Dataset Information

Comparison of cardiac surgery mortality reports using administrative and clinical data sources: a prospective cohort study.

ABSTRACT: BACKGROUND:Outcomes for coronary artery bypass surgery are of broadening interest, but the impact of data type on quality reporting has not been fully examined. We compared the performance of administrative and clinical data-based risk adjustment models at a tertiary-quaternary care hospital. METHODS:We used a prospective study design to test two risk adjustment models, one from administrative (Canadian Institute for Health Information [CIHI] Cardiac Care Quality Indicator) and one from clinical data (Society of Thoracic Surgeons), on cardiac surgical procedures performed between 2013 and 2016 (n = 1635). Our primary outcome was in-hospital mortality within 30 days of surgery. Model performance was established by comparing predicted and observed mortality, model calibration and handling of critical covariates. RESULTS:Observed mortality was 1.96%, which was the same as that predicted by the Society of Thoracic Surgeons model (1.96%), but significantly higher than that predicted by the CIHI model (1.03%). Despite both models having similar C statistics (0.756 CIHI; 0.758 Society of Thoracic Surgeons), the CIHI model showed significant underestimation of mortality among patients at higher risk. There was significant miscalibration of risk associated with 7 covariates: New York Heart Association class IV, congestive heart failure, ejection fraction less than 20%, atrial fibrillation, acute coronary insufficiency, cardiac compromise (shock, myocardial infarction < 24 h, intra-aortic balloon pump, cardiac resuscitation or preprocedure circulatory support) and creatinine concentration of 100 mg/dL or more. Together, these factors accounted for 84% of the difference in predicted mortality between the administrative and clinical models. INTERPRETATION:Risk prediction using administrative data underestimated risk of death, potentially inflating observed-to-predicted mortality ratios at hospitals with patients who are more ill. Caution is warranted when hospital reports of cardiac surgery outcomes are based on administrative data alone.

SUBMITTER: Manlhiot C

PROVIDER: S-EPMC6182118 | biostudies-literature | 2018 Jul-Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparison of cardiac surgery mortality reports using administrative and clinical data sources: a prospective cohort study.

Manlhiot Cedric C Rao Vivek V Rubin Barry B Lee Douglas S DS

CMAJ open 20180701 3

<h4>Background</h4>Outcomes for coronary artery bypass surgery are of broadening interest, but the impact of data type on quality reporting has not been fully examined. We compared the performance of administrative and clinical data-based risk adjustment models at a tertiary-quaternary care hospital.<h4>Methods</h4>We used a prospective study design to test two risk adjustment models, one from administrative (Canadian Institute for Health Information [CIHI] Cardiac Care Quality Indicator) and on ...[more]

PMID: 30181346

Similar Datasets

Project description:ImportanceA variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied.ObjectiveTo examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions.Design, setting, and participantsIn this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021.ExposureCardiac surgery.Main outcomes and measuresPostoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values.ResultsData from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk.Conclusions and relevanceThis study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations.

Project description:IntroductionMore than 30 000 cardiac surgery procedures are performed in the UK each year, however, postoperative complications and long-term failure of interventions are common, leading to repeated surgeries. This represents a significant burden on the patient and health service.Routinely, patients are discharged to their general practitioner 6 weeks postoperatively and research studies typically only report short-term outcomes up to 1 year after surgery, together this makes long-term outcomes of cardiac surgery difficult to monitor. Further, traditional research methods have yet to advance understanding of what causes early complications and why surgical interventions fail.Methods and analysisThis prospective cohort study will characterise participants undergoing cardiac surgery at baseline, describe short-term, medium-term and long-term health outcomes postoperatively and collect tissue samples.All eligible adult patients undergoing cardiac surgery at the Bristol Heart Institute, UK will be approached for consent. Recruitment is expected to continue for up to 10 years resulting in the largest cohort of cardiac patients reported to date. Blood, urine and waste tissue samples will be collected during admission. Samples, along with anonymised data, will be used to investigate outcomes and inform predictive models of complications associated with cardiac surgery.Data about the surgical admission will be obtained from hospital databases and medical notes. Participants may be monitored up to 5 years postoperatively using data obtained from NHS digital. Participants will complete health questionnaires 3 months and 12 months postoperatively.The analysis of data and tissue samples to address specific research questions will require separate research protocols and ethical approval.Ethics and disseminationThis study was approved by the East Midlands Nottingham 2 Research Ethics Committee.Findings will be disseminated through peer-reviewed publications and presentation at national and international meetings. Participants will be informed of results in annual newsletters.Trial registration numberISRCTN90204321.

