A Method to Account for Variation in Congenital Heart Surgery Length of Stay

Risk Factors Associated With Pressure Injury in Critically Ill Children With Congenital Heart Disease

BACKGROUND

Children with congenital heart disease have unique risk factors associated with the pathophysiology of an abnormal heart; hence, this population is most likely at increased risk of acquiring a pressure injury during hospitalization. Few studies have included patients with congenital heart disease or examined the factors unique to these patients.

OBJECTIVE

To identify risk factors associated with pressure injury development in children with congenital heart disease.

METHODS

This retrospective study used a convenience sample from hospital-acquired data at an urban, tertiary, free-standing children's hospital. Patients were admitted to the intensive care unit between 2011 and 2018 with a diagnosis of congenital heart disease. Chi-square analysis was done to compare risk factors between patients, and logistic regression analysis was used to predict the probability that a patient would acquire a pressure injury.

RESULTS

Eighty-two (30.5%) of the 269 patients in this study acquired pressure injuries. Sixty-six patients with pressure injuries met the inclusion criteria for analysis; 82% of those patients had had corticosteroids prescribed, and 71% were receiving anticoagulants. The overall predictive model for acquiring a pressure injury indicated an odds ratio of 3.25 (95% CI, 1.58-6.65) with an anticoagulant and an odds ratio of 9.98 (95% CI, 4.68-21.3) with a prescribed corticosteroid (P < .001 for both factors). Inpatient mortality was significantly associated with pressure injuries.

CONCLUSIONS

Corticosteroid and anticoagulant use were contributing factors in the development of pressure injuries in children with congenital heart disease.

Successful Reduction of Postoperative Chest Tube Duration and Length of Stay After Congenital Heart Surgery: A Multicenter Collaborative Improvement Project

Background Congenital heart disease practices and outcomes vary significantly across centers, including postoperative chest tube (CT) management, which may impact postoperative length of stay (LOS). We used collaborative learning methods to determine whether centers could adapt and safely implement best practices for CT management, resulting in reduced postoperative CT duration and LOS. Methods and Results Nine pediatric heart centers partnered together through 2 learning networks. Patients undergoing 1 of 9 benchmark congenital heart operations were included. Baseline data were collected from June 2017 to June 2018, and intervention-phase data were collected from July 2018 to December 2019. Collaborative learning methods included review of best practices from a model center, regular data feedback, and quality improvement coaching. Center teams adapted CT removal practices (eg, timing, volume criteria) from the model center to their local resources, practices, and setting. Postoperative CT duration in hours and LOS in days were analyzed using statistical process control methodology. Overall, 2309 patients were included. Patient characteristics did not differ between the study and intervention phases. Statistical process control analysis showed an aggregate 15.6% decrease in geometric mean CT duration (72.6 hours at baseline to 61.3 hours during intervention) and a 9.8% reduction in geometric mean LOS (9.2 days at baseline to 8.3 days during intervention). Adverse events did not increase when comparing the baseline and intervention phases: CT replacement (1.8% versus 2.0%, P=0.56) and readmission for pleural effusion (0.4% versus 0.5%, P=0.29). Conclusions We successfully lowered postoperative CT duration and observed an associated reduction in LOS across 9 centers using collaborative learning methodology.

Hospital Length of Stay Prediction Methods: A Systematic Review

OBJECTIVE

This systematic review sought to establish a picture of length of stay (LOS) prediction methods based on available hospital data and study protocols designed to measure their performance.

MATERIALS AND METHODS

An English literature search was done relative to hospital LOS prediction from 1972 to September 2019 according to the PRISMA guidelines. Articles were retrieved from PubMed, ScienceDirect, and arXiv databases. Information were extracted from the included papers according to a standardized assessment of population setting and study sample, data sources and input variables, LOS prediction methods, validation study design, and performance evaluation metrics.

RESULTS

Among 74 selected articles, 98.6% (73/74) used patients' data to predict LOS; 27.0% (20/74) used temporal data; and 21.6% (16/74) used the data about hospitals. Overall, regressions were the most popular prediction methods (64.9%, 48/74), followed by machine learning (20.3%, 15/74) and deep learning (17.6%, 13/74). Regarding validation design, 35.1% (26/74) did not use a test set, whereas 47.3% (35/74) used a separate test set, and 17.6% (13/74) used cross-validation. The most used performance metrics were R2 (47.3%, 35/74), mean squared (or absolute) error (24.4%, 18/74), and the accuracy (14.9%, 11/74). Over the last decade, machine learning and deep learning methods became more popular (P=0.016), and test sets and cross-validation got more and more used (P=0.014).

