of MI. Patients with an adjudicated diagnosis of type 1 MI without a clinical diagnosis were more likely to be women (eg, 336 women [49%] vs 909 women [36%] in Scotland; P < .001) and o

Published
2024
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12. Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis

Author

Butterly, Elaine W., Hanlon, Peter, Shah, Anoop S. V., Hannigan, Laurie J., McIntosh, Emma, Lewsey, Jim, Wild, Sarah H., Guthrie, Bruce, Mair, Frances S., Kent, David M., Dias, Sofia, Welton, Nicky J., and McAllister, David A.

Subjects
Comorbidity -- Risk factors -- Demographic aspects, Chronic diseases -- Demographic aspects -- Patient outcomes, Quality of life -- Health aspects, Biological sciences
Abstract

Background Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. Methods and findings Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had [greater than or equal to]300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (P.sub.Bayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], P.sub.Bayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], P.sub.Bayes = 0.956 and -0.05 [-0.10 to 0.01], P.sub.Bayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], P.sub.Bayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], P.sub.Bayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], P.sub.Bayes = 0.631. Conclusions Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. Trial registration A prespecified protocol was registered on PROSPERO (CRD42018048202)., Author(s): Elaine W. Butterly 1, Peter Hanlon 1, Anoop S. V. Shah 2, Laurie J. Hannigan 3,4,5, Emma McIntosh 1, Jim Lewsey 1, Sarah H. Wild 6, Bruce Guthrie 6, [...]

Published
2023
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13. Adverse health effects associated with household air pollution: a systematic review, meta-analysis, and burden estimation study

Author

Lee, Kuan Ken, Bing, Rong, Kiang, Joanne, Bashir, Sophia, Spath, Nicholas, Stelzle, Dominik, Mortimer, Kevin, Bularga, Anda, Doudesis, Dimitrios, Joshi, Shruti S, Strachan, Fiona, Gumy, Sophie, Adair-Rohani, Heather, Attia, Engi F, Chung, Michael H, Miller, Mark R, Newby, David E, Mills, Nicholas L, McAllister, David A, and Shah, Anoop S V

Published
2020
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16. Multiple Cardiac Biomarkers to Improve Prediction of Cardiovascular Events: Findings from the Generation Scotland Scottish Family Health Study

Author

Welsh, Paul, primary, Kimenai, Dorien M, additional, Shah, Anoop S V, additional, Gadd, Danni A, additional, Marioni, Riccardo E, additional, Woodward, Mark, additional, Sudlow, Cathie L M, additional, Campbell, Archie, additional, Cleland, John G F, additional, Pellicori, Pierpaolo, additional, Hayward, Caroline, additional, Mills, Nicholas L, additional, and Sattar, Naveed, additional

Published
2023
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17. Implementation of a high sensitivity cardiac troponin I assay and risk of myocardial infarction or death at five years: observational analysis of a stepped wedge, cluster randomised controlled trial

Author

Lee, Kuan Ken, primary, Doudesis, Dimitrios, additional, Ferry, Amy V, additional, Chapman, Andrew R, additional, Kimenai, Dorien M, additional, Fujisawa, Takeshi, additional, Bularga, Anda, additional, Lowry, Matthew T H, additional, Taggart, Caelan, additional, Schulberg, Stacey, additional, Wereski, Ryan, additional, Tuck, Chris, additional, Strachan, Fiona E, additional, Newby, David E, additional, Anand, Atul, additional, Shah, Anoop S V, additional, and Mills, Nicholas L, additional

Published
2023
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19. Global burden of atherosclerotic cardiovascular disease in people with hepatitis C virus infection: a systematic review, meta-analysis, and modelling study

Author

Lee, Kuan Ken, Stelzle, Dominik, Bing, Rong, Anwar, Mohamed, Strachan, Fiona, Bashir, Sophia, Newby, David E, Shah, Jasmit S, Chung, Michael H, Bloomfield, Gerald S, Longenecker, Chris T, Bagchi, Shashwatee, Kottilil, Shyamasundaran, Blach, Sarah, Razavi, Homie, Mills, Peter R, Mills, Nicholas L, McAllister, David A, and Shah, Anoop S V

