Comparison of Clinical and Administrative Data Sources for Hospital Coronary Artery Bypass Graft Surgery Report Cards

Estimating the Impact of Hospital-Level Variation on the Use of Inpatient Rehabilitation Facilities Versus Skilled Nursing Facilities on Individual Patients With Stroke

BACKGROUND

There is substantial hospital-level variation in the use of Inpatient Rehabilitation Facilities (IRFs) versus Skilled Nursing Facilities (SNFs) among patients with stroke, which is poorly understood. Our objective was to quantify the net effect of the admitting hospital on the probability of receiving IRF or SNF care for individual patients with stroke.

METHODS

Using Medicare claims data (2011-2013), a cohort of patients with acute stroke discharged to an IRF or SNF was identified. We generated 2 multivariable logistic regression models. Model 1 predicted IRF admission (versus SNF) using only patient-level factors, whereas model 2 added a hospital random effect term to quantify the hospital effect. The statistical significance and direction of the random effect terms were used to categorize hospitals as being either IRF-favoring, SNF-favoring, or neutral with respect to their discharge patterns. The hospital's impact on individual patient's probability of IRF discharge was estimated by taking the change in individual predicted probabilities (change in individual predicted probability) between the 2 models. Hospital-level effects were categorized as small (<10%), moderate (10%-19%), or large (≥20%) depending on change in individual predicted probability.

RESULTS

The cohort included 135 415 patients (average age, 81.5 [SD=8.0] years, 61% female, 91% ischemic stroke) who were discharged from 1816 acute care hospitals to IRFs (n=66 548) or SNFs (n=68 867). Half of hospitals were classified as being either IRF-favoring (n=461, 25.4%) or SNF-favoring (n=485, 26.7%) with the remainder (n=870, 47.9%) considered neutral. Overall, just over half (n=73 428) of patients were treated at hospitals that had moderate or large independent effects on discharge settings. Hospital effects for neutral hospitals were small (ie, change in individual predicted probability <10%) for most patients (72.5%). However, hospital effects were moderate or large for 78.8% and 84.6% of patients treated at IRF- or SNF-favoring hospitals, respectively.

CONCLUSIONS

For most patients with stroke, the admitting hospital meaningfully changed the type of rehabilitation care that they received.

Preoperative smoking status and long-term survival after coronary artery bypass grafting: a competing risk analysis

OBJECTIVES

Patients with severe coronary artery disease who undergo coronary artery bypass grafting consistently demonstrate that continued smoking after surgery increases late mortality rates. Smoking may exert its harmful effects through the ongoing chronic process of atherosclerotic progression both in the grafts and the native system. However, it is not clear whether cardiac mortality is primary and solely responsible for the inferior late survival of current smokers.

METHODS

In this retrospective analysis, we included all consecutive patients undergoing primary isolated coronary artery bypass surgery from 1 January 2000 to 30 September 2015 in an Academic Hospital in Northern Portugal. The predictive or independent variable was the patients' smoking history status, a categorical variable with 3 levels: non-smoker (the comparator), ex-smoker for >1 year (exposure 1) and current smoker (exposure 2). The primary end point was long-term all-cause mortality. Secondary outcomes were long-term cause-specific mortality (cardiovascular and noncardiovascular). We fitted overall and Fine and Gray subdistribution hazard models.

RESULTS

We identified 5242 eligible patients. Follow-up was 99.7% complete (with 17 patients lost to follow-up). The median follow-up time was 12.79 years (interquartile range, 9.51-16.60). Throughout the study, there were 2049 deaths (39.1%): 877 from cardiovascular causes (16.7%), 727 from noncardiovascular causes (13.9%) and 445 from unknown causes (8.5%). Ex-smokers had an identical long-term survival than non-smokers [hazard ratio (HR) 0.99; 95% confidence interval (CI) 0.88, 1.12; P = 0.899]. Conversely, current smokers had a 24% increase in late mortality risk (HR 1.24; 95% CI 1.07, 1.44; P = 0.004) as compared to non-smokers. While the current smoker status increased the relative incidence of noncardiac death by 61% (HR 1.61; 95% CI 1.27, 2.05, P < 0.001), it did confer a 25% reduction in the relative incidence of cardiac death (HR 0.75; 95% CI 0.59, 0.97; P = 0.025).

CONCLUSIONS

Whereas ex-smokers have an identical long-term survival to non-smokers, current smokers exhibit an increase in late all-cause mortality risk at the expense of an increased relative incidence of noncardiac death. By subtracting the inciting risk factor, smoking cessation reduces the relative incidence of cardiac death.

Data-driven coaching to improve statewide outcomes in CABG: before and after interventional study

BACKGROUND

The impact of quality improvement initiatives program (QIP) on coronary artery bypass grafting surgery (CABG) remains scarce, despite improved outcomes in other surgical areas. This study aims to evaluate the impact of a package of QIP on mortality rates among patients undergoing CABG.

MATERIALS AND METHODS

This prospective cohort study utilized data from the multicenter database Registro Paulista de Cirurgia Cardiovascular II (REPLICCAR II), spanning from July 2017 to June 2019. Data from 4018 isolated CABG adult patients were collected and analyzed in three phases: before-implementation, implementation, and after-implementation of the intervention (which comprised QIP training for the hospital team). Propensity Score Matching was used to balance the groups of 2170 patients each for a comparative analysis of the following outcomes: reoperation, deep sternal wound infection/mediastinitis ≤30 days, cerebrovascular accident, acute kidney injury, ventilation time >24 h, length of stay <6 days, length of stay >14 days, morbidity and mortality, and operative mortality. A multiple regression model was constructed to predict mortality outcomes.

RESULTS

Following implementation, there was a significant reduction of operative mortality (61.7%, P =0.046), as well as deep sternal wound infection/mediastinitis ( P <0.001), sepsis ( P =0.002), ventilation time in hours ( P <0.001), prolonged ventilation time ( P =0.009), postoperative peak blood glucose ( P <0.001), total length of hospital stay ( P <0.001). Additionally, there was a greater use of arterial grafts, including internal thoracic ( P <0.001) and radial ( P =0.038), along with a higher rate of skeletonized dissection of the internal thoracic artery.

