Early Hospital Readmission and Mortality Risk after Surgical Treatment of Proximal Humerus Fractures in a Community-Based Health Care Organization
Edward Yian, MD; Hui Zhou, MS, PhD; Ariyon Schreiber; Jeff Sodl, MD;
Ron Navarro, MD; Anshuman Singh, MD; Nikita Bezrukov, MD
Perm J 2016 Winter;20(1):47-52
https://doi.org/10.7812/TPP/15-065
Abstract
Context: Surgical treatment for proximal humerus fractures has increased exponentially. Recent health care policies incentivize centers to reduce hospital readmission rates. Better understanding of risk factors for readmission and early mortality in this population will assist in identifying favorable risk-benefit patient profiles.
Objective: To identify incidence and risk factors of 30-day hospital readmission rate and 1-year mortality rate after open surgery of proximal humerus fractures.
Design: Retrospective cohort analysis from Kaiser Permanente Southern California Region database.
Methods: Using International Classification of Diseases, Ninth Revision, diagnosis and procedure codes, all operative proximal humerus fractures were validated. Hospital readmission, one-year mortality, and demographic and medical data were collected. A logistic regression test was performed to assess potential risk factors for outcomes.
Results: From 1387 surgical patients, the 30-day all-cause readmission rate was 5.6%. Forty percent of hospital readmissions were due to surgery-related reasons. Severe liver disease (odds ratio [OR], 3.48, 95% confidence interval [CI] = 1.42-8.55) and LACE (length of stay, acuity of admission, comorbidities, and number of Emergency Department visits in the previous 6 months) index score ≥ 10 (OR, 4.47, 95% CI = 2.54-7.86) were independent risk factors of readmission on multivariate analysis. The 1-year mortality rate was 4.86%. Multivariate analysis showed length of hospital stay (OR 1.11, 95% CI = 1.05-1.19), cancer (OR 3.38, 95% CI = 1.61-7.10), 30-day readmission (OR 3.31, 95% CI = 1.34-8.21), and Charlson comorbidity index greater than or equal to 4 (OR 13.94, 95% CI = 4.40-44.17) predicted higher mortality risk.
Conclusion: After open treatment of proximal humerus fractures, there was a 5.6% all-cause 30-day hospital readmission rate. Surgical complications accounted for 40% of readmissions. Severe liver disease and LACE score correlated best with postoperative 30-day readmission risk. Length of hospital stay, preexisting cancer, 30-day readmission, and Charlson comorbidity index were predictive of 1-year mortality.
Introduction
More than 19% of Medicare patients who have been hospitalized were readmitted within 30 days, costing Medicare more than $17 billion.1 More than 2600 hospitals in 2015 will have reimbursements from Medicare reduced because of the Hospital Readmissions Reductions Program.2 Medicare has begun evaluating readmissions for elective knee or hip replacements and has fined hospitals if they had higher than expected complication and readmission rates.3 Expansion of these quantitative measures to other surgeries may be implemented in the future. To prepare for future health care policies and to improve surgical patient selection, it is important to determine current morbidity, mortality, and readmission rates for other procedures.4-7 Because surgical treatment of proximal humerus fractures has grown considerably, there is a need to investigate what procedure-specific variables are associated with this metric.8-11
The objective of this study was to identify the incidence and risk factors of 30-day hospital readmission rate and 1-year mortality rate after open surgery of proximal humerus fractures.
Methods
After institutional review board approval, a retrospective cohort of all patients undergoing surgical treatment of proximal humerus fractures in 2005 to 2013 at a statewide integrated community health care organization, Kaiser Permanente Southern California, was analyzed to investigate the 30-day hospital readmission rate. Patients aged 18 years and older who underwent surgery were identified using the health care organization’s electronic medical records database and confirmed by medical chart review.