Published
2019
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21. Mortality risk prediction of high-sensitivity C-reactive protein in suspected acute coronary syndrome: A cohort study

Author

Kaura, Amit, Hartley, Adam, Panoulas, Vasileios, Glampson, Ben, Shah, Anoop S. V., Davies, Jim, Mulla, Abdulrahim, Woods, Kerrie, Omigie, Joe, Shah, Anoop D., Thursz, Mark R., Elliott, Paul, Hemmingway, Harry, Williams, Bryan, Asselbergs, Folkert W., O'Sullivan, Michael, Lord, Graham M., Trickey, Adam, Sterne, Jonathan AC, Haskard, Dorian O., Melikian, Narbeh, Francis, Darrel P., Koenig, Wolfgang, Shah, Ajay M., Kharbanda, Rajesh, Perera, Divaka, Patel, Riyaz S., Channon, Keith M., Mayet, Jamil, and Khamis, Ramzi

Subjects
Mortality -- Risk factors, Acute coronary syndrome -- Prognosis -- Patient outcomes, C-reactive protein -- Measurement -- Health aspects, Biological sciences
Abstract

Background There is limited evidence on the use of high-sensitivity C-reactive protein (hsCRP) as a biomarker for selecting patients for advanced cardiovascular (CV) therapies in the modern era. The prognostic value of mildly elevated hsCRP beyond troponin in a large real-world cohort of unselected patients presenting with suspected acute coronary syndrome (ACS) is unknown. We evaluated whether a mildly elevated hsCRP (up to 15 mg/L) was associated with mortality risk, beyond troponin level, in patients with suspected ACS. Methods and findings We conducted a retrospective cohort study based on the National Institute for Health Research Health Informatics Collaborative data of 257,948 patients with suspected ACS who had a troponin measured at 5 cardiac centres in the United Kingdom between 2010 and 2017. Patients were divided into 4 hsCRP groups ( Following the exclusion criteria, there were 102,337 patients included in the analysis (hsCRP 15 mg/L makes it unlikely that sepsis was a major contributor. Conclusions These multicentre, real-world data from a large cohort of patients with suspected ACS suggest that mildly elevated hsCRP (up to 15 mg/L) may be a clinically meaningful prognostic marker beyond troponin and point to its potential utility in selecting patients for novel treatments targeting inflammation. Trial registration ClinicalTrials.gov - NCT03507309, Author(s): Amit Kaura 1,2, Adam Hartley 1,2, Vasileios Panoulas 1,2, Ben Glampson 2, Anoop S. V. Shah 1,2,3, Jim Davies 4, Abdulrahim Mulla 2, Kerrie Woods 4, Joe Omigie 5, [...]

Published
2022
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23. Performance of a prehospital HEART score in patients with possible myocardial infarction: a prospective evaluation

Author

Cooper, Jamie G, primary, Ferguson, James, additional, Donaldson, Lorna A, additional, Black, Kim M M, additional, Livock, Kate J, additional, Horrill, Judith L, additional, Davidson, Elaine M, additional, Scott, Neil W, additional, Lee, Amanda J, additional, Fujisawa, Takeshi, additional, Lee, Kuan Ken, additional, Anand, Atul, additional, Shah, Anoop S V, additional, and Mills, Nicholas L, additional

Published
2023
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26. Implementation of a high sensitivity cardiac troponin I assay and risk of myocardial infarction or death at five years: observational analysis of a stepped wedge, cluster randomised controlled trial.

Author

Kuan Ken Lee, Doudesis, Dimitrios, Ferry, Amy V., Chapman, Andrew R., Kimenai, Dorien M., Takeshi Fujisawa, Bularga, Anda, Lowry, Matthew T. H., Taggart, Caelan, Schulberg, Stacey, Wereski, Ryan, Tuck, Chris, Strachan, Fiona E., Newby, David E., Anand, Atul, Shah, Anoop S. V., and Mills, Nicholas L.