CONCLUSIONS

QIP was associated with a 61.7% reduction in operative mortality following CABG. Although not all complications exhibited a decline, the reduction in mortality suggests a possible decrease in failure to rescue during the after-implementation period.

Predicting return to work after traumatic brain injury using machine learning and administrative data

BACKGROUND

Accurate patient-specific predictions on return-to-work after traumatic brain injury (TBI) can support both clinical practice and policymaking. The use of machine learning on large administrative data provides interesting opportunities to create such prognostic models.

AIM

The current study assesses whether return-to-work one year after TBI can be predicted accurately from administrative data. Additionally, this study explores how model performance and feature importance change depending on whether a distinction is made between mild and moderate-to-severe TBI.

METHODS

This study used a population-based dataset that combined discharge, claims and social security data of patients hospitalized with a TBI in Belgium during the year 2016. The prediction of TBI was attempted with three algorithms, elastic net logistic regression, random forest and gradient boosting and compared in their performance by their accuracy, sensitivity, specificity and area under the receiver operator curve (ROC AUC).

RESULTS

The distinct modelling algorithms resulted in similar results, with 83% accuracy (ROC AUC 85%) for a binary classification of employed vs. not employed and up to 76% (ROC AUC 82%) for a multiclass operationalization of employment outcome. Modelling mild and moderate-to-severe TBI separately did not result in considerable differences in model performance and feature importance. The features of main importance for return-to-work prediction were related to pre-injury employment.

DISCUSSION

While clearly offering some information beneficial for predicting return-to-work, administrative data needs to be supplemented with additional information to allow further improvement of patient-specific prognose.

A critical analysis of Discovery Health's claims-based risk adjustment of mortality rates in South African private sector hospitals

In 2019, Discovery Health published a risk adjustment model to determine standardised mortality rates across South African private hospital systems, with the aim of contributing towards quality improvement in the private healthcare sector. However, the model suffers from limitations due to its design and its reliance on administrative data. The publication's aim of facilitating transparency is unfortunately undermined by shortcomings in reporting. When designing a risk prediction model, patient-proximate variables with a sound theoretical or proven association with the outcome of interest should be used. The addition of key condition-specific clinical data points at the time of hospital admission will dramatically improve model performance. Performance could be further improved by using summary risk prediction scores such as the EUROSCORE II for coronary artery bypass graft surgery or the GRACE risk score for acute coronary syndrome. In general, model reporting should conform to published reporting standards, and attempts should be made to test model validity by using sensitivity analyses. In particular, the limitations of machine learning prediction models should be understood, and these models should be appropriately developed, evaluated and reported.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Long-term survival of female versus male patients after coronary artery bypass grafting

Background

Several of the most extensively used risk prediction tools for coronary artery bypass grafting outcomes include female sex as an independent risk factor for postoperative outcomes. It is not clear whether this putative increased surgical risk impacts long-term survival. This study aimed to assess sex differences in 10-year all-cause mortality.

Methods

Retrospective analysis of 5340 consecutive patients undergoing primary isolated coronary artery bypass surgery, performed from 2000 to 2015, in a Portuguese level III Hospital. The primary endpoint was all-cause mortality at ten years. We employed an overlap weighting algorithm to minimize confounding. Its target population highlights patients with the most overlap in their observed characteristics, and its corresponding estimand is the average treatment effect in the overlap population.

Results

We identified that 5340 patients underwent isolated CABG: 1104 (20.7%) were female, and 4236 (79.3%) were male. Sixteen patients were lost to follow-up (0.3%). The median follow-up time was 12.79 (IQR, 9.52–16.66) years: 12.68 (IQR, 9.48–16.54) years for the male patient group and 13.13 (IQR, 9.75–16.98) years for the female patient group. The primary endpoint of all-cause mortality at ten years occurred in 1106 patients (26.1%) in the male patient group, compared with 315 (28.5%) in the female patient group. The unweighted survival analysis for both groups reveals the worst long-term prognosis for the female cohort (hazard ratio, 1.22; 95% CI, 1.10 to 1.35; p < 0.001), while in the overlap weighted survival analysis, such long-term difference in prognosis disappears (hazard ratio, 0.98; 95% CI, 0.88 to 1.09; p = 0.693).

Conclusion

In this longitudinal, population-level analysis of patients undergoing primary, isolated CABG, we demonstrated that the female sex is not associated with increased long-term all-cause mortality compared to their male counterparts. Thus, sex should not influence the undertaking of an adequate revascularization strategy.

Long-term survival of single versus bilateral internal mammary artery grafting in patients under 70

OBJECTIVES

As definitive data from randomized controlled trials comparing the effect on long-term survival of using single internal mammary artery (SIMA) or bilateral internal mammary artery (BIMA) grafting are not yet available, observational studies allow for long-term follow-up in large and representative populations, which might complement the information potentially derived from randomized trials. To compare long-term survival in patients under 70 years of age undergoing SIMA or BIMA grafting.

METHODS

Retrospective analysis of 3384 consecutive patients under 70 years undergoing primary isolated coronary artery bypass grafting, performed from 2000 to 2015, in a Portuguese level III Hospital. We identified 2176 and 1208 patients from the study population who underwent SIMA and BIMA grafting, respectively. The primary end point was all-cause mortality at 10 years. We employed inverse probability weighting to restrict confounding by indication.

RESULTS

The mean age of the study population was 59.4 (± 7.6) years, and 567 (16.8%) were females. Inverse probability weighting was effective in eliminating differences in all significant baseline characteristics. Follow-up was 99.88% complete. The median follow-up time was 12.82 (interquartile range, 9.65, 16.74) years: the primary end point of all-cause mortality at 10 years occurred in 463 patients (21.3%) and 166 (13.7%) in the SIMA and BIMA grafting groups, respectively (hazard ratio, 0.78; 95% confidence interval, 0.66-0.92; P = 0.004).

CONCLUSIONS

Bilateral internal mammary grafting is associated with lower long-term mortality than single internal mammary grafting. Moreover, this survival benefit comes at no increased perioperative morbidity or mortality cost.

An overlap-weighted analysis on 10-year survival of off-pump versus on-pump coronary artery grafting in multivessel coronary artery disease

BACKGROUND AND OBJECTIVE

The introduction of off-pump coronary artery bypass surgery intended to overcome some of the conventional on-pump procedure limitations by avoiding potentially harmful adverse effects of extracorporeal circulation and aortic cross-clamping. However, the doubt remains on whether it is associated with worse long-term outcomes. To compare long-term survival in patients with multivessel ischemic heart disease undergoing off-pump versus on-pump coronary artery bypass grafting.