None of the hospitals studied were of a level I trauma center designation. Specific surgical procedures were captured using International Classification of Diseases, Ninth Revision, codes (79.11, 79.31, 78.52) and Current Procedural Terminology codes (23615, 23640, 23670, 23680). Inclusion criteria included patients who underwent surgery for open reduction and internal fixation of the surgical neck, anatomic neck, humeral head, and greater tuberosity and/or lesser tuberosity of the proximal humerus. Only patients who kept their insurance with the health care organization at least 30 days after the surgical procedure were included in the study. Exclusion criteria included humerus fractures located at, or distal to, the diaphysis of the proximal humerus. Medical charts were validated by the study’s authors to confirm anatomic location of the fracture and surgical procedure. A subpopulation of these patients (1068 patients) was analyzed to assess the 1-year mortality after surgery. Mortality was determined by using the California State Death Master Files. The Charlson comorbidity index (CCI) and LACE (length of stay, acuity of admission, comorbidities, and number of Emergency Department visits in the previous 6 months) index scores were calculated on the basis of these data.12,13 For LACE scores, we compared patients with an index less than 10 with those with an index ≥ 10 because prior studies have identified this as a useful criterion to differentiate high-risk patients.14
Frequencies/proportions or mean (standard deviation [SD]) were used to describe the characteristics of study population. A logistic regression test was performed to assess the potential risk factors of two outcomes. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported. Ordinary least squares regression analysis was used to determine the amount of variance answered by the CCI and LACE score for readmission and mortality. Data were analyzed using SAS Enterprise Guide, version 5.1 (SAS Institute, Cary, NC).
Results
From 14 hospital centers, 1387 surgical patients met inclusion criteria. The 30-day all-cause readmission rate was 5.6% (78 among 1387 patients; Table 1). In univariate analysis, readmitted patients were older, were hospitalized longer, had diabetes, had severe liver disease (cirrhosis), and had higher CCI and LACE scores (Table 2). CCI and LACE score predicted 1.60% and 5.20% of variance,
respectively, for 30-day hospital readmission risk. In the multivariate analysis, only severe liver disease (OR 3.48; 95% CI = 1.42-8.55) and LACE ≥ 10 (OR 4.47; CI = 2.54-7.86) were significant independent risk factors of readmission after adjustment for age, sex, length of hospital stay, and diabetes (Table 3).
Thirty-one of 78 hospital readmissions (40%) were due to surgery-related reasons (Figure 1). The reasons for readmission were primarily medically related and included pulmonary- and abdominal-related comorbidities, as well as chest pain diagnosis with management.
The 1-year mortality rate was 4.86% (52 of 1068 patients). CCI predicted 11.50% and LACE predicted 8.30% of the variance for 1-year mortality risk. Univariate logistic analysis showed age, sex, diabetes, severe renal disease, sepsis, cancer, 30-day readmission, LACE, and CCI correlated with risk of mortality within 1 year (Table 4). Multivariate logistic analysis showed length of hospital stay (OR 1.11; 95% CI = 1.05-1.19), cancer (OR 3.38; CI = 1.61-7.10), CCI ≥ 4 (OR 13.94; CI = 4.40-44.17), and 30-day readmission (OR 3.31; CI = 1.34-8.21) predicted a higher mortality risk within 1 year (Table 5).
Discussion
The 30-day hospital readmission penalties after surgical procedures are in development. Hospital readmission rates have been reported between 4% to 15% after elective orthopedic surgical procedures.15-17 Most studies focus on lower extremity joint arthroplasty patient populations from large administrative databases, or nationwide samples with significant variability in medical care organizational resources, patient follow-up, and insurance status.17-20 Past studies suggest that readmission rates in the trauma patient population after surgical procedures may differ.21,22 This study’s patient cohort consisted of a proximal humerus fracture population that received surgical care within a community health care organization network. Multiple integrated care delivery resources were implemented, including an integrated electronic medical records system, coordinated home health care resources, and multidisciplinary care management.
By providing quality-based incentives, current health care policy favors hospitals that can redistribute and increase health care resources to susceptible patient populations. Various predictive models have been proposed to estimate readmission and mortality risk. Most have been applied to broad populations with varying surgical indications. Although some have shown correlation, they may account for only a small percentage of the variation in readmission or mortality.23,24 The CCI and LACE score have both shown some value in predicting readmission probability, but they have not been shown to be as predictive in other specific patient populations, such as patients with congestive heart failure.23-26 It is important to determine whether specific patient cohorts, such as postoperative shoulder fracture patients, provide consistent correlations for these predictive models.