CONCLUSIONS

Methods to predict LOS are more and more elaborate and the assessment of their validity is increasingly rigorous. Reducing heterogeneity in how these methods are used and reported is key to transparency on their performance.

Predicting and Surviving Prolonged Critical Illness After Congenital Heart Surgery

OBJECTIVES

Prolonged critical illness after congenital heart surgery disproportionately harms patients and the healthcare system, yet much remains unknown. We aimed to define prolonged critical illness, delineate between nonmodifiable and potentially preventable predictors of prolonged critical illness and prolonged critical illness mortality, and understand the interhospital variation in prolonged critical illness.

DESIGN

Observational analysis.

SETTING

Pediatric Cardiac Critical Care Consortium clinical registry.

PATIENTS

All patients, stratified into neonates (≤28 d) and nonneonates (29 d to 18 yr), admitted to the pediatric cardiac ICU after congenital heart surgery at Pediatric Cardiac Critical Care Consortium hospitals.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 2,419 neonates and 10,687 nonneonates from 22 hospitals. The prolonged critical illness cutoff (90th percentile length of stay) was greater than or equal to 35 and greater than or equal to 10 days for neonates and nonneonates, respectively. Cardiac ICU prolonged critical illness mortality was 24% in neonates and 8% in nonneonates (vs 5% and 0.4%, respectively, in nonprolonged critical illness patients). Multivariable logistic regression identified 10 neonatal and 19 nonneonatal prolonged critical illness predictors within strata and eight predictors of mortality. Only mechanical ventilation days and acute renal failure requiring renal replacement therapy predicted prolonged critical illness and prolonged critical illness mortality in both strata. Approximately 40% of the prolonged critical illness predictors were nonmodifiable (preoperative/patient and operative factors), whereas only one of eight prolonged critical illness mortality predictors was nonmodifiable. The remainders were potentially preventable (postoperative critical care delivery variables and complications). Case-mix-adjusted prolonged critical illness rates were compared across hospitals; six hospitals each had lower- and higher-than-expected prolonged critical illness frequency.

CONCLUSIONS

Although many prolonged critical illness predictors are nonmodifiable, we identified several predictors to target for improvement. Furthermore, we observed that complications and prolonged critical care therapy drive prolonged critical illness mortality. Wide variation of prolonged critical illness frequency suggests that identifying practices at hospitals with lower-than-expected prolonged critical illness could lead to broader quality improvement initiatives.

A Monte Carlo Simulation Approach to Optimizing Capacity in a High-Volume Congenital Heart Pediatric Surgical Center

Importance

Elective surgeries are primarily scheduled according to surgeon availability with less consideration of patients' postoperative cardiac intensive care unit (CICU) length of stay. Furthermore, the CICU census can exhibit a high rate of variation in which the CICU is operating at over-capacity, resulting in admission delays and cancellations; or under-capacity, resulting in underutilized labor and overhead expenditures.

Objective

To identify strategies to reduce variation in CICU occupancy levels and avoid late patient surgery cancellation.

Design

Monte Carlo simulation study of the daily and weekly CICU census at Boston Children's Hospital Heart Center. Data on all surgical admissions to and discharges from the CICU at Boston Children's Hospital between September 1, 2009 and November 2019 were included to obtain the distribution of length of stay for the simulation study. The available data allows us to model realistic length of stay samples that include short and extended lengths of stay.

Main Outcomes

Annual number of patient surgical cancellations and change in average daily census.

Results

We demonstrate that the models of strategic scheduling would result in up to 57% reduction in patient surgical cancellations, increase the historically low Monday census and decrease the historically higher late-mid-week (Wednesday and Thursday) censuses in our center.

Conclusions and Relevance

Use of strategic scheduling may improve surgical capacity and reduce the number of annual cancellations. The reduction of peaks and valleys in the weekly census corresponds to a reduction of underutilization and overutilization of the system.

The final reason paediatric Cardiac ICU patients require care prior to discharge to the floor: a single-centre survey

OBJECTIVE

To determine the Final ICU Need in the 24 hours prior to ICU discharge for children with cardiac disease by utilising a single-centre survey.

