Predictive Index for 90-Day Postoperative Outcomes Following Gynecologic Surgery

Increasing LACE+ index score is a significant predictor of greater risk of unplanned readmission, emergency department visits, and reoperation after gynecologic surgery.

ABSTRACTObjectives: Assessment of the potential of LACE+ index scores in patients undergoing gynecologic surgery to predict short-term undesirable outcomes.

Study Design: Retrospective study over a 2-year time period (2016-2018).

Methods: Coarsened exact matching was used to assess the predictive capacity of the LACE+ index among all gynecologic surgery cases over a 2-year period (2016-2018) at 1 health system (N = 12,225). Study subjects were matched on characteristics not assessed by LACE+, including race and duration of surgery. For comparison of outcomes, LACE+ score was divided into quartiles and otherwise matched populations were compared in reference to LACE+ quartile (Q): Q4 vs Q1, Q4 vs Q2, Q4 vs Q3.

Results: A total of 1715 patients were matched for Q1 to Q4, 1951 patients were matched for Q2 to Q4, and 1822 patients were matched for Q3 to Q4. Escalating LACE+ score significantly predicted increased readmission, reoperation, and emergency department (ED) visits from 30 to 90 postoperative days as well as readmission, reoperation, and ED visits from 0 to 90 postoperative days.

Conclusions: The results of this study suggest that the LACE+ index is suitable as a prediction model for important patient outcomes in a gynecologic surgery population.

Am J Manag Care. 2020;26(7):303-309.

Takeaway Points

The present study assessed the ability of the LACE+ index to identify patients at risk of adverse outcomes after gynecologic surgery.

  • Patients with highest quartile LACE+ scores were nearly 3 times more likely to be readmitted than patients with the lowest LACE+ scores. Outcomes were similar with respect to rates of reoperation and emergency department visits.
  • The LACE+ index has shown utility in identifying patients at increased risk of suboptimal postoperative outcomes.
  • Future studies will target ways to pursue targeted interventions designed to improve postdischarge outcomes.

The optimization of surgical care and disposition planning continues to be relevant for surgeons and patients. Readmissions to the hospital are a major source of patient dissatisfaction1 and a leading cause of health care expenditures, with readmission costs accounting for more than $12 billion per year.2,3 This issue is particularly concerning for surgical patients, with a prior investigation showing that 27% of Medicare and Medicaid patients were readmitted within 90 postoperative days.2 Hospital readmissions are utilized by CMS as a marker of quality of care.3 As hospitals are incentivized to reduce readmissions, optimized patient transitions of care have become an essential component of effective care delivery. A potential opportunity to improve upon surgical care and discharge planning is through appropriately engaging resources to support high-risk patients, hence decreasing preventable hospital readmissions.

Due to a moderate rate of hospital readmissions and postoperative complications, gynecologic surgery is of particular interest for evaluating methods for improving surgical care and discharge planning. Readmissions in this population predominately result from wound infection and peritoneal adhesions leading to bowel obstruction, infertility, and pain.4-6 Prior work in the literature reports that following gynecologic surgery, readmission rates range from 1.4% to 34.2%5-9 and postoperative complication rates range from 8% to 21%.7,10,11 Independent risk factors for readmission identified by previous studies are severity of preoperative medical comorbidities, prolonged operative time, and postoperative complications.9 However, there is currently no validated tool to predict a patient’s risk of 90-day readmission at the time of discharge for the gynecologic surgery population.

The LACE (Length of stay [LOS], Acuity of admission, Charlson Comorbidity Index [CCI] score, and Emergency department [ED] visits in the past 6 months) index was developed to predict readmissions among hospital discharges/medical patients.12 The LACE index has been assessed in various surgical populations and disease-specific medical populations, such as congestive heart failure and chronic obstructive pulmonary disease, with mixed results to date. In an attempt to expand upon the LACE index, Van Walraven et al assessed a LACE+ index (Figure 1) that captures additional variables such as age, sex, and other covariates to expand the accuracy of the model.13 None of the prior reports in the literature evaluated readmission patterns in a gynecologic surgical population. In an attempt to minimize confounding factors, the study herein utilizes coarsened exact matching to assess the predictive capacity of LACE+ for gynecologic surgery patients. Coarsened exact matching, per its creator, is “a monotonic imbalance bounding matching method which balances between the treated and control groups by the user ex ante rather than discovered through the usual laborious process of checking after the fact and repeatedly reestimating”14 (Figure 2 [A]). The objective of this study was to determine, in a preliminary analysis, if the LACE+ index has the potential of being leveraged for postoperative management of a population of patients undergoing an array of gynecologic surgeries.


