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The Impact of Functional Dependence and Related Surgical Complications on Postoperative Mortality

  • Implementation Science & Operations Management
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Purpose

Functional dependency is a known determinant of surgical risk. To enhance our understanding of the relationship between dependency and adverse surgical outcomes, we studied how postoperative mortality following a surgical complication was impacted by preoperative functional dependency.

Methods

We explored a historical cohort of 6,483,387 surgical patients within the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). All patients ≥ 18 years old within the ACS-NSQIP from 2007 to 2017 were included.

Results

There were 6,222,611 (96.5%) functionally independent, 176,308 (2.7%) partially dependent, and 47,428 (0.7%) totally dependent patients. Within 30 days postoperatively, 57,652 (0.9%) independent, 15,075 (8.6%) partially dependent, and 10,168 (21.4%) totally dependent patients died. After adjusting for confounders, increasing functional dependency was associated with increased odds of mortality (Partially Dependent OR: 1.72, 99% CI: 1.66 to 1.77; Totally Dependent OR: 2.26, 99% CI: 2.15 to 2.37). Dependency also significantly impacted mortality following a complication; however, independent patients usually experienced much stronger increases in the odds of mortality. There were six complications not associated with increased odds of mortality. Model diagnostics show our model was able to distinguish between patients who did and did not suffer 30-day postoperative mortality nearly 96.7% of the time.

Conclusions

Within our cohort, dependent surgical patients had higher rates of comorbidities, complications, and odds of 30-day mortality. Preoperative functional status significantly impacted the level of postoperative mortality following a complication, but independent patients were most affected.

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References

  1. Shah, R., et al., Association of Frailty with Failure to Rescue After Low-Risk and High-Risk Inpatient Surgery. JAMA Surg, 2018. 153(5): p. e180214.

  2. Scarborough, J.E., et al., The impact of functional dependency on outcomes after complex general and vascular surgery. Ann Surg, 2015. 261(3): p. 432-7.

    Article  Google Scholar 

  3. Pel-Littel, R.E., et al., Frailty: defining and measuring of a concept. J Nutr Health Aging, 2009. 13(4): p. 390-4.

    Article  CAS  Google Scholar 

  4. Akyar, S., et al., The Impact of Frailty on Postoperative Cardiopulmonary Complications in the Emergency General Surgery Population. Surg J (N Y), 2018. 4(2): p. e66-e77.

    Article  Google Scholar 

  5. Puts, M.T., P. Lips, and D.J. Deeg, Static and dynamic measures of frailty predicted decline in performance-based and self-reported physical functioning. J Clin Epidemiol, 2005. 58(11): p. 1188-98.

    Article  CAS  Google Scholar 

  6. Rockwood, K., et al., A global clinical measure of fitness and frailty in elderly people. Cmaj, 2005. 173(5): p. 489-95.

    Article  Google Scholar 

  7. Theou, O., et al., Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc, 2013. 61(9): p. 1537-51.

    Article  Google Scholar 

  8. Lally F, Crome P. Understanding frailty. Postgrad Med J. 2007;83(975):16-20. https://doi.org/10.1136/pgmj.2006.048587A

    Article  PubMed  PubMed Central  Google Scholar 

  9. Linda P. Fried, Catherine M. Tangen, Jeremy Walston, Anne B. Newman, Calvin Hirsch, John Gottdiener, Teresa Seeman, Russell Tracy, Willem J. Kop, Gregory Burke, Mary Ann McBurnie, Frailty in Older Adults: Evidence for a Phenotype, The Journals of Gerontology: Series A, Volume 56, Issue 3, 1 March 2001, Pages M146–M157. https://doi.org/10.1093/gerona/56.3.M146A

    Article  CAS  Google Scholar 

  10. Makary MA, Segev DL, Pronovost PJ Syin D, Bandeen-Roche K, Patel P, Takenaga R, Devgan L, Holzmueller CG, Tian J, Fried LP. Frailty as a predictor of surgical outcomes in older patients J Am Coll Surg. 2010 Jun;210(6):901–8. https://doi.org/10.1016/j.jamcollsurg.2010.01.028. Epub 2010 Apr 28 PMID: 20510798.