METHODS

Retrospective analysis of 4788 consecutive patients undergoing primary isolated multivessel coronary artery bypass grafting surgery, performed from 2000 to 2015, in Northern Portugal. Among the study population, we identified 1616 and 3172 patients that underwent off-pump and on-pump coronary artery grafting, respectively. We employed a propensity-score-based overlap weighting (OW) algorithm to restrict confounding by indication. The primary endpoint was all-cause mortality at 10 years.

RESULTS

The mean age of the study population was 63.9 (±9.8) years, and 951 (19.9%) were females. OW was effective in eliminating differences in all major baseline characteristics. Follow-up was 100% complete. The median follow-up time was 12.80 (9.62, 16.62) years. The primary endpoint of all-cause mortality at 10 years occurred in 431 patients (26.7%) in the off-pump group, as compared with 863 (27.2%) in the on-pump group (hazard ratio, 0.93; 95% confidence interval, 0.83-1.04; p = .196).

CONCLUSIONS

In this longitudinal, population-level comparison of off-pump versus on-pump coronary artery bypass surgery for treating multivessel coronary artery disease, the primary outcome of long-term mortality was identical among both patients' groups.

Hospital Characteristics and Early Enrollment Trends in the American College of Cardiology Voluntary Public Reporting Program

IMPORTANCE

Limited data exist regarding the characteristics of hospitals that do and do not participate in voluntary public reporting programs.

OBJECTIVE

To describe hospital characteristics and trends associated with early participation in the American College of Cardiology (ACC) voluntary reporting program for cardiac catheterization-percutaneous coronary intervention (CathPCI) and implantable cardioverter-defibrillator (ICD) registries.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed enrollment trends and characteristics of hospitals that did and did not participate in the ACC voluntary public reporting program. All hospitals reporting procedure data to the National Cardiovascular Data Registry (NCDR) CathPCI or ICD registries that were eligible for the public reporting program from July 2014 (ie, program launch date) to May 2017 were included. Stepwise logistic regression was used to identify hospital characteristics associated with voluntary participation. Enrollment trends were evaluated considering the date US News & World Report (USNWR) announced that it would credit participating hospitals. Data analysis was performed from March 2017 to January 2018.

MAIN OUTCOMES AND MEASURES

Hospital characteristics and participation in the public reporting program.

RESULTS

By May 2017, 561 of 1747 eligible hospitals (32.1%) had opted to participate in the program. Enrollment increased from 240 to 376 hospitals (56.7%) 1 month after the USNWR announcement that program participation would be considered as a component of national hospital rankings. Compared with hospitals that did not enroll, program participants had increased median (IQR) procedural volumes for PCI (481 [280-764] procedures vs 332 [186-569] procedures; P < .001) and ICD (114 [56-220] procedures vs 62 [25-124] procedures; P < .001). Compared with nonparticipating hospitals, an increased mean (SD) proportion of participating hospitals adhered to composite discharge medications after PCI (0.96 [0.03] vs 0.92 [0.07]; P < .001) and ICD (0.88 [0.10] vs 0.81 [0.12]; P < .001). Hospital factors associated with enrollment included participation in 5 or more NCDR registries (odds ratio [OR],1.98; 95% CI, 1.24-3.19; P = .005), membership in a larger hospital system (ie, 3-20 hospitals vs ≤2 hospitals in the system: OR, 2.29; 95% CI, 1.65-3.17; P = .001), participation in an NCDR pilot public reporting program of PCI 30-day readmissions (OR, 2.93; 95% CI, 2.19-3.91; P < .001), university affiliation (vs government affiliation: OR, 3.85, 95% CI, 1.03-14.29; P = .045; vs private affiliation: OR, 2.22; 95% CI, 1.35-3.57; P < .001), Midwest location (vs South: OR, 1.47; 95% CI, 1.06-2.08; P = .02), and increased comprehensive quality ranking (4 vs 1-2 performance stars in CathPCI: OR, 8.08; 95% CI, 5.07-12.87; P < .001; 4 vs 1 performance star in ICD: OR, 2.26; 95% CI, 1.48-3.44; P < .001) (C statistic = 0.829).

CONCLUSIONS AND RELEVANCE

This study found that one-third of eligible hospitals participated in the ACC voluntary public reporting program and that enrollment increased after the announcement that program participation would be considered by USNWR for hospital rankings. Several hospital characteristics, experience with public reporting, and quality of care were associated with increased odds of participation.

Quantifying injury severity for traumatic brain injury with routinely collected health data

BACKGROUND

Routinely collected health data (RCHD) offers many opportunities for traumatic brain injury (TBI) research, in which injury severity is an important factor.

OBJECTIVE

The use of clinical injury severity indices in a context of RCHD is explored, as are alternative measures created for this specific purpose. To identify useful scales for full body injury severity and TBI severity this study focuses on their performance in predicting these currently used indices, while accounting for age and comorbidities.

DATA

This study utilized an extensive population-based RCHD dataset consisting of all patients with TBI admitted to any Belgian hospital in 2016.

METHODS

Full body injury severity is scored based on the (New) Injury Severity Score ((N)ISS) and the ICD-based Injury Severity Score (ICISS). For TBI specifically, the Abbreviated Injury Scale (AIS) Head, Loss of Consciousness and the ICD-based Injury Severity Score for TBI injuries (ICISS) were used in the analysis. These scales were used to predict three outcome variables strongly related to injury severity: in-hospital death, admission to intensive care and length of hospital stay. For the prediction logistic regressions of the different injury severity scales and TBI severity indices were used, and error rates and the area under the receiver operating curve were evaluated visually.

RESULTS

In general, the ICISS had the best predictive performance (error rate between 0.06 and 0.23; AUC between 0.82 [0.81;0.83] and 0.86 [0.85;0.86]). A clearly increasing error rate can be noticed with advancing age and accumulating comorbidity.

CONCLUSION

Both for full body injury severity and TBI severity, the ICISS tends to outperform other scales. It is therefore the preferred scale for use in research on TBI in the context of RCHD. In their current form, the severity scales are not suitable for use in older populations.