Subjects
MYOCARDIAL infarction risk factors, MORTALITY risk factors, TROPONIN, EVALUATION of medical care, SCIENTIFIC observation, CONFIDENCE intervals, ACUTE coronary syndrome, TERTIARY care, HUMAN services programs, RISK assessment, SEX distribution, DESCRIPTIVE statistics, RESEARCH funding, SECONDARY care (Medicine), SECONDARY analysis, PROPORTIONAL hazards models
Published
2023
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27. Performance of a prehospital HEART score in patients with possible myocardial infarction: a prospective evaluation

Author

Cooper, Jamie G, Ferguson, James, Donaldson, Lorna A, Black, Kim M M, Livock, Kate J, Horrill, Judith L, Davidson, Elaine M, Scott, Neil W, Lee, Amanda J, Fujisawa, Takeshi, Lee, Kuan Ken, Anand, Atul, Shah, Anoop S V, and Mills, Nicholas L

Abstract

Introduction: The History, Electrocardiogram (ECG), Age, Risk Factors and Troponin (HEART) score is commonly used to risk stratify patients with possible myocardial infarction as low risk or high risk in the Emergency Department (ED). Whether the HEART score can be used by paramedics to guide care were high-sensitivity cardiac troponin testing available in a prehospital setting is uncertain.Methods: In a prespecified secondary analysis of a prospective cohort study where paramedics enrolled patients with suspected myocardial infarction, a paramedic Heart, ECG, Age, Risk Factors (HEAR) score was recorded contemporaneously, and a prehospital blood sample was obtained for subsequent cardiac troponin testing. HEART and modified HEART scores were derived using laboratory contemporary and high-sensitivity cardiac troponin I assays. HEART and modified HEART scores of ≤3 and ≥7 were applied to define low-risk and high-risk patients, and performance was evaluated for an outcome of major adverse cardiac events (MACEs) at 30 days.Results: Between November 2014 and April 2018, 1054 patients were recruited, of whom 960 (mean 64 (SD 15) years, 42% women) were eligible for analysis and 255 (26%) experienced a MACE at 30 days. A HEART score of ≤3 identified 279 (29%) as low risk with a negative predictive value of 93.5% (95% CI 90.0% to 95.9%) for the contemporary assay and 91.4% (95% CI 87.5% to 94.2%) for the high-sensitivity assay. A modified HEART score of ≤3 using the limit of detection of the high-sensitivity assay identified 194 (20%) patients as low risk with a negative predictive value of 95.9% (95% CI 92.1% to 97.9%). A HEART score of ≥7 using either assay gave a lower positive predictive value than using the upper reference limit of either cardiac troponin assay alone.Conclusions: A HEART score derived by paramedics in the prehospital setting, even when modified to harness the precision of a high-sensitivity assay, does not allow safe rule-out of myocardial infarction or enhanced rule-in compared with cardiac troponin testing alone.

Published
2023

29. Sex Differences in Oral Anticoagulation Therapy in Patients Hospitalized With Atrial Fibrillation: A Nationwide Cohort Study

Author

Lee, Kuan Ken, primary, Doudesis, Dimitrios, additional, Bing, Rong, additional, Astengo, Federica, additional, Perez, Jesus R., additional, Anand, Atul, additional, McIntyre, Shauna, additional, Bloor, Nicholas, additional, Sandler, Belinda, additional, Lister, Steven, additional, Pollock, Kevin G., additional, Qureshi, Ayesha C., additional, McAllister, David A., additional, Shah, Anoop S. V., additional, and Mills, Nicholas L., additional

Published
2023
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30. High-sensitivity cardiac troponin I at presentation in patients with suspected acute coronary syndrome: a cohort study

Author

Shah, Anoop S V, Anand, Atul, Sandoval, Yader, Lee, Kuan Ken, Smith, Stephen W, Adamson, Philip D, Chapman, Andrew R, Langdon, Timothy, Sandeman, Dennis, Vaswani, Amar, Strachan, Fiona E, Ferry, Amy, Stirzaker, Alexandra G, Reid, Alan, Gray, Alasdair J, Collinson, Paul O, McAllister, David A, Apple, Fred S, Newby, David E, and Mills, Nicholas L

Published
2015
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32. Additional file 1 of Trajectories of cardiac troponin in the decades before cardiovascular death: a longitudinal cohort study

Author

Kimenai, Dorien M., Anand, Atul, de Bakker, Marie, Shipley, Martin, Fujisawa, Takeshi, Lyngbakken, Magnus N., Hveem, Kristian, Omland, Torbjørn, Valencia-Hernández, Carlos A., Lindbohm, Joni V., Kivimaki, Mika, Singh-Manoux, Archana, Strachan, Fiona E., Shah, Anoop S. V., Kardys, Isabella, Boersma, Eric, Brunner, Eric J., and Mills, Nicholas L.