Sample Selection

In this University of Pennsylvania institutional review board (IRB)—approved study (approval number 831722), patients undergoing gynecologic surgical intervention at a multihospital, 1659-bed university health system were enrolled retrospectively over 2 years (January 1, 2016, to January 1, 2018). A waiver of informed consent was granted by the IRB because this study was considered to be of minimal risk to patients. A total of 12,225 gynecologic procedures were studied (Figure 2 [B]). Key data were acquired using the EpiLog tool, a nonproprietary data acquisition system built and layered on top of the existing electronic health record architecture to enhance charting, workflow, quality improvement efforts, and cost reduction (by the senior author [N.R.M.] of this article and his department).15-21

Data Collection

Relevant patient information including sex, admission type, LOS, CCI score, alternative level of care (ALC) status, recent ED visits, surgical history, and history of hospital admissions was scored and incorporated into a composite LACE+ index value (—2 to 90).

Patient characteristics not used for LACE+ index score calculation served as matching criteria. Included were operative duration, surgery type (elective, emergent, urgent), surgical cost, median household income, insurance type (private, government, workers’ compensation), wound class (clean or contaminated), admission type (routine, emergency, transfer) and patient class (inpatient, outpatient), as well as median household income22 and cost of surgical supplies and implants (Table 1 [part A and part B]). Outcomes for patients from each LACE+ quartile were then analyzed and compared with matched patients in the fourth quartile (ie, those patients with the highest LACE+ scores). Other characteristics were recorded, such as body mass index (BMI), history of prior lifetime surgery, and surgery within 90 days before the operation. Lastly, mortality during the 90-day follow-up period, as well as occurrence of 90-day readmission, ED visit, and reoperation, were recorded.

Statistical Analysis

Matches were sought between patients with different LACE+ score quartiles but otherwise identical characteristics (matching criteria) (eAppendix Figure [eAppendix available at]). During this process, patients in the highest LACE+ score quartile (Q4) were matched to patients in lower LACE+ score quartiles (Q3, Q2, Q1). Unmatched patients from each LACE+ quartile were removed from the data set and not included in further analysis. The result was a series of matched groups (Q4-Q1, Q4-Q2, Q4-Q3). Groups in these pairs differed only with respect to LACE+ score and had otherwise identical patient composition.

McNemar’s test was used to assess the ability of the LACE+ index and subsequent single variables to accurately predict the outcome measures by comparing the means between groups created via coarsened exact matching. Univariate analysis was performed with significance set at P <.05. Data were extracted from EpiLog and pushed into defined spreadsheets. Binning of the matching variables and removal of missing values were performed using SAS version 9.4 (SAS Institute Inc). Matching was completed using the MatchIt programming package23 in R Statistics (R Core Team), with subsequent analysis executed through SAS version 9.4.


Patient Characteristics

The sample included all consecutive patients undergoing gynecologic surgery over 2 calendar years (N&thinsp;=&thinsp;12,225) (Table 1). Median BMI was 29.65. Median LOS was 31 hours, and median operative duration was 48 minutes for all patients. Median total cost of surgical supplies and implants was $115.51. Median household income for patients was $65,123 for the overall cohort.