Association of the hospital readmission reduction program with readmission and mortality outcomes after coronary artery bypass graft surgery

The Affordable Care Act established the Hospital Readmissions Reduction Program (HRRP) to reduce payments to hospitals with excessive readmissions in an effort to link payment to the quality of hospital care. Prior studies demonstrating an association of HRRP implementation with increased mortality after heart failure discharges have prompted concern for potential unintended adverse consequences of the HRRP. We examined the impact of these policies on coronary artery bypass graft (CABG) surgery outcomes using the Nationwide Readmissions Database and found that, in line with previously observed readmission trends for CABG, readmission rates continued to decline in the era of the HRRP, but that this did not come at the expense of increased mortality. These results suggest that inclusion of surgical procedures, such as CABG in the HRRP might be an effective cost-reducing measure that does not adversely affect quality of hospital care.

Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions

PURPOSE OF REVIEW

The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease.

RECENT FINDINGS

Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.

Spanish Video-Assisted Thoracic Surgery Group: Method, Auditing, and Initial Results From a National Prospective Cohort of Patients Receiving Anatomical Lung Resections

INTRODUCTION

Our study sought to know the current implementation of video-assisted thoracoscopic surgery (VATS) for anatomical lung resections in Spain. We present our initial results and describe the auditing systems developed by the Spanish VATS Group (GEVATS).

METHODS

We conducted a prospective multicentre cohort study that included patients receiving anatomical lung resections between 12/20/2016 and 03/20/2018. The main quality controls consisted of determining the recruitment rate of each centre and the accuracy of the perioperative data collected based on six key variables. The implications of a low recruitment rate were analysed for "90-day mortality" and "Grade IIIb-V complications".

RESULTS

The series was composed of 3533 cases (1917 VATS; 54.3%) across 33 departments. The centres' median recruitment rate was 99% (25-75th:76-100%), with an overall recruitment rate of 83% and a data accuracy of 98%. We were unable to demonstrate a significant association between the recruitment rate and the risk of morbidity/mortality, but a trend was found in the unadjusted analysis for those centres with recruitment rates lower than 80% (centres with 95-100% rates as reference): grade IIIb-V OR=0.61 (p=0.081), 90-day mortality OR=0.46 (p=0.051).

CONCLUSIONS

More than half of the anatomical lung resections in Spain are performed via VATS. According to our results, the centre's recruitment rate and its potential implications due to selection bias, should deserve further attention by the main voluntary multicentre studies of our speciality. The high representativeness as well as the reliability of the GEVATS data constitute a fundamental point of departure for this nationwide cohort.

National Quality Forum Guidelines for Evaluating the Scientific Acceptability of Risk-adjusted Clinical Outcome Measures: A Report From the National Quality Forum Scientific Methods Panel

: Quality measurement is at the heart of efforts to achieve high-quality surgical and medical care at a lower cost. Without accurate quality measures, it is not possible to appropriately align incentives with quality. The aim of these National Quality Forum (NQF) guidelines is to provide measure developers and other stakeholders with guidance on the standards used by the NQF to evaluate the scientific acceptability of performance measures. Using a methodologically rigorous and transparent process for evaluating health care quality measures is the best insurance that alternative payment plans will truly reward and promote higher quality care. Performance measures need to be credible in order for physicians and hospitals to willingly partner with payers in efforts to improve population outcomes. Our goal in creating this position paper is to promote the transparency of NQF evaluations, improve the quality of performance measurements, and engage surgeons and all other stakeholders to work together to advance the science of performance measurement.

Mortality Trends for Veterans Hospitalized With Heart Failure and Pneumonia Using Claims-Based vs Clinical Risk-Adjustment Variables

IMPORTANCE

Prior studies have reported declines in mortality for patients admitted to Veterans Health Administration (VA) and non-VA hospitals using claims-based risk adjustment. These apparent mortality reductions may be influenced by changes in coding practices.

OBJECTIVE

To compare trends in the VA for 30-day mortality following hospitalization for heart failure (HF) and pneumonia using claims-based and clinical risk-adjustment models.

DESIGN, SETTING, AND PARTICIPANTS

This observational time-trend study analyzed admissions to a VA Medical Center with a principal diagnosis of HF, pneumonia, or sepsis/respiratory failure (RF) with a secondary diagnosis of pneumonia. Exclusion criteria included having less than 12 months of VA enrollment, being discharged alive within 24 hours, leaving against medical advice, and hospice utilization.

EXPOSURES

Admission to a VA hospital from January 2009 through September 2015.

MAIN OUTCOMES AND MEASURES

The primary outcome was 30-day, all-cause mortality. All models included age and sex. Claims-based covariates included 22 (30) comorbidities for HF (pneumonia). Clinical covariates included vital signs, laboratory values, and ejection fraction.

RESULTS

Among the 146 924 HF admissions, the mean (SD) age was 71.6 (11.4) years and 144 502 (98.4%) were men; among the 131 325 admissions for pneumonia, the mean (SD) age was 70.8 (12.3) years and 127 491 (97.1%) were men. Unadjusted 30-day mortality rates were 6.45% (HF) and 11.22% (pneumonia). Claims-based models showed an increased predicted risk of 30-day mortality over time (0.019 percentage points per quarter for HF [95% CI, 0.015 to 0.023]; 0.053 percentage points per quarter for pneumonia [95% CI, 0.043 to 0.063]). Clinical models showed declines or no change in predicted risk (-0.014 percentage points per quarter for HF [95% CI, -0.020 to -0.008]; -0.004 percentage points per quarter for pneumonia [95% CI, -0.017 to 0.008]). Claims-based risk adjustment yielded declines in 30-day mortality of 0.051 percentage points per quarter for HF (95% CI, -0.074 to -0.027) and 0.084 percentage points per quarter for pneumonia (95% CI, -0.111 to -0.056). Models adjusting for clinical covariates attenuated or eliminated these changes for HF (-0.017 percentage points per quarter; 95% CI, -0.039 to 0.006) and for pneumonia (-0.026 percentage points per quarter; 95% CI, -0.052 to 0.001). Compared with the claims-based models, the clinical models for HF and pneumonia more accurately differentiated between patients who died after 30 days and those who did not.

