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Inicio Cirugía Española (English Edition) Charlson index and the surgical risk scale in the analysis of surgical mortality
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Vol. 88. Núm. 3.
Páginas 174-179 (septiembre 2010)
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Vol. 88. Núm. 3.
Páginas 174-179 (septiembre 2010)
Acceso a texto completo
Charlson index and the surgical risk scale in the analysis of surgical mortality
Valor de los índices de Charlson y la escala de riesgo quirúrgico en el análisis de la mortalidad operatoria
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1637
Jesús Gil-Bonaa, Antoni Sabatéa,
Autor para correspondencia
asabatep@bellvitgehospital.cat

Corresponding author.
, Jose María Miguelena Bovadillab, Romà Adroera, Maylin Kooa, Eduardo Jaurrietab
a Servicio de Anestesiología, Reanimación y Terapéutica del Dolor, Hospital Universitari de Bellvitge, IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
b Servicio de Cirugía General y Digestiva, Hospital Universitari de Bellvitge, IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
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Abstract
Introduction

There is controversy over how to assess surgical mortality risks after different operations. The purpose of this study was to assess the surgical factors that influenced surgical mortality and the ability of the Charlson Index and The Surgical Risk Scale (SRS) to determine low risk patients.

Material and methods

All patients who died during the period 2004–2007 were included. The score of both indices (Charlson and SRS) were recorded. A score of “0” for the Charlson Index and «8» for the SRS were chosen as the cut-off point between a low and high probability of death. Three risk groups were established: Low when the Charlson was =0 and SRS was <8; Intermediate when the Charlson was >0 and the SRS <8 or Charlson=0 and SRS≥8; and high when the Charlson was >0 and the SRS≥8. The risks factors before, during and after surgery were compared between the groups.

Results

A total of 72,771 patients were surgically intervened, of which 7011 were urgent. One in every 1455 patients died during surgery and 1 in every 112 died during their hospital stay. Thirteen (2%) patients who died belonged to the low risk group, 199 (30.7%) to the intermediate risk group, and 434 (67.2%) to the high risk group. Heart disease was associated with the high risk group. The urgency of the operation was a determining factor associated with surgical complexity. Re-intervention and sepsis predominated as a cause of death in the low risk group, and in the rest of the groups a cardiac cause was the predominant factor.

Conclusions

The combination of the Charlson Index and SRS detected those patients with a low risk of death, thus making it a useful tool to audit surgical results.

Keywords:
Morbidity
Mortality
Surgical patients
Risk indices
Resumen
Introducción

Existe controversia sobre cómo valorar los riesgos de mortalidad quirúrgica tras distintas intervenciones. El objetivo de este estudio es valorar los factores operatorios que influyeron en la mortalidad quirúrgica y la capacidad de los índices de Charlson y la Escala de Riesgo Quirúrgico (SRS) en determinar los pacientes de bajo riesgo.

Material y métodos

Se incluyeron todos los pacientes que fallecieron en el periodo 2004–2007. Se recogió la puntuación de ambos índices. Se escogió el punto de división entre baja y alta probabilidad de muerte una puntuación de «0» para el índice de Charlson y de «8» para el SRS. Se han establecido tres grupos de riesgo: bajo, cuando el Charlson fue = 0 y el SRS fue < 8. Intermedio, cuando el Charlson fue > 0 y SRS < 8 o Charlson = 0 y SRS ≥8. Alto, cuando el Charlson fue > 0 y el SRS ≥8. Se han comparado los factores de riesgo pre-intrapostoperatorios entre los grupos.

Resultados

Se intervinieron 72.771 pacientes, de los cuales 7.011 lo fueron en régimen de urgencia. Fallecieron uno de cada 1.455 pacientes en el intraoperatorio y 1 de cada 112 pacientes durante su ingreso hospitalario. Trece (2%) pacientes fallecidos pertenecían al grupo bajo riesgo, 199 (30,7%) al de riesgo intermedio y 434 (67,2%) al de riesgo alto. Se asoció enfermedad cardiaca al grupo de alto riesgo. La urgencia fue un factor determinante que se asoció a la complejidad quirúrgica. En el grupo de bajo riesgo predominó la reintervención y la sepsis como causa de muerte; para el resto de los grupos predominó la causa cardiaca.

Conclusiones

La combinación del índice de Charlson y el SRS detectó aquellos pacientes de bajo riesgo de muerte siendo una herramienta útil para auditar los resultados operatorios.