  11. Bergman H, Ferrucci L, Guralnik J, et al. Frailty: an emerging research and clinical paradigm--issues and controversies. J Gerontol A Biol Sci Med Sci. 2007;62(7):731-737. https://doi.org/10.1093/gerona/62.7.731A

    Article  PubMed  Google Scholar 

  12. Linda P. Fried, Luigi Ferrucci, Jonathan Darer, Jeff D. Williamson, Gerard Anderson, Untangling the Concepts of Disability, Frailty, and Comorbidity: Implications for Improved Targeting and Care, The Journals of Gerontology: Series A, Volume 59, Issue 3, March 2004, Pages M255–M263. https://doi.org/10.1093/gerona/59.3.M255A

    Article  Google Scholar 

  13. Thomas N. Robinson, Ben Eiseman, Jeffrey I. Wallace, Skotti D. Church, Kim K. McFann, Shirley M. Pfister, Terra J. Sharp, Marc Moss, Redefining Geriatric Preoperative Assessment Using Frailty, Disability and Co-Morbidity, Annals of Surgery: September 2009 - Volume 250 - Issue 3 - p 449–455. https://doi.org/10.1097/SLA.0b013e3181b45598

  14. Cheung A, Haas B, Ringer TJ, McFarlan A, Wong CL. Canadian Study of Health and Aging Clinical Frailty Scale: Does It Predict Adverse Outcomes among Geriatric Trauma Patients?. J Am Coll Surg. 2017 Nov;225(5):658–665.e3. https://doi.org/10.1016/j.jamcollsurg.2017.08.008. Epub 2017 Sep 6. PMID: 28888692.

  15. Chow, W.B., et al., Optimal preoperative assessment of the geriatric surgical patient: a best practices guideline from the American College of Surgeons National Surgical Quality Improvement Program and the American Geriatrics Society. J Am Coll Surg, 2012. 215(4): p. 453-66.

    Article  Google Scholar 

  16. Hall DE, Arya S, Schmid KK, Blaser C, Carlson MA, Bailey TL, Purviance G, Bockman T, Lynch TG, Johanning J. Development and Initial Validation of the Risk Analysis Index for Measuring Frailty in Surgical Populations. JAMA Surg. 2017 Feb 1;152(2)175–182. https://doi.org/10.1001/jamasurg.2016.4202. PMID: 27893030; PMCID: PMC7140150.

  17. Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. New 5-Factor Modified Frailty Index Using American College of Surgeons NSQIP Data. J Am Coll Surg. 2018 Feb;226(2)173–181.e8. https://doi.org/10.1016/j.jamcollsurg.2017.11.005. Epub 2017 Nov 16. PMID: 29155268.

  18. E Dent P Kowal EO Hoogendijk 2016 Jun Frailty measurement in research and clinical practice: A review Eur J Intern Med. 31 3 10 https://doi.org/10.1016/j.ejim.2016.03.007. Epub 2016 Mar 31 PMID: 27039014

  19. McDonald VS, Thompson KA, Lewis PR, Sise CB, Sise MJ, Shackford SR. Frailty in trauma: A systematic review of the surgical literature for clinical assessment tools. J Trauma Acute Care Surg. 2016 May;80(5)824–834. https://doi.org/10.1097/TA.0000000000000981. PMID: 26881488.

  20. Joseph B, Pandit V, Zangbar B, Kulvatunyou N, Tang A, O'Keeffe T, Green DJ, Vercruysse G, Fain MJ, Friese RS, Rhee P. Validating trauma-specific frailty index for geriatric trauma patients: a prospective analysis. J Am Coll Surg. 2014 Jul;219(1):10–17.e1. https://doi.org/10.1016/j.jamcollsurg.2014.03.020. Epub 2014 Mar 19. Erratum in: J Am Coll Surg. 2016 Mar;222(3):336. PMID: 24952434.

