Elixhauser Comorbidity Score Is the Best Risk Score in Predicting Survival After Mitraclip Implantation

Discrepancy between estimated glomerular filtration rate by creatinine versus cystatin C in different patient care settings

OBJECTIVE

Estimated glomerular filtration rate (eGFR) calculated by cystatin C (cysC) has been recommended for broader adoption. This study assessed the discrepancy between eGFR calculated by cysC (eGFRcys) and creatinine (eGFRcr) in different patient care settings and explored potential contributing factors to such discrepancies.

METHODS

This retrospective study included 2072 patients with paired cysC and creatinine results in different patient care settings. Delta eGFRcr-cys (eGFRcr - eGFRcys) was analyzed in relationship to patient care settings and the Elixhauser Comorbidity index. The 90-day survival in patients with different delta eGFR was assessed by Kaplan-Meier analysis, univariate and multivariate Cox proportional hazard models. In addition, discrepancy between eGFRcys and eGFRcr was analyzed in 50 ambulatory patients with systemic inflammation but normal kidney function.

RESULTS

Inpatients had higher cysC (median 1.91 mg/L), lower eGFRcys (median 31 mL/min/1.73 m2), and larger delta eGFRcr-cys (median 18 mL/min/1.73 m2) than outpatients (cysC median 1.53 mg/L, p < 0.0001, eGFRcys median 41 mL/min/1.73 m2, p < 0.0001, delta eGFRcr-cys median 4 mL/min/1.73 m2, p < 0.0001). Higher Elixhauser Comorbidity index correlated with lower eGFRcys and larger delta eGFRcr-cys, with median delta eGFRcr-cys 11 and 6 mL/min/1.73 m2 in patients with a Comorbidity index > 15 and ≤ 15, respectively (p < 0.0001). Increased delta eGFRcr-cys was associated with worse 90-day survival. Patients with systemic inflammation but normal kidney function had lower eGFRcys (median 77.5 mL/min/1.73 m2) than eGFRcr (median 97 mL/min/1.73 m2, p < 0.001), with red blood cell abnormalities as associated factors.

CONCLUSION

Inflammation and comorbidities are associated with decreased eGFRcys and large discrepancies between eGFRcr and eGFRcys independent of kidney function and are most apparent in inpatients. Creatinine-cysC combined eGFR reduces this discrepancy and should be broadly adopted.

Machine learning vs. conventional methods for prediction of 30-day readmission following percutaneous mitral edge-to-edge repair

BACKGROUND

Identifying predictors of readmissions after mitral valve transcatheter edge-to-edge repair (MV-TEER) is essential for risk stratification and optimization of clinical outcomes.

AIMS

We investigated the performance of machine learning [ML] algorithms vs. logistic regression in predicting readmissions after MV-TEER.

METHODS

We utilized the National-Readmission-Database to identify patients who underwent MV-TEER between 2015 and 2018. The database was randomly split into training (70 %) and testing (30 %) sets. Lasso regression was used to remove non-informative variables and rank informative ones. The top 50 informative predictors were tested using 4 ML models: ML-logistic regression [LR], Naive Bayes [NB], random forest [RF], and artificial neural network [ANN]/For comparison, we used a traditional statistical method (principal component analysis logistic regression PCA-LR).

RESULTS

A total of 9425 index hospitalizations for MV-TEER were included. Overall, the 30-day readmission rate was 14.6 %, and heart failure was the most common cause of readmission (32 %). The readmission cohort had a higher burden of comorbidities (median Elixhauser score 5 vs. 3) and frailty score (3.7 vs. 2.9), longer hospital stays (3 vs. 2 days), and higher rates of non-home discharges (17.4 % vs. 8.5 %). The traditional PCA-LR model yielded a modest predictive value (area under the curve [AUC] 0.615 [0.587-0.644]). Two ML algorithms demonstrated superior performance than the traditional PCA-LR model; ML-LR (AUC 0.692 [0.667-0.717]), and NB (AUC 0.724 [0.700-0.748]). RF (AUC 0.62 [0.592-0.677]) and ANN (0.65 [0.623-0.677]) had modest performance.