Patient Outcomes

Q1 to Q4 comparison. For the Q1 to Q4 comparison, 1715 patients were matched (n&thinsp;=&thinsp;3430; a 66.3% match rate) (Table 2). Among matched patients, escalating LACE+ score significantly predicted increased readmission from 30 to 90 postoperative days (0.41% vs 1.05%; odds ratio [OR], 2.57; 95% CI, 1.07-6.16; P&thinsp;=&thinsp;.028) and ED visits from 30 to 90 days (2.57% vs 6.18%; OR, 2.55; 95% CI, 1.77-3.68; P&thinsp;<&thinsp;.001) (Figure 3 [A]). Increasing LACE+ score predicted higher rates of readmission, reoperation, and ED visits from 0 to 90 postoperative days (readmission: 2.27% vs 4.26%; OR, 1.94; 95% CI, 1.30-2.91; P&thinsp;=&thinsp;.001; reoperation: 0.58% vs 1.58%; OR, 2.70; 95% CI, 1.31-5.58; P&thinsp;=&thinsp;.005; ED visits: 7.58% vs 13.88%; OR, 1.93; 95% CI, 1.54-2.42; P&thinsp;<&thinsp;.001) (Figure 3 [B]). Escalating LACE+ score did not predict higher rates of reoperation from 30 to 90 postoperative days (OR, 2.00; 95% CI, 0.54-9.08; P&thinsp;=&thinsp;.388).

Q2 to Q4 comparison. For the Q2 to Q4 comparison, 1951 patients were matched (n&thinsp;=&thinsp;3902; a 63.3% match rate) (Table 2). Among matched patients, escalating LACE+ score significantly predicted increased readmission from 30 to 90 postoperative days (0.41% vs 1.64%; OR, 4.00; 95% CI, 1.84-8.68; P&thinsp;<&thinsp;.001), reoperation from 30 to 90 days (0.21% vs 0.87%; OR, 4.25; 95% CI, 1.43-12.63; P&thinsp;=&thinsp;.008), and ED visits from 30 to 90 days (2.31% vs 6.15%; OR, 2.79; 95% CI, 1.96-3.96; P&thinsp;<&thinsp;.001) (Figure 3 [A]). Increasing LACE+ score significantly predicted higher rates of readmission, reoperation, and ED visits from 0 to 90 postoperative days (readmission: 2.26% vs 5.33%; OR, 2.46; 95% CI, 1.71-3.54; P&thinsp;<&thinsp;.001; reoperation: 0.72% vs 1.64%; OR, 2.29; 95% CI, 1.22-4.28; P&thinsp;=&thinsp;.008; ED visits: 7.74% vs 14.20%; OR, 2.02; 95% CI, 1.63-2.51; P&thinsp;<&thinsp;.001) (Figure 3 [B]).

Q3 to Q4 comparison. For the Q3 to Q4 comparison, 1822 patients were matched (n&thinsp;=&thinsp;3644; a 59.0% match rate) (Table 2). Among matched patients, escalating LACE+ score significantly predicted increased ED visits from 30 to 90 postoperative days (2.25% vs 5.88%; OR, 2.69; 95% CI, 1.86-3.89; P&thinsp;<&thinsp;.001) (Figure 3 [A]). Increasing LACE+ score significantly predicted higher rates of readmission from 0 to 90 postoperative days (4.17% vs 5.98%; OR, 1.48; 95% CI, 1.09-2.01; P&thinsp;=&thinsp;.012) and ED visits from 0 to 90 postoperative days (5.65% vs 14.38%; OR, 2.92; 95% CI, 2.27-3.74; P&thinsp;<&thinsp;.001) (Figure 3 [B]). Escalating LACE+ score did not predict higher rates of readmission or reoperation from 30 to 90 postoperative days (readmission: OR, 1.54; 95% CI, 0.94-2.52; P&thinsp;=&thinsp;.085; reoperation: OR, 1.73; 95% CI, 0.92-3.27; P&thinsp;=&thinsp;.086). In addition, LACE+ score did not predict a higher rate of reoperation within 90 postoperative days (OR, 1.26; 95% CI, 0.79-2.02; P&thinsp;=&thinsp;.339).