  21. Program, A.C.o.S.N.S.Q.I. User Guide for the 2017 ACS NSQIP Participant Use Data File. 12/16/2020]; Available from: https://www.facs.org/~/media/files/quality%20programs/nsqip/nsqip_puf_userguide_2017.ashx.

  22. Fried, L.P., et al., Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci, 2004;59(3): p. 255–263.

  23. Khan, M.A., et al., Perioperative risk factors for 30-day mortality after bariatric surgery: is functional status important? Surg Endosc, 2013. 27(5): p. 1772-7.

    Article  Google Scholar 

  24. Ponzetto, M., et al., Risk factors for early and late mortality in hospitalized older patients: the continuing importance of functional status. J Gerontol A Biol Sci Med Sci, 2003. 58(11): p. 1049-54.

    Article  Google Scholar 

  25. Cohen, M.E., et al., Effect of subjective preoperative variables on risk-adjusted assessment of hospital morbidity and mortality. Ann Surg, 2009. 249(4): p. 682-9.

    Article  Google Scholar 

  26. Kilic, A., et al., Functional status is highly predictive of outcomes after redo lung transplantation: an analysis of 390 cases in the modern era. Ann Thorac Surg, 2013. 96(5): p. 1804–11; discussion 1811.

  27. de la Fuente, S.G., K.M. Bennett, and J.E. Scarborough, Functional status determines postoperative outcomes in elderly patients undergoing hepatic resections. J Surg Oncol, 2013;107(8):865–70.

  28. Lentine, K.L., et al., Impact of Functional Status on Outcomes of Simultaneous Pancreas-kidney Transplantation: Risks and Opportunities for Patient Benefit. Transplant Direct, 2020. 6(9): p. e599.

  29. Stienen, M.N. et al., The influence of preoperative dependency on mortality, functional recovery and complications after microsurgical resection of intracranial tumors. J Neurooncol, 2018;139(2):441–448.

  30. Network, E. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. 12/16/20]; Available from: https://www.equator-network.org/reporting-guidelines/strobe/.

  31. White, I.R., P. Royston, and A.M. Wood, Multiple imputation using chained equations: Issues and guidance for practice. Stat Med, 2011;30(4): p. 377–99.

  32. Buuren, S. and C. Groothuis-Oudshoorn, MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 2011. 45.

  33. Jakobsen, J.C., et al., When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol, 2017. 17(1): p. 162.

    Article  Google Scholar 

  34. Little, R.J., et al., The prevention and treatment of missing data in clinical trials. The New England journal of medicine, 2012;367(14): p. 1355–1360.

  35. Groenwold, R.H., et al., Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. Cmaj, 2012. 184(11): p. 1265-9.

    Article  Google Scholar 

  36. Jones, M.P., Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression. Journal of the American Statistical Association, 1996. 91(433): p. 222-230.

    Article  Google Scholar 

  37. Wasserstein, R.L. and N.A. Lazar, The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 2016. 70(2): p. 129-133.

    Article  Google Scholar 

  38. Tibshirani, R., Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 1996. 58(1): p. 267-288.

    Google Scholar 

  39. Bien, J., J. Taylor, and R. Tibshirani, A LASSO FOR HIERARCHICAL INTERACTIONS. Ann Stat, 2013. 41(3): p. 1111-1141.

    Article  Google Scholar 

  40. Zhao, Y. and Q. Long, Variable Selection in the Presence of Missing Data: Imputation-based Methods. Wiley Interdiscip Rev Comput Stat, 2017. 9(5).

  41. Steyerberg, E.W., et al., Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology, 2010. 21(1): p. 128-38.

    Article  Google Scholar 

  42. Cook, N.R., Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem, 2008. 54(1): p. 17-23.

    Article  CAS  Google Scholar 

  43. Freundlich, R.E., et al., Complications Associated With Mortality in the National Surgical Quality Improvement Program Database. Anesth Analg, 2018. 127(1): p. 55-62.

    Article  Google Scholar 

  44. Naseer, M., H. Forssell, and C. Fagerström, Malnutrition, functional ability and mortality among older people aged ⩾ 60 years: a 7-year longitudinal study. Eur J Clin Nutr, 2016. 70(3): p. 399-404.