CONCLUSION

Machine learning algorithms may provide a useful tool for predicting readmissions after MV-TEER using administrative databases.

Predictivity of the comorbidity indices for geriatric syndromes

Background

The aging population and increasing chronic diseases make a tremendous burden on the health care system. The study evaluated the relationship between comorbidity indices and common geriatric syndromes.

Methods

A total of 366 patients who were hospitalized in a university geriatric inpatient service were included in the study. Sociodemographic characteristics, laboratory findings, and comprehensive geriatric assessment(CGA) parameters were recorded. Malnutrition, urinary incontinence, frailty, polypharmacy, falls, orthostatic hypotension, depression, and cognitive performance were evaluated. Comorbidities were ranked using the Charlson Comorbidity Index(CCI), Elixhauser Comorbidity Index(ECM), Geriatric Index of Comorbidity(GIC), and Medicine Comorbidity Index(MCI). Because, the CCI is a valid and reliable tool used in different clinical settings and diseases, patients with CCI score higher than four was accepted as multimorbid. Additionally, the relationship between geriatric syndromes and comorbidity indices was assessed with regression analysis.

Results

Patients’ mean age was 76.2 ± 7.25 years(67.8% female). The age and sex of multimorbid patients according to the CCI were not different compared to others. The multimorbid group had a higher rate of dementia and polypharmacy among geriatric syndromes. All four indices were associated with frailty and polypharmacy(p < 0.05). CCI and ECM scores were related to dementia, polypharmacy, and frailty. Moreover, CCI was also associated with separately slow walking speed and low muscle strength. On the other hand, unlike CCI, ECM was associated with malnutrition.

Conclusions

In the study comparing the four comorbidity indices, it is revealed that none of the indices is sufficient to use alone in geriatric practice. New indices should be developed considering the complexity of the geriatric cases and the limitations of the existing indices.

Outcomes of Elderly Patients Undergoing Left Atrial Appendage Closure

Background Elderly patients have a higher burden of comorbidities that influence clinical outcomes. We aimed to compare in-hospital outcomes in patients ≥80 years old to younger patients, and to determine the factors associated with increased risk of major adverse events (MAE) after left atrial appendage closure. Methods and Results The National Inpatient Sample was used to identify discharges after left atrial appendage closure between October 2015 and December 2018. The primary outcome was in-hospital MAE defined as the composite of postprocedural bleeding, vascular and cardiac complications, acute kidney injury, stroke, and death. A total of 6779 hospitalizations were identified, of which, 2371 (35%) were ≥80 years old and 4408 (65%) were <80 years old. Patients ≥80 years old experienced a higher rate of MAE compared with those aged <80 years old (6.0% versus 4.6%, P=0.01), and this difference was driven by a numerically higher rate of cardiac complications (2.4% versus 1.8%, P=0.09) and death (0.3% versus 0.1%, P=0.05) among individuals ≥80 years old. In patients ≥80 years old, higher odds of in-hospital MAE were observed in women (1.61-fold), and those with preprocedural congestive heart failure (≈2-fold), diabetes (≈1.5-fold), renal disease (≈2.6-fold), anemia (≈2.7-fold), and dementia (≈5-fold). In patients <80 years old, a higher risk of in-hospital MAE was encountered among women (≈1.4-fold) and those with diabetes (≈1.3-fold), renal disease (≈2.6-fold), anemia (≈2-fold), and dyslipidemia (≈1.2-fold). Conclusions Patients ≥80 years old had higher rates of in-hospital MAE compared with patients aged <80 years old. Female sex and the presence of heart failure, diabetes, renal disease, and anemia were factors associated with in-hospital MAE among both groups.