Escalating LACE+ score, among otherwise matched patients, appears to effectively predict numerous undesirable outcomes for patients undergoing gynecologic surgery interventions. The primary objective of this study was to evaluate the predictive ability of the LACE+ index on a number of patient outcomes in a gynecologic surgery population. Through comparison of the LACE+ index between otherwise matched Q1 and Q4 patients, the results of this study demonstrate that increasing LACE+ score predicts higher rates of hospital readmission, need for reoperation, and visits to the ED at 0 to 90 postoperative days. These data additionally suggest a significant difference in readmission and ED visit rates from 30 to 90 postoperative days. Escalating LACE+ index score led to a 1.9-fold increased risk of 0-to-90-day readmission in the Q1 vs Q4 comparison, a 2.5-fold increase in the Q2 vs Q4 comparison, and a 1.5-fold increase in the Q3 vs Q4 comparison. These results build upon those of prior studies that report on the predictive capability of the LACE index for readmissions.

Integrating a prediction model into the electronic health record, with the ability to assist in postsurgical care planning, is essential for reducing readmissions. Although the LACE and LACE+ indices provide the ability to curtail the abundance of patient data into a simple value, they have fallen short in numerous studies.24-26 The results of the study herein demonstrate that the LACE+ index may be suitable as a prediction model for readmission in a gynecologic surgery population. Through coarsened exact matching, these results offer increased assurance that successful application of the LACE+ index is not a result of variables outside the scope of the LACE+ covariates. In the present study, well-matched patients, when compared by LACE+ quartile, had multiple outcomes that were significant both clinically and statistically.

Postoperative peritoneal adhesions are the primary concern in gynecologic surgery patients and the leading cause of hospital readmission in this population.4 The Surgical and Clinical Adhesions Research Group found, in a number of studies, that patients undergoing ovarian surgery had a 7.5% readmission rate due to peritoneal adhesions.4,6 Additionally, these investigations found that roughly one-third of patients who underwent open pelvic surgery were readmitted over the subsequent 10 years, with greater than 20% of readmissions occurring during the first year after surgery.4-6 Further, there is evidence that preoperative factors have an impact on readmission rates following gynecologic procedures. A 2017 study by Cory et al found a 2.8-fold increased risk for readmission for patients receiving surgical treatment for oncologic conditions compared with those treated for benign conditions.9 Adhesion-related health care costs also carry a tremendous financial burden, totaling in excess of $1 billion annually.4,27 As evidenced by the CMS homing in on readmissions as a target for reducing health care cost, it is clear that reducing readmission rates is in the interest of the health care system, the hospital, and the patient. The current literature on patients receiving gynecologic surgery establishes that preoperative factors and postoperative complications play a significant role in hospital readmissions28; thus, these factors need to be considered in attempts at discharge planning.

The results of this large and coarsened exact matched gynecologic surgery cohort study show that the LACE+ index can be used as an effective predictor of short-term patient outcomes. This suggests that the LACE+ index has potential utility to assist in appropriately engaging resources toward programs focused on reducing readmissions, such as shorter intervals between follow-up appointments or in-home assistance, but this necessitates further investigation. For example, rather than sending in-home nursing care to Q1 and Q4 patients for an equivalent amount of time, nursing care could be sent more frequently to the Q4 cohort, shown to be at high risk of readmission. The ability to reduce Q4 readmissions without increasing Q1 readmissions represents a powerful example of low-cost health care improvement, with minimal workflow interruption, through applying predictive measures.

This study was designed to elucidate the capacity of the LACE+ index to discern differences between groups. The LACE+ index is a risk score; therefore, patients who have similar risk profiles (Q3 and Q4) are less likely to have significant differences in outcomes. However, this study provides valuable data regarding population management for the highest-risk, Q4 patients. Expanded, prospective studies to assess outcome differences between each of the quartiles are a target of future investigation, which the authors plan to pursue. The study herein was able to differentiate significant outcomes between quartiles of LACE+ index score, which suggests that effective prediction and application for optimal resource engagement is possible using this tool.


Potential inaccuracies in data recording may have influenced the analysis and contributed to inherent selection bias. However, one benefit of coarsened exact matching is that these hypothetical inaccuracies would be equally distributed among comparison groups. The study design is also reliant on the index university hospital system as the recipient institution for the readmissions to be recorded in the electronic health record. This means that the true readmission rate could potentially be underreported, creating challenges for the LACE+ index to accurately fit the sample. In addition, there is a slight limit in generalizability because this sample was selected from 1 institution.

