Publication/Presentation Date




The success of an initial cardiac surgery promotes longer survival rates among patients who may live long enough to require further surgical intervention. Unfortunately these reoperations are associated with higher risks. Retrospective analysis is successful in identifying risk factors that promote better patient stratification and surgical management. The Lehigh Valley Health Network has a high volume of reoperation records that can be converted into a microcomputer database on the institutional level. This research strives to begin the first model of this database intended to compute meaningful and concrete statistical data.


In an effort to compile information regarding cardiothoracic surgery on an international scale, the Society of Thoracic Surgeons (STS) began the STS National Database in 1989 (Shahian et al., 2013). This project served as a stepping-stone towards providing data for clinical research that would surface any overlooked complications and generate concrete evidence for improving the quality of surgeries and patient outcomes (Edwards, Clark, & Schwartz, 1994). The continuous innovations in cardiothoracic surgery call for constant revision of the data collection and algorithms within the database. For example, STS recently renovated its “Procedure Type” input to include immediate surgeries, terminated plans, and the relatively new transcatheter valve procedure. STS graciously provides free resources, including training guides, user-friendly software, and analogous data collection worksheets (Shahian et al., 2013).

Several questions can be answered through analysis of an established network that exists within a database. The STS National Database has provided data to identify risk factors associated with reoperative surgery (Jamieson et al., 1999). At the institutional level, a recent study tested for the effects of previous Coronary Artery Bypass Grafting (CABG) on redo surgery for valves by using a 10-year volume of 1,000+ patients (Breglio et al., 2013). With precision comes the ability to hone in on specific postoperative instances and factors most likely associated with those outcomes (Vivacqua et al., 2011). With the proper structure and sample size, the Lehigh Valley Health Network (LVHN) has the materials and potential to create a database and can examine the patient pool as a whole and generate meaningful statistics.

The Cardiothoracic Department within LVHN contains an extensive history of cases that, if correctly compiled, may provide key insight into readmitted-patient risk factors associated with specific operations. Due to the positive results of initial cardiac surgeries, patients survive long enough to develop more complications that require a reoperation (Balsam et al., 2010). The mortality risk increases with each cardiac surgery for a single patient, and constructing a database of this caliber will both neatly organize the retrospective data for generating meaningful statistics and draw LVHN towards the platform of the groundbreaking STS Database and becoming a major contributor to the international effort. Past work from Research Scholars within the department regarded the quantitative study of past surgical patients cases, and presently we are striving towards preparing these cases for database construction and future statistical analysis.


Upon literature review of reoperative cardiac surgeries, a hybridized microcomputer database was constructed with Microsoft Access (Friedrichsen, 2010). The design was specialized to capture important information that could be conveniently extracted from given patient records. Presently LVHN has records of redo patients from the Cedar Crest and Muhlenberg campuses, and this project constructed the first version of the database and recorded information from 2009 and 2010 with a resultant sample size of 122 patients. Published database studies are typically of ≥10 year volumes, and therefore this study should be further innovated and supplemented in order to obtain an acceptable amount of information. Thus far, the database captures patient medical and surgical histories, perioperative cardiac conditions, the type of reoperation itself, and the postoperative outcomes.

The framework of Microsoft Access allows one to design a form with which the user can enter data that translates into a corresponding table. While every preexisting condition may have a specificity to the patient, general categories have been shown to be powerful tools for examining multiple records as a whole (Jones et al., 2001). The database form included the following operative information: Patient name, Medical Record Number (MRN), Surgeon, hospital campus, age (years), weight (pounds), height (inches), cardiac surgical history, history of smoking, drinking, drug abuse, Diabetes Mellitus, dyslipidemia, dialysis, hypertension, Endocarditis, lung disease, renal insufficiencies, Coronary Artery Disease, previous valve surgery and valve operated on, other cardiac interventions, NSTEMI (Non-ST Segment Elevation Myocardial Infarction), STEMI, angina, heart failure, cardiogenic shock, arrhythmia, ejection fraction, valve etiologies and lesions, other diseases, presence of a ventricular assist device, type of redo surgery, postoperative outcomes, and mortality (within 30 days of redo). Categories were inputted as a discrete number, free-response text, or binary true/false for appropriate categories.