Abstract

Additional file 1: Supplementary Appendix. Principle of joint modelling. Table S1. Clinical characteristics of study population at first, second and third troponin measurement. Table S2. Association between clinical characteristics and longitudinal cardiac troponin I. Table S3. Baseline characteristics of those who experienced an event. Table S4. Association between the cardiac troponin I level at baseline and cardiovascular death. Table S5. Baseline characteristics of individuals without cardiac disease at baseline. Table S6. Association between the temporal evolution of cardiac troponin I and cardiovascular death in individuals without cardiac disease at baseline. Table S7. The longitudinal cardiac troponin’s accuracy. Table S8. Association between serial cardiac troponin measurements and cardiovascular death in HUNT. Fig S1. Study flow diagram. Fig S2. Cardiac troponin I levels at baseline, 10 years and 15 years, stratified by sex and age groups. Fig S3. Trajectories of cardiac troponin I with 95% confidence intervals before non-cardiovascular death occurred or at end of follow-up. Fig S4. Trajectories of cardiac troponin I with 95% confidence intervals before cardiovascular death or non-cardiovascular death occurred or at end of follow-up. Fig S5. Trajectories of cardiac troponin I with 95% confidence intervals before cardiac death occurred or at end of follow-up. Fig S6. Trajectories of cardiac troponin I with 95% confidence intervals before non-fatal myocardial infarction, fatal myocardial infarction and no myocardial infarction event. Fig S7. Trajectories of cardiac troponin I with 95% confidence intervals in individuals without baseline cardiac disease before cardiovascular death occurred or at end of follow-up. Fig S8. Trajectories of cardiac troponin I with 95% confidence intervals before cardiovascular death and death from any causein HUNT.

Published
2023
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37. Influence of age on the diagnosis of myocardial infarction

Author

Lowry, Matthew TH, Doudesis, Dimitrios, Wereski, Ryan, Kimenai, Dorien M, Tuck, Chris, Ferry, Amy V, Bularga, Anda, Taggart, Caelan, Lee, Kuan Ken, Chapman, Andrew R, Shah, Anoop S V, Newby, David E, Mills, Nicholas L, and Anand, Atul

Subjects
Male, troponin, Troponin I, aging, Myocardial Infarction, Risk Assessment, frail elderly, acute coronary syndrome, myocardial infarction, Physiology (medical), Humans, Female, Acute Coronary Syndrome, Cardiology and Cardiovascular Medicine, Biomarkers, Aged
Abstract

Background: The 99th centile of cardiac troponin, derived from a healthy reference population, is recommended as the diagnostic threshold for myocardial infarction, but troponin concentrations are strongly influenced by age. Our aim was to assess the diagnostic performance of cardiac troponin in older patients presenting with suspected myocardial infarction. Methods: In a secondary analysis of a multicenter trial of consecutive patients with suspected myocardial infarction, we assessed the diagnostic accuracy of high-sensitivity cardiac troponin I at presentation for the diagnosis of type 1, type 2, or type 4b myocardial infarction across 3 age groups ( Results: In 46 435 consecutive patients aged 18 to 108 years (mean, 61±17 years), 5216 (11%) had a diagnosis of myocardial infarction. In patients Conclusions: Age alters the diagnostic performance of cardiac troponin, with reduced specificity and positive predictive value in older patients when applying the guideline-recommended or age-adjusted 99th centiles. Individualized diagnostic approaches rather than the adjustment of binary thresholds are needed in an aging population.

