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

Brennan, Andrew; Connor, Jean Anne; Almodovar, Melvin C.; DiNardo, James A.; Banka, Puja; Nathan, Meena; Mathieu, Derek; Kaza, Aditya K.; Mayer, John E.; Bergersen, Lisa

doi:10.1097/pcc.0000000000001168

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…Implementation of this approach is predicated on the ability to predict patient CICU LOS prior to surgical scheduling. We have previously demonstrated that pre-operative characteristics of this patient population can be used to predict post-operative CICU LOS with operationally adequate accuracy, and have incorporated these models into local capacity predictions (4,6). In short, we derived Congenital Heart Surgical Stay categories, which closely approximate STAT Categories, to identify those patients with a short anticipated CICU course of 1-2 days (category 1-2), compared to those with a longer anticipated LOS (categories 3-5) (4).…”

Section: Discussionmentioning

confidence: 99%

“…In total, there were 14,526 admissions. Descriptive data of the informing surgical population is included in recent reports of our program (4,6). In brief, these admissions are typically comprised of 19% neonates (<30 days); 32% infants (30 days to <1year), 41% children (1 to <18 years) and 9% over age 18 years, with surgical complexity according to The Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) Category 1 25%,Category 2 29%, Category 3 15%, Category 4 25% and Category 5 5%.…”

Section: Participantsmentioning

confidence: 99%

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

Elia

Bergersen

et al. 2022

Front.Health Serv.

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ImportanceElective 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.ObjectiveTo identify strategies to reduce variation in CICU occupancy levels and avoid late patient surgery cancellation.DesignMonte 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 OutcomesAnnual number of patient surgical cancellations and change in average daily census.ResultsWe 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 RelevanceUse 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.

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Section: Discussionmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

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

Elia

Bergersen

et al. 2022

Front.Health Serv.

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“…22,24,26,28,30,38,43,46,51,54,[56][57][58][59][60]62,63,[65][66][67]69,70,73,82 ** Patient drugs administered. 20,32,46,48,49,51,66,76,81,84 † † An event occurring during the hospital stay, for example, development of complications. 21,23,24,26,30,42,47,48,55,81 ‡ ‡ Patient risk scores, for example, Injury Severity Score.…”

Section: Data Sources and Input Variablesmentioning

confidence: 99%

“…20,32,46,48,49,51,66,76,81,84 † † An event occurring during the hospital stay, for example, development of complications. 21,23,24,26,30,42,47,48,55,81 ‡ ‡ Patient risk scores, for example, Injury Severity Score. 7,21,24-26,29,33,34,36-38,41,43, LOS modeling format was a continuous variable without transformation, a continuous variable with transformation (logarithmic or polynomial), or a discrete variable in 54.1% (40/74), 21.6% (16/74), and 24.3% (18/74) of the articles, respectively.…”

Section: Data Sources and Input Variablesmentioning

confidence: 99%

Hospital Length of Stay Prediction Methods

et al. 2021

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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 R 2 (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.

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“…Many factors influence resource utilisation and Paediatric Cardiac ICU length of stay, including admitting diagnosis, surgical operation, post-operative complications, and unplanned hospital and Cardiac ICU readmissions. [1][2][3][4][5][6][7][8][9][10] Beyond severity of illness, variation in clinical outcomes is driven by individual providers' perceptions of "optimal care" and the system's ability to deliver the provider's construct to the patient. However, the downstream influence of provider perception on care delivery is not routinely assessed.…”

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confidence: 99%

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

Winder

Presson

et al. 2020

Cardiol Young

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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.

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

Cited by 11 publications

References 23 publications

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

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

Hospital Length of Stay Prediction Methods

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

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