Dataset Information

Variation in Case-Mix Adjusted Unplanned Pediatric Cardiac ICU Readmission Rates.

ABSTRACT: OBJECTIVES:To identify modifiable factors leading to unplanned readmission and characterize differences in adjusted unplanned readmission rates across hospitals. DESIGN:Retrospective cohort study using prospectively collected clinical registry data SETTING:: Pediatric Cardiac Critical Care Consortium clinical registry. PATIENTS:Patients admitted to a pediatric cardiac ICU at Pediatric Cardiac Critical Care Consortium hospitals. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We examined pediatric cardiac ICU encounters in the Pediatric Cardiac Critical Care Consortium registry from October 2013 to March 2016. The primary outcomes were early (< 48?hr from pediatric cardiac ICU transfer) and late (2-7 d) unplanned readmission. Generalized logit models identified independent predictors of unplanned readmission. We then calculated observed-to-expected ratios of unplanned readmission and identified higher-than or lower-than-expected unplanned readmission rates for those with an observed-to-expected ratios greater than or less than 1, respectively, and a 95% CI that did not cross 1. Of 11,301 pediatric cardiac ICU encounters (16 hospitals), 62% were surgical, and 18% were neonates. There were 175 (1.6%) early unplanned readmission, and 300 (2.7%) late unplanned readmission, most commonly for respiratory (31%), or cardiac (28%) indications. In multivariable analysis, unique modifiable factors were associated with unplanned readmission. Although shorter time between discontinuation of vasoactive infusions and pediatric cardiac ICU transfer was associated with early unplanned readmission, nighttime discharge was independently associated with a greater likelihood of late unplanned readmission. Two hospitals had lower-than-expected unplanned readmission in both the early and late categories, whereas two other hospitals were higher-than-expected in both. CONCLUSIONS:This analysis demonstrated time from discontinuation of critical care therapies to pediatric cardiac ICU transfer as a significant, modifiable predictor of unplanned readmission. We identified two hospitals with lower-than-expected adjusted rates of both early and late unplanned readmission, suggesting that their systems are well designed to prevent unplanned readmission. This offers the possibility of disseminating best practices to other hospitals through collaborative learning.

SUBMITTER: Smith AH

PROVIDER: S-EPMC6239958 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Variation in Case-Mix Adjusted Unplanned Pediatric Cardiac ICU Readmission Rates.

Smith Andrew H AH Anand Vijay V Banerjee Mousumi M Bates Katherine E KE Brunetti Marissa A MA Cooper David S DS Lehrich Jessica J Mistry Kshitij P KP Pasquali Sara K SK Shin Andrew Y AY Tabbutt Sarah S Gaies Michael M

Critical care medicine 20181201 12

<h4>Objectives</h4>To identify modifiable factors leading to unplanned readmission and characterize differences in adjusted unplanned readmission rates across hospitals.<h4>Design</h4>Retrospective cohort study using prospectively collected clinical registry data SETTING:: Pediatric Cardiac Critical Care Consortium clinical registry.<h4>Patients</h4>Patients admitted to a pediatric cardiac ICU at Pediatric Cardiac Critical Care Consortium hospitals.<h4>Interventions</h4>None.<h4>Measurements and ...[more]