Further, although patients were matched on many key criteria, it is possible that in the population being studied, additional criteria might have resulted in more perfect matches. The matching criteria were selected based on supporting literature correlating these factors with outcomes. Prior investigations on length of surgery have shown that an increase in surgical time leads to increased risk of complication and predicts length of surgical intensive care unit stay.29,30 History of previous surgery, evaluated in abdominal surgery31 and cervical spine surgery,32 was found to lower surgical success rate and postoperative improvement. Connolly et al found that uninsured and Medicaid patients incurred increased odds of postsurgical inpatient mortality.33 This result was similarly found in patients with low income status following lung cancer resection.34 Numerous studies have reported minority race,35,36 BMI (particularly obesity),37-39 and case type40,41 as being independently associated with increased postoperative morbidity.

Future studies should expand the use of the LACE+ index in a larger, perhaps multicenter, gynecologic population that may prove robust enough for the accuracy of the LACE+ index to be definitively assessed. Additionally, a prospective trial using the LACE+ index to inform resource allocation could offer further conclusive results.


The results of the study herein are novel, as this is the first report in the literature of studying the LACE+ index in gynecologic surgery. The utility of a predictive tool, such as the LACE+ index, to forecast readmissions has the potential to generate reductions in avoidable readmissions that cause substantial economic strain and inhibit patient recovery. Our results show that the LACE+ index, when evaluated in a coarsened exact matched cohort, is a valuable predictive model when applied to the gynecologic surgery population. Although there appears to be evidence supporting the use of the LACE+ index as it stands in this population, future research should aim to expand the population analyzed by LACE+ and study its utility in a prospective manner.


The authors want to acknowledge The EpiLog Project and The Bernadette and Kevin McKenna Family Research Fund for their continued support.Author Affiliations: Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania (GG, EW, IFC, NRM), Philadelphia, PA; McKenna EpiLog Fellowship in Population Health, University of Pennsylvania (SG, NRM), Philadelphia, PA; West Chester Statistical Institute and Department of Mathematics, West Chester University (SG, SDM), West Chester, PA; Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania (SKS), Philadelphia, PA.

Source of Funding: The Bernadette and Kevin McKenna Family Research Fund.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (GG, NRM); acquisition of data (GG, NRM); analysis and interpretation of data (GG, EW, IFC, SG, SDM, NRM); drafting of the manuscript (GG, EW, IFC, NRM); critical revision of the manuscript for important intellectual content (GG, EW, IFC, SG, SDM, SKS, NRM); statistical analysis (SG, SDM); provision of patients or study materials (SKS, NRM); administrative, technical, or logistic support (GG, NRM); and supervision (SKS, NRM).

Address Correspondence to: Neil R. Malhotra, MD, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Silverstein Pavilion, 3400 Spruce St, 3rd Floor, Philadelphia, PA 19104. Email:

1. Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17(1):41-48.

2. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428. doi:10.1056/NEJMsa0803563

3. Axon RN, Williams MV. Hospital readmission as an accountability measure. JAMA. 2011;305(5):504-505. doi:10.1001/jama.2011.72

4. Practice Committee of the American Society for Reproductive Medicine; Society of Reproductive Surgeons. Pathogenesis, consequences, and control of peritoneal adhesions in gynecologic surgery. Fertil Steril. 2007;88(1):21-26. doi:10.1016/j.fertnstert.2007.04.066

5. Ellis H, Moran BJ, Thompson JN, et al. Adhesion-related hospital readmissions after abdominal and pelvic surgery: a retrospective cohort study. Lancet. 1999;353(9163):1476-1480. doi:10.1016/S0140-6736(98)09337-4

6. Lower AM, Hawthorn RJ, Ellis H,O’Brien F, Buchan S, Crowe AM. The impact of adhesions on hospital readmissions over ten years after 8849 open gynaecological operations: an assessment from the Surgical and Clinical Adhesions Research Study. BJOG. 2000;107(7):855-862. doi:10.1111/j.1471-0528.2000.tb11083.x

7. Walker JL, Piedmonte MR, Spirtos NM, et al. Laparoscopy compared with laparotomy for comprehensive surgical staging of uterine cancer: Gynecologic Oncology Group Study LAP2. J Clin Oncol. 2009;27(32):5331-5336. doi:10.1200/JCO.2009.22.3248