Published
2022

38. Assessment of Cardiac, Vascular, and Pulmonary Pathobiology In Vivo During Acute COVID‐19

Author

Alam, Shirjel R., primary, Vinayak, Sudhir, additional, Shah, Adeel, additional, Doolub, Gemina, additional, Kimeu, Redemptar, additional, Horn, Kevin P., additional, Bowen, Stephen R., additional, Jeilan, Mohamed, additional, Lee, Kuan Ken, additional, Gachoka, Sylvia, additional, Riunga, Felix, additional, Adam, Rodney D., additional, Vesselle, Hubert, additional, Joshi, Nikhil, additional, Obino, Mariah, additional, Makhdomi, Khalid, additional, Ombati, Kevin, additional, Nganga, Edward, additional, Gitau, Samuel, additional, Chung, Michael H., additional, and Shah, Anoop S. V., additional

Published
2022
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40. 18F-fluoride positron emission tomography for identification of ruptured and high-risk coronary atherosclerotic plaques: a prospective clinical trial

Author

Joshi, Nikhil V, Vesey, Alex T, Williams, Michelle C, Shah, Anoop S V, Calvert, Patrick A, Craighead, Felicity H M, Yeoh, Su Ern, Wallace, William, Salter, Donald, Fletcher, Alison M, van Beek, Edwin J R, Flapan, Andrew D, Uren, Neal G, Behan, Miles W H, Cruden, Nicholas L M, Mills, Nicholas L, Fox, Keith A A, Rudd, James H F, Dweck, Marc R, and Newby, David E

Published
2014
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43. Diagnostic and prognostic benefits of computed tomography coronary angiography using the 2016 National Institute for Health and Care Excellence guidance within a randomised trial

Author

Adamson, Philip D, Hunter, Amanda, Williams, Michelle C, Shah, Anoop S V, McAllister, David A, Pawade, Tania A, Dweck, Marc R, Mills, Nicholas L, Berry, Colin, Boon, Nicholas A, Clark, Elizabeth, Flather, Marcus, Forbes, John, McLean, Scott, Roditi, Giles, van Beek, Edwin J R, Timmis, Adam D, and Newby, David E

Published
2018
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45. Association of High-Sensitivity Cardiac Troponin I Concentration With Cardiac Outcomes in Patients With Suspected Acute Coronary Syndrome

Author

Chapman, Andrew R., Lee, Kuan Ken, McAllister, David A., Cullen, Louise, Greenslade, Jaimi H., Parsonage, William, Worster, Andrew, Kavsak, Peter A., Blankenberg, Stefan, Neumann, Johannes, Söerensen, Nils A., Westermann, Dirk, Buijs, Madelon M., Verdel, Gerard J. E., Pickering, John W., Than, Martin P., Twerenbold, Raphael, Badertscher, Patrick, Sabti, Zaid, Mueller, Christian, Anand, Atul, Adamson, Philip, Strachan, Fiona E., Ferry, Amy, Sandeman, Dennis, Gray, Alasdair, Body, Richard, Keevil, Brian, Carlton, Edward, Greaves, Kim, Korley, Frederick K., Metkus, Thomas S., Sandoval, Yader, Apple, Fred S., Newby, David E., Shah, Anoop S. V., and Mills, Nicholas L.

Published
2017
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46. Patient selection for high sensitivity cardiac troponin testing and diagnosis of myocardial infarction: prospective cohort study

Author

Shah, Anoop S V, Sandoval, Yader, Noaman, Ala, Sexter, Anne, Vaswani, Amar, Smith, Stephen W, Gibbins, Mathew, Griffiths, Megan, Chapman, Andrew R, Strachan, Fiona E, Anand, Atul, Denvir, Martin A, Adamson, Philip D, DʼSouza, Michelle S, Gray, Alasdair J, McAllister, David A, Newby, David E, Apple, Fred S, and Mills, Nicholas L

Published
2017
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47. Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study

