48 results on '"Doudesis, Dimitrios"'
Search Results
2. Personalized diagnosis in suspected myocardial infarction.
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Neumann, Johannes, Twerenbold, Raphael, Ojeda, Francisco, Aldous, Sally, Allen, Brandon, Apple, Fred, Babel, Hugo, Christenson, Robert, Cullen, Louise, Di Carluccio, Eleonora, Doudesis, Dimitrios, Ekelund, Ulf, Giannitsis, Evangelos, Greenslade, Jaimi, Inoue, Kenji, Jernberg, Tomas, Kavsak, Peter, Keller, Till, Lee, Kuan, Lindahl, Bertil, Lorenz, Thiess, Mahler, Simon, Mills, Nicholas, Mokhtari, Arash, Parsonage, William, Pickering, John, Pemberton, Christopher, Reich, Christoph, Richards, A, Sandoval, Yader, Than, Martin, Toprak, Betül, Troughton, Richard, Worster, Andrew, Zeller, Tanja, Ziegler, Andreas, and Blankenberg, Stefan
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Acute myocardial infarction ,Biomarker ,Machine learning ,Probability ,Super learner ,Troponin ,Validation ,Humans ,Angina Pectoris ,Biomarkers ,Myocardial Infarction ,ROC Curve ,Troponin I ,Troponin T ,Clinical Studies as Topic - Abstract
BACKGROUND: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS: gov ; NCT01852123), LUND ( www. CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS: gov ; NCT03111862), ROMI ( www. CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS: gov ; NCT02060760).
- Published
- 2023
3. Improving diagnosis in acute cardiac care using statistical machine learning
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Doudesis, Dimitrios, Mills, Nick, Anand, Atul, and Tsanas, Thanasis
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circulatory disease ,heart disease ,artificial intelligence ,AI-guided tools ,diagnosis ,cardiac troponin ,CoDE-HF evaluation - Abstract
Cardiovascular disease affects more than half of people in the United Kingdom and remains the most common cause of death. Each year more than 25 million persons attend an Emergency Department, with chest pain or breathlessness being the most common presentations. These patients are often admitted to hospital because of concerns that they may have a life-threatening condition, such as acute myocardial infarction or decompensated heart failure. Despite the availability of specific and sensitive cardiac biomarkers, the diagnosis is not straightforward, resulting in unnecessary hospital admission or misdiagnosis. The aim of this thesis is to use cardiac biomarkers and statistical machine learning to develop clinical decision support tools that improve the diagnosis of patients presenting to the Emergency Department with possible acute cardiac conditions. In 20,761 consecutive patients from the High Sensitivity Troponin in the Evaluation of Acute Coronary Syndrome (High-STEACS) trial, we validated a previously developed machine learning algorithm to assess its diagnostic performance for myocardial infarction in routine clinical practice. The myocardial-ischemic-injury-index (MI3) algorithm, which incorporates age, sex, and two troponin measurements of a patient, had excellent discrimination for the index diagnosis of myocardial infarction, and moreover, it predicted subsequent events too. However, the analysis showed that MI3 performance was not well calibrated in patients with intermediate probability, and there was considerable heterogeneity across important subgroups such as age, sex, presenting symptom of chest pain, cerebrovascular disease and renal function. It is well known that cardiac troponin concentrations are influenced not only by the age and sex of the patient, but also by the time since symptom onset and comorbidities. Hence, we used patients from the High-STEACS trial and three external cohorts to develop and validate CoDE-ACS (Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome); a decision support tool that uses machine learning to incorporate clinical variables along with serial troponin measures and other laboratory tests. CoDE-ACS accurately predicts the likelihood of myocardial infarction and provides a more individualised diagnostic assessment. When CoDE-ACS was compared with the guideline-recommended clinical pathways, the performance was more consistent across important patient subgroups with better negative and positive predicted value. We have subsequently developed and validated a decision support tool for patients with suspected acute heart failure; a condition where symptoms mimic many other conditions making the diagnosis challenging. To address this, we developed and externally validated CoDE-HF (Collaboration for the Diagnosis and Evaluation of Heart Failure) in 10,369 patients from 13 countries to improve the diagnosis and evaluation of acute heart failure. CoDE-HF combines blood natriuretic peptide concentrations as a continuous measure and simple objective clinical variables known to be associated with acute heart failure. First, we used the N-terminal pro-B-type natriuretic peptide (NT-proBNP), as it is the most common test used in clinical practice. Then, we retrained CoDE-HF to support the use of two other natriuretic peptides (B-type natriuretic peptide [BNP] and mid-regional pro atrial natriuretic peptide [MR-proANP]). Last, we compared my solution with the guideline-recommended approach showing that my new decision support tool could achieve a better overall performance, including in complex patients with comorbidities. My findings suggest that a precision medicine approach combining machine learning algorithms with clinical variables and cardiac biomarkers could improve the diagnostic information provided to clinicians when assessing patients with suspected myocardial infarction and heart failure in the Emergency Department.