8. Rivard C, Casserly K, Anderson M, Isaksson Vogel R, Teoh D. Factors influencing same-day hospital discharge and risk factors for readmission after robotic surgery in the gynecologic oncology patient population. J Minim Invasive Gynecol. 2015;22(2):219-226. doi:10.1016/j.jmig.2014.10.001

9. Cory L, Latif N, Brensinger C, et al. Readmission after gynecologic surgery: a comparison of procedures for benign and malignant indications. Obstet Gynecol. 2017;130(2):285-295. doi:10.1097/AOG.0000000000002141

10. Kamat AA, Brancazio L, Gibson M. Wound infection in gynecologic surgery. Infect Dis Obstet Gynecol. 2000;8(5-6):230-234. doi:10.1155/S1064744900000338

11. Morris MS, Deierhoi RJ, Richman JS, Altom LK, Hawn MT. The relationship between timing of surgical complications and hospital readmission. JAMA Surg. 2014;149(4):348-354. doi:10.1001/jamasurg.2013.4064

12. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551-557. doi:10.1503/cmaj.091117

13. van Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med. 2012;6(3):e80-e90.

14. Iacus SM, King G, Porro G. Causal inference without balance checking: coarsened exact matching. Polit Anal. 2012;20(1):1-24. doi:10.1093/pan/mpr013

15. Gawande A. Why doctors hate their computers. The New Yorker. November 5, 2018. Accessed January 30, 2019.

16. Agarwal P, Ramayya AG, Osiemo B, et al. Association of overlapping neurosurgery with patient outcomes at a large academic medical center. Neurosurgery. 2019;85(6):E1050-E1058. doi: 10.1093/neuros/nyz243.

17. Glauser G, Agarwal P, Ramayya AG, et al. Association of surgical overlap during wound closure with patient outcomes among neurological surgery patients at a large academic medical center. Neurosurgery. 2019;85(5):E882-E888. doi:10.1093/neuros/nyz142

18. Berger I, Piazza M, Sharma N, et al. Evaluation of the Risk Assessment and Prediction Tool for postoperative disposition needs after cervical spine surgery. Neurosurgery. 2019;85(5):E902-E909. doi:10.1093/neuros/nyz161

19. Caplan IF, Sinha S, Schuster JM, et al. The utility of cervical spine bracing as a postoperative adjunct to single-level anterior cervical spine surgery. Asian J Neurosurg. 2019;14(2):461-466. doi:10.4103/ajns.AJNS_236_18

20. Piazza M, Sharma N, Osiemo B, et al. Initial assessment of the Risk Assessment and Prediction Tool in a heterogeneous neurosurgical patient population. Neurosurgery. 2019;85(1):50-57. doi:10.1093/neuros/nyy197

21. Malhotra NR, Piazza M, Demoor R, et al. Impact of reduced preincision antibiotic infusion time on surgical site infection rates: a retrospective cohort study. Ann Surg. 2020;271(4):774-780. doi:10.1097/SLA.0000000000003030

22. Median income in the past 12 months (in 2016 inflation-adjusted dollars). United States Census Bureau. Accessed January 12, 2019.,%20Families,%20Individuals%29&vintage=2018&hidePreview=false

23. Ho DE, Imai K, King G, Stuart EA. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw. 2011;42(8):1-28. doi:10.18637/jss.v042.i08

24. Robinson R, Hudali T. The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital. PeerJ. 2017;5:e3137. doi:10.7717/peerj.3137

25. Wang H, Robinson RD, Johnson C, et al. Using the LACE index to predict hospital readmissions in congestive heart failure patients. BMC Cardiovasc Disord. 2014;14:97. doi:10.1186/1471-2261-14-97

26. Teh R, Janus E. Identifying and targeting patients with predicted 30-day hospital readmissions using the revised LACE index score and early postdischarge intervention. Int J Evid Based Healthc. 2018;16(3):174-181. doi:10.1097/XEB.0000000000000142

