Derivation and external validation of a simple risk tool to predict 30-day hospital readmissions after transcatheter aortic valve replacement

Thirty-Day Readmission Rate & Healthcare Economic Effects of patients with Transcatheter Aortic Valve Replacement and Co-Existing Chronic Congestive Heart Failure

BACKGROUND

Transcatheter aortic valve replacement (TAVR) procedures have increased since adoption in 2010. Readmission for TAVR patients with underlying chronic congestive heart failure (cCHF) remains challenging. Therefore, we sought to determine the 30-day readmission rate (30-DRr) of patients who undergo TAVR & co-existing cCHF & its impact on mortality & healthcare utilization in the United States.

METHODS

We performed a retrospective study using the national readmission database year 2017 and 2018. The patients studied were discharged with TAVR as a principal diagnosis & underlying cCHF as a secondary diagnosis according to ICD-10 codes. The primary outcome was a 30-day readmission rate and mortality, while secondary outcomes were the most common diagnoses for readmission, & resource utilization.

RESULTS

A total of 76,892 index hospitalization for TAVR with co-existing cCHF: mean age was 79.7 years [SD: ± 2], & 54.5% of patients were males. In-hospital mortality rate for index admission was 1.63%. The 30-DRr was 9.5%. Among the group of readmitted patients, in-hospital mortality rate was 3.13%. Readmission mortality showed a statistically significant increase compared to index mortality (3.13% vs. 1.63%, adjusted p=<0.001, aOR: 2.1, 95% CI: 1.6-2.9). The total healthcare in-hospital economic spending was $94.4 million, and total patient charge of $412 million.

CONCLUSION

Approximately 1 in 10 patients who underwent TAVR with underlying cCHF had 30-DRr, with subsequent readmissions associated with increased healthcare spending. Readmission mortality showed a statistically significant increase when compared to index mortality. TAVR patients with cCHF are a vulnerable subset requiring additional outpatient care.

Derivation and Validation of Risk Prediction Model for 30-Day Readmissions Following Transcatheter Mitral Valve Repair

Transcatheter mitral valve repair (TMVr) has shown to reduce heart failure (HF) rehospitalization and all cause mortality. However, the 30-day all-cause readmission remains high (∼15%) after TMVr. Therefore, we sought to develop and validate a 30-day readmission risk calculator for TMVr. Nationwide Readmission Database from January 2014 to December 2017 was utilized. A linear calculator was developed to determine the probability for 30-day readmission. Internal calibration with bootstrapped calculations was conducted to assess model accuracy. The root mean square error and mean absolute error were calculated to determine model performance. Of 8339 patients who underwent TMVr, 1246 (14.2%) were readmitted within 30 days. The final 30-day readmission risk prediction tool included the following variables: Heart failure, Atrial Fibrillation, Anemia, length of stay ≥4 days, Acute kidney injury (AKI), and Non-Home discharge, Non-Elective admission and Bleeding/Transfusion. The c-statistic of the prediction model was 0.63. The validation c-statistic for readmission risk tool was 0.628. On internal calibration, our tool was extremely accurate in predicting readmissions up to 20%. A simple and easy to use risk prediction tool identifies TMVr patients at increased risk of 30-day readmissions. The tool can guide in optimal discharge planning and reduce resource utilization.

Predicting short-term outcomes after transcatheter aortic valve replacement for aortic stenosis

BACKGROUND

The approved use of transcatheter aortic valve replacement (TAVR) for aortic stenosis has expanded substantially over time. However, gaps remain with respect to accurately delineating risk for poor clinical and patient-centered outcomes. Our objective was to develop prediction models for 30-day clinical and patient-centered outcomes after TAVR within a large, diverse community-based population.

