Punkaj Gupta, MBBS Division of Pediatric Cardiology Arkansas Children’s Hospital March 26, 2015

None for all authors VPS data was provided by the VPS, LLC. No endorsement or editorial restriction of the interpretation of these data or opinions of the authors has been implied or stated

Building Trust in the Power of “Big Data” For Outcomes Research to Serve the Public Good

Study of the end results of particular health care practices and interventions Uses retrospective, non-interventional data from existing multi-center databases

The nation is spending over $800 billion dollars on health care, yet very little is known about what that $800 billion is buying Outcomes research helps us understand the most effective and efficient ways to provide high quality health care

Existing data may be used to conduct studies that are not amenable to a randomized trial format Existing data often describe “real-world” care and may be used to define practice variation

Nickname in computer science, business, and public policy for the application of sophisticated analytic techniques to large and rapidly growing databases In medicine applicable to electronic health records, clinical registries, and administrative databases

Virtual PICU Performance System (VPS, LLC): ~ 1 million ICU patients from 130 Pediatric ICUs The Pediatric Health Information System (PHIS): ~ 3 million patients from 43 free- standing children’s hospitals in United States

Big data provides great potential for extracting useful knowledge to achieve the ‘triple aim’ in health care –better care for individuals, –better care for all, and –greater value for dollars spent. Okun S, McGraw D, Stang P, et al. Discussion Paper: Making the Case for Continuous Learning From Routinely Collected Data. Institute of Medicine

Health care lags behind other industries in leveraging advances in information technology and analytical techniques. If “Big Data” using databases like VPS, LLC applied to health care, it would potentially improve quality and efficiency of the system.

Affordable Care Act: Incentives are increasing for stakeholders (including clinicians, insurers, purchasers, and patients) to collect, analyze, and exchange health care information

Study two examples from VPS, LLC database Demonstrate strength and weakness of “Big Data” through these examples

Punkaj Gupta, MBBS; Xinyu Tang, PhD; Casey Lauer, BA; Robert M. Kacmarek, PhD, RRT; Tom B. Rice, MD; Barry P. Markovitz, MD, MPH; Randall C. Wetzel, MBBS

Little is known about the effects of clinical education, and hospital structure on medical outcomes in children with critical illness Increasing concerns regarding trainee inexperience as a contributing factor to outcomes in children with critical illness

Similar concerns for non-university, and non-free standing children’s hospitals providing a lower level of care for critically ill children

To evaluate outcomes associated with training programs (such as residency or fellowship training), and hospital structure (such as free-standing children’s or university hospital) using the Virtual PICU Systems (VPS, LLC) Database

Odds of ICU mortality Time to ICU discharge Odds of mechanical ventilation Time to liberation from mechanical ventilation

The Virtual PICU Systems (VPS, LLC) is an online pediatric critical care network Prospective observational cohort from ~130 PICUs with interrater reliability (IRR) testing > 95% Formed by NACHRI (now part of CHA), Children’s Hospital of Los Angeles, and Children’s Hospital of Wisconsin

Patients <18 years of age admitted to one of the participating PICUs in the VPS database were included Patients with both cardiac (cardiac- medical and cardiac-surgical), and non- cardiac diagnoses were included

Patient characteristics and outcomes summarized between the study hospitals and control hospitals Multivariable logistic regression models and Cox proportional hazards models were fitted to evaluate association of training programs and hospital structure with study outcomes

A total of 308,082 patients from 102 centers were included Patients in the study hospitals had greater severity of illness (PIM-2 and PRISM-3 scores), and had higher incidence of cardiopulmonary resuscitation

Compared to the control groups, resource utilization was also greater among the four hospital categories, e.g., –the use of mechanical ventilation and –high frequency ventilation, and –use of arterial and invasive central lines

Compared to patients in control hospitals, patients in the four hospital categories were older, and had significant comorbidities, such as –developmental disorder –genetic syndrome –low birth weight –prematurity

ICU mortality was significantly lower among the study hospitals- as compared to the control hospitals Despite caring for more complex and sicker patients, time to ICU discharge was shorter among the study hospitals- as compared to the control hospitals

Could not account for the potential impact of variables such as- –hospital structure and process measures, –training or availability of ICU personnel, or –nursing factors on study outcomes

Our study did not address the financial burden of training program or hospital structure as an outcome measure. Use of ICU Mortality, time to ICU discharge, and time to liberation from mechanical ventilation as outcome measures

Punkaj Gupta, MBBS; Xinyu Tang, PhD; Casey Lauer, BA; Tom B. Rice, MD; Randall C. Wetzel, MBBS

Clinical practice variations are common in children undergoing congenital heart surgery None of the existing literature to-date has truly compared the volume-outcome relationship with mechanical ventilation after pediatric cardiac surgery as an outcome

To evaluate the –odds of mechanical ventilation, and –duration of mechanical ventilation after pediatric cardiac surgery across centers of varying center volume using the Virtual PICU Systems (VPS, LLC) Database

The Virtual PICU Systems (VPS, LLC) is an online pediatric critical care network Prospective observational cohort from ~130 PICUs with interrater reliability (IRR) testing > 95% Formed by NACHRI (now part of CHA), Children’s Hospital of Los Angeles, and Children’s Hospital of Wisconsin

Patients <18 years of age undergoing operations (with or without CPB) for heart disease at one of the participating ICUs in the VPS database were included

Patients receiving high frequency oscillatory ventilation (HFOV), or jet ventilation were also excluded Centers with >10% missing data were excluded

Average number of cardiac surgery cases per year for each center Study centers were categorized using the center volume tertiles: –Low-volume: <175 cases/year –Medium volume: ≥175 to <275 cases/year –High-volume: ≥275 cases/year

Patient characteristics, procedural data, post-operative outcomes Outcomes –Odds of mechanical ventilation –Duration of mechanical ventilation after pediatric cardiac surgery

Multivariable logistic regression models and Cox proportional hazards models used to evaluate the relationship between: –Center volume and odds of MV –Center volume and duration of MV Models adjusted for patient factors and center effects

10,378 patients from 43 centers were included LowMediumHigh Number of Centers 3643 Number of Patients 3,657 (35%)3,176 (31%)3,545 (34%)

LowMediumHigh Mortality 3% (127)3% (102)2% (72) Use of MV 73% (2675)81% (2576)68% (2397) Duration of MV 24 (8, 96)27 (8, 99)45 (19, 119)

UnadjustedAdjusted OR (95% CI) P P Low 1.26 (1.14, 1.39) < (2.15, 3.35) <0.001 Medium 1.78 (1.60, 1.98) < (1.12, 1.52) <0.001 High Reference

Higher volume centers were associated with lower odds of mechanical ventilation in the lower risk patients (STS-EACTS categories 1-3) No significant relationship between center volume and odds of mechanical ventilation in the higher risk patients (STS-EACTS categories 4-5)

UnadjustedAdjusted HR (95% CI) P P Low 1.16 (1.10, 1.23) < (1.16, 1.37) <0.001 Medium 1.14 (1.08, 1.21) < (1.11, 1.28) <0.001 High Reference

Higher volume centers were associated with longer duration of mechanical ventilation in both high risk (STS-EACTS categories 4-5) and low risk patients (STS- EACTS categories 1-3)

Large clinical practice variations were demonstrated for MV following pediatric cardiac surgery among ICUs of varied center volumes Both odds of mechanical ventilation and duration of mechanical ventilation following cardiac surgery vary substantially across hospitals

Multi-institutional databases can be powerful tool for doing outcomes research If used methodically, database research can have significant impact on clinical practice and health care outcomes