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- 2022
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4. Uniform or Sex-Specific Cardiac Troponin Thresholds to Rule Out Myocardial Infarction at Presentation
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Mills, Nicholas L., Strachan, Fiona E., Tuck, Christopher, Anand, Atul, Akinlade, Olawale Mathias, Barker, Stephanie, Blades, Jennifer, Boeddinghaus, Jasper, Bularga, Anda, de Bakker, Marie, Chapman, Andrew R., Doudesis, Dimitrios, Ferry, Amy V., Fujisawa, Takeshi, Georgiev, Konstantin, Kimenai, Dorien M., Lee, Kuan Ken, Lyell, Iona, Li, Ziwen, Lowry, Matthew TH., McKinlay, Lynn, McDermott, Michael, McPherson, Jean, Mendusic, Filip, Sorbie, Andrew, Souter, Grace, Schulberg, Stacey D., Taggart, Caelan, Thurston, Alexander JF., Tew, Yong Yong, Perez-Vicencio, Daniel, Wang, Yiqing, Wereski, Ryan, Williams, Kelly, Newby, David E., Fox, Keith AA., Berry, Colin, Walker, Simon, Weir, Christopher J., Ford, Ian, 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, Shah, Anoop SV., Andrews, Jack PM., Adamson, Philip D., Moss, Alastair, Anwar, Mohamed S., Hung, John, Malo, Jonathan, Fischbacher, Colin M., Croal, Bernard L., Leslie, Stephen J., Keerie, Catriona, Parker, Richard A., Walker, Allan, Harkess, Ronnie, Wackett, Tony, Weir, Christopher, Armstrong, Roma, Stirling, Laura, MacDonald, Claire, Sadat, Imran, Finlay, Frank, Harrison, Kathy, Linksted, Pamela, Lavenberg, Stephen, Lowry, Matthew T.H., Tuck, Chris, and Shah, Anoop S.V.
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- 2024
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5. Machine Learning for Myocardial Infarction Compared With Guideline-Recommended Diagnostic Pathways
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Boeddinghaus, Jasper, Doudesis, Dimitrios, Lopez-Ayala, Pedro, Lee, Kuan Ken, Koechlin, Luca, Wildi, Karin, Nestelberger, Thomas, Borer, Raphael, Miró, Òscar, Martin-Sanchez, F. Javier, Strebel, Ivo, Rubini Giménez, Maria, Keller, Dagmar I., Christ, Michael, Bularga, Anda, Li, Ziwen, Ferry, Amy V., Tuck, Chris, Anand, Atul, Gray, Alasdair, Mills, Nicholas L., Mueller, Christian, Richards, A. Mark, Pemberton, Chris, Troughton, Richard W., Aldous, Sally J., Brown, Anthony F.T., Dalton, Emily, Hammett, Chris, Hawkins, Tracey, O’Kane, Shanen, Parke, Kate, Ryan, Kimberley, Schluter, Jessica, Barker, Stephanie, Blades, Jennifer, Chapman, Andrew R., Fujisawa, Takeshi, Kimenai, Dorien M., McDermott, Michael, Newby, David E., Schulberg, Stacey D., Shah, Anoop S.V., Sorbie, Andrew, Soutar, Grace, Strachan, Fiona E., Taggart, Caelan, Vicencio, Daniel Perez, Wang, Yiqing, Wereski, Ryan, Williams, Kelly, Weir, Christopher J., Berry, Colin, Reid, Alan, Maguire, Donogh, Collinson, Paul O., Sandoval, Yader, Smith, Stephen W., Wussler, Desiree, Muench-Gerber, Tamar, Glaeser, Jonas, Spagnuolo, Carlos, Huré, Gabrielle, Gehrke, Juliane, Puelacher, Christian, Gualandro, Danielle M., Shrestha, Samyut, Kawecki, Damian, Morawiec, Beata, Muzyk, Piotr, Buergler, Franz, Buser, Andreas, Rentsch, Katharina, Twerenbold, Raphael, López, Beatriz, Martinez-Nadal, Gemma, Adrada, Esther Rodriguez, Parenica, Jiri, and von Eckardstein, Arnold
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- 2024
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6. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations
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Doudesis, Dimitrios, Lee, Kuan Ken, Boeddinghaus, Jasper, Bularga, Anda, Ferry, Amy V., Tuck, Chris, Lowry, Matthew T. H., Lopez-Ayala, Pedro, Nestelberger, Thomas, Koechlin, Luca, Bernabeu, Miguel O., Neubeck, Lis, Anand, Atul, Schulz, Karen, Apple, Fred S., Parsonage, William, Greenslade, Jaimi H., Cullen, Louise, Pickering, John W., Than, Martin P., Gray, Alasdair, Mueller, Christian, and Mills, Nicholas L.
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- 2023
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7. 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
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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
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- 2022
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8. Data science in undergraduate medicine: Course overview and student perspectives
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Doudesis, Dimitrios and Manataki, Areti
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- 2022
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9. Uniform or Sex-Specific Cardiac Troponin Thresholds to Rule Out Myocardial Infarction at Presentation
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Li, Ziwen, primary, Wereski, Ryan, additional, Anand, Atul, additional, Lowry, Matthew T.H., additional, Doudesis, Dimitrios, additional, McDermott, Michael, additional, Ferry, Amy V., additional, Tuck, Chris, additional, Chapman, Andrew R., additional, Lee, Kuan Ken, additional, Shah, Anoop S.V., additional, Mills, Nicholas L., additional, Kimenai, Dorien M., additional, Strachan, Fiona E., additional, Tuck, Christopher, additional, Akinlade, Olawale Mathias, additional, Barker, Stephanie, additional, Blades, Jennifer, additional, Boeddinghaus, Jasper, additional, Bularga, Anda, additional, de Bakker, Marie, additional, Fujisawa, Takeshi, additional, Georgiev, Konstantin, additional, Lyell, Iona, additional, Li, Ziwen, additional, Lowry, Matthew TH., additional, McKinlay, Lynn, additional, McPherson, Jean, additional, Mendusic, Filip, additional, Sorbie, Andrew, additional, Souter, Grace, additional, Schulberg, Stacey D., additional, Taggart, Caelan, additional, Thurston, Alexander JF., additional, Tew, Yong Yong, additional, Perez-Vicencio, Daniel, additional, Wang, Yiqing, additional, Williams, Kelly, additional, Newby, David E., additional, Fox, Keith AA., additional, Berry, Colin, additional, Walker, Simon, additional, Weir, Christopher J., additional, Ford, Ian, 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, Shah, Anoop SV., additional, Andrews, Jack PM., additional, Adamson, Philip D., additional, Moss, Alastair, additional, Anwar, Mohamed S., additional, Hung, John, additional, Malo, Jonathan, additional, Fischbacher, Colin M., 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, Weir, Christopher, additional, Armstrong, Roma, additional, Stirling, Laura, additional, MacDonald, Claire, additional, Sadat, Imran, additional, Finlay, Frank, additional, Harrison, Kathy, additional, Linksted, Pamela, additional, and Lavenberg, Stephen, additional
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- 2024
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10. P20 Risk scores and coronary artery disease in patients with suspected acute coronary syndrome and intermediate cardiac troponin concentrations
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Vicencio, Daniel Perez, primary, Thurston, Alexander JF, additional, Doudesis, Dimitrios, additional, O’Brien, Rachel, additional, Ferry, Amy V, additional, Fujisawa, Takeshi, additional, Williams, Michelle C, additional, Gray, Alasdair J, additional, Mills, Nicholas L, additional, and Lee, Kuan Ken, additional
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- 2024
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11. Troponin-Guided Coronary Computed Tomographic Angiography After Exclusion of Myocardial Infarction
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Lee, Kuan Ken, Bularga, Anda, O’Brien, Rachel, Ferry, Amy V., Doudesis, Dimitrios, Fujisawa, Takeshi, Kelly, Shauna, Stewart, Stacey, Wereski, Ryan, Cranley, Denise, van Beek, Edwin J.R., Lowe, David J., Newby, David E., Williams, Michelle C., Gray, Alasdair J., and Mills, Nicholas L.