METHODS

A cross-sectional survey was utilised to determine Final ICU Need, which was categorised as "Cardiovascular", "Respiratory", "Feeding", "Sedation", "Systems Issue", or "Other" for each encounter. Survey responses were obtained from attending physicians who discharged children (≤18 years of age with ICU length of stay >24 hours) from the Cardiac ICU between April 2016 and July 2018.

MEASUREMENTS AND RESULTS

Survey response rate was 99% (n = 1073), with 667 encounters eligible for analysis. "Cardiovascular" (61%) and "Respiratory" (26%) were the most frequently chosen Final ICU Needs. From a multivariable mixed effects logistic regression model fitted to "Cardiovascular" and "Respiratory", operations with significantly reduced odds of having "Cardiovascular" Final ICU Need included Glenn palliation (p = 0.003), total anomalous pulmonary venous connection repair (p = 0.024), truncus arteriosus repair (p = 0.044), and vascular ring repair (p < 0.001). Short lengths of stay (<7.9 days) had significantly higher odds of "Cardiovascular" Final ICU Need (p < 0.001). "Cardiovascular" and "Respiratory" Final ICU Needs were also associated with provider and ICU discharge season.

CONCLUSIONS

Final ICU Need is a novel metric to identify variations in Cardiac ICU utilisation and clinical trajectories. Final ICU Need was significantly influenced by benchmark operation, length of stay, provider, and season. Future applications of Final ICU Need include targeting quality and research initiatives, calibrating provider and family expectations, and identifying provider-level variability in care processes and mental models.

Treating Center Volume and Congenital Diaphragmatic Hernia Outcomes in California

OBJECTIVE

To examined outcomes for infants born with congenital diaphragmatic hernias (CDH), according to specific treatment center volume indicators.

STUDY DESIGN

A population-based retrospective cohort study was conducted involving neonatal intensive care units in California. Multivariable analysis was used to examine the outcomes of infants with CDH including mortality, total days on ventilation, and respiratory support at discharge. Significant covariables of interest included treatment center surgical and overall neonatal intensive care unit volumes.

RESULTS

There were 728 infants in the overall CDH cohort, and 541 infants (74%) in the lower risk subcohort according to a severity-weighted congenital malformation score and never requiring extracorporeal membrane oxygenation. The overall cohort mortality was 28.3% (n = 206), and 19.8% (n = 107) for the subcohort. For the lower risk subcohort, the adjusted odds of mortality were significantly lower at treatment centers with higher CDH repair volume (OR, 0.41; 95% CI, 0.23-0.75; P = .003), ventilator days were significantly lower at centers with higher thoracic surgery volume (OR, 0.56; 9 5% CI, 0.33-0.95; P = .03), and respiratory support at discharge trended lower at centers with higher neonatal intensive care unit admission volumes (OR, 0.51; 9 5% CI, 0.26-1.02; P = .06).

CONCLUSIONS

Overall and surgery-specific institutional experience significantly contribute to optimized outcomes for infants with CDH. These data and follow-on studies may help inform the ongoing debate over the optimal care setting and relevant quality indicators for newborn infants with major surgical anomalies.

Center Variation in Chest Tube Duration and Length of Stay After Congenital Heart Surgery

BACKGROUND

Nearly every child undergoing congenital heart surgery has chest tubes placed intraoperatively. Center variation in removal practices and impact on outcomes has not been well described. This study evaluated variation in chest tube management practices and outcomes across centers.

METHODS

The study included patients undergoing any of 10 benchmark operations from June 2017 to May 2018 at participating Pediatric Acute Care Cardiology Collaborative (PAC³) and Pediatric Cardiac Critical Care Consortium (PC⁴) centers. Clinical data from PC⁴ centers were merged with chest tube data from PAC³ centers. Practices and outcomes were compared across centers in univariate and multivariable analysis.

RESULTS

The cohort included 1029 patients (N = 9 centers). Median chest tube duration varied significantly across centers for 9 of 10 benchmark operations (all P ≤ .03), with a "model" center noted to have the shortest duration for 9 of 10 operations (range, 27.9% to 87.4% shorter duration vs other centers across operations). This effect persisted in multivariable analysis (P < .0001). The model center had higher volumes of chest tube output before removal (median, 8.5 mL/kg/24 h [model] vs 2.2 mL/kg/24 h [other centers]; P < .001], but it did not have higher rates of chest tube reinsertion (model center 1.3% vs 2.1%; P = .59) or readmission for pleural effusion (model center 4.4% vs 3.0%; P = .31), and had the shortest length of stay for 7 of 10 operations.

