Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions
Objectives: This study sought to determine the most risk-adjustment model for 30-day all-cause mortality in order to report risk-adjusted outcomes. The study also explored whether the exclusion of extreme high-risk conditions of cardiogenic shock, intubated out-of-hospital cardiac arrest (OHCA), or...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
ELSEVIER SCIENCE INC
2019
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/nhmrc/1111170 http://hdl.handle.net/20.500.11937/93081 |
| _version_ | 1848765692011085824 |
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| author | Tacey, M. Dinh, D.T. Andrianopoulos, N. Brennan, A.L. Stub, D. Liew, D. Reid, Christopher Duffy, S.J. Lefkovits, J. |
| author_facet | Tacey, M. Dinh, D.T. Andrianopoulos, N. Brennan, A.L. Stub, D. Liew, D. Reid, Christopher Duffy, S.J. Lefkovits, J. |
| author_sort | Tacey, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Objectives: This study sought to determine the most risk-adjustment model for 30-day all-cause mortality in order to report risk-adjusted outcomes. The study also explored whether the exclusion of extreme high-risk conditions of cardiogenic shock, intubated out-of-hospital cardiac arrest (OHCA), or the need for mechanical ventricular support affected the model's predictive accuracy. Background: Robust risk-adjustment models are a critical component of clinical quality registries, allowing outcomes to be reported in a fair and meaningful way. The Victorian Cardiac Outcomes Registry encompasses all 30 hospitals in the state of Victoria, Australia, that undertake percutaneous coronary intervention. Methods: Data were collected on 27,544 consecutive percutaneous coronary intervention procedures from 2014 to 2016. Twenty-eight patient risk factors and procedural variables were considered in the modeling process. The multivariable logistic regression analysis considered derivation and validation datasets, along with a temporal validation period. Results: The model included risk-adjustment for cardiogenic shock, intubated OHCA, estimated glomerular filtration rate, left ventricular ejection fraction, angina type, mechanical ventricular support, ≥80 years of age, lesion complexity, percutaneous access site, and peripheral vascular disease. The C-statistic for the derivation dataset was 0.921 (95% confidence interval: 0.905 to 0.936), with C-statistics of 0.931 and 0.934 for 2 validation datasets reflecting the 2014 to 2016 and 2017 periods. Subgroup modeling excluding cardiogenic shock and intubated OHCA provided similar risk-adjusted outcomes (p = 0.32). Conclusions: Our study has developed a highly predictive risk-adjustment model for 30-day mortality that included high-risk presentations. Therefore, we do not need to exclude high-risk cases in our model when determining risk-adjusted outcomes. |
| first_indexed | 2025-11-14T11:39:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-93081 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:39:17Z |
| publishDate | 2019 |
| publisher | ELSEVIER SCIENCE INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-930812023-10-16T07:37:11Z Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions Tacey, M. Dinh, D.T. Andrianopoulos, N. Brennan, A.L. Stub, D. Liew, D. Reid, Christopher Duffy, S.J. Lefkovits, J. Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology 30-day mortality clinical quality registry percutaneous coronary intervention risk-adjustment PERCUTANEOUS CORONARY INTERVENTION 30-DAY MORTALITY 30-day mortality clinical quality registry percutaneous coronary intervention risk-adjustment Aged Aged, 80 and over Cause of Death Coronary Artery Disease Female Glomerular Filtration Rate Health Status Heart-Assist Devices Humans Intubation, Intratracheal Male Out-of-Hospital Cardiac Arrest Percutaneous Coronary Intervention Quality Indicators, Health Care Registries Reproducibility of Results Risk Assessment Risk Factors Shock, Cardiogenic Stroke Volume Time Factors Treatment Outcome Ventricular Function, Left Victoria Humans Shock, Cardiogenic Stroke Volume Glomerular Filtration Rate Treatment Outcome Heart-Assist Devices Registries Cause of Death Risk Assessment Risk Factors Reproducibility of Results Intubation, Intratracheal Health Status Ventricular Function, Left Time Factors Aged Aged, 80 and over Quality Indicators, Health Care Victoria Female Male Coronary Artery Disease Out-of-Hospital Cardiac Arrest Percutaneous Coronary Intervention Objectives: This study sought to determine the most risk-adjustment model for 30-day all-cause mortality in order to report risk-adjusted outcomes. The study also explored whether the exclusion of extreme high-risk conditions of cardiogenic shock, intubated out-of-hospital cardiac arrest (OHCA), or the need for mechanical ventricular support affected the model's predictive accuracy. Background: Robust risk-adjustment models are a critical component of clinical quality registries, allowing