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- 2021
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12. Application of the Universal Definition of Myocardial Infarction in Clinical Practice in Scotland and Sweden
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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
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- 2024
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13. The electronic frailty index and outcomes in patients with myocardial infarction.
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Lowry, Matthew T H, Kimenai, Dorien M, Doudesis, Dimitrios, Georgiev, Konstantin, McDermott, Michael, Bularga, Anda, Taggart, Caelan, Wereski, Ryan, Ferry, Amy V, Stewart, Stacey D, Tuck, Christopher, Newby, David E, Mills, Nicholas L, and Anand, Atul
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MYOCARDIAL infarction complications ,MORTALITY risk factors ,RISK assessment ,PEARSON correlation (Statistics) ,RESEARCH funding ,FRAIL elderly ,SCIENTIFIC observation ,TREATMENT effectiveness ,RETROSPECTIVE studies ,REPORTING of diseases ,CHI-squared test ,LONGITUDINAL method ,HOSPITAL care of older people ,CONFIDENCE intervals ,REGRESSION analysis ,OLD age - Abstract
Background Frailty is increasingly present in patients with acute myocardial infarction. The electronic Frailty Index (eFI) is a validated method of identifying vulnerable older patients in the community from routine primary care data. Our aim was to assess the relationship between the eFI and outcomes in older patients hospitalised with acute myocardial infarction. Study design and setting Retrospective cohort study using the DataLoch Heart Disease Registry comprising consecutive patients aged 65 years or over hospitalised with a myocardial infarction between October 2013 and March 2021. Methods Patients were classified as fit, mild, moderate, or severely frail based on their eFI score. Cox-regression analysis was used to determine the association between frailty category and all-cause mortality. Results In 4670 patients (median age 77 years [71–84], 43% female), 1865 (40%) were classified as fit, with 1699 (36%), 798 (17%) and 308 (7%) classified as mild, moderate and severely frail, respectively. In total, 1142 patients died within 12 months of which 248 (13%) and 147 (48%) were classified as fit and severely frail, respectively. After adjustment, any degree of frailty was associated with an increased risk of all-cause death with the risk greatest in the severely frail (reference = fit, adjusted hazard ratio 2.87 [95% confidence intervals 2.24 to 3.66]). Conclusion The eFI identified patients at high risk of death following myocardial infarction. Automatic calculation within administrative data is feasible and could provide a low-cost method of identifying vulnerable older patients on hospital presentation. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Adverse health effects associated with household air pollution: a systematic review, meta-analysis, and burden estimation study
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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
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- 2020
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15. 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
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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
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- 2023
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16. A data-driven typology of asthma medication adherence using cluster analysis
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Tibble, Holly, Chan, Amy, Mitchell, Edwin A., Horne, Elsie, Doudesis, Dimitrios, Horne, Rob, Mizani, Mehrdad A., Sheikh, Aziz, and Tsanas, Athanasios
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- 2020
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17. Abstract 16401: Duration of Dual Antiplatelet Therapy in Coronary Heart Disease: A 60,000-patient Meta-analysis of Randomised Controlled Trials
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Bularga, Anda, Meah, Mohammed, Doudesis, Dimitrios, Shah, Anoop S, Mills, Nicholas L, Lee, Kuan Ken, and Newby, David E
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- 2020
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18. Understanding quantity and intensity of hospital rehabilitation using electronic health record data
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Georgiev, Konstantin Stefanov, primary, Doudesis, Dimitrios, additional, McPeake, Joanne, additional, Shenkin, Susan D, additional, Fleuriot, Jacques, additional, and Anand, Atul, additional
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- 2023
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19. Personalized diagnosis in suspected myocardial infarction
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Neumann, Johannes Tobias, Twerenbold, Raphael, Ojeda, Francisco, Aldous, Sally, Allen, Brandon, Apple, Fred M., Babel, Hugo, Christenson, Robert, Cullen, Louise, Di Carluccio, Eleonora, Doudesis, Dimitrios, Ekelund, Ulf M., Giannitsis, Evangelos, Greenslade, Jaimi, Inoue, Kenji, Jernberg, Tomas, Kavsak, Peter, Keller, Till, Lee, Kuan Ken, Lindahl, Bertil, Lorenz, Thiess, Mahler, Simon, Mills, Nicholas, Mokhtari, Arash, Parsonage, William, Pickering, John, Pemberton, Christopher, Reich, Christoph, Richards, A. Mark, Sandoval, Yader, Than, Martin A., Toprak, Betül, Troughton, Richard, Worster, Andrew, Zeller, Tanja, Ziegler, Andreas, Blankenberg, Stefan, Neumann, Johannes Tobias, Twerenbold, Raphael, Ojeda, Francisco, Aldous, Sally, Allen, Brandon, Apple, Fred M., Babel, Hugo, Christenson, Robert, Cullen, Louise, Di Carluccio, Eleonora, Doudesis, Dimitrios, Ekelund, Ulf M., Giannitsis, Evangelos, Greenslade, Jaimi, Inoue, Kenji, Jernberg, Tomas, Kavsak, Peter, Keller, Till, Lee, Kuan Ken, Lindahl, Bertil, Lorenz, Thiess, Mahler, Simon, Mills, Nicholas, Mokhtari, Arash, Parsonage, William, Pickering, John, Pemberton, Christopher, Reich, Christoph, Richards, A. Mark, Sandoval, Yader, Than, Martin A., Toprak, Betül, Troughton, Richard, Worster, Andrew, Zeller, Tanja, Ziegler, Andreas, and Blankenberg, Stefan
- Abstract
Background: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hscTn)- based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. Methods: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability ( ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. Results: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline- recommended strategy. Conclusion We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.