    Article  CAS  Google Scholar 

  45. Curtis, G.L., et al., Dependent Functional Status is a Risk Factor for Perioperative and Postoperative Complications After Total Hip Arthroplasty. J Arthroplasty, 2019. 34(7s): p. S348-s351.

    Article  Google Scholar 

  46. Minhas, S.V., A.S. Mazmudar, and A.A. Patel, Pre-operative functional status as a predictor of morbidity and mortality after elective cervical spine surgery. Bone Joint J, 2017. 99-b(6): p. 824–828.

  47. Kaplan, R.M., D.A. Chambers, and R.E. Glasgow, Big data and large sample size: a cautionary note on the potential for bias. Clin Transl Sci, 2014. 7(4): p. 342-6.

    Article  Google Scholar 

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Authors and Affiliations

Authors

Corresponding author

Correspondence to Jacob C. Clifton.

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Conflict of Interest

Dr. Robert Freundlich has stock in Pfizer, 3 M, Johnson and Johnson, and Gilead Pharmaceuticals.

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Appendices

Appendix 1: Patient Demographics

  

Summary Statistics

  

Whole dataset

Totally Independent

Partially Dependent

Totally Dependent

Total Number of Patients

6,483,387

6,222,611

176,308

47,428

Death (%)

84,170 (1.3)

57,652 (0.9)

15,075 (8.6)

10,168 (21.4)

Demographics

Year (%)

2007

210,405 (3.2)

195,729 (3.1)

10,236 (5.8)

4440 (9.4)

2008

269,847 (4.2)

251,709 (4.0)

12,963 (7.4)

5175 (10.9)

2009

336,185 (5.2)

313,595 (5.0)

16,503 (9.4)

6087 (12.8)

2010

363,431 (5.6)

343,089 (5.5)

14,923 (8.5)

5210 (11.0)

2011

442,149 (6.8)

420,960 (6.8)

14,120 (8.0)

3878 (8.2)

2012

543,885 (8.4)

525,316 (8.4)

13,542 (7.7)

2903 (6.1)

2013

651,940 (10.1)

628,574 (10.1)

14,715 (8.3)

3145 (6.6)

2014

750,937 (11.6)

726,360 (11.7)

16,006 (9.1)

3184 (6.7)

2015

885,502 (13.7)

855,685 (13.8)

20,042 (11.4)

4178 (8.8)

2016

1,000,393 (15.4)

966,924 (15.5)

21,245 (12.0)

4549 (9.6)

2017

1,028,713 (15.9)

994,670 (16.0)

22,013 (12.5)

4679 (9.9)

Race (%)

White

4,747,446 (73.4)

4,561,675 (73.5)

126,902 (72.2)

32,109 (68.0)

Black

643,386 (9.9)

608,546 (9.8)

23,364 (13.3)

8426 (17.9)

Asian

172,860 (2.7)

166,434 (2.7)

3801 (2.2)

1015 (2.2)

Other

903,309 (14.0)

870,373 (14.0)

21,695 (12.3)

5644 (12.0)

Sex = Female (%)

3,691,976 (57.0)

3,549,090 (57.1)

98,076 (55.7)

24,242 (51.2)

Age (years)—median (IQR)

58 [45, 69]

57 [44, 68]

72 [60, 82]

69 [56, 80]

Height (cm)—median (IQR)

168 [160, 175]

168 [160, 175]

165 [157, 175]

168 [157, 175]

Weight (kg)—median (IQR)

81.6 [68.0, 97.5]

81.6 [68.0, 97.5]

74.4 [61.2, 90.7]

72.6 [59.0, 88.9]

Preoperative Comorbidities and Procedural Variables

Surgical Specialty (%)

General Surgery

3,325,982 (51.3)

3,211,754 (51.6)

71,363 (40.5)

26,090 (55.0)

Vascular

484,793 (7.5)

437,155 (7.0)

36,437 (20.7)

8857 (18.7)

Orthopedics

1,189,299 (18.3)

1,129,604 (18.2)

44,290 (25.1)

6708 (14.1)

Urology

321,597 (5.0)