Comorbidity burden in patients undergoing left atrial appendage closure

OBJECTIVE

To estimate the risk of in-hospital complications after left atrial appendage closure (LAAC) in relationship with comorbidity burden.

METHODS

Cohort-based observational study using the US National Inpatient Sample database, 1 October 2015 to 31 December 2017. The main outcome of interest was the occurrence of in-hospital major adverse events (MAE) defined as the composite of bleeding complications, acute kidney injury, vascular complications, cardiac complications and postprocedural stroke. Comorbidity burden and thromboembolic risk were assessed by the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Score (ECS) and CHA2DS2-VASc score. MAE were identified using International Classification of Diseases, Tenth Revision, Clinical Modification codes. The associations of comorbidity with in-hospital MAE were evaluated using logistic regression models.

RESULTS

A total of 3294 hospitalisations were identified, among these, the mean age was 75.7±8.2 years, 60% were male and 86% whites. The mean CHA2DS2-VASc score was 4.3±1.5 and 29.5% of the patients had previous stroke or transient ischaemic attack. The mean CCI and ECS were 2.2±1.9 and 9.7±5.8, respectively. The overall composite rate of in-hospital MAE after LAAC was 4.6%. Females and non-whites had about 1.5 higher odds of in-hospital AEs as well participants with higher CCI (adjusted OR (aOR): 1.19, 95% CI: 1.13 to 1.24, p<0.001), ECS (aOR: 1.06, 95% CI: 1.05 to 1.08, p<0.001) and CHA2DS2-VASc score (aOR: 1.08, 95% CI: 1.02 to 1.15, p=0.01) were significantly associated with in-hospital MAE.

CONCLUSION

In this large cohort of LAAC patients, the majority of them had significant comorbidity burden. In-hospital MAE occurred in 4.6% and female patients, non-whites and those with higher burden of comorbidities were at higher risk of in-hospital MAE after LAAC.

Area Deprivation Index and Cardiac Readmissions: Evaluating Risk-Prediction in an Electronic Health Record

Background Assessment of the social determinants of post-hospital cardiac care is needed. We examined the association and predictive ability of neighborhood-level determinants (area deprivation index, ADI), readmission risk, and mortality for heart failure, myocardial ischemia, and atrial fibrillation. Methods and Results Using a retrospective (January 1, 2011-December 31, 2018) analysis of a large healthcare system, we assess the predictive ability of ADI on 30-day and 1-year readmission and mortality following hospitalization. Cox proportional hazards models analyzed time-to-event. Log rank analyses determined survival. C-statistic and net reclassification index determined the model's discriminative power. Covariates included age, sex, race, comorbidity, number of medications, length of stay, and insurance. The cohort (n=27 694) had a median follow-up of 46.5 months. There were 14 469 (52.2%) men and 25 219 White (91.1%) patients. Patients in the highest ADI quintile (versus lowest) were more likely to be admitted within 1 year of index heart failure admission (hazard ratio [HR], 1.25; 95% CI, 1.03‒1.51). Patients with myocardial ischemia in the highest ADI quintile were twice as likely to be readmitted at 1 year (HR, 2.04; 95% CI, 1.44‒2.91]). Patients with atrial fibrillation living in areas with highest ADI were less likely to be admitted within 1 year (HR, 0.79; 95% CI, 0.65‒0.95). As ADI increased, risk of readmission increased, and risk reclassification was improved with ADI in the models. Patients in the highest ADI quintile were 25% more likely to die within a year (HR, 1.25 1.08‒1.44). Conclusions Residence in socioeconomically disadvantaged communities predicts rehospitalization and mortality. Measuring neighborhood deprivation can identify individuals at risk following cardiac hospitalization.

Sex Differences in Outcomes Following Left Atrial Appendage Closure

OBJECTIVE

To evaluate the effects of female sex on in-hospital outcomes and to provide estimates for sex-specific prediction models of adverse outcomes following left atrial appendage closure (LAAC).