Zhang et al8 studied a large administrative multistate database and found a 30-day readmission rate of 8% in a population of proximal humerus fracture patients treated with both surgical fixation methods and arthroplasty. They found that 75% of readmissions were because of medical reasons, although surgical fixation procedures (compared with arthroplasty) led to a higher rate (29%) of surgically related readmissions. Although they did not find any added risk with older age, they noted that female sex, race, discharge home with home health services, and discharge to a nursing facility all carried higher future readmission risk. On the other hand, Hageman et al23 found a significant association between 30-day readmission and older age in their postoperative general orthopedic skeletal trauma patient cohort. In our study, we found a 30-day readmission rate of 5.6%, with 60% of readmissions for nonsurgical-related (medical) reasons. We did not find a correlation of age or sex with readmission. It is interesting to note that despite the use of improved technology for fracture fixation (ie, locking plates), the most common reasons for surgical procedure failure and subsequent readmission for revision surgery were fixation failure and infection.
Our study supports the notion that patient comorbidities contribute to higher readmission risk. Although readmission metrics may offer hospitals financial incentives, they must also provide improvement in health care quality to be pertinent to the patient. This study identifies hospital readmission as a risk factor for early 1-year mortality after shoulder fracture surgery, highlighting the importance of reducing readmission risk. Risk stratification for future shared preoperative patient counseling may assist the decision for surgical fracture treatment as well as medical optimization of comorbidities before surgery. Interestingly, we did not find any significant correlation of specific medical conditions such as diabetes status, hemoglobin A1c levels, or severe renal disease with early one-year mortality or hospital readmission. However, we found LACE, CCI score, and severe liver disease to be correlative with 30-day hospital readmission risk. In our analysis, we noted that the LACE score predicted a higher proportion of the variability for 30-day hospital readmission, compared with the CCI. However, similar to the findings of Hageman et al,23 most variability could not be accounted for by the factors studied, which highlights the difficulty in developing an accurate predictive model for this outcome.
Mahoney et al27 analyzed 680 shoulder arthroplasties at a single academic medical center and found a 30-day readmission rate of 3.4%. Their surgical indications combined elective and traumatic shoulder diagnoses, which may produce differing readmission rates if analyzed separately. They reported a 1.0% incidence of "never events," including inhospital falls, postoperative infections, and catheter-associated urinary tract infections, which eliminate reimbursement payments for avoidable complications. Our study found a similar 0.9% "never event" incidence, although our cohort strictly encompassed those with proximal humerus fractures.
Recent studies suggest that early mortality is increased after surgery for proximal humerus fractures. Petrigliano et al11 studied a large statewide database of hospital discharges from 1994 to 2005, after surgical fixation of proximal humeral fractures. They noted that older patients with comorbidities were at increased risk for perioperative complications. CCI (OR, 1.5), age older than 75 years (10.2), and male sex (1.7) carried increased risk of early 90-day mortality. Neuhaus et al28 reported from a nationwide database of proximal humerus fractures and found a 2.3% inhospital mortality rate. They found that sex, age, associated femur fracture or head trauma, and operative fracture care influenced the risk of inpatient adverse events and death. Johnell et al29 studied 237 patients and found that mortality immediately after shoulder fracture in the 60- to 80-year-old population was significantly higher compared with the general population and decreased after the first year. They found mortality was not influenced by sex when adjusted for sex-specific population risks. In our study, cancer history, CCI, LACE score, as well as 30-day hospital readmission all carried a greater degree of increased risk of 1-year mortality. Unlike their study, we did not find a significant correlation with age or sex. Morin et al30 found 1-year mortality rates ranging from 5.3% to 22.6% in a Canadian database of older patients with shoulder fracture surgery. Our lower mortality rate (4.86%) may be owing to other factors that were not studied, including social support network, integrative medical care structure, and socioeconomic differences. Contrary to readmission risk, the CCI provided a better point estimate to predict 1-year mortality compared with the LACE score.
Strengths of the study include that our study sample’s data were individually reviewed and validated to confirm data accuracy. In very large sample databases, validity and accuracy may be compromised because the data are not individually confirmed and input is based upon multiple discretions.15,18,31 However, our study did not control for surgeon experience or intraoperative parameters such as blood loss, which may influence postoperative patient recovery and fracture fixation outcomes.
Conclusion
After open treatment of proximal humerus fractures, there was a 5.6% all-cause hospital readmission rate. Surgical complications accounted for 40% of readmissions. LACE scores higher than 10 and liver disease correlated best to predict postoperative 30-day readmission. CCI higher than 4, cancer history, 30-day hospital readmission, and length of hospital stay were predictive of 1-year mortality.