METHODS

We identified all adults who underwent TAVR between 2013-2019 at Kaiser Permanente Northern California, an integrated healthcare delivery system, and were monitored for the following 30-day outcomes: all-cause death, improvement in quality of life, all-cause hospitalizations, all-cause emergency department (ED) visits, heart failure (HF)-related hospitalizations, and HF-related ED visits. We developed prediction models using gradient boosting machines using linked demographic, clinical and other data from the Society for Thoracic Surgeons (STS)/American College of Cardiology (ACC) TVT Registry and electronic health records. We evaluated model performance using area under the curve (AUC) for model discrimination and associated calibration plots. We also evaluated the association of individual predictors with outcomes using logistic regression for quality of life and Cox proportional hazards regression for all other outcomes.

RESULTS

We identified 1,565 eligible patients who received TAVR. The risks of adverse 30-day post-TAVR outcomes ranged from 1.3% (HF hospitalizations) to 15.3% (all-cause ED visits). In models with the highest discrimination, discrimination was only moderate for death (AUC 0.60) and quality of life (AUC 0.62), but better for HF-related ED visits (AUC 0.76). Calibration also varied for different outcomes. Importantly, STS risk score only independently predicted death and all-cause hospitalization but no other outcomes. Older age also only independently predicted HF-related ED visits, and race/ethnicity was not significantly associated with any outcomes.

CONCLUSIONS

Despite using a combination of detailed STS/ACC TVT Registry and electronic health record data, predicting short-term clinical and patient-centered outcomes after TAVR remains challenging. More work is needed to identify more accurate predictors for post-TAVR outcomes to support personalized clinical decision making and monitoring strategies.

Development and Validation of a Machine Learning Score for Readmissions After Transcatheter Aortic Valve Implantation

Background

Identifying predictors of readmissions after transcatheter aortic valve implantation (TAVI) is an important unmet need.

Objectives

We sought to explore the role of machine learning (ML) in predicting readmissions after TAVI.

Methods

We included patients who underwent TAVI between 2016 and 2019 in the Nationwide Readmission Database. A total of 917 candidate predictors representing all International Classification of Diseases, Tenth Revision, diagnosis and procedure codes were included. First, we used lasso regression to remove noninformative variables and rank informative ones. Next, we used an unsupervised ML model (K-means) to identify patterns/clusters in the data. Furthermore, we used Light Gradient Boosting Machine and Shapley Additive exPlanations to specify the impact of individual predictors. Finally, we built a parsimonious model to predict 30-day readmission.

Results

A total of 117,398 and 93,800 index TAVI hospitalizations were included in the 30- and 90-day analyses, respectively. Lasso regression identified 138 and 199 informative predictors for the 30- and 90-day readmission, respectively. Next, K-means recognized 2 distinct clusters: low risk and high risk. In the 30-day cohort, the readmission rate was 10.1% in the low risk group and 23.3% in the high risk group. In the 90-day cohort, the rates were 17.4% and 35.3%, respectively. The top predictors were the length of stay, frailty score, total discharge diagnoses, acute kidney injury, and Elixhauser score. These predictors were incorporated into a risk score (TAVI readmission score), which exhibited good performance in an external validation cohort (area under the curve 0.74 [0.7-0.78]).

Conclusions

ML methods can leverage widely available administrative databases to identify patients at risk for readmission after TAVI, which could inform and improve post-TAVI care.

All-cause readmission after transcatheter aortic valve replacement in a community hospital - Long term follow-up: Readmissions after aortic valve replacement

BACKGROUND

Post-procedure readmissions are associated with lower quality of life and increased economic burden. The study aimed to identify predictors for long-term all-cause readmissions in patients who underwent transcatheter aortic valve replacement (TAVR) in a community hospital.

METHODS

A Historical cohort study of all adults who underwent TAVR at Cape-Cod hospital between June 2015 and December 2017 was performed and data on readmissions was collected up-to May 2020 (median follow up of 3.3 years). Pre-procedure, procedure and in-hospital post-procedure parameters were collected. Readmission rate was evaluated, and univariate and multivariable analyses were applied to identify predictors for readmission.