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- 2023
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20. Troponin in early presenters to rule out myocardial infarction
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Lowry, Matthew T H, Doudesis, Dimitrios, Boeddinghaus, Jasper, Kimenai, Dorien M, Bularga, Anda, Taggart, Caelan, Wereski, Ryan, Ferry, Amy V, Stewart , Stacey D, Tuck, Christopher, Koechlin, Luca, Nestelberger, Thomas, Lopez-Ayala, Pedro, Huré , Gabrielle, Lee, Kuan Ken, Chapman, Andrew R, Newby, David E, Anand, Atul, Collinson, Paul O, Mueller , Christian, and Mills, Nicholas L
- Abstract
Background and aims: Whether a single cardiac troponin measurement can safely rule-out myocardial infarction in patients presenting within a few hours of symptom onset is uncertain. The study aim was to assess the performance of troponin in early presenters.Methods: In patients with possible myocardial infarction, the diagnostic performance of a single measurement of high-sensitivity cardiac troponin I at presentation was evaluated and externally validated in those tested ≤3, 4-12 and >12 hours from symptom onset. The limit of detection (2 ng/L), rule-out (5 ng/L) and sex-specific 99th centile (16 ng/L women, 34 ng/L men) thresholds were compared. Results: In 41,103 consecutive patients (60 [17] years, 46% women), 12,595 (31%) presented within 3 hours and 3,728 (9%) had myocardial infarction. In those presenting ≤3 hours, a threshold of 2 ng/L had greater sensitivity and negative predictive value (99.4% [95% confidence interval 99.2-99.5%] and 99.7% [99.6-99.8%]) compared to 5 ng/L (96.5% [96.2-96.8%] and 99.3% [99.1- 99.4%]). In those presenting ≥3 hours, the sensitivity and negative predictive value were similar for both thresholds. The sensitivity of the 99th centile was low in early and late presenters at 71.4% [70.6-72.2%] and 92.5% [92.0-93.0%], respectively. Findings were consistent in an external validation cohort of 7,088 patients.Conclusions: In early presenters, a single measurement of high-sensitivity cardiac troponin I below the limit of detection may facilitate the safe rule out of myocardial infarction. The 99th centile should not be used to rule out myocardial infarction at presentation even in those presenting later following symptom onset.
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- 2023
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21. Personalized diagnosis in suspected myocardial infarction
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Neumann , Johannes T, Twerenbold , Raphael, Ojeda, Francisco M, Aldous, Sally J., Allen, Brandon R., Apple, Fred S, Babel, Hugo, Christenson, Robert H., Cullen , Louise, Di Carluccio, Eleonora, Doudesis, Dimitrios, Ekelund, Ulf, Giannitsis, Evangelos, Greenslade, Jaimi H, Inoue, Kenji, Jernberg, Tomas, Kavsak, Peter A, Keller, Till, Lee, Kuan Ken, Lindahl, Bertil, Lorenz, Thiess, Mahler, Simon, Mills, Nicholas L, Mokhtari, Arash, Parsonage, William, Pickering, John W, Pemberton, Christopher J, Reichetzeder, Christoph, Richards, A Mark, Sandoval, Yader, Than , Martin P, Toprak, Betül, Troughton, Richard W, Worster, Andrew, Zeller, Tanja, Ziegler, Andreas, and Blankenberg, Stefan
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machine learning ,troponin ,probability ,Validation ,biomarker ,Acute myocardial infarction ,super learner - Abstract
Background. In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.Methods. In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1,688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.Results. Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92-0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy.Conclusion. We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.
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- 2023
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22. 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.
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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.