outcomes to be reported in a fair and meaningful way. The Victorian Cardiac Outcomes Registry encompasses all 30 hospitals in the state of Victoria, Australia, that undertake percutaneous coronary intervention. Methods: Data were collected on 27,544 consecutive percutaneous coronary intervention procedures from 2014 to 2016. Twenty-eight patient risk factors and procedural variables were considered in the modeling process. The multivariable logistic regression analysis considered derivation and validation datasets, along with a temporal validation period. Results: The model included risk-adjustment for cardiogenic shock, intubated OHCA, estimated glomerular filtration rate, left ventricular ejection fraction, angina type, mechanical ventricular support, ≥80 years of age, lesion complexity, percutaneous access site, and peripheral vascular disease. The C-statistic for the derivation dataset was 0.921 (95% confidence interval: 0.905 to 0.936), with C-statistics of 0.931 and 0.934 for 2 validation datasets reflecting the 2014 to 2016 and 2017 periods. Subgroup modeling excluding cardiogenic shock and intubated OHCA provided similar risk-adjusted outcomes (p = 0.32). Conclusions: Our study has developed a highly predictive risk-adjustment model for 30-day mortality that included high-risk presentations. Therefore, we do not need to exclude high-risk cases in our model when determining risk-adjusted outcomes. 2019 Journal Article http://hdl.handle.net/20.500.11937/93081 10.1016/j.jcin.2019.07.002 English http://purl.org/au-research/grants/nhmrc/1111170 http://purl.org/au-research/grants/nhmrc/1045862 http://purl.org/au-research/grants/nhmrc/1090302 ELSEVIER SCIENCE INC unknown |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology 30-day mortality clinical quality registry percutaneous coronary intervention risk-adjustment PERCUTANEOUS CORONARY INTERVENTION 30-DAY MORTALITY 30-day mortality clinical quality registry percutaneous coronary intervention risk-adjustment Aged Aged, 80 and over Cause of Death Coronary Artery Disease Female Glomerular Filtration Rate Health Status Heart-Assist Devices Humans Intubation, Intratracheal Male Out-of-Hospital Cardiac Arrest Percutaneous Coronary Intervention Quality Indicators, Health Care Registries Reproducibility of Results Risk Assessment Risk Factors Shock, Cardiogenic Stroke Volume Time Factors Treatment Outcome Ventricular Function, Left Victoria Humans Shock, Cardiogenic Stroke Volume Glomerular Filtration Rate Treatment Outcome Heart-Assist Devices Registries Cause of Death Risk Assessment Risk Factors Reproducibility of Results Intubation, Intratracheal Health Status Ventricular Function, Left Time Factors Aged Aged, 80 and over Quality Indicators, Health Care Victoria Female Male Coronary Artery Disease Out-of-Hospital Cardiac Arrest Percutaneous Coronary Intervention Tacey, M. Dinh, D.T. Andrianopoulos, N. Brennan, A.L. Stub, D. Liew, D. Reid, Christopher Duffy, S.J. Lefkovits, J. Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions |
| title | Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions |
| title_full | Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions |
| title_fullStr | Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions |
| title_full_unstemmed | Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions |
| title_short | Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions |
| title_sort | risk-adjusting key outcome measures in a clinical quality pci registry: development of a highly predictive model without the need to exclude high-risk conditions |
| topic | Science & Technology Life Sciences & Biomedicine Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology 30-day mortality clinical quality registry percutaneous coronary intervention risk-adjustment PERCUTANEOUS CORONARY INTERVENTION 30-DAY MORTALITY 30-day mortality clinical quality registry percutaneous coronary intervention risk-adjustment Aged Aged, 80 and over Cause of Death Coronary Artery Disease Female Glomerular Filtration Rate Health Status Heart-Assist Devices Humans Intubation, Intratracheal Male Out-of-Hospital Cardiac Arrest Percutaneous Coronary Intervention Quality Indicators, Health Care Registries Reproducibility of Results Risk Assessment Risk Factors Shock, Cardiogenic Stroke Volume Time Factors Treatment Outcome Ventricular Function, Left Victoria Humans Shock, Cardiogenic Stroke Volume Glomerular Filtration Rate Treatment Outcome Heart-Assist Devices Registries Cause of Death Risk Assessment Risk Factors Reproducibility of Results Intubation, Intratracheal Health Status Ventricular Function, Left Time Factors Aged Aged, 80 and over Quality Indicators, Health Care Victoria Female Male Coronary Artery Disease Out-of-Hospital Cardiac Arrest Percutaneous Coronary Intervention |
| url | http://purl.org/au-research/grants/nhmrc/1111170 http://purl.org/au-research/grants/nhmrc/1111170 http://purl.org/au-research/grants/nhmrc/1111170 http://hdl.handle.net/20.500.11937/93081 |