RESULTS

The study included 262 patients (mean age 83.7±7.9 years, 59.9% males). The median Society of Thoracic Surgeons (STS) probability of mortality (PROM) score was 4.9 (IQR, 3.1-7.9). Overall, 120 patients were readmitted. Ten percent were readmitted within 1-month, 20.8% within 3-months, 32.0% within 6-months and 44.5% within 1-year. New readmissions after 1-year were rare. STS PROM 5% or above (HR 1.50, p=0.039), pre-procedure anemia (HR 1.63, p=0.034), severely decreased pre-procedure renal function (HR 1.93, p=0.040) and procedural complication (HR 1.65, p=0.013) were independent predictors for all-cause readmission.

CONCLUSIONS

Elevated procedural risk, anemia, renal dysfunction and procedural complication are important predictors for readmission. Pre-procedure and ongoing treatment of the patient's background diseases and completion of treatment for complications prior to discharge may contribute to a reduction in the rate of readmissions.

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

OBJECTIVE

To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.

DESIGN

Systematic review and meta-analysis.

DATA SOURCE

Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.

PRIMARY AND SECONDARY OUTCOME MEASURES

Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.

RESULTS

Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.

CONCLUSION

Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.

PROSPERO REGISTRATION NUMBER

CRD42020159839.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Hospital Variation in 30-Day Readmissions Following Transcatheter Aortic Valve Replacement

Background Data on hospital variation in 30-day readmission rates after transcatheter aortic valve replacement (TAVR) are limited. Further, whether such variation is explained by differences in hospital characteristics and hospital practice patterns remains unknown. Methods and Results We used the 2017 Nationwide Readmissions Database to identify hospitals that performed at least 5 TAVRs. Hierarchical logistic regression models were used to examine between-hospital variation in 30-day all-cause risk-standardized readmission rate (RSRR) after TAVR and to explore reasons underlying hospital variation in 30-day RSRR. The study included 27 091 index TAVRs performed across 325 hospitals. The median (interquartile range) hospital-level 30-day RSRR was 11.9% (11.1%-12.8%) ranging from 8.8% to 16.5%. After adjusting for differences in patient characteristics, there was significant between-hospital variation in 30-day RSRR (hospital odds ratio, 1.59; 95% CI, 1.39-1.77). Differences in length of stay and discharge disposition accounted for 15% of the between-hospital variance in RSRRs. There was no significant association between hospital characteristics and 30-day readmission rates after TAVR. There was statistically significant but weak correlation between 30-day RSRR after TAVR and that after surgical aortic valve replacement, percutaneous coronary intervention, acute myocardial infarction, heart failure, and pneumonia (r=0.132-0.298; P<0.001 for all). Causes of 30-day readmission varied across hospitals, with noncardiac readmissions being more common at the bottom 5% hospitals (ie, those with the highest RSRRs). Conclusions There is significant variation in 30-day RSRR after TAVR across hospitals that is not entirely explained by differences in patient or hospital characteristics as well as hospital-wide practice patterns. Noncardiac readmissions are more common in hospitals with the highest RSRRs.

Trends in Costs and Risk Factors of 30-Day Readmissions for Transcatheter Aortic Valve Implantation