Author

Lee, Kuan Ken, Doudesis, Dimitrios, Anwar, Mohamed, Astengo, Federica, Chenevier-Gobeaux, Camille, Claessens, Yann-Erick, Wussler, Desiree, Kozhuharov, Nikola, Strebel, Ivo, Sabti, Zaid, deFilippi, Christopher, Seliger, Stephen, Moe, Gordon, Fernando, Carlos, Bayes-Genis, Antoni, van Kimmenade, Roland R. J., Pinto, Yigal, Gaggin, Hanna K., Wiemer, Jan C., Möckel, Martin, Rutten, Joost H. W., van den Meiracker, Anton H., Gargani, Luna, Pugliese, Nicola R., Pemberton, Christopher, Ibrahim, Irwani, Gegenhuber, Alfons, Mueller, Thomas, Neumaier, Michael, Behnes, Michael, Akin, Ibrahim, Bombelli, Michele, Grassi, Guido, Nazerian, Peiman, Albano, Giovanni, Bahrmann, Philipp, Newby, David E., Japp, Alan G., Tsanas, Athanasios, Shah, Anoop S. V., Richards, A. Mark, McMurray, John J. V., Mueller, Christian, Januzzi, James L., Mills, Nicholas L., on behalf of the CoDE-HF investigators, Cardiology, ACS - Heart failure & arrhythmias, Internal Medicine, Lee, K, Doudesis, D, Anwar, M, Astengo, F, Chenevier-Gobeaux, C, Claessens, Y, Wussler, D, Kozhuharov, N, Strebel, I, Sabti, Z, Defilippi, C, Seliger, S, Moe, G, Fernando, C, Bayes-Genis, A, van Kimmenade, R, Pinto, Y, Gaggin, H, Wiemer, J, Möckel, M, Rutten, J, van den Meiracker, A, Gargani, L, Pugliese, N, Pemberton, C, Ibrahim, I, Gegenhuber, A, Mueller, T, Neumaier, M, Behnes, M, Akin, I, Bombelli, M, Grassi, G, Nazerian, P, Albano, G, Bahrmann, P, Newby, D, Japp, A, Tsanas, A, Shah, A, Richards, A, Mcmurray, J, Mueller, C, Januzzi, J, and Mills, N

Subjects
Heart Failure, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], Predictive Value of Test, General Medicine, Biomarker, Peptide Fragments, Diagnosis, Differential, Prospective Studie, Observational Studies as Topic, Peptide Fragment, SDG 3 - Good Health and Well-being, Predictive Value of Tests, Natriuretic Peptide, Brain, Humans, Prospective Studies, Biomarkers, Human
Abstract

ObjectivesTo evaluate the diagnostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds for acute heart failure and to develop and validate a decision support tool that combines NT-proBNP concentrations with clinical characteristics.DesignIndividual patient level data meta-analysis and modelling study.SettingFourteen studies from 13 countries, including randomised controlled trials and prospective observational studies.ParticipantsIndividual patient level data for 10 369 patients with suspected acute heart failure were pooled for the meta-analysis to evaluate NT-proBNP thresholds. A decision support tool (Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF)) that combines NT-proBNP with clinical variables to report the probability of acute heart failure for an individual patient was developed and validated.Main outcome measureAdjudicated diagnosis of acute heart failure.ResultsOverall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure (73.3% (2286/3119) and 29.0% (1802/6208) in those with and without previous heart failure, respectively). The negative predictive value of the guideline recommended rule-out threshold of 300 pg/mL was 94.6% (95% confidence interval 91.9% to 96.4%); despite use of age specific rule-in thresholds, the positive predictive value varied at 61.0% (55.3% to 66.4%), 73.5% (62.3% to 82.3%), and 80.2% (70.9% to 87.1%), in patients aged 75 years, respectively. Performance varied in most subgroups, particularly patients with obesity, renal impairment, or previous heart failure. CoDE-HF was well calibrated, with excellent discrimination in patients with and without previous heart failure (area under the receiver operator curve 0.846 (0.830 to 0.862) and 0.925 (0.919 to 0.932) and Brier scores of 0.130 and 0.099, respectively). In patients without previous heart failure, the diagnostic performance was consistent across all subgroups, with 40.3% (2502/6208) identified at low probability (negative predictive value of 98.6%, 97.8% to 99.1%) and 28.0% (1737/6208) at high probability (positive predictive value of 75.0%, 65.7% to 82.5%) of having acute heart failure.ConclusionsIn an international, collaborative evaluation of the diagnostic performance of NT-proBNP, guideline recommended thresholds to diagnose acute heart failure varied substantially in important patient subgroups. The CoDE-HF decision support tool incorporating NT-proBNP as a continuous measure and other clinical variables provides a more consistent, accurate, and individualised approach.Study registrationPROSPERO CRD42019159407.

