Database Research for Fellows-in-Training and Early-Career Cardiologists

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY

VOL. 66, NO. 12, 2015

ª 2015 BY THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION

ISSN 0735-1097/$36.00

PUBLISHED BY ELSEVIER INC.

http://dx.doi.org/10.1016/j.jacc.2015.08.001

FELLOWS-IN-TRAINING & EARLY CAREER PAGE

Database Research for Fellows-in-Training and Early-Career Cardiologists Tarek Alsaied, MD, Muhammad Shoaib Khan, MD, James Cnota, MD

I

still remember meeting with 1 of my first-year

quality improvement and meaningful collaboration

cardiology fellows who was seeking a mentor

between national and international centers (1,2).

and a research project. During that meeting, I

Observations

felt his enthusiasm to have a well-designed research

from

database

research

may

also

generate hypotheses for further clinical trials (3).

project that may affect the field of pediatric cardiol-

Database research is very valuable for fellows-in-

ogy. I told him that although the time is limited

training for multiple reasons. The availability of the

in fellowship, there are always ways to do that.

raw data in a database may provide a time benefit and

One way I recommended was using multi-institu-

save you many steps in your research project. Data-

tional databases, which have become an important

bases also provide the sample size and statistical

data resource in pediatric cardiology (1). We sought

power that is very difficult to achieve with a single-

to provide a review of the potential research

center study (3). The data contributed by many

opportunities

centers result in generalizable results. For these

these

databases

may

provide

for

fellows and early-career cardiologists.

reasons, approaching such databases with a good

Over the last 50 years, tremendous progress has

research question and sound analytic methodology

been achieved in caring for children with congenital

will often lead to results that are accepted for

heart disease (CHD). Research in patients with CHD

presentation in scientific meetings—a goal for all

has never been easy, primarily due to its rarity and

fellows (4). Furthermore, as a good portion of the

challenges with the complicated nature of the dis-

evidence-based

ease. Over the last 2 decades, the importance of

databases, this type of research will provide a good

collecting and reporting data for patients undergoing

understanding of the source of evidence we have (5).

practice

in

CHD

comes

from

congenital heart surgery was recognized (1). This led

The most important aspect of having a successful

many national organizations and congenital heart

research project using multi-institutional databases is

societies to start databases for the collection of

finding a good mentor (4). As most institutions

outcomes data that eventually led to the creation

nowadays contribute to databases, you will likely be

of

heart

able to find a mentor at your institution with

programs (2). The collection of data on a national

experience in databases. Having a mentor with

scale addressed the problem of small sample sizes.

some experience in database research is important,

The multi-institutional nature of the data helped in

as your mentor will know what research questions a

understanding institutional variations in treating

certain database will be able to answer and the

different patients and adapting the most successful

limitations for the database you will be using. Your

ones (1). In the past few years, there has been rapid

mentor also will guide you through the process of

growth in database research projects in CHD, due to

obtaining the

the increased awareness of its usefulness and the

multiple mentors is helpful, and your mentors can

national

benchmarks

for

congenital

data from the database. Having

massive evolution in information technology (2).

be in different institutions (6). Even if your local

Using databases has not only permitted tracking

mentors do not have experience in the database

and comparison of outcomes, but has also led to

in which you are interested, they will be able to connect you with a mentor in a different department or institution who may help you in your

From the Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.

project.

Alsaied et al.

JACC VOL. 66, NO. 12, 2015 SEPTEMBER 22, 2015:1404–7

Fellows-in-Training & Early Career Page

An important example of multi-institutional datasets in CHD is the pediatric heart network (PHN). The

completed by early-career cardiologists and fellowsin-training (10).

PHN was established in 2001 by the National Heart,

When pursuing database research in CHD, try to be

Lung, and Blood Institute of the National Institutes of

aware of the limitations, which include variability in

Health. It was created to help advance clinical

terminology and nomenclature between different

research in CHD. Since it was established, the PHN

institutions and organizations. Furthermore, admin-

has offered multiple educational and funding oppor-

istrative databases utilize data that are collected for

tunities in clinical research for fellows-in-training

hospital billing purposes, which may limit the validity

and early-career pediatric cardiologists. Furthermore,

and reliability of data. High-quality clinical databases

the PHN has 3 large datasets for public use that are

are now available, but they may also lack the granular

available free of charge (5).