As transcatheter aortic valve implantation (TAVI) continues its rapid growth as a treatment approach for aortic stenosis, costs associated with TAVI, and its burden to healthcare systems will assume greater importance. Patients undergoing TAVI between January 2012 and November 2017 in the Nationwide Readmission Database were identified. Trends in cause-specific readmissions were assessed using Poisson regression. Thirty-day TAVI cost burden (cost of index TAVI hospitalization plus total 30-day readmissions cost) was adjusted to 2017 U.S. dollars and trended over year from 2012 to 2017. Overall, 47,255 TAVI were included and 30-day readmissions declined from 20% to 12% (p <0.0001). Most common causes of readmission (heart failure, infection/sepsis, gastrointestinal causes, and respiratory) declined as well, except arrhythmia/heart block which increased (1.0% to 1.4%, p <0.0001). Cost of TAVI hospitalization ($52,024 to $44,110, p <0.0001) and 30-day cost burden ($54,122 to $45,252, p <0.0001) declined. Whereas costs of an average readmission did not change ($9,734 to $10,068, p = 0.06), cost burden of readmissions (per every TAVI performed) declined ($4,061 to $1,883, p <0.0001), including reductions in each of the top 5 causes except arrhythmia/heart block ($171 to $263, p = 0.04). Index TAVI hospitalizations complicated by acute kidney injury, length of stay ≥5 days, low hospital procedural volume, and skilled nursing facility discharge were associated with increased odds of 30-day readmissions. In conclusion, the costs of index hospitalizations and 30-day cost burden for TAVI in the U.S. significantly declined from 2012 to 2017. However, readmissions due to arrhythmia/heart block and their associated costs increased. Continued strategies to prevent readmissions, especially those for conduction disturbances, are crucial in the efforts to optimize outcomes and costs with the ongoing expansion of TAVI.

Impact of Frailty on Mortality, Readmissions, and Resource Utilization After TAVI

With aging population and preponderance of severe aortic stenosis occurring in elderly patients, the number of transcatheter aortic valve implantations (TAVI) performed in the elderly are growing. Frailty is common in the elderly and is known to be associated with worse outcomes. We aimed to evaluate the impact of frailty on hospital readmissions rates after TAVI. We used the 2016 Nationwide Readmission Database and categorized patients who underwent TAVI low, intermediate, and high frailty status. The primary outcome was 6-months readmission rates across the 3 frailty categories. Secondary outcomes included causes of readmissions, in-hospital mortality and cost of care. STATA 16.0 was used for survey-specific statistical tests. Of 20,504 patients who underwent TAVI, 58.9% were low-, 39.6% were intermediate-, and 1.5% were in the high-frailty group. Overall in-hospital mortality was 1.9% (n = 396), and was 0.6%, 3.3%, and 16.8% (p <0.01) with increasing frailty. Of the 20,108 patients who survived to discharge, 6,427 (32%) patients were readmitted within 6-months after TAVI. Readmission rates increased across the categories from 27.9% in low, 37.6% in intermediate and 51.1% in high frailty group (p <0.01). While cardiac causes (mostly heart failure) were the predominant readmission etiologies across frailty categories (low: 51.2%, intermediate: 34.1%, high: 27.2%), rates of infectious and injury-related readmissions increased (low: 11%, intermediate: 30%, high: 45%). Mortality during readmissions also worsened from 0.8%, 5.3%, and 8.5% (p <0.01). Over 40% of patients undergoing TAVI were of intermediate-high frailty. In conclusion, an increasing frailty was associated with significantly worse postprocedure mortality, readmissions, and related mortality.

Identifying patients likely to be readmitted after transcatheter aortic valve replacement

Hospital readmission following transcatheter aortic valve replacement (TAVR) contributes considerably to the costs of care. Readmission rates following TAVR have been reported to be as high as 17.4% at 30 days and 53.2% at 1 year. Patient and procedural factors predict an increased likelihood of readmission including non-transfemoral access, acute and chronic kidney impairment, chronic lung disease, left ventricular systolic dysfunction, atrial fibrillation, major bleeding and prolonged index hospitalisation. Recent studies have also found the requirement for new pacemaker implantation and the severity of paravalvular aortic regurgitation and tricuspid regurgitation to be novel predictors of readmission. Post-TAVR readmission within 30 days of discharge is more likely to occur for non-cardiac than cardiac pathology, although readmission for cardiac causes, especially heart failure, predicts higher mortality than readmission for non-cardiac causes. To combat the risk of readmission and associated mortality, the routine practice of calculating and considering readmission risk should be adopted by the heart team. Furthermore, because most readmissions following TAVR occur for non-cardiac reasons, more holistic approaches to readmission prevention are necessary. Familiarity with the most common predictors and causes of readmission should guide the development of initiatives to address these conditions proactively.