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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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23. Sex Differences in Oral Anticoagulation Therapy in Patients Hospitalized With Atrial Fibrillation: A Nationwide Cohort Study
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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
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- 2023
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24. APPLICATION OF THE UNIVERSAL DEFINITION OF MYOCARDIAL INFARCTION IN PRACTICE
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Taggart, Caelan, primary, Kimenai, Dorothea, additional, Kadesjo, Erik, additional, Anand, Atul, additional, Bularga, Anda, additional, Wereski, Ryan, additional, Doudesis, Dimitrios, additional, Lowry, Matthew, additional, Lee, Kuan Ken, additional, Chapman, Andrew R., additional, Edgren, Gustaf, additional, Roos, Andreas, additional, and Mills, Nicholas L., additional
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- 2023
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25. Improving Risk Stratification for Patients With Type 2 Myocardial Infarction
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Taggart, Caelan, primary, Monterrubio-Gómez, Karla, additional, Roos, Andreas, additional, Boeddinghaus, Jasper, additional, Kimenai, Dorien M., additional, Kadesjo, Erik, additional, Bularga, Anda, additional, Wereski, Ryan, additional, Ferry, Amy, additional, Lowry, Matthew, additional, Anand, Atul, additional, Lee, Kuan Ken, additional, Doudesis, Dimitrios, additional, Manolopoulou, Ioanna, additional, Nestelberger, Thomas, additional, Koechlin, Luca, additional, Lopez-Ayala, Pedro, additional, Mueller, Christian, additional, Mills, Nicholas L., additional, Vallejos, Catalina A., additional, and Chapman, Andrew R., additional
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- 2023
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26. 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
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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
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- 2022
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27. Influence of age on the diagnosis of myocardial infarction
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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
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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.
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- 2022
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28. Assessment of oxygen supply-demand imbalance and outcomes among patients with type 2 myocardial infarction: a secondary analysis of the High-STEACS cluster randomized clinical trial
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Bularga, Anda, Taggart, Caelan, Mendusic, Filip, Kimenai, Dorien M., Wereski, Ryan, Lowry, Matthew T. H., Lee, Kuan K., Ferry, Amy V., Stewart, Stacey S., McAllister, David A., Shah, Anoop S.V., Anand, Atul, Newby, David E., Mills, Nicholas L., Chapman, Andrew R., Strachan, Fiona E, Tuck, Christopher, Doudesis, Dimitrios, Sandeman, Dennis, Adamson, Philip D, Andrews, Jack P M, Moss, Alastair, Anwar, Mohamed S, Hung, John, Stables, Catherine L, Vallejo, Catalina A, Tsanas, Athanasios, Marshal, Lucy, Fujisawa, Takeshi, Hautvast, Mischa, McPherson, Jean, McKinley, Lynn, Fox, Keith A A, Berry, Colin, Walker, Simon, Weir, Christopher, Ford, Ian, Gray, Alasdair, Collinson, Paul O, Apple, Fred S, Reid, Alan, Cruikshank, Anne, Findlay, Iain, Amoils, Shannon, Maguire, Donogh, Stevens, Jennifer, Norrie, John, Malo, Jonathan, Fischbacher, Colin M, Croal, Bernard L, Leslie, Stephen J, Keerie, Catriona, Parker, Richard A, Walker, Allan, Harkess, Ronnie, Wackett, Tony, Armstrong, Roma, Flood, Marion, Stirling, Laura, MacDonald, Claire, Sadat, Imran, Finlay, Frank, Charles, Heather, Linksted, Pamela, Young, Stephen, Alexander, Bill, and Duncan, Chris
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Importance: Type 2 myocardial infarction occurs owing to multiple factors associated with myocardial oxygen supply-demand imbalance, which may confer different risks of adverse outcomes.\ud \ud Objective: To evaluate the prevalence and outcomes of different factors associated with oxygen supply-demand imbalance among patients with type 2 myocardial infarction.\ud \ud Design, Setting, and Participants: In this secondary analysis of a stepped-wedge, cluster randomized clinical trial conducted at 10 secondary and tertiary care hospitals in Scotland, 6096 patients with an adjudicated diagnosis of type 1 or type 2 myocardial infarction from June 10, 2013, to March 3, 2016, were identified, and the findings were reported on August 28, 2018. The trial enrolled consecutive patients with suspected acute coronary syndrome. The diagnosis of myocardial infarction was adjudicated according to the Fourth Universal Definition of Myocardial Infarction and the primary factor associated with oxygen supply-demand imbalance in type 2 myocardial infarction was defined. This secondary analysis was not prespecified. Statistical analysis was performed from July 7 to 30, 2020.\ud \ud Intervention: Implementation of a high-sensitivity cardiac troponin I assay.\ud \ud Main Outcomes and Measures: All-cause death at 1 year according to the factors associated with oxygen supply-demand imbalance among patients with type 2 myocardial infarction.\ud \ud Results: Of 6096 patients (2602 women [43%]; median age, 70 years [IQR, 58-80 years]), 4981 patients had type 1 myocardial infarction, and 1115 patients had type 2 myocardial infarction. The most common factor associated with oxygen supply-demand imbalance was tachyarrhythmia (616 of 1115 [55%]), followed by hypoxemia (219 of 1115 [20%]), anemia (95 of 1115 [9%]), hypotension (89 of 1115 [8%]), severe hypertension (61 of 1115 [5%]), and coronary mechanisms (35 of 1115 [3%]). At 1 year, all-cause mortality occurred for 15% of patients (720 of 4981) with type 1 myocardial infarction and 23% of patients (285 of 1115) with type 2 myocardial infarction. Compared with patients with type 1 myocardial infarction, those with type 2 myocardial infarction owing to hypoxemia (adjusted odds ratio [aOR], 2.35; 95% CI, 1.72-3.18) and anemia (aOR, 1.83; 95% CI, 1.14-2.88) were at greatest risk of death, whereas those with type 2 myocardial infarction owing to tachyarrhythmia (aOR, 0.83; 95% CI, 0.65-1.06) or coronary mechanisms (aOR, 1.07; 95% CI, 0.17-3.86) were at similar risk of death as patients with type 1 myocardial infarction.\ud \ud Conclusions and Relevance: In this secondary analysis of a randomized clinical trial, mortality after type 2 myocardial infarction was associated with the underlying etiologic factor associated with oxygen supply-demand imbalance. Most type 2 myocardial infarctions were associated with tachyarrhythmia, with better prognosis, whereas hypoxemia and anemia accounted for one-third of cases, with double the mortality of type 1 myocardial infarction. These differential outcomes should be considered by clinicians when determining which cases need to be managed if patient outcomes are to improve.\ud \ud Trial Registration: ClinicalTrials.gov Identifier: NCT01852123.