CONCLUSIONS AND RELEVANCE

Among HF and pneumonia hospitalizations, adjusting for clinical covariates attenuated declines in mortality rates identified using claims-based models. Assessments of temporal trends in 30-day mortality using claims-based risk adjustment should be interpreted with caution.

Coronary artery bypass grafting in Spain. Influence of procedural volume on outcomes

INTRODUCTION AND OBJECTIVES

To analyze the association between volume and outcomes in coronary artery bypass grafting (CABG) in the Spanish National Health System.

METHODS

We analyzed CABG episodes from 2013 to 2015. The selected outcome variables were in-hospital mortality in the index episode, 30-day cardiac-related readmissions, and mortality during readmission. Risk-adjusted rates of in-hospital mortality (RAMR) and 30-day readmissions (RARR) were calculated using multilevel logistic regression. High- and low-volume hospitals for CABG were identified by a nonconditioned analysis (k-means) and by compliance with the volume recommendation of clinical practice guidelines.

RESULTS

A total of 17 335 CABG index episodes were included, with a crude in-hospital mortality rate of 5.0%. Episodes attended in low-volume centers for CABG (< 155 CABG per year) showed 17% higher RAMR (5.81%±2.07% vs 4.96%±1.76%; P <.001) and a negative linear correlation between volume and RARR (r=-0.318; P=.029), as well as a higher percentage of complications during the episode. The same association between volume and more favorable outcomes was found in isolated CABG.

CONCLUSIONS

The mean CABG volume is low in Spanish National Health System hospitals. Higher volume was associated with better outcomes in CABG, both total and isolated. The findings of this study indicate the need for a higher concentration of CABG programs, as well as the publication of risk-adjusted outcomes of coronary intervention.

Comparison of administrative data and the American College of Surgeons National Surgical Quality Improvement Program data in a New South Wales Hospital

BACKGROUND

The National Surgical Quality Improvement Program (NSQIP) is widely used in North America for benchmarking. In 2015, NSQIP was introduced to four New South Wales public hospitals. The aim of this study is to investigate the agreement between NSQIP and administrative data in the Australian setting; to compare the performance of models derived from each data set to predict 30-day outcomes.

METHODS

The NSQIP and administrative data variables were mapped to select variables available in both data sets where coding may be influenced by interpretation of the clinical information. These were compared for agreement. Logistic regression models were fitted to estimate the probability of adverse outcomes within 30 days. Models derived from NSQIP and administrative data were compared by receiver operating characteristic curve analysis.

RESULTS

A total of 2240 procedures over 21 months had matching records. Functional status demonstrated poor agreement (kappa 0.02): administrative data recorded only one (1%) patient with partial- or total-dependence as recorded by NSQIP data. The American Society of Anesthesiologists class demonstrated excellent agreement (kappa 0.91). Other perioperative variables demonstrated poor to fair agreement (kappa 0.12-0.61). Predictive model based on NSQIP data was excellent at predicting mortality but was less accurate for complications and readmissions. The NSQIP model was better in predicting mortality and complications (receiver operating characteristic curve 0.93 versus 0.87; P = 0.029 and 0.71 versus 0.64; P = 0.027).

CONCLUSIONS

There is poor agreement between NSQIP data and administrative data. Predictive models associated with NSQIP data were more accurate at predicting surgical outcomes than those from administrative data. To drive quality improvement in surgery, high-quality clinical data are required and we believe that NSQIP fulfils this function.

Risk Stratification for Transcatheter Aortic Valve Replacement

Risk assessment models developed from administrative and clinical databases are used for clinical decision making. Since these models are derived from a database, they have an inherent limitation of being as good as the data they are derived from. Many of these models under or overestimate certain clinical outcomes particularly mortality in certain group of patients. Undeniably, there is significant variability in all these models on account of patient population studied, the statistical analysis used to develop the model and the period during which these models were developed. This review aims to shed light on development and application of risk assessment models for cardiac surgery with special emphasis on risk stratification in severe aortic stenosis to select patients for transcatheter aortic valve replacement.

Validity of acute cardiovascular outcome diagnoses in European electronic health records: a systematic review protocol

INTRODUCTION

Cardiovascular diseases (CVDs) are among the leading causes of death globally. Electronic health records (EHRs) provide a rich data source for research on CVD risk factors, treatments and outcomes. Researchers must be confident in the validity of diagnoses in EHRs, particularly when diagnosis definitions and use of EHRs change over time. Our systematic review provides an up-to-date appraisal of the validity of stroke, acute coronary syndrome (ACS) and heart failure (HF) diagnoses in European primary and secondary care EHRs.

METHODS AND ANALYSIS

We will systematically review the published and grey literature to identify studies validating diagnoses of stroke, ACS and HF in European EHRs. MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library, OpenGrey and EThOS will be searched from the dates of inception to April 2019. A prespecified search strategy of subject headings and free-text terms in the title and abstract will be used. Two reviewers will independently screen titles and abstracts to identify eligible studies, followed by full-text review. We require studies to compare clinical codes with a suitable reference standard. Additionally, at least one validation measure (sensitivity, specificity, positive predictive value or negative predictive value) or raw data, for the calculation of a validation measure, is necessary. We will then extract data from the eligible studies using standardised tables and assess risk of bias in individual studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesised into a narrative format and heterogeneity assessed. Meta-analysis will be considered when a sufficient number of homogeneous studies are available. The overall quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation tool.

ETHICS AND DISSEMINATION

This is a systematic review, so it does not require ethical approval. Our results will be submitted for peer-review publication.

PROSPERO REGISTRATION NUMBER

CRD42019123898.

Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers

Background

The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded in Epithor through a comparison with the French PMSI database, which is the national medico-administrative reference database. The aim of this study was to demonstrate the influence of data quality with respect to identifying 30-day mortality hospital outliers.

Methods

We used each hospital’s individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group.

Results

Of the hospitals that use Epithor, 25 were classified in the GQD group and 75 in the LQD group. The 30-day mortality rate was 2.8% (n = 300) in the GQD group vs. 1.9% (n = 181) in the LQD group (P <0.0001). The between-hospital differences in SMR1 appeared substantial (interquartile range (IQR) 0–1.036), and they were even higher in SMR2 (IQR 0–1.19). SMR1 identified 7 hospitals as high-mortality outliers. SMR2 identified 4 hospitals as high-mortality outliers. Some hospitals went from non-outlier to high mortality and vice-versa. Kappa values were roughly 0.46 and indicated moderate agreement.