details of individual cases. To overcome these chal-

Another important database registry is IMPACT

lenges, it is crucial to have a good study design and to

(Improving Pediatric and Congenital Treatment),

be aware of the limitations and confounding factors

which is among the American College of Cardiology’s

in the dataset (11). From a public health standpoint,

(ACC’s) National Cardiovascular Data Registries (1).

the

The

financial burden to the health care system. Although

IMPACT

registry

collects

pediatric

cardiac

cost

of

maintaining

these

databases

adds

catheterization data from >80 institutions across

no current mechanisms for efficient, real-time data

the United States (1,7). It has the potential to

are available, there is an ongoing effort to link

set the benchmarks in congenital interventional

multiple datasets to each other and to the electronic

cardiology and supports powerful research studies

health record systems to decrease cost and increase

(7).

Interestingly,

abstracts

efficiency (12). These costs are outweighed by the

fellows-in-

knowledge gained, and as today’s fellows become

training at the 2015 ACC Scientific Sessions and later

increasingly familiar with the resource, we believe

published in the Journal (8) from the IMPACT

the collaborative efforts needed to maintain the

registry. A third success story is the NPC-QIC

databases will further benefit patients and families

(National Pediatric Cardiology Quality Improvement

affected by CHD.

presented

as

oral

there

were

presentations

some by

Collaborative), which is the product of collaboration between multiple national societies, including the ACC and American Heart Association, to improve the

REPRINT REQUESTS AND CORRESPONDENCE: Dr.

care of children with CHD (9). The information from

Tarek Alsaied, Pediatric Cardiology, The Heart Insti-

this database led to significant improvement in

tute, Cincinnati Children’s Hospital Medical Center,

outcomes in children with single ventricle physiology

3333 Burnet Avenue MLC, Cincinnati, Ohio 45229.

(10). Multiple publications using these data were

E-mail: [email protected].

REFERENCES 1. Landzberg MJ. The impact of IMPACT: a game changer for congenital cardiology. J Am Coll Cardiol 2014;64:2452–4. 2. Jenkins KJ, Beekman RH III, Bergersen LJ, et al. Databases for assessing the outcomes of the treatment of patients with congenital and paediatric cardiac disease—the perspective of cardiology. Cardiol Young 2008;18 Suppl 2: 116–23. 3. McLaughlin VV, Suissa S. Prognosis of pulmonary arterial hypertension: the power of clinical registries of rare diseases. Circulation 2010;122: 106–8.

heart network clinical research skills development conference. Am Heart J 2011;161:13–67. 6. Sharma GV, Freeman AM. Mentoring: why it matters even after training. J Am Coll Cardiol 2014;64:1964–5. 7. Moore JW, Vincent RN, Beekman RH 3rd, et al. Procedural results and safety of common interventional procedures in congenital heart disease: initial report from the National Cardiovascular Data Registry. J Am Coll Cardiol 2014;64:2439–51.

4. Negi SI, Koifman E. Research during fellowship: walking the tight rope. J Am Coll Cardiol 2015;65: 625–7.

8. O’Byrne ML, Gillespie M, Dori Y, et al. The influence of deficient retro-aortic rim on technical success and early adverse events following device closure of secundum atrial septal defects: an analysis of the IMPACT registry (abstr). J Am Coll Cardiol 2015;65 Suppl 10S:A503.

5. Lai WW, Vetter VL, Richmond M, et al. Clinical research careers: reports from a NHLBI pediatric

9. Kugler JD, Beekman RH III, Rosenthal GL, et al. Development of a pediatric cardiology

quality improvement collaborative: from inception to implementation. From the Joint Council on Congenital Heart Disease Quality Improvement Task Force. Congenit Heart Dis 2009;4: 318–28. 10. Anderson JB, Beekman RH 3rd, Kugler JD, et al. Use of a learning network to improve variation in interstage weight gain after the Norwood operation. Congenit Heart Dis 2014;9: 512–20. 11. Pasquali SK, Peterson ED, Jacobs JP, et al. Differential case ascertainment in clinical registry versus administrative data and impact on outcomes assessment for pediatric cardiac operations. Ann Thorac Surg 2013;95:197–203. 12. Cooke CR, Iwashyna TJ. Using existing data to address important clinical questions in critical care. Crit Care Med 2013;41:886–96.

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Alsaied et al.