Published
2022

48. Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study

Author

Lee, Kuan Ken, primary, Doudesis, Dimitrios, additional, Anwar, Mohamed, additional, Astengo, Federica, additional, Chenevier-Gobeaux, Camille, additional, Claessens, Yann-Erick, additional, Wussler, Desiree, additional, Kozhuharov, Nikola, additional, Strebel, Ivo, additional, Sabti, Zaid, additional, deFilippi, Christopher, additional, Seliger, Stephen, additional, Moe, Gordon, additional, Fernando, Carlos, additional, Bayes-Genis, Antoni, additional, van Kimmenade, Roland R J, additional, Pinto, Yigal, additional, Gaggin, Hanna K, additional, Wiemer, Jan C, additional, Möckel, Martin, additional, Rutten, Joost H W, additional, van den Meiracker, Anton H, additional, Gargani, Luna, additional, Pugliese, Nicola R, additional, Pemberton, Christopher, additional, Ibrahim, Irwani, additional, Gegenhuber, Alfons, additional, Mueller, Thomas, additional, Neumaier, Michael, additional, Behnes, Michael, additional, Akin, Ibrahim, additional, Bombelli, Michele, additional, Grassi, Guido, additional, Nazerian, Peiman, additional, Albano, Giovanni, additional, Bahrmann, Philipp, additional, Newby, David E, additional, Japp, Alan G, additional, Tsanas, Athanasios, additional, Shah, Anoop S V, additional, Richards, A Mark, additional, McMurray, John J V, additional, Mueller, Christian, additional, Januzzi, James L, additional, and Mills, Nicholas L, additional

Published
2022
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49. Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis

Author

Doudesis, Dimitrios, primary, Lee, Kuan Ken, additional, Yang, Jason, additional, Wereski, Ryan, additional, Shah, Anoop S V, additional, Tsanas, Athanasios, additional, Anand, Atul, additional, Pickering, John W, additional, Than, Martin P, additional, Mills, Nicholas L, additional, Strachan, Fiona E, additional, Tuck, Christopher, additional, Shah, Anoop SV, additional, Chapman, Andrew R, additional, Ferry, Amy V, additional, Doudesis, Dimitrios, additional, Bularga, Anda, additional, Taggart, Caelan, additional, Lowry, Matthew TH, additional, Mendusic, Filip, additional, Kimenai, Dorien M, additional, Sandeman, Dennis, additional, Adamson, Philip D, additional, Stables, Catherine L, additional, Vallejos, Catalina A, additional, Marshall, Lucy, additional, Stewart, Stacey D, additional, Fujisawa, Takeshi, additional, Hautvast, Mischa, additional, McPherson, Jean, additional, McKinlay, Lynn, additional, Ford, Ian, additional, Newby, David E, additional, Fox, Keith AA, additional, Berry, Colin, additional, Walker, Simon, additional, Weir, Christopher J, additional, Gray, Alasdair, additional, Collinson, Paul O, additional, Apple, Fred S, additional, Reid, Alan, additional, Cruikshank, Anne, additional, Findlay, Iain, additional, Amoils, Shannon, additional, McAllister, David A, additional, Maguire, Donogh, additional, Stevens, Jennifer, additional, Norrie, John, additional, Andrews, Jack PM, additional, Moss, Alastair, additional, Anwar, Mohamed S, additional, Hung, John, additional, Malo, Jonathan, additional, Fischbacher, Colin, additional, Croal, Bernard L, additional, Leslie, Stephen J, additional, Keerie, Catriona, additional, Parker, Richard A, additional, Walker, Allan, additional, Harkess, Ronnie, additional, Wackett, Tony, additional, Armstrong, Roma, additional, Stirling, Laura, additional, MacDonald, Claire, additional, Sadat, Imran, additional, Finlay, Frank, additional, Charles, Heather, additional, Linksted, Pamela, additional, Young, Stephen, additional, Alexander, Bill, additional, and Duncan, Chris, additional

Published
2022
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