E-ISSN: 1305-9327 ISSN: 1305-9319
The Comparison of ASA, SORT, CCI and CACI Indexes to Predict Postoperative Intensive Care Requirement in Patients with Gastrointestinal Malignancy Surgery
PDF
Cite
Share
Request
Research
VOLUME: 15 ISSUE: 2
P: 142-149
June 2019

The Comparison of ASA, SORT, CCI and CACI Indexes to Predict Postoperative Intensive Care Requirement in Patients with Gastrointestinal Malignancy Surgery

Med J Bakirkoy 2019;15(2):142-149
DOI: 10.4274/BTDMJB.galenos.2018.20180318093225
Mustafa Kılıç 1
Elif Bombacı 2
Banu Çevik 2
1. İstanbul Yakacık Doğum ve Çocuk Hastalıkları Hastanesi, Anesteziyoloji ve Reanimasyon Kliniği, İstanbul, Türkiye
2. Sağlık Bilimleri Üniversitesi İstanbul Dr. Lütfi Kırdar Eğitim ve Araştırma Hastanesi, Anesteziyoloji ve Reanimasyon Kliniği, İstanbul, Türkiye
No information available.
No information available
Received Date: 18.03.2018
Accepted Date: 06.06.2018
Publish Date: 27.05.2019
PDF
Cite
Share
Request

ABSTRACT

Objective:

In this retrospective study, it has been aimed to investigate the effectiveness of Charlson Comorbidity Index (CCI), Charlson Age-added Comorbidity Index (CACI), Surgical Risk Result Tool (SORT) and American Society of Anesthesiologists, (ASA) classification and indexes to predict the intensive care (IC) needs of the patients who will have gastrointestinal system tumor operations.

Methods:

In this study, data of the patients who had oncologic gastrointestinal system operation between 01 April 2015 and 31 May 2017 have been scanned retrospectively. In the direction of these assessments 4 groups have been created as; Group 1) Patients in whom intensive care necessity was foreseen in preoperative evaluation and needed intensive care after the surgery, Group 2) Patients in whom postoperative intensive care necessity was foreseen but they have been transferred to the surgical department without the need of postoperative intensive care follow up, Group 3) Patients in whom intensive care need was not foreseen but they have been followed up intensive care unit after surgery, and Group 4) Patients in whom intensive care need was not foreseen and they have been transferred to the surgical department after the operation. Each patient’s ASA, SORT score, CCI, CACI indexes were calculated and recorded. Furthermore the patients’ age, gender, type of the surgery, comorbidities etc. were recorded. For the statistical analysis IBM SPSS Statistics version 22.0 program was used. By using ROC graph, the effects of SORT, CACI, and CCI indexes were analyzed to anticipate if the patients will need intensive care following surgery.

Results:

The prevalence of advanced age, multiple comorbid disease and some operations (esophageal or pancreatic operations), which were predicted as intensive care indications in preoperative evaluation, were significantly higher in patients requiring intensive care after surgery. In evaluation of sensitivity and originality, the area that remains under the ROC graph was SORT: 0,746 CACI: 0,795 and CCI: 0,706.

Conclusion:

While CACI gave the best rates in determination of the patients’ need for the intensive care, it was observed that CCI index had the weakest precision among these 4 methods. It has been concluded that the efficacy SORT, which includes all the determinants of the patient’s age, ASA physical condition, comorbid diseases and the characteristics of the surgery, was not found superior to other indexes, to determine the need for intensive care in elective patients.

Keywords:
Intensive care ındication, Charlson Comorbidity Index, Charlson Age-added Comorbidity Index, surgical risk result tool, ASA physical status