Data analysis was performed by exporting information into Microsoft Excel. Gender stratification was utilized as this has been one of the previously recognized risk factors in certain reoperative cardiac surgeries (Lytle et al., 1986).


The database currently contains the information of 122 patients including all redo cardiac patients from 2009 and 2010 from both Cedar Crest and Muhlenberg. Of the current pool, females (n=44, 36.1%) had an average age of 68 ± 13 years and morality rate of 9.09% (n=4). Males (n=78, 63.9%) were at a mean age of 69 ± 11 years and morality rate of 3.85% (n=3). The order of most frequent preoperative conditions for both genders were hypertension (n=105, 86.1%), Coronary Artery Disease (n=98, 80.3%), hyperlipidemia (n=91, 74.6%), history of smoking (n=66, 54.1%), Diabetes Mellitus (n=50, 41.0%), history of drinking (n=42, 34.4%), and renal insufficiencies (n=34, 27.9%). All other conditions were frequencies below 10%. 48 patients (39.3%) had a prior valve surgery. Most preexisting conditions were extracted from transcripts taken by Physician Assistants and Catheter Lab Reports. Conditions regarding cardiac physiology and performance were recorded by Cardiac Anesthesiologists and Cardiothoracic Surgeons.

Of the procedures performed, the CABG procedure and valve replacements and repairs were dominant. Operations that were categorized as “other” were underrepresented in this investigation, including Ascending Aortic Aneurysm repair, Septal Myectomy, root reconstruction, tumor removal, and specific revascularization procedures. Categorization of operations for females (Figure 1) and males (Figure 2) were developed with a systematic approach by creating networks.

Figure 1. Categorization of Redo Operations for Females

Figure 1. The networks described correspond to the findings within the database for 2009 and 2010. Each initial node corresponds to the first cardiac surgery the patient underwent (as denoted by a ratio out of 44). Edges leading to subsequent nodes are weighted in order to emphasize which reoperations are of a higher occurrence. Final nodes correspond to the actual reoperation that was performed at Lehigh Valley Heath Network. Ratios within these nodes correspond to the frequency within that subcategory.

Figure 1. Categorization of Redo Operations for Males

Figure 2. The architecture of the male network was constructed in a similar hierarchical manner. Considering the edge weights, there are visible differences in operation frequencies between genders.

Additionally, the free-text response capability of Microsoft Access allows one to enter any postoperative outcomes that may be specific to the patient. Using a counting function, the program can determine how many times a specific outcome arises, and this may be an indication of common outcomes from specific operations. If programmed correctly and categorized efficiently, one can set up a relationship within Microsoft Access that allows for recording the frequency of certain outcomes for a specific redo.

Figure 3. Most Common Postoperative Outcomes

Frequency of Postoperative Outcomes (Females)

Acute Blood Loss Anemia

Atrial Fibrillation

Pleural Effusions



Other Notable Occurrences: Arrhythmia, Acute Renal Issues, Respiratory Insufficiency, Seizure

Frequency of Postoperative Outcomes (Males)

Acute Blood Loss Anemia


Atrial Fibrillation

Acute Renal Issues

Atrial Fibrillation

Volume Overload


Other Notable Occurrences: Respiratory Insufficiency, Arrhythmia, Pleural Effusions, Seizures

Figure 3. Outcomes are listed in descending order. Separating this data by gender shows differences in postoperative patient conditions.

Due to the small sample size, it is difficult to presently confirm any significant risk factors associated with mortality. At this time, there are underlying themes present within the failed reoperations. For example, females who did not survive the reoperation after 30 days underwent some form of revascularization operation and may have had a substantial and established disease, such as lung disease or endocarditis. Similarly, the males who did not survive were also at a critical state prior to a revascularization surgery, including history of angina, myocardial infarction, cardiogenic shock, or other risk statuses. In almost all cases of postoperative results, patients were under ventilator-dependent respiratory failure, which may serve as an indicator that pulmonary status is vital to risk stratification if additional data also presents this motif.


Creating a database of this institutional caliber has, thus far, proven to be an indispensible tool for efficiently harvesting retrospective data and utilizing mathematical modeling to identify odds and trends. Beginning this new research with a mature Database will accelerate the process for compiling data and, once the patient sample size is sufficiently large, generate meaningful statistics. Differences in ratios between categorical variables can then be tested for using a two-tailed Fischer’s Exact Test with a Contingency Table to determine significance (Breglio et al., 2013).