312,158 (5.0)

5857 (3.3)

1365 (2.9)

Neurosurgery

286,015 (4.4)

273,362 (4.4)

9168 (5.2)

1799 (3.8)

Other

875,700 (13.5)

858,577 (13.8)

9193 (5.2)

2609 (5.5)

Wound Classification (%)

1 – Clean

3,575,426 (55.1)

3,439,672 (55.3)

96,463 (54.7)

17,940 (37.8)

2 – Clean/ Contaminated

2,140,662 (33.0)

2,081,106 (33.4)

38,687 (21.9)

10,981 (23.2)

3 – Contaminated

423,012 (6.5)

399,617 (6.4)

14,776 (8.4)

5693 (12.0)

4 – Dirty/Infected

344,282 (5.3)

302,214 (4.9)

26,382 (15.0)

12,814 (27.0)

ASA Class (%)

1

583,875 (9.0)

579,093 (9.3)

1235 (0.7)

87 (0.2)

2

2,912,622 (45.1)

2,876,798 (46.4)

18,893 (10.7)

1886 (4.0)

3

2,556,923 (39.6)

2,417,971 (39.0)

104,291 (59.3)

20,239 (42.8)

4/5

410,794 (6.4)

330,433 (5.3)

51,355 (29.2)

25,078 (53.0)

Diabetes (%)

0 – No

5,486,362 (84.6)

5,302,856 (85.2)

120,327 (68.2)

32,787 (69.1)

1 – Insulin

377,319 (5.8)

330,895 (5.3)

33,704 (19.1)

9797 (20.7)

2 – Non-Insulin

619,698 (9.6)

588,860 (9.5)

22,277 (12.6)

4844 (10.2)

Dyspnea (%)

0 – No

6,044,616 (93.2)

5,826,783 (93.6)

145,917 (82.8)

37,516 (79.1)

1 – At Rest

42,863 (0.7)

29,051 (0.5)

7206 (4.1)

6149 (13.0)

2 – Moderate Exertion

395,878 (6.1)

366,762 (5.9)

23,182 (13.1)

3761 (7.9)

Sepsis (%)

0 – None

6,084,721 (94.0)

5,884,964 (94.8)

140,007 (79.6)

26,515 (56.0)

1 – Systemic Inflammatory Response Syndrome (SIRS)

219,814 (3.4)

192,436 (3.1)

18,442 (10.5)

6972 (14.7)

2 – Sepsis

135,212 (2.1)

114,743 (1.8)

13,177 (7.5)

6082 (12.8)

3 – Septic Shock

31,359 (0.5)

18,641 (0.3)

4320 (2.5)

7790 (16.4)

Functional Status (%)

0 – Totally Independent

6,222,611 (96.5)

6,222,611 (100.0)

0 (0.0)

0 (0.0)

1 – Partially Dependent

176,308 (2.7)

0 (0.0)

176,308 (100.0)

0 (0.0)

2 – Totally Dependent

47,428 (0.7)

0 (0.0)

0 (0.0)

47,428 (100.0)

Outpatient (%)

2,550,335 (39.3)

2,511,457 (40.4)

17,433 (9.9)

2786 (5.9)

Transfer Status = Admitted from other (%)

274,086 (4.2)

202,184 (3.3)

47,389 (26.9)

21,499 (45.4)

Emergency (%)

636,169 (9.8)

575,060 (9.2)

37,692 (21.4)

18,058 (38.1)

Smoker (%)

1,189,267 (18.3)

1,142,322 (18.4)

32,105 (18.2)

7512 (15.8)

Ventilator Dependent (%)

29,861 (0.5)

15,185 (0.2)

2453 (1.4)

11,582 (24.4)

History of Severe Chronic Obstructive Pulmonary Disease (%)

296,716 (4.6)

263,668 (4.2)

24,312 (13.8)

6339 (13.4)

Ascites (%)

30,524 (0.5)

24,623 (0.4)

3268 (1.9)

2401 (5.1)

History of Congestive Heart Failure (%)

56,817 (0.9)

41,922 (0.7)

10,120 (5.7)

4161 (8.8)

Hypertension Requiring Medication (%)

2,943,652 (45.4)

2,771,762 (44.5)

123,744 (70.2)

30,723 (64.8)

Acute Renal Failure (%)

25,937 (0.4)

19,058 (0.3)

3786 (2.1)

2738 (5.8)

Dialysis (%)

94,879 (1.5)

75,327 (1.2)

13,473 (7.6)

5052 (10.7)

Disseminated Cancer (%)

142,302 (2.2)

132,313 (2.1)

7499 (4.3)

1738 (3.7)

Open Wound/Wound Infection (%)

216,208 (3.3)

157,509 (2.5)

39,712 (22.5)

16,980 (35.8)

Steroid Use for Chronic Condition (%)

226,678 (3.5)

207,868 (3.3)

13,610 (7.7)

3680 (7.8)

Weight Loss (%)

92,197 (1.4)

80,291 (1.3)

8687 (4.9)

2723 (5.7)

Bleeding Disorder (%)

294,254 (4.5)

250,997 (4.0)

30,855 (17.5)

9800 (20.7)

Transfusion (%)

58,983 (0.9)

45,321 (0.7)

8075 (4.6)

4904 (10.3)

Intraoperative/ Postoperative Complications

Return to Operating Room = Yes (%)

214,429 (3.3)

189,899 (3.1)

16,015 (9.1)

7192 (15.2)

Superficial Incisional Surgical Site Infection (%)

114,112 (1.8)

106,398 (1.7)

5692 (3.2)

1397 (2.9)

Deep Incisional Surgical Site Infection (%)

36,876 (0.6)

33,148 (0.5)

2670 (1.5)

834 (1.8)

Organ Space Surgical Site Infection (%)

81,794 (1.3)

76,040 (1.2)

3792 (2.2)

1507 (3.2)

Wound Disruption (%)

27,445 (0.4)

24,442 (0.4)

2058 (1.2)

785 (1.7)

Pneumonia (%)

82,053 (1.3)

66,004 (1.1)

9817 (5.6)

5376 (11.3)

Unplanned Intubation (%)

58,243 (0.9)

47,258 (0.8)

6996 (4.0)

3437 (7.2)

Pulmonary Embolism (%)

21,471 (0.3)

19,776 (0.3)

1172 (0.7)

418 (0.9)

Progressive Renal Insufficiency (%)

17,862 (0.3)

15,411 (0.2)

1618 (0.9)

690 (1.5)

Acute Renal Failure (%)

20,581 (0.3)

16,516 (0.3)

2226 (1.3)

1621 (3.4)

Urinary Tract Infection (%)

90,406 (1.4)

78,808 (1.3)

7861 (4.5)

3135 (6.6)

Stroke/Cerebrovascular Accident (%)

13,548 (0.2)

11,419 (0.2)

1454 (0.8)

576 (1.2)

Cardiac Arrest (%)

21,281 (0.3)

16,569 (0.3)

2743 (1.6)

1703 (3.6)

Myocardial Infarction (%)

23,347 (0.4)

19,872 (0.3)

2545 (1.4)

705 (1.5)

Transfusions (%)

329,261 (5.1)

288,116 (4.6)

29,329 (16.6)

9115 (19.2)

Deep Vein Thrombosis Requiring Therapy (%)

38,829 (0.6)

34,039 (0.5)

3085 (1.7)

1433 (3.0)

Sepsis (%)

107,620 (1.7)

93,228 (1.5)

9392 (5.3)

4127 (8.7)

Septic Shock (%)

54,799 (0.8)

42,644 (0.7)

7368 (4.2)

3980 (8.4)

Lab Values

Blood Urea Nitrogen (mg/dL)—median (IQR)

15.0 [11.0, 19.0]

15.00 [11.0, 19.0]

18.5 [13.0, 27.0]

21.0 [13.0, 33.6]

Serum Albumin (g/dL)—median (IQR)

4.0 [3.6, 4.3]

4.0 [3.6, 4.3]

3.3 [2.7, 3.8]

2.8 [2.2, 3.4]

Total Bilirubin (mg/dL)—median (IQR)

0.5 [0.4, 0.8]

0.5 [0.4, 0.8]

0.5 [0.36, 0.8]

0.6 [0.4, 0.9]

Serum Glutamic Oxaloacetic Transaminase (units/L)—median (IQR)

22 [17,29]

22 [17,29]

22 [17,31]

25 [18,41]

Alkaline Phosphatase (units/L)—median (IQR)

77 [62, 97]

76 [61, 96]

87 [68, 118]

90 [68, 127]

White Blood Cell Count (K/uL)—median (IQR)

7.40 [5.90, 9.40]

7.30 [5.90, 9.30]

8.50 [6.51, 11.40]

10.0 [7.30, 14.14]

Hematocrit (volume %)—median (IQR)

40.0 [36.5, 42.9]

40.0 [36.8, 43.0]

35.0 [30.5, 39.2]

32.3 [28.2, 37.1]

Platelet Count (K/uL)—median (IQR)

241 [198, 291]

241 [198, 290]

236 [182, 306]

234 [167, 319]

International Normalized Ratio of Prothrombin Time values—median (IQR)

1.0 [1.0, 1.1]

1.0 [1.0, 1.1]

1.1 [1.0, 1.2]

1.2 [1.1, 1.4]

Appendix 2: Missingness Rates

 

Count

Proportion

Age

24,622

0.38%

Sex

4014

0.06%

Race

16,386

0.25%

Height

130,609

2.01%

Weight

69,082

1.07%

Work Relative Value Unit

5742

0.09%

In/outpatient

4

 < 0.00%

Transfer Status

5711

0.09%

Anesthesia

1545

0.02%

Surgical Service

1

 < 0.00%

Emergency

32

 < 0.00%

Wound Classification

5

 < 0.00%

ASA Classification

19,173

0.30%

Diabetes

8

 < 0.00%

Smoke

25

 < 0.00%

Dyspnea

30

 < 0.00%

Functional Status

37,040

0.57%

Ventilator

11

 < 0.00%

History of Chronic Obstructive Pulmonary Disease

13

 < 0.00%

Ascites

20

 < 0.00%

History of Congestive Heart Failure

13

 < 0.00%

Hypertension

16

 < 0.00%

Preoperative Acute Renal Failure

19

 < 0.00%

Dialysis

24

 < 0.00%

Disseminated Cancer

17

 < 0.00%

Open Wound/Wound Infection

17

 < 0.00%

Steroid

17

 < 0.00%

Weight Loss

18

 < 0.00%

Bleeding Disorder

15

 < 0.00%

Preop Transfusion

20

 < 0.00%

Systemic Sepsis

12,281

0.19%

Operation Time

945

0.01%

Hospital Length of Stay

5683

0.09%

Days from Admission to Surgery

239

 < 0.00%

Return to Operating Room

55

 < 0.00%

Serum Sodium

1,232,739

19.01%

Blood Urea Nitrogen

1,416,070

21.84%

Serum Creatinine

1,186,261

18.30%

Serum Albumin

3,150,009

48.59%

Total Bilirubin

3,151,874

48.61%

Serum Glutamic Oxaloacetic Transaminase

3,145,901

48.52%

Alkaline Phosphatase

3,131,423

48.30%

White Blood Cell Count

1,033,486

15.94%

Hematocrit

942,893

14.54%

Platelet Count

1,037,770

16.01%

Partial Thromboplastin Time

4,278,329

65.99%

International Normalized Ratio (INR) of Prothrombin Time values

3,732,736

57.57%

Prothrombin Time

5,809,086

89.60%

Appendix 3: Totally Dependent Patients with Unplanned Intubation Cohort Summary

 

Lived

Died

Count (%)

2112 (5.4)

1326 (7.2)

Sex = Female (%)

945 (44.8)

618 (46.6)

Age (years)—median (IQR)

65 [54, 75]

72 [61, 81]

Weight (kg)—median (IQR)

76 [63, 94]

71 [59, 87]

Wound Classification (%)

1 – Clean

560 (26.5)

372 (28.1)

2 – Clean/ Contaminated

510 (24.1)

299 (22.5)

3 – Contaminated

328 (15.5)

210 (15.8)

4 – Dirty/Infected

714 (33.8)

445 (33.6)

ASA Class (%)

1

1 (0.0)

1 (0.1)

2

38 (1.8)

13 (1.0)

3

677 (32.1)

379 (28.6)

4/5

1391 (66.0)

933 (70.4)

Diabetes (%)

0 – No

1492 (70.6)

896 (67.6)

1 – Insulin

398 (18.8)

286 (21.6)

2 – Non-Insulin

222 (10.5)

144 (10.9)

Dyspnea (%)

0 – No

1420 (67.2)

872 (65.8)

1 – At Rest

462 (21.9)

297 (22.4)

2 – Moderate Exertion

230 (10.9)

157 (11.8)

Sepsis (%)

0 – None

750 (35.5)

478 (36.1)

1 – Systemic Inflammatory Response Syndrome (SIRS)

403 (19.1)

270 (20.4)

2 – Sepsis

312 (14.8)

269 (20.3)

3 – Septic Shock

646 (30.6)

307 (23.2)

Outpatient (%)

14 (0.7)

14 (1.1)

Transfer Status = Admitted from other (%)

927 (43.9)

624 (47.1)

Emergency (%)

1144 (54.2)

662 (49.9)

Smoker (%)

474 (22.4)

250 (18.9)

Ventilator Dependent (%)

863 (40.9)

352 (26.5)

History of Severe Chronic Obstructive Pulmonary Disease (%)

373 (17.7)

251 (18.9)

Ascites (%)

174 (8.2)

138 (10.4)

History of Congestive Heart Failure (%)

282 (13.4)

205 (15.5)

Hypertension Requiring Medication (%)

1384 (65.5)

936 (70.6)

Acute Renal Failure (%)

182 (8.6)

109 (8.2)

Dialysis (%)

225 (10.7)

241 (18.2)

Disseminated Cancer (%)

62 (2.9)

85 (6.4)

Open Wound/Wound Infection (%)

678 (32.1)

526 (39.7)

Steroid Use for Chronic Condition (%)

200 (9.5)

176 (13.3)

Weight Loss (%)

141 (6.7)

136 (10.3)

Bleeding Disorder (%)

497 (23.5)

352 (26.5)

Transfusion (%)

268 (12.7)

180 (13.6)

Return to Operating Room = Yes (%)

743 (35.2)

315 (23.8)

Superficial Incisional Surgical Site Infection (%)

112 (5.3)

33 (2.5)

Deep Incisional Surgical Site Infection (%)

70 (3.3)

33 (2.5)

Organ Space Surgical Site Infection (%)

168 (8.0)

76 (5.7)

Wound Disruption (%)

95 (4.5)

53 (4.0)

Pneumonia (%)

959 (45.4)

494 (37.3)

Unplanned Intubation (%)

2112 (100.0)

1326 (100.0)

Pulmonary Embolism (%)

59 (2.8)

30 (2.3)

Progressive Renal Insufficiency (%)

85 (4.0)

53 (4.0)

Acute Renal Failure (%)

168 (8.0)

137 (10.3)

Urinary Tract Infection (%)

264 (12.5)

115 (8.7)

Stroke/Cerebrovascular Accident (%)

62 (2.9)

49 (3.7)

Cardiac Arrest (%)

174 (8.2)

472 (35.6)

Myocardial Infarction (%)

71 (3.4)

87 (6.6)

Transfusions (%)

472 (22.3)

349 (26.3)

Deep Vein Thrombosis Requiring Therapy (%)

186 (8.8)

69 (5.2)

Sepsis (%)

393 (18.6)

156 (11.8)

Septic Shock (%)

614 (29.1)

527 (39.7)

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Clifton, J.C., Engoren, M., Shotwell, M.S. et al. The Impact of Functional Dependence and Related Surgical Complications on Postoperative Mortality. J Med Syst 46, 6 (2022). https://doi.org/10.1007/s10916-021-01779-8

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