References

1
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;40:373-83.
2
Koppie TM, Serio AM, Vickers AJ, Vora K, Dalbagni G, Donat SM, et al. Age-adjusted Charlson comorbidity score is associated with treatment decisions and clinical outcomes for patients undergoing radical cystectomy for bladder cancer. Cancer 2008;112:2384-92.
3
Protopapa KL, Simpson JC, Smith NCE, Moonesinghe SR. Development and validation of the Surgical Outcome Risk Tool (SORT). Br J Surg 2014;101:1774-83.
4
Guidelines for intensive care unit admission, discharge, and triage. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine. Crit Care Med 1999;27:633-8.
5
Pearse RM, Harrison DA, James P, Watson D, Hinds C, Rhodes A, et al. Identification and characterisation of the high-risk surgical population in the United Kingdom. Crit Care 2006;10:R81.
6
Sobol JB, Wunsch H. Triage of high-risk surgical patients for intensive care. Crit Care 2011;15:217.
7
Senagore AJ, Duepree HJ, Delaney CP, Brady KM, Fazio VW. Results of a Standardized Technique and Postoperative Care Plan for Laparoscopic Sigmoid Colectomy. Dis Colon Rectum 2003;46:503-9.
8
Uzman S, Yilmaz Y, Toptas M, Akkoc I, Gul YG, Daskaya H, et al. A retrospective analysis of postoperative patients admitted to the intensive care unit. Hippokratia 2016;20:38-43.
9
Pearse RM, Moreno RP, Bauer P, Pelosi P, Metnitz P, Spies C, et al. Mortality after surgery in Europe: a 7 day cohort study. Lancet 2012;380:1059-65.
10
Simpson JC, Moonesinghe SR. Introduction to the postanaesthetic care unit. Perioper Med (Lond) 2013;2:5.
11
Hoekstra HJ. Cancer surgery in the elderly. Eur J Cancer 2001;37 Suppl 7:S235-44.
12
Broens SJ, He X, Evley R, Olofsen E, Niesters M, Mahajan RP, et al. Frequent respiratory events in postoperative patients aged 60 years and above. Ther Clin Risk Manag 2017;13:1091-8.
13
Chandrasinghe PC, Ediriweera DS, Nazar T, Kumarage S, Hewavisenthi J, Deen KI. Overall Survival of Elderly Patients Having Surgery for Colorectal Cancer Is Comparable to Younger Patients: Results from a South Asian Population. Gastroenterol Res Pract 2017;2017:9670512.
14
Oruç MT, Uzun S, Saylam B, Karakahya M, Karadağ Ç, Düzgün AP, et al. İleri yaşta acil ve elektif şartlarda cerrahi tedavi. Türk Geriatri Dergisi 2004;7:37-40.
15
Leung JM, Dzankic S. Relative importance of preoperative health status versus intraoperative factors in predicting postoperative adverse outcomes in geriatric surgical patients. J Am Geriatr Soc 2001;49:1080-5.
16
Leung E, McArdle K, Wong LS. Risk-adjusted scoring systems in colorectal surgery. Int J Surg 2011;9:130-5.
17
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;173:676-82.
18
Valent F, Tonutti L, Grimaldi F. Does diabetes mellitus comorbidity affect in-hospital mortality and length of stay? Analysis of administrative data in an Italian Academic Hospital. Acta Diabetol 2017;54:1081-90.
19
Lemmens VEPP, Janssen-Heijnen MLG, Houterman S, Verheij KDGW, Martijn H, van de Poll-Franse L, et al. Which comorbid conditions predict complications after surgery for colorectal cancer? World J Surg 2007;31:192-9.
20
Haroon N, Mh A, Chafiki Z. Age Adjusted Charlson Comorbidity Index: Predictor of 90-Day Mortality after Radical Cystectomy. Journal of Surgery and Operative Care 2016;1:1-6.
21
Christensen S, Johansen MB, Christiansen CF, Jensen R, Lemeshow S. Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care. Clin Epidemiol 2011;3:203-11.
22
Huang Y, Zhang Y, Li J, Liu G. Charlson comorbidity index for evaluation of the outcomes of elderly patients undergoing laparoscopic surgery for colon cancer. J BUON 2017;22:686-91.
23
Asano T, Yamada S, Fujii T, Yabusaki N, Nakayama G, Sugimoto H, et al. The Charlson age comorbidity index predicts prognosis in patients with resected pancreatic cancer. Int J Surg 2017;39:169-75.
24
St-Louis E, Iqbal S, Feldman LS, Sudarshan M, Deckelbaum DL, Razek TS, et al. Using the age-adjusted Charlson comorbidity index to predict outcomes in emergency general surgery. J Trauma Acute Care Surg 2015;78:318-23.