Disclosure Statement
The author(s) have no conflicts of interest to disclose.
Acknowledgements
Mary Corrado, ELS, provided editorial assistance.
References
1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009 Apr 2;360(14):1418-28. DOI: https://doi.org/10.1056/NEJMsa0803563.
2. Rau J. A guide To Medicare’s readmissions penalties and data [Internet]. Menlo Park, CA: Kaiser Health News; 2014 Oct 2 [cited 2014 Oct 2]. Available from: http://kaiserhealthnews.org/news/a-guide-to-medicare-readmissions-penalties-and-data/.
3. Mednick RE, Alvi HM, Krishnan V, Lovecchio F, Manning DW. Factors affecting readmission rates following primary total hip arthroplasty. J Bone Joint Surg Am 2014 Jul 16;96(14):1201-9. DOI: https://doi.org/10.2106/JBJS.M.00556.
4. Waterman BR, Dunn JC, Bader J, Urrea L, Schoenfeld AJ, Belmont PJ Jr. Thirty-day morbidity and mortality after elective total shoulder arthroplasty: patient-based and surgical risk factors. J Shoulder Elbow Surg 2015 Jan;24(1):24-30. DOI: http:dx.doi.org/10.1016/j.jse.2014.05.016.
5. Griffin JW, Hadeed MM, Novicoff WM, Browne JA, Brockmeier SF. Patient age is a factor in early outcomes after shoulder arthroplasty. J Shoulder Elbow Surg 2014 Dec;23(12):1867-71. DOI: https://doi.org/10.1016/j.jse.2014.04.004.
6. Ricchetti ET, Abboud JA, Kuntz AF, Ramsey ML, Glaser DL, Williams GR Jr. Total shoulder arthroplasty in older patients: increased perioperative morbidity? Clin Orthop Relat Res 2011 Apr;469(4):1042-9. DOI: hhttps://doi.org/10.1007/s11999-010-1582-3.
7. Maeda JL, Lee KM, Horberg M. Comparative health systems research among Kaiser Permanente and other integrated delivery systems: a systematic literature review. Perm J 2014 Summer;18(3):66-77. DOI: https://doi.org/10.7812/TPP/13-159.
8. Zhang AL, Schairer WW, Feeley BT. Hospital readmissions after surgical treatment of proximal humerus fractures: is arthroplasty safer than open reduction internal fixation? Clin Orthop Relat Res 2014 Aug;472(8):2317-24. DOI: https://doi.org/10.1007/s11999-014-3613-y.
9. Khatib O, Onyekwelu I, Zuckerman JD. The incidence of proximal humeral fractures in New York State from 1990 through 2010 with an emphasis on operative management in patients aged 65 years or older. J Shoulder Elbow Surg 2014 Sep;23(9):1356-62. DOI: https://doi.org/10.1016/j.jse.2013.12.034.
10. Bell JE, Leung BC, Spratt KF, et al. Trends and variation in incidence, surgical treatment, and repeat surgery of proximal humeral fractures in the elderly.
J Bone Joint Surg Am 2011 Jan 19;93(2):121-31. DOI: https://doi.org/10.2106/JBJS.I.01505.
11. Petrigliano FA, Bezrukov N, Gamradt SC, SooHoo NF. Factors predicting complication and reoperation rates following surgical fixation of proximal humeral fractures. J Bone Joint Surg Am 2014 Sep 17;96(18):1544-51. DOI: https://doi.org/10.2106/JBJS.M.01039.
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 Apr 6;182(6):551-7. DOI: https://doi.org/10.1503/cmaj.091117.
13. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373-83. DOI: https://doi.org/10.1016/0021-9681(87)90171-8.
14. Gruneir A, Dhalla IA, van Walraven C, et al. Unplanned readmissions after
hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med 2011;5(2):e104-11.
15. Dailey EA, Cizik A, Kasten J, Chapman JR, Lee MJ. Risk factors for readmission of orthopaedic surgical patients. J Bone Joint Surg Am 2013 Jun 5;95(11):1012-9. DOI: https://doi.org/10.2106/JBJS.K.01569.
16. Khan MA, Hossain FS, Dashti Z, Muthukumar N. Causes and predictors of early re-admission after surgery for a fracture of the hip. J Bone Joint Surg Br 2012 May;94(5):690-7. DOI: https://doi.org/10.1302/0301-620X.94B5.28933.