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- 2022
29. Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study
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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
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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.
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- 2022
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30. Development and validation of a decision support tool for the diagnosis of acute heart failure: Systematic review, meta-analysis, and modelling study
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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, Mills, N, 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, 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, Mills, N, 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, and Mills, Nicholas L
- Abstract
Objectives: To 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. Design: Individual patient level data meta-analysis and modelling study. Setting: Fourteen studies from 13 countries, including randomised controlled trials and prospective observational studies. Participants: Individual 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 measure: Adjudicated diagnosis of acute heart failure. Results: Overall, 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 [removed]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
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- 2022
31. Development and validation of a decision support tool for the diagnosis of acute heart failure:systematic review, meta-analysis, and modelling study
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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, 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, and Mills, Nicholas L
- Abstract
Abstract:Objectives: To 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. Design: Individual patient level data meta-analysis and modelling study. Setting: Fourteen studies from 13 countries, including randomised controlled trials and prospective observational studies. Participants: Individual 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 measure: Adjudicated diagnosis of acute heart failure. Results: Overall, 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 <50 years, 50-75 years, and >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 co
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- 2022
32. Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study
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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
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- 2022
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33. 40 Impact of time from symptom onset on the diagnostic performance of high-sensitivity cardiac troponin for type 1 myocardial infarction
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Lowry, Matthew, primary and Doudesis, Dimitrios, additional
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- 2022
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34. UNIFORM AND SEX-SPECIFIC CARDIAC TROPONIN THRESHOLDS TO RULE OUT MYOCARDIAL INFARCTION AT PRESENTATION
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Li, Ziwen, Wereski, Ryan, Anand, Atul, Lowry, Matthew, Doudesis, Dimitrios, Tuck, Chris, Shah, Anoop S., Mills, Nicholas L., and Kimenai, Dorothea
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- 2024
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35. Sex Differences in Oral Anticoagulation Therapy in Patients Hospitalized With Atrial Fibrillation: A Nationwide Cohort Study.
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Kuan Ken Lee, Doudesis, Dimitrios, Rong Bing, Astengo, Federica, Perez, Jesus R., Anand, Atul, McIntyre, Shauna, Bloor, Nicholas, Sandler, Belinda, Lister, Steven, Pollock, Kevin G., Qureshi, Ayesha C., McAllister, David A., Shah, Anoop S. V., and Mills, Nicholas L.
- Published
- 2023
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36. Abstract 8905: Cardiac Troponin Thresholds and Kinetics to Differentiate Myocardial Injury and Myocardial Infarction
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Wereski, Ryan, primary, Kimenai, Dorien, additional, Taggart, Caelan, additional, Doudesis, Dimitrios, additional, Lee, Kuan Ken, additional, Lowry, Mathew T, additional, Bularga, Anda, additional, Lowe, David, additional, Fujisawa, Takeshi, additional, Apple, Fred S, additional, Collinson, Paul O, additional, Anand, Atul, additional, Chapman, Andrew, additional, and Mills, Nicholas L, additional
- Published
- 2021
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37. Influence of Age on the Diagnosis of Myocardial Infarction.
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Lowry, Matthew T.H., Doudesis, Dimitrios, Wereski, Ryan, Kimenai, Dorien M., Tuck, Christopher, Ferry, Amy V., Bularga, Anda, Taggart, Caelan, Lee, Kuan K., Chapman, Andrew R., Shah, Anoop S.V., Newby, David E., Mills, Nicholas L., Anand, Atul, and High-STEACS Investigators
- Published
- 2022
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38. Cardiac Troponin Thresholds and Kinetics to Differentiate Myocardial Injury and Myocardial Infarction
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Wereski, Ryan, primary, Kimenai, Dorien M., additional, Taggart, Caelan, additional, Doudesis, Dimitrios, additional, Lee, Kuan Ken, additional, Lowry, Matthew T.H., additional, Bularga, Anda, additional, Lowe, David J., additional, Fujisawa, Takeshi, additional, Apple, Fred S., additional, Collinson, Paul O., additional, Anand, Atul, additional, Chapman, Andrew R., additional, and Mills, Nicholas L., additional
- Published
- 2021
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39. Duration of dual antiplatelet therapy and stability of coronary heart disease: a 60 000-patient meta-analysis of randomised controlled trials
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Bularga, Anda, primary, Meah, Mohammed N, additional, Doudesis, Dimitrios, additional, Shah, Anoop S V, additional, Mills, Nicholas L, additional, Newby, David E, additional, and Lee, Kuan Ken, additional
- Published
- 2021
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40. Additional file 1 of Diagnostic performance of the combined nasal and throat swab in patients admitted to hospital with suspected COVID-19
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Lee, Kuan Ken, Doudesis, Dimitrios, Ross, Daniella A., Bularga, Anda, MacKintosh, Claire L., Koch, Oliver, Ingolfur Johannessen, Templeton, Kate, Jenks, Sara, Chapman, Andrew R., Anoop S. V. Shah, Anand, Atul, Perry, Meghan R., and Mills, Nicholas L.
