Objectives: The study compared cardiac scintigraphy stress scanning practices applied in a National Maltese Nuclear Medicine centre and in international centres. This was achieved through the design of an online surve...Objectives: The study compared cardiac scintigraphy stress scanning practices applied in a National Maltese Nuclear Medicine centre and in international centres. This was achieved through the design of an online survey which investigated participant knowledge of stress testing, and current procedural practice. Methods: An online survey comprising 12 questions was prepared using Survey Monkey. Professional Nuclear Medicine groups such as the Medical-Physics-Engineering community and Virtual Radiopharmacy were targeted. Access to the survey remained open for eight months during which a periodic reminder was sent to optimise the response rate. Forty-three members responded from across Europe and Australasia. Chi-square tests and comparisons between multiple responses using IBM SPSS 20 were used to evaluate the results. Information related to Maltese practice was collated separately for review and comparative purposes. Results: The online survey participants comprised United Kingdom [72%], other European countries [18%] and Australasia [9%]. The majority of respondents [n = 39] reported pharmacological stress testing as being performed either alone or in conjunction with exercise stress testing as the preferred option. Most participants [60%] were aware of local stress test protocols but had limited knowledge in relation to guidelines designed for cases where patients were not suitable for pharmacological stress testing. Conclusion: The survey provided information about procedures within participating centres for scintigraphic cardiac stress scanning. Differences were identified with regards to the preferred radiopharmaceutical tracers and procedural protocols. Further investigation of examination techniques is warranted, with the aim of increasing standardisation of protocol compliance and the application of more suitable practice.展开更多
BACKGROUND Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value(NPV)of 99%.However,due to low positive predictive va...BACKGROUND Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value(NPV)of 99%.However,due to low positive predictive value(PPV),current stratification tools result in unwarranted investigations such as serial laboratory tests and cardiac stress tests(CSTs).AIM To create a machine learning model(MLM)for risk stratification of chest pain with a better PPV.METHODS This retrospective cohort study used de-identified hospital data from January 2016 until November 2021.Inclusion criteria were patients aged>21 years who presented to the ER,had at least two serum troponins measured,were subsequently admitted to the hospital,and had a CST within 4 d of presentation.Exclusion criteria were elevated troponin value(>0.05 ng/mL)and missing values for body mass index.The primary outcome was abnormal CST.Demographics,coronary artery disease(CAD)history,hypertension,hyperlipidemia,diabetes mellitus,chronic kidney disease,obesity,and smoking were evaluated as potential risk factors for abnormal CST.Patients were also categorized into a high-risk group(CAD history or more than two risk factors)and a low-risk group(all other patients)for comparison.Bivariate analysis was performed using a χ^(2) test or Fisher’s exact test.Age was compared by t test.Binomial regression(BR),random forest,and XGBoost MLMs were used for prediction.Bootstrapping was used for the internal validation of prediction models.BR was also used for inference.Alpha criterion was set at 0.05 for all statistical tests.R software was used for statistical analysis.RESULTS The final cohort of the study included 2328 patients,of which 245(10.52%)patients had abnormal CST.When adjusted for covariates in the BR model,male sex[risk ratio(RR)=1.52,95%confidence interval(CI):1.2-1.94,P<0.001],CAD history(RR=4.46,95%CI:3.08-6.72,P<0.001),and hyperlipidemia(RR=3.87,95%CI:2.12-8.12,P<0.001)remained statistically significant.Incidence of abnormal CST was 12.2%in the high-risk group and 2.3%in the low-risk group(RR=5.31,95%CI:2.75-10.24,P<0.001).The XGBoost model had the best PPV of 24.33%,with an NPV of 91.34%for abnormal CST.CONCLUSION The XGBoost MLM achieved a PPV of 24.33%for an abnormal CST,which is better than current stratification tools(13.00%-17.50%).This highlights the beneficial potential of MLMs in clinical decision-making.展开更多
文摘Objectives: The study compared cardiac scintigraphy stress scanning practices applied in a National Maltese Nuclear Medicine centre and in international centres. This was achieved through the design of an online survey which investigated participant knowledge of stress testing, and current procedural practice. Methods: An online survey comprising 12 questions was prepared using Survey Monkey. Professional Nuclear Medicine groups such as the Medical-Physics-Engineering community and Virtual Radiopharmacy were targeted. Access to the survey remained open for eight months during which a periodic reminder was sent to optimise the response rate. Forty-three members responded from across Europe and Australasia. Chi-square tests and comparisons between multiple responses using IBM SPSS 20 were used to evaluate the results. Information related to Maltese practice was collated separately for review and comparative purposes. Results: The online survey participants comprised United Kingdom [72%], other European countries [18%] and Australasia [9%]. The majority of respondents [n = 39] reported pharmacological stress testing as being performed either alone or in conjunction with exercise stress testing as the preferred option. Most participants [60%] were aware of local stress test protocols but had limited knowledge in relation to guidelines designed for cases where patients were not suitable for pharmacological stress testing. Conclusion: The survey provided information about procedures within participating centres for scintigraphic cardiac stress scanning. Differences were identified with regards to the preferred radiopharmaceutical tracers and procedural protocols. Further investigation of examination techniques is warranted, with the aim of increasing standardisation of protocol compliance and the application of more suitable practice.
基金supported by the Clinical and Translational Science Award from the National Center for Advancing Translational Sciences,which has been awarded to the University of Kansas Clinical and Translational Science Institute.
文摘BACKGROUND Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value(NPV)of 99%.However,due to low positive predictive value(PPV),current stratification tools result in unwarranted investigations such as serial laboratory tests and cardiac stress tests(CSTs).AIM To create a machine learning model(MLM)for risk stratification of chest pain with a better PPV.METHODS This retrospective cohort study used de-identified hospital data from January 2016 until November 2021.Inclusion criteria were patients aged>21 years who presented to the ER,had at least two serum troponins measured,were subsequently admitted to the hospital,and had a CST within 4 d of presentation.Exclusion criteria were elevated troponin value(>0.05 ng/mL)and missing values for body mass index.The primary outcome was abnormal CST.Demographics,coronary artery disease(CAD)history,hypertension,hyperlipidemia,diabetes mellitus,chronic kidney disease,obesity,and smoking were evaluated as potential risk factors for abnormal CST.Patients were also categorized into a high-risk group(CAD history or more than two risk factors)and a low-risk group(all other patients)for comparison.Bivariate analysis was performed using a χ^(2) test or Fisher’s exact test.Age was compared by t test.Binomial regression(BR),random forest,and XGBoost MLMs were used for prediction.Bootstrapping was used for the internal validation of prediction models.BR was also used for inference.Alpha criterion was set at 0.05 for all statistical tests.R software was used for statistical analysis.RESULTS The final cohort of the study included 2328 patients,of which 245(10.52%)patients had abnormal CST.When adjusted for covariates in the BR model,male sex[risk ratio(RR)=1.52,95%confidence interval(CI):1.2-1.94,P<0.001],CAD history(RR=4.46,95%CI:3.08-6.72,P<0.001),and hyperlipidemia(RR=3.87,95%CI:2.12-8.12,P<0.001)remained statistically significant.Incidence of abnormal CST was 12.2%in the high-risk group and 2.3%in the low-risk group(RR=5.31,95%CI:2.75-10.24,P<0.001).The XGBoost model had the best PPV of 24.33%,with an NPV of 91.34%for abnormal CST.CONCLUSION The XGBoost MLM achieved a PPV of 24.33%for an abnormal CST,which is better than current stratification tools(13.00%-17.50%).This highlights the beneficial potential of MLMs in clinical decision-making.