Conclusion

We found that most hospitals provided Epithor with high-quality data, but other hospitals needed to improve the quality of the information provided. Quality control is essential for this type of database and necessary for the unbiased adjustment of regression models.

Sensitivity of Administrative Coding in Identifying Inpatient Acute Strokes Complicating Procedures or Other Diseases in UK Hospitals

Background

Administrative hospital diagnostic coding data are increasingly used in “big data” research and to assess complication rates after surgery or acute medical conditions. Acute stroke is a common complication of several procedures/conditions, such as carotid interventions, but data are lacking on the sensitivity of administrative coding in identifying acute stroke during inpatient stay.

Methods and Results

Using all acute strokes ascertained in a population‐based cohort (2002–2017) as the reference, we determined the sensitivity of hospital administrative diagnostic codes (International Classification of Diseases, Tenth Revision; ICD‐10) for identifying acute strokes that occurred during hospital admission for other reasons, stratified by coding strategies, study periods, and stroke severity (National Institutes of Health Stroke Score</≥5). Of 3011 acute strokes, 198 (6.6%) occurred during hospital admissions for procedures/other diseases, including 122 (61.6%) major strokes. Using stroke‐specific codes (ICD‐10=I60–I61 and I63–I64) in the primary diagnostic position, 66 of the 198 cases were correctly identified (sensitivity for any stroke, 33.3%; 95% CI, 27.1–40.2; minor stroke, 30.3%; 95% CI, 21.0–41.5; major stroke, 35.2%; 95% CI, 27.2–44.2), with no improvement of sensitivity over time (P_trend=0.54). Sensitivity was lower during admissions for surgery/procedures than for other acute medical admissions (n/% 17/23.3% versus 49/39.2%; P=0.02). Sensitivity improved to 60.6% (53.6–67.2) for all and 61.6% (50.0–72.1) for surgery/procedures if other diagnostic positions were used, and to 65.2% (58.2–71.5) and 68.5% (56.9–78.1) respectively if combined with use of all possible nonspecific stroke‐related codes (ie, adding ICD‐10=I62 and I65–I68).

Conclusions

Low sensitivity of administrative coding in identifying acute strokes that occurred during admission does not support its use alone for audit of complication rates of procedures or hospitalization for other reasons.

Identifying cardiac surgery operations in hospital episode statistics administrative database, with an OPCS-based classification of procedures, validated against clinical data

OBJECTIVES

Administrative databases with dedicated coding systems in healthcare systems where providers are funded based on services recorded have been shown to be useful for clinical research, although their reliability is still questioned. We devised a custom classification of procedures and algorithms based on OPCS, enabling us to identify open heart surgeries from the English administrative database, Hospital Episode Statistics, with the objective of comparing the incidence of cardiac procedures in administrative and clinical databases.

DESIGN

A comparative study of the incidence of cardiac procedures in administrative and clinical databases.

SETTING

Data from all National Health Service Trusts in England, performing cardiac surgery.

PARTICIPANTS

Patients classified as having cardiac surgery across England between 2004 and 2015, using a combination of procedure codes, age >18 and consultant specialty, where the classification was validated against internal and external benchmarks.

RESULTS

We identified a total of 296 426 cardiac surgery procedures, of which majority of the procedures were coronary artery bypass grafting (CABG), aortic valve replacement (AVR), mitral repair and aortic surgery. The matching at local level was 100% for CABG and transplant, >90% for aortic valve and major aortic procedures and >80% for mitral. At national level, results were similar for CABG (IQR 98.6%-104%), AVR (IQR 105%-118%) and mitral valve replacement (IQR 86.2%-111%).

CONCLUSIONS

We set up a process which can identify cardiac surgeries in England from administrative data. This will lead to the development of a risk model to predict early and late postoperative mortality, useful for risk stratification, risk prediction, benchmarking and real-time monitoring. Once appropriately adjusted, the system can be applied to other specialties, proving especially useful in those areas where clinical databases are not fully established.

Challenges in outlier surgeon assessment in the era of public reporting

OBJECTIVE

To assess the effect of various evaluation and reporting strategies in determining outlier surgeons, defined by having worse-than-expected mortality after cardiac surgery.

METHODS

Our study included 33 394 isolated coronary artery bypass graft (CABG) procedures performed by 136 surgeons and 12 172 surgical aortic valve replacement (SAVR) procedures performed by 113 surgeons between 2010 and 2014. Three current methodologies based on the framework of comparing observed and expected (O/E ratio) mortality, with different distributional assumptions, were examined. We further assessed the consistency of outliers detected by these three methods and the impact of using different time windows and aggregating data of CABG and SAVR procedures.

RESULTS

The three methods were consistent and detected same outliers, with the least conservative method detecting additional outliers (outliers detected for methods 1, 2 and 3: CABG 3 (2.2%), 2 (1.5%) and 8 (5.9%); SAVR 1 (0.9%), 0 (0.0%) and 11 (9.7%)). When numbers of cases recorded were low and events were rare, the two more conservative methods were unlikely to detect outliers unless the O/E ratios were extremely high. However, these two methods were more consistent in detecting the same surgeons as outliers across different time windows for assessment. Of the surgeons who performed both CABG and SAVR, none was an outlier for both procedures when assessed separately. Aggregating data from CABG and SAVR may lead to results to be dominated by the procedure that had a higher caseload.

CONCLUSIONS

The choices of outlier assessment method, time window for assessment and data aggregation have an intertwined impact on detecting outlier surgeons, often representing different value assumptions toward patient protection and provider penalty. It is desirable to use different methods as sensitivity analyses, avoid aggregating procedures and avoid rare-event endpoints if possible.

Effect of data validation audit on hospital mortality ranking and pay for performance

Background

Quality improvement and epidemiology studies often rely on database codes to measure performance or impact of adjusted risk factors, but how validity issues can bias those estimates is seldom quantified.

Objectives

To evaluate whether and how much interhospital administrative coding variations influence a typical performance measure (adjusted mortality) and potential incentives based on it.

Design

National cross-sectional study comparing hospital mortality ranking and simulated pay-for-performance incentives before/after recoding discharge abstracts using medical records.