PATIENTS AND METHODS

Cohort-based observational study querying the National Inpatient Sample database between October 1, 2015, and December 31, 2017. Demographics, baseline characteristics, and comorbidities were assessed with the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index score (ECS), and CHA₂DS₂-VASc score. The primary outcome was in-hospital major adverse events (MAEs) defined as the composite of bleeding, vascular, cardiac complications, post-procedural stroke, and acute kidney injury. The associations of the CCI, ECS, and CHA₂DS₂-VASc score with in-hospital MAE were examined using logistic regression models for women and men, respectively.

RESULTS

A total of 3294 hospitalizations were identified, of which 1313 (40%) involved women and 1981 (60%) involved men. Women were older (76.3±7.7 vs 75.2±8.4 years, P<.001), had a higher CHA₂DS₂-VASc score (4.9±1.4 vs 3.9±1.4, P<.001) but showed lower CCI and ECS compared with men (2.1±1.9 vs 2.3±1.9, P=.01; and 9.3±5.9 vs 9.9±5.7, P=.002, respectively). The primary composite outcome occurred in 4.6% of patients and was higher in women compared with men (women 5.6% vs men 4.0%, P=.04), and this was mainly driven by the occurrence of cardiac complications (2.4% vs 1.2%, P=.01). In women, older age, higher median income, and higher CCI (adjusted odds ratio [aOR], 1.32; 95% confidence interval [CI], 1.21 to 1.44; P<.001), ECS (aOR, 1.04; 95% CI, 1.02 to 1.07; P=.002), and CHA₂DS₂-VASc score (aOR, 1.24; 95% CI, 1.10 to 1.39; P<.001) were associated with increased risk of in-hospital MAE. In men, non-White race/ethnicity, lower median income, and higher ECS (aOR, 1.06; 95% CI, 1.04 to 1.09; P<.001) were associated with increased risk of in-hospital MAE.

CONCLUSION

Women had higher rates of in-hospital adverse events following LAAC than men did. Women with older age and higher median income, CCI, ECS, and CHA₂DS₂-VASc scores were associated with in-hospital adverse events, whereas men with non-White race/ethnicity, lower median income, and higher ECS were more likely to experience adverse events. Further research is warranted to identify sex-specific, racial/ethnic, and socioeconomic pathways during the patient selection process to minimize complications in patients undergoing LAAC.

Can stress echocardiography identify patients who will benefit from percutaneous mitral valve repair?

The aim of the current study was to investigate whether stress echocardiography improves selection of patients who might have clinical benefit from percutaneous mitral valve repair with the MitraClip. In total, 39 patients selected for MitraClip implantation underwent preprocedural low-dose stress (dobutamine or handgrip) echocardiography from which stroke volume, ejection fraction and MR grade were measured. Outcome after MitraClip implantation was determined by New York Heart Association classification and Quality of Life questionnaires. Clinical benefit from MitraClip treatment was defined as survival and NYHA class I–II at 6 months follow-up. In total, 36 patients with a technically successful procedure were included in the analysis (mean age 79 ± 8 years, 47% male, 50% functional MR). Clinical benefit was achieved in 18 patients. All seven patients with MR decreasing during stress remained in NYHA III–IV or died within 6 months, while 62% (18 out of 29) of the patients with stable or increased MR during stress had clinical benefit (p = 0.008). Significant increase in Quality of Life on 4/8 subscales of the RAND Short Form-36 questionnaire was observed: Physical Functioning (p < 0.001), Social Functioning (p < 0.001), Mental Health (p = 0.022) and Vitality (p = 0.026) was seen in patients with an increase in stroke volume during stress echocardiography. Patients with a decreased MR during preprocedural stress echocardiography remained more symptomatic than patients with a stable or increased MR during stress. Stress echocardiography may support patient selection for percutaneous mitral valve repair.