CONCLUSIONS

This study suggests significant center variation in chest tube removal practices and associated outcomes after congenital heart surgery. Best practices used at the model center have informed the design of an ongoing collaborative learning project aimed at reducing chest tube duration and length of stay.

Predictors of Extended Length of Hospital Stay Following Surgical Repair of Congenital Heart Diseases

The purpose of this study is to evaluate post-operative length of stay (LOS) following surgical repair of congenital heart defects (CHD) and to investigate baseline pre-operative factors and predictors of post-operative LOS (pLOS). Retrospective chart review of all cases of corrective surgery for CHD performed at the Pediatric Cardiology Unit, King Abdulaziz University Hospital, Jeddah during January 2013-December 2016. Baseline demographics, clinical factors, pre-operative, intra-operative, post-operative cardiac and extra-cardiac complications were analyzed as independent factors of pLOS using stepwise linear regression. Kaplan-Meier (KM) survival analysis was used to analyze the correlation of pLOS (in days) with the independent variables and estimate the probability to exceeding a given pLOS. A total 191 patients (52.4% male, 49.7% aged ≤ 1 year) were included with a median [range] LOS = 10 [3, 158] days. Several baseline clinical factors were associated with longer pLOS such as complex CHD types (tetralogy of Fallot, transposition of great arteries, etc.), high-risk RACHS categories and low weight at surgery. Independent risk factors of pLOS included pre-operative hemoglobin level (unstandardized regression coefficient: B = 2.96, p = 0.036) as the only pre-operative predictor of LOS, besides intra-operative complications (B = 11.72, p = 0.009) and posto-perative factors including MV duration (B = 9.39, p < 0.001), diet/feeding problems (B = 10.27, p = 0.001) and drain tube stay (B = 3.82, p = 0.003). KM survival curves confirmed that these factors increased the probability for longer LOS. Post-operative LOS was associated with several baseline and peri-operative factors; however, it was independently predicted by abnormal baseline hemoglobin level, the occurrence of intra-operative complications, besides post-operative feeding problems, chest drain stay, and MV duration.

Accurate Prediction of Congenital Heart Surgical Length of Stay Incorporating a Procedure-Based Categorical Variable

OBJECTIVES

There is increasing demand for the limited resource of Cardiac ICU care. In this setting, there is an expectation to optimize hospital resource use without restricting care delivery. We developed methodology to predict extended cardiac ICU length of stay following surgery for congenital heart disease.

DESIGN

Retrospective analysis by multivariable logistic regression of important predictive factors for outcome of postoperative ICU length of stay greater than 7 days.

SETTING

Cardiac ICU at Boston Children's Hospital, a large, pediatric cardiac surgical referral center.

PATIENTS

All patients undergoing congenital heart surgery at Boston Children's Hospital from January 1, 2010, to December 31, 2015.

INTERVENTIONS

No study interventions.

MEASUREMENTS AND MAIN RESULTS

The patient population was identified. Clinical variables and Congenital Heart Surgical Stay categories were recorded based on surgical intervention performed. A model was built to predict the outcome postoperative ICU length of stay greater than 7 days at the time of surgical intervention. The development cohort included 4,029 cases categorized into five Congenital Heart Surgical Stay categories with a C statistic of 0.78 for the outcome ICU length of stay greater than 7 days. Explanatory value increased with inclusion of patient preoperative status as determined by age, ventilator dependence, and admission status (C statistic = 0.84). A second model was optimized with inclusion of intraoperative factors available at the time of postoperative ICU admission, including cardiopulmonary bypass time and chest left open (C statistic 0.87). Each model was tested in a validation cohort (n = 1,008) with equivalent C statistics.

CONCLUSIONS

Using a model comprised of basic patient characteristics, we developed a robust prediction tool for patients who will remain in the ICU longer than 7 days after cardiac surgery, at the time of postoperative ICU admission. This model may assist in patient counseling, case scheduling, and capacity management. Further examination in external settings is needed to establish generalizability.