27. Ray NF, Denton WG, Thamer M, Henderson SC, Perry S. Abdominal adhesiolysis: inpatient care and expenditures in the United States in 1994. J Am Coll Surg. 1998;186(1):1-9. doi:10.1016/s1072-7515(97)00127-0

28. Giannini A, Di Donato V, Schiavi MC, May J, Panici PB, Congiu MA. Predictors of postoperative overall and severe complications after surgical treatment for endometrial cancer: the role of the fragility index. Int J Gynaecol Obstet. 2020;148(2):174-180. doi:10.1002/ijgo.13020

29. Kim JYS, Khavanin N, Rambachan A, et al. Surgical duration and risk of venous thromboembolism. JAMA Surg. 2015;150(2):110-117. doi:10.1001/jamasurg.2014.1841

30. Chu D, Bakaeen FG, Wang XL, LeMaire SA, Coselli JS, Huh J. Does the duration of surgery affect outcomes in patients undergoing coronary artery bypass grafting? Am J Surg. 2008;196(5):652-656. doi:10.1016/j.amjsurg.2008.07.003

31. Goneidy A, Ong EGP. Effect of previous abdominal surgery and gallbladder appearance on biliary atresia outcomes. J Pediatr Surg. 2018;53(2):274-276. doi:10.1016/j.jpedsurg.2017.11.018

32. Radcliff K, Jalai C, Vira S, et al. Two-year results of the prospective spine treatment outcomes study: analysis of postoperative clinical outcomes between patients with and without a history of previous cervical spine surgery. World Neurosurg. 2018;109:e144-e149. doi:10.1016/j.wneu.2017.09.122

33. Connolly TM, White RS, Sastow DL, Gaber-Baylis LK, Turnbull ZA, Rong LQ. The disparities of coronary artery bypass grafting surgery outcomes by insurance status: a retrospective cohort study, 2007-2014. World J Surg. 2018;42(10):3240-3249. doi:10.1007/s00268-018-4631-9

34. LaPar DJ, Bhamidipati CM, Harris DA, et al. Gender, race, and socioeconomic status affects outcomes after lung cancer resections in the United States. Ann Thorac Surg. 2011;92(2):434-439. doi:10.1016/j.athoracsur.2011.04.048

35. Stone JS, Fukuoka H, Weinreb RN, Afshari NA. Relationship between race, insurance coverage, and visual acuity at the time of cataract surgery. Eye Contact Lens. 2018;44(6):393-398. doi:10.1097/ICL.0000000000000443

36. Causey MW, McVay D, Hatch Q, et al. The impact of race on outcomes following emergency surgery: an American College of Surgeons National Surgical Quality Improvement Program assessment. Am J Surg. 2013;206(2):172-179. doi:10.1016/j.amjsurg.2012.11.022

37. Smits A, Lopes A, Das N, et al. Surgical morbidity and clinical outcomes in ovarian cancer — the role of obesity. BJOG. 2016;123(2):300-308. doi:10.1111/1471-0528.13585

38. Causey MW, Johnson EK, Miller S, Martin M, Maykel J, Steele SR. The impact of obesity on outcomes following major surgery for Crohn’s disease: an American College of Surgeons National Surgical Quality Improvement Program assessment. Dis Colon Rectum. 2011;54(12):1488-1495. doi:10.1097/DCR.0b013e3182342ccb

39. Wilson JR, Tetreault LA, Schroeder G, et al. Impact of elevated body mass index and obesity on long-term surgical outcomes for patients with degenerative cervical myelopathy: analysis of a combined prospective dataset. Spine (Phila Pa 1976). 2017;42(3):195-201. doi:10.1097/BRS.0000000000001859

40. Solsky I, Friedmann P, Muscarella P, In H. Poor outcomes of gastric cancer surgery after admission through the emergency department. Ann Surg Oncol. 2017;24(5):1180-1187. doi:10.1245/s10434-016-5696-z

41. Akbarian-Tefaghi H, Kalakoti P, Sun H, et al. Impact of hospital caseload and elective admission on outcomes after extracranial-intracranial bypass surgery. World Neurosurg. 2017;108:716-728. doi:10.1016/j.wneu.2017.09.082

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