Setting

Twenty-four public and private hospitals located in France

Participants

All inpatient stays from the 78 deadliest diagnosis-related groups over 1 year.

Interventions

Elixhauser and Charlson comorbidities were derived, and mortality ratios were computed for each hospital. Thirty random stays per hospital were then recoded by two central reviewers and used in a Bayesian hierarchical model to estimate hospital-specific and comorbidity-specific predictive values. Simulations then estimated shifts in adjusted mortality and proportion of incentives that would be unfairly distributed by a typical pay-for-performance programme in this situation.

Main outcome measures

Positive and negative predictive values of routine coding of comorbidities in hospital databases, variations in hospitals’ mortality league table and proportion of unfair incentives.

Results

A total of 70 402 hospital discharge abstracts were analysed, of which 715 were recoded from full medical records. Hospital comorbidity-level positive predictive values ranged from 64.4% to 96.4% and negative ones from 88.0% to 99.9%. Using Elixhauser comorbidities for adjustment, 70.3% of hospitals changed position in the mortality league table after correction, which added up to a mean 6.5% (SD 3.6) of a total pay-for-performance budget being allocated to the wrong hospitals. Using Charlson, 61.5% of hospitals changed position, with 7.3% (SD 4.0) budget misallocation.

Conclusions

Variations in administrative data coding can bias mortality comparisons and budget allocation across hospitals. Such heterogeneity in data validity may be corrected using a centralised coding strategy from a random sample of observations.

Beyond borders-international database collaboration in thoracic surgery

Thoracic surgery databases continue to emerge as pillars for institutional quality improvement and research endeavors. This paper reviews the current state of the largest thoracic surgery databases: the Thoracic Surgeons General Thoracic Surgery Database (STS-GTSD) and the European Society of Thoracic Surgery Database (ESTSD). In addition, we utilize these as a platform to evaluate the role and key ingredients for successful international database collaborations. Ultimately, collaborative efforts among large databases unify research efforts, foster cohesion, serve as benchmarks for quality improvement locally, nationally and internationally, promote comparative innovation, and ultimately improve patient outcomes.

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

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.

A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

BACKGROUND

Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals.

METHODS

We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010-2015 was analyzed.

RESULTS

The 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant.

CONCLUSIONS

The risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality.

The Impact of Differential Case Ascertainment in Clinical Registry Versus Administrative Data on Assessment of Resource Utilization in Pediatric Heart Surgery

BACKGROUND

Resource utilization in congenital heart surgery is typically assessed using administrative data sets. Recent analyses have called into question the accuracy of coding of cases in administrative data; however, it is unclear whether miscoding impacts assessment of associated resource use.

METHODS

We merged data coded within both an administrative data set and clinical registry on children undergoing heart surgery (2004-2010) at 33 hospitals. The impact of differences in coding of operations between data sets on reporting of postoperative length of stay (PLOS) and total hospital costs associated with these operations was assessed.

RESULTS

For each of the eight operations of varying complexity evaluated (total n = 57,797), there were differences in coding between data sets, which translated into differences in the reporting of associated resource utilization for the cases coded in either data set. There were statistically significant differences in PLOS and cost for seven of the eight operations, although most PLOS differences were relatively small with the exception of the Norwood operation and truncus repair (differences of two days, P < .001). For cost, there was a >5% difference for three of the eight operations and >10% difference for truncus repair (US$10,570; P < .01). Grouping of operations into categories of similar risk appeared to mitigate many of these differences.

CONCLUSION

Differences in coding of cases in administrative versus clinical registry data can translate into differences in assessment of associated PLOS and cost for certain operations. This may be minimized through evaluating larger groups of operations when using administrative data or using clinical registry data to accurately identify operations of interest.

Database Audit in Thoracic Surgery

Administrative data are less accurate and relevant than specialty-specific, procedure-specific, risk-adjusted data collected in voluntary registries such as the Society of Thoracic Surgeons-General Thoracic Surgery Database (GTSD). Voluntary clinical databases must be proven accurate and complete before they are accepted as credible information sources. With substantial growth of the GTSD, an annual audit was initiated in 2010 to assess the completeness, accuracy, and quality of the data collected. The audit process is essential in validating data quality and adding credibility and value to volunteer clinical registries. It serves as an important tool for improvement of patient care.

Trends in Coronary Revascularization and Ischemic Heart Disease-Related Mortality in Israel

BACKGROUND

We investigated national trends in volume and outcomes of percutaneous coronary angioplasty (PCI), coronary artery bypass grafting (CABG), and ischemic heart disease-related mortality in Israel.

METHODS AND RESULTS

Using International Classification of Diseases 9th and 10th revision codes, we linked 5 Israeli national databases, including the Israel Center for Disease Control National PCI and CABG Registries, the Ministry of Health Hospitalization Report, the Center of Bureau of Statistics, and the Ministry of Interior Mortality Report, to assess the annual PCI and CABG volume, procedural mortality, comorbidities, and ischemic heart disease-related mortality between 2002 and 2014. Trends over time were analyzed using linear regression, assuming a Poisson distribution. A total of 298 390 revascularization procedures (PCI: 255 724, CABG: 42 666) were performed during the study period. PCI volume increased by 9% from 2002 to 2008 (387.4/100 000 to 423.2/100 000), steadily decreasing by 10.5% to 378.5/100 000 in 2014 (P=0.70 for the trend). CABG volume decreased by 59% (109.0/100 000 to 45.2/100 000) from 2002 to 2013, leveling at 46.4/100 000 (P<0.0001). PCI/CABG ratio increased from 3.6 in 2002 to 8.5 in 2013, slightly decreasing to 8.2 by 2014 (P<0.0001). In-hospital procedural mortality remained stable (PCI: 1.2-1.6%, P=0.34, CABG: 3.7-4.4%, P=0.29) despite a significant change in patient clinical profile. During the course of the study, ischemic heart disease-related mortality decreased by 46% (84.6-46/100 000, P<0.001).

CONCLUSIONS

We observed a dramatic change in coronary revascularization procedures type and volume, and a marked decrease in ischemic heart disease-related mortality in Israel. The reasons for the observed changes remain unclear and need to be further investigated.