- Abstract
Additional file 1: eTable 1. Virology, laboratory tests at presentation with suspected COVID-19. eTable 2. Baseline characteristics of patients stratified according to whether the diagnosis of COVID-19 was confirmed or probable. eTable 3. Virology, laboratory tests at presentation stratified according to whether the diagnosis of COVID-19 was confirmed or probable. eTable 4. Use of serial testing in patients with suspected COVID-19. eTable 5. Diagnostic performance of the index and serial combined nasal and throat swab for the secondary outcome of a diagnosis of confirmed COVID-19 on serial testing. eFigure 1. Stack plot of the number of RT-PCR tests performed stratified according to whether the test was positive (red) or negative (blue). eFigure 2. Heat map of RT-PCR testing in patients with confirmed COVID-19 stratified according to whether the index test was negative (a) or positive (b). eFigure 3. Sensitivity of serial testing using the combined nasal and throat swab for the primary (confirmed and probable COVID-19) and secondary (confirmed COVID-19) outcome in patients who were tested at least four times. eFigure 4. Negative predictive value of serial testing using the combined nasal and throat swab for the primary (confirmed and probable COVID-19) and secondary (confirmed COVID-19) outcome in patients who were tested at least four times. eFigure 5. Forest plot of the (a) sensitivity and (b) negative predictive value of the index combined nasal and throat swab for a diagnosis of confirmed COVID-19 stratified by subgroups.
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- 2021
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41. Clinical decision support using machine learning and natriuretic peptides for the diagnosis of acute heart failure.
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Lee, Kuan Ken, Doudesis, Dimitrios, Mair, Johannes, Mills, Nicholas L, Lindahl, Bertil, Boeddinghaus, Jasper, Cullen, Louise, Daniels, Lori, Hammarsten, Ola, Huber, Kurt, Giannitsis, Evangelos, Jaffe, Allan S, Kimenai, Dorien M, Krychtiuk, Konstantin, Möckel, Martin, Mueller, Christian, Thielmann, Matthias, and Thygesen, Kristian
- Published
- 2024
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42. High-Sensitivity Cardiac Troponin on Presentation to Rule Out Myocardial Infarction: A Stepped-Wedge Cluster Randomized Controlled Trial
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Anand, Atul, primary, Lee, Kuan Ken, additional, Chapman, Andrew R., additional, Ferry, Amy V., additional, Adamson, Phil D., additional, Strachan, Fiona E., additional, Berry, Colin, additional, Findlay, Iain, additional, Cruikshank, Anne, additional, Reid, Alan, additional, Collinson, Paul O., additional, Apple, Fred S., additional, McAllister, David A., additional, Maguire, Donogh, additional, Fox, Keith A.A., additional, Newby, David E., additional, Tuck, Chris, additional, Harkess, Ronald, additional, Keerie, Catriona, additional, Weir, Christopher J., additional, Parker, Richard A., additional, Gray, Alasdair, additional, Shah, Anoop S.V., additional, Mills, Nicholas L., additional, Alexander, Bill, additional, Amoils, Shannon, additional, Armstrong, Roma, additional, Bularga, Anda, additional, Croal, Bernard L., additional, Doudesis, Dimitrios, additional, Fischbacher, Colin M., additional, Ford, Ian, additional, Fujisawa, Takeshi, additional, Harkess, Ronnie, additional, Kimenai, Dorien M., additional, Leslie, Stephen J., additional, Linksted, Pamela, additional, Lowry, Matthew T.H., additional, MacDonald, Claire, additional, Marshall, Lucy, additional, Mendusic, Filip, additional, Norrie, John, additional, Sadat, Imran, additional, Stables, Catherine L., additional, Stevens, Jennifer, additional, Stewart, Stacey D., additional, Stirling, Laura, additional, Taggart, Caelan, additional, Wereski, Ryan, additional, and Young, Stephen, additional
- Published
- 2021
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43. Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study.
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Kuan Ken Lee, 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., and Möckel, Martin
- Subjects
EXPERIMENTAL design ,MEDICAL databases ,META-analysis ,MEDICAL information storage & retrieval systems ,RESEARCH methodology ,RESEARCH methodology evaluation ,SYSTEMATIC reviews ,DESCRIPTIVE statistics ,PEPTIDE hormones ,MEDLINE ,HEART failure ,ACUTE diseases - Published
- 2022
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44. Data-Driven Insights towards Risk Assessment of Postpartum Depression
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Valavani, Evdoxia, primary, Doudesis, Dimitrios, primary, Kourtesis, Ioannis, primary, Chin, Richard, primary, MacIntyre, Donald, primary, Fletcher-Watson, Sue, primary, Boardman, James, primary, and Tsanas, Athanasios, primary
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- 2020
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45. Adverse health effects associated with household air pollution: a systematic review, meta-analysis, and burden estimation study
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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;Shah, Anoop S V and 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;Shah, Anoop S V
- Published
- 2019
46. Diagnostic performance of the combined nasal and throat swab in patients admitted to hospital with suspected COVID-19.