JACC VOL. 66, NO. 12, 2015 SEPTEMBER 22, 2015:1404–7

Fellows-in-Training & Early Career Page

RESPONSE: An Important Clinical Question,

Rather Than a Database, Should Spark Research Sara K. Pasquali, MD, MHS Department of Pediatrics and Communicable Diseases, C.S. Mott Children’s Hospital, and Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan E-mail: [email protected] Over the past 2 decades, the number and variety of data

that not all associations found in a large dataset are neces-

sources available to medical researchers has increased

sarily clinically meaningful. Analytic guidance from a

exponentially, mirroring similar data trends across many

mentor with expertise in working with large datasets is

other fields. In pediatric cardiovascular medicine, there

critical, as this often requires specific knowledge of

are now numerous datasets capturing information that

advanced methodologies to deal with confounders, differ-

may facilitate research, including national clinical regis-

ences in case-mix across centers, center effects, and other

tries, multicenter research datasets, administrative data-

factors.

bases, the electronic health record, and emerging data

Overall, strong mentorship is critically important. This

sources containing genetic and biomarker information,

may involve reaching out not only to others outside of

data streams from monitoring systems, and longitudinal

one’s institution, as described by Alsaied and colleagues,

follow-up data, such as neurodevelopmental outcomes (1).

but also to those outside of the field of cardiology.

There are several key considerations regarding the use

Although many cardiology programs now participate in

of these datasets by trainees and junior faculty for research

these datasets, there is a different skillset involved in

purposes, in addition to the important points raised by

understanding how to submit data to a database versus

Alsaied and colleagues. First, it is important to note that

the use of large datasets for research. As described

“database research” is not a type of research. Rather, out-

previously, this includes knowledge of the overall

comes research, health services and policy research, and

strengths and weaknesses of the dataset and appropriate

comparative-effectiveness research are examples of types

methodologic and analytic techniques, in addition to an

of research that, by their nature, may be facilitated by or

understanding of the clinical question. My own men-

require the use of large datasets. For example, it is not

torship team has included several individuals outside

possible to study the national landscape of care delivery

of cardiology, including a bariatric surgeon, infectious

and outcomes without using a large multicenter dataset.

disease specialist, and health economist, who have all

However, there may be other research questions not well

brought different and important expertise in various

suited to the use of an existing dataset. It is best to develop

aspects of health services and policy research. For those

a research project from the perspective of “How do I

interested in pursuing this type of research as a major

answer this important question?” (which may or may not

part of their career, obtaining formal master’s-level

involve the use of a certain database) versus “What anal-

training in addition to practical guidance from a group

ysis could I perform with this dataset?”

of expert mentors is often necessary to position yourself

In addition, it is critical to understand the strengths and

for success.

limitations of different datasets to gauge the types of

Finally, newer database management and linkage

questions that may or may not be answered. There can be

methodologies may further expand the types of research

wide differences in standards regarding data definitions,

possible and the range of questions that may be answered

data entry, data audits, level of missing data, or capture of

(2). For example, integrating continuous data streams

pertinent variables to the area of study. For example, as

generated by monitoring systems with clinical outcomes

noted by Alsaied and colleagues, the limited granularity of

data may enable better prediction and treatment of

International Classification of Diseases–9 (and 10) codes

adverse events in intensive care settings, and leveraging

with regard to congenital heart diagnoses and procedures

existing registry data may hold the potential to more

is important to recognize when considering the use of

efficiently power clinical trials (3). These and other

administrative datasets.

developments noted by Alsaied and colleagues make it

From a methodological standpoint, it is important to remember that correlation does not imply causation and

an exciting time for early-career cardiologists to get involved in this area of research.

Alsaied et al.

JACC VOL. 66, NO. 12, 2015 SEPTEMBER 22, 2015:1404–7

Fellows-in-Training & Early Career Page

REFERENCES 1. Barach P, Jacobs JP, Lipshultz SE, Laussen P, editors. Pediatric and Congenital Cardiac Disease: Outcomes Analysis, Quality Improvement, and Patient Safety. London: Springer-Verlag, 2015.

2. Pasquali SK, Schumacher KR, Davies RR. Can linking databases answer questions about pediatric heart failure? Cardiol Young 2015. In press.

3. Lauer MS, D’Agostino RB. The randomized registry trial—the next disruptive technology in clinical research? N Engl J Med 2013;369: 1579–81.

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