Development and Validation of a Predictive Model for Short- and Medium-Term Hospital Readmission Following Heart Valve Surgery

Background

Although models exist for predicting hospital readmission after coronary artery bypass surgery, no such models exist for predicting readmission after heart valve surgery (HVS).

Methods and Results

Using a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database, January 2007–June 2011), we examined patient, hospital, and clinical factors predictive of short‐ and medium‐term hospital readmission post‐HVS. We set aside 20% of hospitals for model validation. A generalized estimating equation model accounted for clustering within hospitals. At 219 hospitals, we identified 38 532 patients (67 years, 56% male, 62% aortic valve surgery) who underwent HVS. A total of 3125 (7.8%) and 4943 (12.8%) patients were readmitted to the index hospital within 1 and 3 months, respectively. Our 3‐month model predicted readmission rates between 3% and 61% with fair discrimination (C‐statistic, 0.67) and good calibration (predicted vs observed differences in validation cohort averaged 1.9% across all deciles of predicted readmission risk). Results were similar for our 1‐month model and our simplified 3‐month model (suitable for clinical use), which used the 5 strongest predictors of readmission: transfused units of packed Red blood cells, presence of End‐stage renal disease, type of Valve surgery, Emergency hospital admission, and hospital Length of stay (REVEaL).

Conclusions

We described and validated key factors that predict short‐ and medium‐term hospital readmission post‐HVS. These models should enable clinicians to identify individuals with HVS who are at increased risk for hospital readmission and are most likely to benefit from improved postdischarge care and follow‐up.

Appending Limited Clinical Data to an Administrative Database for Acute Myocardial Infarction Patients: The Impact on the Assessment of Hospital Quality

BACKGROUND

Hospitals' risk-standardized mortality rates and outlier status (significantly higher/lower rates) are reported by the Centers for Medicare and Medicaid Services (CMS) for acute myocardial infarction (AMI) patients using Medicare claims data. New York now has AMI claims data with blood pressure and heart rate added.

OBJECTIVE

The objective of this study was to see whether the appended database yields different hospital assessments than standard claims data.

METHODS

New York State clinically appended claims data for AMI were used to create 2 different risk models based on CMS methods: 1 with and 1 without the added clinical data. Model discrimination was compared, and differences between the models in hospital outlier status and tertile status were examined.

RESULTS

Mean arterial pressure and heart rate were both significant predictors of mortality in the clinically appended model. The C statistic for the model with the clinical variables added was significantly higher (0.803 vs. 0.773, P<0.001). The model without clinical variables identified 10 low outliers and all of them were percutaneous coronary intervention hospitals. When clinical variables were included in the model, only 6 of those 10 hospitals were low outliers, but there were 2 new low outliers. The model without clinical variables had only 3 high outliers, and the model with clinical variables included identified 2 new high outliers.

CONCLUSION

Appending even a small number of clinical data elements to administrative data resulted in a difference in the assessment of hospital mortality outliers for AMI. The strategy of adding limited but important clinical data elements to administrative datasets should be considered when evaluating hospital quality for procedures and other medical conditions.

Understanding Differences in Administrative and Audited Patient Data in Cardiac Surgery: Comparison of the University HealthSystem Consortium and Society of Thoracic Surgeons Databases

BACKGROUND

The University HealthSystem Consortium (UHC) administrative database has been used increasingly as a quality indicator for hospitals and even individual surgeons. We aimed to determine the accuracy of cardiac surgical data in the administrative UHC database vs data in the clinical Society of Thoracic Surgeons database.

STUDY DESIGN

We reviewed demographic and outcomes information of patients with aortic valve replacement (AVR), mitral valve replacement (MVR), and coronary artery bypass grafting (CABG) surgery between January 1, 2012, and December 31, 2013. Data collected in aggregate and compared across the databases included case volume, physician specialty coding, patient age and sex, comorbidities, mortality rate, and postoperative complications.

RESULTS

In these 2 years, the UHC database recorded 1,270 AVRs, 355 MVRs, and 1,473 CABGs. The Society of Thoracic Surgeons database case volumes were less by 2% to 12% (1,219 AVRs; 316 MVRs; and 1,442 CABGs). Errors in physician specialty coding occurred in UHC data (AVR, 0.6%; MVR, 0.8%; and CABG, 0.7%). In matched patients from each database, demographic age and sex information was identical. Although definitions differed in the databases, percentages of patients with at least one comorbidity were similar. Hospital mortality rates were similar as well, but postoperative recorded complications differed greatly.

CONCLUSIONS

In comparing the 2 databases, we found similarity in patient demographic information and percentage of patients with comorbidities. The small difference in volumes of each operation type and the larger disparity in postoperative complications between the databases were related to differences in data definition, data collection, and coding errors.

Congenital Heart Surgery Case Mix Across North American Centers and Impact on Performance Assessment

BACKGROUND

Performance assessment in congenital heart surgery is challenging due to the wide heterogeneity of disease. We describe current case mix across centers, evaluate methodology inclusive of all cardiac operations versus the more homogeneous subset of Society of Thoracic Surgeons benchmark operations, and describe implications regarding performance assessment.

METHODS

Centers (n = 119) participating in the Society of Thoracic Surgeons Congenital Heart Surgery Database (2010 through 2014) were included. Index operation type and frequency across centers were described. Center performance (risk-adjusted operative mortality) was evaluated and classified when including the benchmark versus all eligible operations.

RESULTS

Overall, 207 types of operations were performed during the study period (112,140 total cases). Few operations were performed across all centers; only 25% were performed at least once by 75% or more of centers. There was 7.9-fold variation across centers in the proportion of total cases comprising high-complexity cases (STAT 5). In contrast, the benchmark operations made up 36% of cases, and all but 2 were performed by at least 90% of centers. When evaluating performance based on benchmark versus all operations, 15% of centers changed performance classification; 85% remained unchanged. Benchmark versus all operation methodology was associated with lower power, with 35% versus 78% of centers meeting sample size thresholds.

CONCLUSIONS

There is wide variation in congenital heart surgery case mix across centers. Metrics based on benchmark versus all operations are associated with strengths (less heterogeneity) and weaknesses (lower power), and lead to differing performance classification for some centers. These findings have implications for ongoing efforts to optimize performance assessment, including choice of target population and appropriate interpretation of reported metrics.