- Author
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Lee, Kuan Ken, Doudesis, Dimitrios, Ross, Daniella A., Bularga, Anda, MacKintosh, Claire L., Koch, Oliver, Johannessen, Ingolfur, Templeton, Kate, Jenks, Sara, Chapman, Andrew R., Shah, Anoop S. V., Anand, Atul, Perry, Meghan R., Mills, Nicholas L., on behalf of the DataLoch COVID-19 Collaboration, Harrison, Kathy, Stables, Catherine, Hume, Ally, Homan, David, and Waugh, Catriona
- Subjects
- *
SARS-CoV-2 , *COVID-19 , *HOSPITAL patients , *VIRAL transmission , *THROAT - Abstract
Background: Accurate diagnosis in patients with suspected coronavirus disease 2019 (COVID-19) is essential to guide treatment and limit spread of the virus. The combined nasal and throat swab is used widely, but its diagnostic performance is uncertain.Methods: In a prospective, multi-centre, cohort study conducted in secondary and tertiary care hospitals in Scotland, we evaluated the combined nasal and throat swab with reverse transcriptase-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in consecutive patients admitted to hospital with suspected COVID-19. Diagnostic performance of the index and serial tests was evaluated for a primary outcome of confirmed or probable COVID-19, and a secondary outcome of confirmed COVID-19 on serial testing. The diagnosis was adjudicated by a panel, who recorded clinical, laboratory and radiological features blinded to the test results.Results: We enrolled 1368 consecutive patients (median age 68 [interquartile range, IQR 53-80] years, 47% women) who underwent a total of 3822 tests (median 2 [IQR 1-3] tests per patient). The primary outcome occurred in 36% (496/1368), of whom 65% (323/496) and 35% (173/496) had confirmed and probable COVID-19, respectively. The index test was positive in 255/496 (51%) patients with the primary outcome, giving a sensitivity and specificity of 51.4% (95% confidence interval [CI] 48.8 to 54.1%) and 99.5% (95% CI 99.0 to 99.8%). Sensitivity increased in those undergoing 2, 3 or 4 tests to 60.1% (95% CI 56.7 to 63.4%), 68.3% (95% CI 64.0 to 72.3%) and 77.6% (95% CI 72.7 to 81.9%), respectively. The sensitivity of the index test was 78.9% (95% CI 74.4 to 83.2%) for the secondary outcome of confirmed COVID-19 on serial testing.Conclusions: In patients admitted to hospital, a single combined nasal and throat swab with RT-PCR for SARS-CoV-2 has excellent specificity, but limited diagnostic sensitivity for COVID-19. Diagnostic performance is significantly improved by repeated testing. [ABSTRACT FROM AUTHOR]- Published
- 2021
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47. Risk scores and coronary artery disease in patients with suspected acute coronary syndrome and intermediate cardiac troponin concentrations.
- Author
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Perez-Vicencio D, Thurston AJF, Doudesis D, O'Brien R, Ferry A, Fujisawa T, Williams MC, Gray AJ, Mills NL, and Lee KK
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- Humans, Female, Male, Middle Aged, Prospective Studies, Risk Assessment methods, Aged, Risk Factors, Computed Tomography Angiography, Predictive Value of Tests, Prognosis, Biomarkers blood, Coronary Angiography, Coronary Artery Disease blood, Coronary Artery Disease diagnosis, Coronary Artery Disease epidemiology, Acute Coronary Syndrome blood, Acute Coronary Syndrome diagnosis, Acute Coronary Syndrome epidemiology, Troponin I blood
- Abstract
Background: Guidelines recommend the use of risk scores to select patients for further investigation after myocardial infarction has been ruled out but their utility to identify those with coronary artery disease is uncertain., Methods: In a prospective cohort study, patients with intermediate high-sensitivity cardiac troponin I concentrations (5 ng/L to sex-specific 99th percentile) in whom myocardial infarction was ruled out were enrolled and underwent coronary CT angiography (CCTA) after hospital discharge. History, ECG, Age, Risk factors, Troponin (HEART), Emergency Department Assessment of Chest Pain Score (EDACS), Global Registry of Acute Coronary Event (GRACE), Thrombolysis In Myocardial Infarction (TIMI), Systematic COronary Risk Evaluation 2 and Pooled Cohort Equation risk scores were calculated and the odds ratio (OR) and diagnostic performance for obstructive coronary artery disease were determined using established thresholds., Results: Of 167 patients enrolled (64±12 years, 28% female), 29.9% (50/167) had obstructive coronary artery disease. The odds of having obstructive disease were increased for all scores with the lowest and highest increase observed for an EDACS score ≥16 (OR 2.2 (1.1-4.6)) and a TIMI risk score ≥1 (OR 12.9 (3.0-56.0)), respectively. The positive predictive value (PPV) was low for all scores but was highest for a GRACE score >88 identifying 39% as high risk with a PPV of 41.9% (30.4-54.2%). The negative predictive value (NPV) varied from 77.3% to 95.2% but was highest for a TIMI score of 0 identifying 26% as low risk with an NPV of 95.2% (87.2-100%)., Conclusions: In patients with intermediate cardiac troponin concentrations in whom myocardial infarction has been excluded, clinical risk scores can help identify patients with and without coronary artery disease, although the performance of established risk thresholds is suboptimal for utilisation in clinical practice., Trial Registration Number: NCT04549805., Competing Interests: Competing interests: MCW has received speaker fees from Cannon Medical Systems, Siemens Healthineers and Novartis. NLM reports research grants awarded to the University of Edinburgh from Abbott Diagnostics, Siemens Healthineers and Roche Diagnostics outside the submitted work, and honoraria from Abbott Diagnostics, Siemens Healthineers, Roche Diagnostics, LumiraDx and Psyros Diagnostics. All other authors have no interests to declare., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)
- Published
- 2024
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48. Application of the Universal Definition of Myocardial Infarction in Clinical Practice in Scotland and Sweden.
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Taggart C, Roos A, Kadesjö E, Anand A, Li Z, Doudesis D, Lee KK, Bularga A, Wereski R, Lowry MTH, Chapman AR, Ferry AV, Shah ASV, Gard A, Lindahl B, Edgren G, Mills NL, and Kimenai DM
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- Male, Humans, Female, Aged, Aged, 80 and over, Middle Aged, Sweden epidemiology, Cohort Studies, Prospective Studies, Scotland epidemiology, Myocardial Infarction diagnosis, Myocardial Infarction epidemiology
- 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 older (mean [SD] age, 71 [14] v 67 [14] years in Scotland, P < .001) and, when adjusting for competing risk from noncardiovascular death, were at similar or increased risk of subsequent MI or cardiovascular death compared with patients with a clinical diagnosis of MI (eg, 29% vs 18% in Scotland; P < .001)., Conclusions and Relevance: In this cohort study, the universal definition of MI was not consistently applied in clinical practice, with a minority of patients with type 2 MI identified, and type 1 MI underrecognized in women and older persons, suggesting uncertainty remains regarding the diagnostic criteria or value of the classification.
- Published
- 2024
- Full Text
- View/download PDF
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