17. Schairer WW, Vail TP, Bozic KJ. What are the rates and causes of hospital readmission after total knee arthroplasty? Clin Orthop Relat Res 2014 Jan;472(1):181-7. DOI: https://doi.org/10.1007/s11999-013-3030-7.
18. McCormack R, Michels R, Ramos N, Hutzler L, Slover JD, Bosco JA. Thirty-day readmission rates as a measure of quality: causes of readmission after orthopedic surgeries and accuracy of administrative data. J Healthc Manag 2013 Jan-Feb;58(1):64-76; discussion 76-7.
19. Belmont PJ Jr, Goodman GP, Waterman BR, Bader JO, Schoenfeld AJ. Thirty-day postoperative complications and mortality following total knee arthroplasty: incidence and risk factors among a national sample of 15,321 patients.
J Bone Joint Surg Am 2014 Jan 1;96(1):20-6. DOI: https://doi.org/10.2106/JBJS.M.00018.
20. Belmont PJ Jr, Goodman GP, Hamilton W, Waterman BR, Bader JO, Schoenfeld AJ. Morbidity and mortality in the thirty-day period following total hip arthroplasty: risk factors and incidence. J Arthroplasty 2014 Oct;29(10):2025-30. DOI: https://doi.org/10.1016/j.arth.2014.05.015.
21. Morris DS, Rohrbach J, Sundaram LM, et al. Early hospital readmission in the trauma population: are the risk factors different? Injury 2014 Jan;45(1):56-60. DOI: https://doi.org/10.1016/j.injury.2013.04.029.
22. Morris DS, Rohrbach J, Rogers M, et al. The surgical revolving door: risk factors for hospital readmission. J Surg Res 2011 Oct;170(2):297-301. DOI: https://doi.org/10.1016/j.jss.2011.04.049.
23. Hageman MG, Bossen JK, Smith RM, Ring D. Predictors of readmission in orthopaedic trauma surgery. J Orthop Trauma 2014 Oct;28(10):e247-9. DOI: https://doi.org/10.1097/BOT.0000000000000094.
24. Voskuijl T, Hageman M, Ring D. Higher Charlson Comorbidity Index Scores are associated with readmission after orthopaedic surgery. Clin Orthop Relat Res 2014 May;472(5):1638-44. DOI: https://doi.org/10.1007/s11999-013-3394-8.
25. Sathiyakumar V, Molina CS, Thakore RV, Obremskey WT, Sethi MK. ASA score as a predictor of 30-day perioperative readmission in patients with orthopaedic trauma injuries: a NSQIP analysis. J Orthop Trauma 2015 Mar;29(3):e127-32. DOI: https://doi.org/10.1097/BOT.0000000000000200.
26. 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 Aug 7;14:97. DOI: https://doi.org/10.1186/1471-2261-14-97.
27. Mahoney A, Bosco JA 3rd, Zuckerman JD. Readmission after shoulder arthroplasty. J Shoulder Elbow Surg 2014 Mar;23(3):377-81. DOI: https://doi.org/10.1016/j.jse.2013.08.007.
28. Neuhaus V, Swellengrebel CH, Bossen JK, Ring D. What are the factors influencing outcome among patients admitted to a hospital with a proximal humeral fracture? Clin Orthop Relat Res 2013 May;471(5):1698-706. DOI: https://doi.org/10.1007/s11999-013-2876-z.
29. Johnell O, Kanis JA, Odén A, et al. Mortality after osteoporotic fractures. Osteoporos Int 2004 Jan;15(1):38-42. DOI: https://doi.org/10.1007/s00198-003-1490-4.
30. Morin S, Lix LM, Azimaee M, Metge C, Caetano P, Leslie WD. Mortality rates after incident non-traumatic fractures in older men and women. Osteoporos Int 2011 Sep;22(9):2439-48. DOI: https://doi.org/10.1007/s00198-010-1480-2.
31. Losina E, Barrett J, Baron JA, Katz JN. Accuracy of Medicare claims data for rheumatologic diagnoses in total hip replacement recipients. J Clin Epidemiol 2003 Jun;56(6):515-9. DOI: https://doi.org/10.1016/S0895-4356(03)00056-8.