Thus far we have noted gender differences in necessary reoperations and the outcomes of those reoperations. However, further classification may be necessary so as to not generalize findings or establish conclusions prematurely. The relationship function within Microsoft Access would allow for this type of intricate analysis. Additionally, further database form renovation may be necessary once medical records start transcribing recently developed procedures, such as the transcatheter aortic valve replacement (TAVR) (Shahian et al., 2013). This may make the institutional database a great tool for analyzing this new procedure once enough cases have been recorded. The database would already be established, and the data analysis can be performed and broadcasted quickly, putting the Lehigh Valley Health Network at the forefront of research in the TAVR procedure. This database is at the preliminary model stage and carries great implications if supported and completed gradually over time.


Special thanks to Dr. James Wu for his guidance and invaluable insight, Heather Geist, a fellow research scholar for assistance with database completion, Dr. Phillips for additional help, and the rest of the surgical team here at LVHN-Cedar Crest.


Balsam, L. B., Grossi, E. A., Greenhouse, D. G., Ursomanno, P., Deanda, A., Ribakove, G. H., … Galloway, A. C. (2010). Reoperative valve surgery in the elderly: predictors of risk and long-term survival. The Annals of Thoracic Surgery, 90(4), 1195–200; discussion 1201. doi:10.1016/j.athoracsur.2010.04.057

Breglio, A., Anyanwu, A., Itagaki, S., Polanco, A., Adams, D. H., & Chikwe, J. (2013). Does prior coronary bypass surgery present a unique risk for reoperative valve surgery? The Annals of Thoracic Surgery, 95(5), 1603–8. doi:10.1016/j.athoracsur.2013.01.073

Edwards, F. H., Clark, R. E., & Schwartz, M. (1994). Coronary artery bypass grafting: The Society of Thoracic Surgeons National Database experience. The Annals of Thoracic Surgery, 57(1), 12–19. doi:10.1016/0003-4975(94)90358-1

Friedrichsen, L. (2010). Microsoft Access 2010: Illustrated Introductory (p. 256). Cengage Learning. Retrieved from

Jamieson, W. R. E., Edwards, F. H., Schwartz, M., Bero, J. W., Clark, R. E., & Grover, F. L. (1999). Risk stratification for cardiac valve replacement. National Cardiac Surgery Database. The Annals of Thoracic Surgery, 67(4), 943–951. doi:10.1016/S0003-4975(99)00175-7

Jones, J. M., O’kane, H., Gladstone, D. J., Sarsam, M. A., Campalani, G., MacGowan, S. W., … Cran, G. W. (2001). Repeat heart valve surgery: risk factors for operative mortality. The Journal of Thoracic and Cardiovascular Surgery, 122(5), 913–8. doi:10.1067/mtc.2001.116470

Lytle, B. W., Cosgrove, D. M., Taylor, P. C., Gill, C. C., Goormastic, M., Golding, L. R., … Loop, F. D. (1986). Reoperations for Valve Surgery: Perioperative Mortality and Determinants of Risk for 1,000 Patients, 1958–1984. The Annals of Thoracic Surgery, 42(6), 632–643. doi:10.1016/S0003-4975(10)64597-3

Shahian, D. M., Jacobs, J. P., Edwards, F. H., Brennan, J. M., Dokholyan, R. S., Prager, R. L., … Grover, F. L. (2013). The society of thoracic surgeons national database. Heart (British Cardiac Society), 99(20), 1494–501. doi:10.1136/heartjnl-2012-303456

Vivacqua, A., Koch, C. G., Yousuf, A. M., Nowicki, E. R., Houghtaling, P. L., Blackstone, E. H., & Sabik, J. F. (2011). Morbidity of bleeding after cardiac surgery: is it blood transfusion, reoperation for bleeding, or both? The Annals of Thoracic Surgery, 91(6), 1780–90. doi:10.1016/j.athoracsur.2011.03.105


Mentor: Dr. James Wu


Research Scholars, Research Scholars - Posters, Department of Surgery, Department of Surgery Faculty

Document Type