Project description:BackgroundEstimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources.MethodsBaseline survey data for 90,352 participants of the 45 and Up Study-a cohort study of residents of New South Wales, Australia, aged 45 years and over-were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (?).The characteristics of people classified as multimorbid were compared using logistic regression modelling.ResultsAgreement was found to be highest for diabetes in both hospital and claims data (? = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data.ConclusionsDifferent individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions.

Project description:BackgroundHigher levels of plasma neutrophil gelatinase-associated lipocalin (pNGAL) are an early marker of acute kidney injury and are associated with increased risk of short-term adverse outcomes. The independent association between pNGAL and long-term mortality is unknown.MethodsIn this prospective observational cohort study, we studied 1191 adults who underwent cardiac surgery between 2007 and 2009 at 6 centers in the TRIBE-AKI cohort. We measured the pNGAL on the pre-operative and first 3 post-operative days and assessed the relationship of peri-operative pNGAL concentrations with all-cause mortality.ResultsDuring a median follow-up of 3.0 years, 139 participants died (50/1000 person-years). Pre-operative levels of pNGAL were associated with 3-year mortality (unadjusted HR 1.96, 95% CI 1.34,2.85) and the association persisted after adjustment for pre-operative variables including estimated glomerular filtration rate (adjusted HR 1.48, 95% CI 1.04-2.12). After adjustment for pre- and intra-operative variables, including pre-operative NGAL levels, the highest tertiles of first post-operative and peak post-operative pNGAL were also independently associated with 3-year mortality risk (adjusted HR 1.31, 95% CI 1.0-1.7 and adjusted HR 1.78, 95% CI 1.2-2.7, respectively). However, after adjustment for peri-operative changes in serum creatinine, there was no longer an independent association between the first post-operative and peak post-operative pNGAL and long-term mortality (adjusted HR 0.98,95% CI 0.79-1.2 for first pNGAL and adjusted HR 1.19, 95% CI 0.87-1.61 for peak pNGAL).ConclusionsPre-operative pNGAL levels were independently associated with 3-year mortality after cardiac surgery. While post-operative pNGAL levels were also associated with 3-year mortality, this relationship was not independent of changes in serum creatinine. These findings suggest that while pre-operative pNGAL adds prognostic value for mortality beyond routinely available serum creatinine, post-operative pNGAL measurements may not be as useful for this purpose.

Project description:BackgroundIt is important to find a comorbidity measure with better performance for use with administrative data. The new method proposed by Elixhauser et al. has never been validated and compared to the widely used Charlson method in the Asia region. The objective of this study was to compare the performance of three comorbidity measures using information from different data periods in predicting short- and long-term mortality among patients with acute myocardial infarction (AMI) and chronic obstructive pulmonary disease (COPD).MethodsWe conducted a retrospective cohort study using National Health Insurance claims data (2001-2002) in Taiwan. We constructed the Elixhauser, the Charlson/Deyo, and the Charlson/Romano methods based on the International Classification of Disease, 9th Revision, Clinical Modification codes in the claims data. Two data periods, including the index hospitalization as well as the index and prior 1-year hospitalizations, were used in the analysis. The performances were compared using the c-statistics derived from multiple logistic regression models that included age, gender, race, and whether the patient received surgery or not. The outcomes of interest were in-hospital and 1-year mortality.ResultsThe performance was in the same rank order among both populations regardless of the outcome and data period: Elixhauser > Charlson/Romano > Charlson/Deyo. In predicting in-hospital mortality, the Elixhauser models using information from the index hospitalization performed best, even better than the Charlson/Deyo or Charlson/Romano models using information from the index and prior hospitalizations. Nevertheless, in predicting 1-year mortality, the Elixhauser models using information from the index and 1-year prior hospitalizations performed better than using information from the index hospitalization only.ConclusionsThis is so far the first study to validate the Elixhauser method and compare it to other methods in the Asia region, and is the first to report its differences in data periods between short- and long-term outcomes. The comorbidity measurement developed by Elixhauser et al. has relatively good predictive validity, and researchers should consider its use in claims-based studies.

Dataset Information

Comparison of cardiac surgery mortality reports using administrative and clinical data sources: a prospective cohort study.

Publications

Comparison of cardiac surgery mortality reports using administrative and clinical data sources: a prospective cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets