Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText##

Original article

Vol. 149 No. 3738 (2019)

Development and validation of a multivariable risk score for prolonged length of stay in the surgical intensive care unit

  • Conrad Wesch
  • Kris Denhaerynck
  • Ursi Barandun Schaefer
  • Martin Siegemund
  • Michael Wehrli
  • Hans Pargger
  • Susanne Look
DOI
https://doi.org/10.4414/smw.2019.20122
Cite this as:
Swiss Med Wkly. 2019;149:w20122
Published
12.09.2019

Summary

BACKGROUND

Chronically critical illness is highly relevant in intensive care units, but the definitions in literature vary greatly. The timely detection of prolonged intensive care unit length of stay could support care planning for chronically critical ill patients.

AIM

To develop and validate a risk score for predicting prolonged length of stay in the surgical intensive care unit.

METHODS

This single centre cohort study formed part of a nursing-led project in one surgical intensive care unit. We examined the performance of seven predefined predictive factors of prolonged (>20 days) intensive care unit length of stay in adults on the seventh day of stay in intensive care to develop (n = 304) and validate (n = 101) a risk score. Candidate variables (Charlson Comorbidity Index, Simplified Acute Physiology Score II, minimum plasma albumin, need for anti-infective drugs, time of mechanical ventilation, main feeding method and score on the Sedation-Agitation Scale) were analysed using multiple logistical regression analysis.

RESULTS

Our risk score assigned different points to the following conditions: Charlson Comorbidity Index >2, minimum albumin <20 g/l between days 1 and 7, mechanical ventilation >14 hr on day 7 and the need for parenteral nutrition on day 7. For a validation data set (n = 101), the area under the receiver operating characteristic curve was 0.89 (95% confidence interval 0.77­0.87). At a cut-off value of 100 points, the degree of sensitivity was 88%, the specificity 75%, the positive predictive value 53%, the negative predictive value 95%, and the model fit R2 0.40.

CONCLUSIONS

Our model allowed the timely detection of prolonged intensive care unit length of stay with four candidate predictive factors. The timely identification of patients with prolonged intensive care unit length of stay is possible and could influence the person-centred prevention of chronically critical illness and adequate resource allocation. (Trial registration no DRKS 00017073)

References

  1. Rosseau S, Suttorp N. Der chronisch kritisch kranke Patient [The chronic critically ill patient]. Med Klin Intensivmed Notf Med. 2013;108(4):266. In German. doi:.https://doi.org/10.1007/s00063-012-0162-6
  2. Carson SS. Definitions and epidemiology of the chronically critically ill. Respir Care. 2012;57(6):848–56, discussion 856–8. doi:.https://doi.org/10.4187/respcare.01736
  3. Kahn JM, Le T, Angus DC, Cox CE, Hough CL, White DB, et al.; ProVent Study Group Investigators. The epidemiology of chronic critical illness in the United States. Crit Care Med. 2015;43(2):282–7. doi:.https://doi.org/10.1097/CCM.0000000000000710
  4. MacIntyre NR, Epstein SK, Carson S, Scheinhorn D, Christopher K, Muldoon S ; National Association for Medical Direction of Respiratory Care. Management of patients requiring prolonged mechanical ventilation: report of a NAMDRC consensus conference. Chest. 2005;128(6):3937–54. doi:.https://doi.org/10.1378/chest.128.6.3937
  5. Wiencek C, Winkelman C. Chronic critical illness: prevalence, profile, and pathophysiology. AACN Adv Crit Care. 2010;21(1):44–61, quiz 63. doi:.https://doi.org/10.1097/NCI.0b013e3181c6a162
  6. Nelson JE, Cox CE, Hope AA, Carson SS. Chronic critical illness. Am J Respir Crit Care Med. 2010;182(4):446–54. doi:.https://doi.org/10.1164/rccm.201002-0210CI
  7. Beckie TM. A systematic review of allostatic load, health, and health disparities. Biol Res Nurs. 2012;14(4):311–46. doi:.https://doi.org/10.1177/1099800412455688
  8. Bellar A, Kunkler K, Burkett M. Understanding, recognizing, and managing chronic critical illness syndrome. J Am Acad Nurse Pract. 2009;21(11):571–8. doi:.https://doi.org/10.1111/j.1745-7599.2009.00451.x
  9. Rodríguez Villar S, Barrientos Yuste RM. Long-term admission to the intensive care unit: a cost-benefit analysis. Rev Esp Anestesiol Reanim. 2014;61(9):489–96. doi:.https://doi.org/10.1016/j.redar.2014.02.008
  10. Jeitziner MM, Massarotto P, Barandun Schäfer U. Symptombelastung und entsprechende Interventionen. Intensiv. 2015;23(03):123–7. In German. doi:.https://doi.org/10.1055/s-0035-1550608
  11. Loss SH, Marchese CB, Boniatti MM, Wawrzeniak IC, Oliveira RP, Nunes LN, et al. Prediction of chronic critical illness in a general intensive care unit. Rev Assoc Med Bras (1992). 2013;59(3):241–7. doi:.https://doi.org/10.1016/j.ramb.2012.12.002
  12. Chen HY, Vanness DJ, Golestanian E. A simplified score for transfer of patients requiring mechanical ventilation to a long-term care hospital. Am J Crit Care. 2011;20(6):e122–30. doi:.https://doi.org/10.4037/ajcc2011775
  13. Szubski CR, Tellez A, Klika AK, Xu M, Kattan MW, Guzman JA, et al. Predicting discharge to a long-term acute care hospital after admission to an intensive care unit. Am J Crit Care. 2014;23(4):e46–53. doi:.https://doi.org/10.4037/ajcc2014985
  14. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676–82. doi:.https://doi.org/10.1093/aje/kwq433
  15. Higgins TL, McGee WT, Steingrub JS, Rapoport J, Lemeshow S, Teres D. Early indicators of prolonged intensive care unit stay: impact of illness severity, physician staffing, and pre-intensive care unit length of stay. Crit Care Med. 2003;31(1):45–51. doi:.https://doi.org/10.1097/00003246-200301000-00007
  16. Boniatti MM, Friedman G, Castilho RK, Vieira SR, Fialkow L. Characteristics of chronically critically ill patients: comparing two definitions. Clinics (São Paulo). 2011;66(4):701–4. doi:.https://doi.org/10.1590/S1807-59322011000400027
  17. Lee JJ, Waak K, Grosse-Sundrup M, Xue F, Lee J, Chipman D, et al. Global muscle strength but not grip strength predicts mortality and length of stay in a general population in a surgical intensive care unit. Phys Ther. 2012;92(12):1546–55. doi:.https://doi.org/10.2522/ptj.20110403
  18. Estenssoro E, Reina R, Canales HS, Saenz MG, Gonzalez FE, Aprea MM, et al. The distinct clinical profile of chronically critically ill patients: a cohort study. Crit Care. 2006;10(3):R89. doi:.https://doi.org/10.1186/cc4941
  19. Marchioni A, Fantini R, Antenora F, Clini E, Fabbri L. Chronic critical illness: the price of survival. Eur J Clin Invest. 2015;45(12):1341–9. doi:.https://doi.org/10.1111/eci.12547
  20. Wesch C. Master Thesis: Developing and validating a multivariable tool to predict a prolonged length of stay in the ICU: A retrospective exploratory cohort study, in Institute of Nursing Science. 2017, University of Basel.
  21. Higgins PA, Daly BJ, Lipson AR, Guo SE. Assessing nutritional status in chronically critically ill adult patients. Am J Crit Care. 2006;15(2):166–76, quiz 177.
  22. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270(24):2957–63. doi:.https://doi.org/10.1001/jama.1993.03510240069035
  23. Cox CE. Persistent systemic inflammation in chronic critical illness. Respir Care. 2012;57(6):859–64, discussion 864–6. doi:.https://doi.org/10.4187/respcare.01719
  24. Riker RR, Picard JT, Fraser GL. Prospective evaluation of the Sedation-Agitation Scale for adult critically ill patients. Crit Care Med. 1999;27(7):1325–9. doi:.https://doi.org/10.1097/00003246-199907000-00022
  25. Riley RD, Hayden JA, Steyerberg EW, Moons KG, Abrams K, Kyzas PA, et al.; PROGRESS Group. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380. doi:.https://doi.org/10.1371/journal.pmed.1001380
  26. Moons KGM, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683–90. doi:.https://doi.org/10.1136/heartjnl-2011-301246
  27. Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al.; PROGRESS Group. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ. 2013;346(feb05 1):e5595. doi:.https://doi.org/10.1136/bmj.e5595
  28. Lei Q, Chen L, Jin M, Ji H, Yu Q, Cheng W, et al. Preoperative and intraoperative risk factors for prolonged intensive care unit stay after aortic arch surgery. J Cardiothorac Vasc Anesth. 2009;23(6):789–94. doi:.https://doi.org/10.1053/j.jvca.2009.05.028
  29. Hein OV, Birnbaum J, Wernecke K, England M, Konertz W, Spies C. Prolonged intensive care unit stay in cardiac surgery: risk factors and long-term-survival. Ann Thorac Surg. 2006;81(3):880–5. doi:.https://doi.org/10.1016/j.athoracsur.2005.09.077
  30. Vincent J-L, Dubois M-J, Navickis RJ, Wilkes MM. Hypoalbuminemia in acute illness: is there a rationale for intervention? A meta-analysis of cohort studies and controlled trials. Ann Surg. 2003;237(3):319–34. doi:.https://doi.org/10.1097/01.SLA.0000055547.93484.87
  31. Lee JH, Kim J, Kim K, Jo YH, Rhee J, Kim TY, et al. Albumin and C-reactive protein have prognostic significance in patients with community-acquired pneumonia. J Crit Care. 2011;26(3):287–94. doi:.https://doi.org/10.1016/j.jcrc.2010.10.007
  32. de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity. a critical review of available methods. J Clin Epidemiol. 2003;56(3):221–9. doi:.https://doi.org/10.1016/S0895-4356(02)00585-1
  33. Rochon PA, Katz JN, Morrow LA, McGlinchey-Berroth R, Ahlquist MM, Sarkarati M, et al. Comorbid illness is associated with survival and length of hospital stay in patients with chronic disability. A prospective comparison of three comorbidity indices. Med Care. 1996;34(11):1093–101. doi:.https://doi.org/10.1097/00005650-199611000-00004
  34. Puntillo KA, Morris AB, Thompson CL, Stanik-Hutt J, White CA, Wild LR. Pain behaviors observed during six common procedures: results from Thunder Project II. Crit Care Med. 2004;32(2):421–7. doi:.https://doi.org/10.1097/01.CCM.0000108875.35298.D2
  35. Widyastuti Y, Stenseth R, Wahba A, Pleym H, Videm V. Length of intensive care unit stay following cardiac surgery: is it impossible to find a universal prediction model? Interact Cardiovasc Thorac Surg. 2012;15(5):825–32. doi:.https://doi.org/10.1093/icvts/ivs302
  36. Caironi P, Tognoni G, Masson S, Fumagalli R, Pesenti A, Romero M, et al.; ALBIOS Study Investigators. Albumin replacement in patients with severe sepsis or septic shock. N Engl J Med. 2014;370(15):1412–21. doi:.https://doi.org/10.1056/NEJMoa1305727
  37. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, et al.; PROGRESS Group. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi:.https://doi.org/10.1371/journal.pmed.1001381
  38. Kasotakis G, Schmidt U, Perry D, Grosse-Sundrup M, Benjamin J, Ryan C, et al. The surgical intensive care unit optimal mobility score predicts mortality and length of stay. Crit Care Med. 2012;40(4):1122–8. doi:.https://doi.org/10.1097/CCM.0b013e3182376e6d
  39. Reuschenbach B, Mahler C, Ahlsdorf E. Pflegebezogene Assessmentinstrumente. Internationales Handbuch für Pflegeforschung und-praxis. 2011. Bern, Hans Huber.

Most read articles by the same author(s)

1 2 > >>