PMID: 30252712

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Project description:OBJECTIVES:Pediatric cardiac ICUs should be adept at treating both critical medical and surgical conditions for patients with cardiac disease. There are no case-mix adjusted quality metrics specific to medical cardiac ICU admissions. We aimed to measure case-mix adjusted cardiac ICU medical mortality rates and assess variation across cardiac ICUs in the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Pediatric Cardiac Critical Care Consortium clinical registry. PATIENTS:All cardiac ICU admissions that did not include cardiac surgery. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary endpoint was cardiac ICU mortality. Based on multivariable logistic regression accounting for clustering, we created a case-mix adjusted model using variables present at cardiac ICU admission. Bootstrap resampling (1,000 samples) was used for model validation. We calculated a standardized mortality ratio for each cardiac ICU based on observed-to-expected mortality from the fitted model. A cardiac ICU was considered a statistically significant outlier if the 95% CI around the standardized mortality ratio did not cross 1. Of 11,042 consecutive medical admissions from 25 cardiac ICUs (August 2014 to May 2017), the observed mortality rate was 4.3% (n = 479). Final model covariates included age, underweight, prior surgery, time of and reason for cardiac ICU admission, high-risk medical diagnosis or comorbidity, mechanical ventilation or extracorporeal membrane oxygenation at admission, and pupillary reflex. The C-statistic for the validated model was 0.87, and it was well calibrated. Expected mortality ranged from 2.6% to 8.3%, reflecting important case-mix variation. Standardized mortality ratios ranged from 0.5 to 1.7 across cardiac ICUs. Three cardiac ICUs were outliers; two had lower-than-expected (standardized mortality ratio <1) and one had higher-than-expected (standardized mortality ratio >1) mortality. CONCLUSIONS:We measured case-mix adjusted mortality for cardiac ICU patients with critical medical conditions, and provide the first report of variation in this quality metric within this patient population across Pediatric Cardiac Critical Care Consortium cardiac ICUs. This metric will be used by Pediatric Cardiac Critical Care Consortium cardiac ICUs to assess and improve outcomes by identifying high-performing (low-mortality) centers and engaging in collaborative learning.

Project description:ImportanceThe Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.ObjectiveTo identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.Design, setting, and participantsThis cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.ExposuresPresent-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.Main outcomes and measuresHospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).ResultsIncluded in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).Conclusions and relevanceThis cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

Project description:OBJECTIVE:To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Multicenter Pediatric Cardiac Critical Care Consortium clinical registry. PARTICIPANTS:All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality. CONCLUSIONS:For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

Project description:Background:Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions. Objective:To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile. Materials:An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost. Methods:Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size. Results:Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms. Conclusions:We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Project description:BackgroundDespite recent reductions in national unplanned readmission rates, we have relatively little understanding of which hospital strategies are most associated with changes in risk-standardized readmission rates (RSRR).ObjectiveWe examined associations between the change in hospital 30-day RSRR for patients with heart failure and the uptake of strategies over 12-18 months in a national sample of hospitals.DesignWe conducted a prospective study of hospitals using a Web-based survey at baseline (November 2010-May 2011, n = 599, 91.0% response rate) and 12-18 months later (November 2011-October 2012, n = 501, 83.6% response rate), with RSRR measured at the same time points. The final analytic sample included 478 hospitals.ParticipantsThe study included hospitals participating in the Hospital-to-Home (H2H) and State Action on Avoidable Rehospitalizations (STAAR) initiatives.Main measuresWe examined associations between change in hospital 30-day RSRR for patients with heart failure and the uptake of strategies previously demonstrated to have increased between baseline and follow-up, using unadjusted and adjusted linear regression.Key resultsThe average number of strategies taken up from baseline to follow-up was 1.6 (SE = 0.06); approximately one-quarter (25.3%) of hospitals took up at least three new strategies. Hospitals that adopted the strategy of routinely discharging patients with a follow-up appointment already scheduled experienced significant reductions in RSRR (reduction of 0.63 percentage point, p value < 0.05). Hospitals that took up three or more strategies had significantly greater reductions in RSRR compared to hospitals that took up only zero to two strategies (reduction of 1.29 versus 0.57 percentage point, p value < 0.05). Among the 117 hospitals that took up three or more strategies, 93 unique combinations of strategies were used.ConclusionsAlthough most individual strategies were not associated with RSRR reduction, hospitals that took up any three or more strategies showed significantly greater reduction in RSRR compared to hospitals that took up fewer than three strategies.

Dataset Information

Variation in Case-Mix Adjusted Unplanned Pediatric Cardiac ICU Readmission Rates.

Publications

Variation in Case-Mix Adjusted Unplanned Pediatric Cardiac ICU Readmission Rates.

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