Palabras clave:
Morbilidad
Mortalidad
Pacientes quirúrgicos
Índices de riesgo
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References
[1.]
M.E. Charlson, P. Pompei, K.L. Ales, C.R. Mackenzie.
A new method of classifying prognostic comorbidity in longitudinal studies: a development and validation.
J Chronic Dis, 40 (1987), pp. 373-383
[2.]
V. De Groot, H. Beckerman, G.J. Lankhorst, L.M. Bouter.
How to measure comorbidity. A critical review of available methods.
J Clin Epidemiol, 56 (2003), pp. 221-229
[3.]
D.A. Southern, H. Quan, W.A. Ghali.
Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data.
Med Care, 42 (2004), pp. 355-360
[4.]
R. Sutton, S. Bann, M. Brooks, S. Sarin.
The surgical risk scale as an improved tool for risk-adjusted analysis in comparative surgical audit.
[5.]
J. Gil-Bona, A. Sabate, A. Pi, R. Adroer, E. Jaurrieta.
Mortality risk factors in surgical patients in a tertiary hospital: A study of patients records in the period 2004-2006.
[6.]
V. Sundararajan, T. Henderson, C.L. Perry, A. Muggivan, H. Quan, W.A. Ghali, et al.
New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality.
J Clin Epidemiol, 57 (2004), pp. 1288-1294
[7.]
R.L. Keenan.
Epidemiological aspects.
Outcome after anaesthesia and surgery, pp. 477-490
[8.]
R.A. Deyo, D.C. Cherkin, M.A. Ciol.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.
J Clin Epidemiol, 45 (1992), pp. 613-619
[9.]
W.A. Ghali, R.E. Hall, A.K. Rosen, A.S. Ash, M.A. Moskowitz.
Searching for an improved clinical comorbidity index for use with ICD-9- CM administrative data.
J Clin Epidemiol, 49 (1996), pp. 273-278
[10.]
J.R. Landis, G.G. Koch.
The measurement of observer agreement for categorical data.
Biometrics, 33 (1977), pp. 159-174
[11.]
R.M. Poses, D.K. McClish, W.R. Smith, C. Bekes, W.E. Scott.
Prediction of survival of critically ill patients by admission comorbidity.
J Clin Epidemiol, 49 (1996), pp. 743-747
[12.]
W.A. Ghali, H. Quan, R. Brant.
Risk adjustment using administrative data: Impact of a diagnosis-type indicator.
J Cen Intern Med, 16 (2001), pp. 519-524
[13.]
M.J. Brooks, R. Sutton, S. Sarin.
Comparison of surgical Risk Score POSSUM and p-POSSUM in higher-risk surgical patients.
Br J Surg, 92 (2005), pp. 1288-1292
[14.]
G.P. Copeland, D. Jones, M. Walters, Possum:.
a scoring system for surgical audit.
Br J Surg, 78 (1991), pp. 355-360
[15.]
L.D. Wijensinghe, T. Mahmood, D.J.A. Scott, D.C. Berridge, P.J. Kent, R.C. Kester, et al.
Comparison of POSSUM and the Portsmouth predictor equation for predicting death following vascular surgery.
[16.]
D.R. Prytherch, M.S. Whiteley, B. Higgins, P.C. Weaver, W.G. Prout, S.J. Powell, et al.
POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and operative severity scores for the enumeration of mortality an morbidity.
Br J Surg, 85 (1998), pp. 1217-1220
[17.]
K.V. Menon, R. Farouk.
An analysis of the accuracy of P-POSSUM scoring for mortality risk assesment after surgery for colorectal cancer.
Colorectal Dis, 4 (2002), pp. 197-200
[18.]
S.B. Pillai, A.M. Van Rij, S. Williams, I.A. Thomson, M.J. Putteril, S. Greig, et al.
Complexity and risk adjusted model for measuring surgical outcome.
Br J Surg, 86 (1999), pp. 1567-1572
[19.]
A. Elixhauser, C. Steiner, D.R. Harris, R.M. Coffey.
Comorbidity measures for use with administrative data.
Med Care, 36 (1998), pp. 8-27
[20.]
M. Pine, H.S. Jordan, A. Elixhauser, D.E. Fry, D.C. Hoaglin, B. Jones, et al.
Enhancement of claims data to improve risk adjustment of hospital F mortality.
JAMA, 297 (2007), pp. 71-76
[21.]
R.S. Lagasse.
Anesthesia safety: model or myth?.
Anesthesiology, 97 (2002), pp. 1609-1617
[22.]
W.R. Best, S.F. Khuri, M. Phelan, K. Hut, W.G. Henderson, J.G. Demakis, et al.
Identifying patients preoperative risk factors and postoperative adverse events in administrative databases: Results from the department of veterans affairs national surgical quality improvement program.
J Am Coll Surg, 194 (2002), pp. 257-266
[23.]
M. Renshaw, C. Vaughan, M. Ottewill, A. Ireland, J. Carmody.
Clinical incident reporting: wrong time wrong place.
Int J Health Care Qual Assur, 21 (2008), pp. 380-384
[24.]
C.H. Vincent, K. Moorthy, S.K. Sarker, A. Chang, A. Darzi.
Systems approaches to surgical quality and safety: from concept to measurement.
Ann Surg, 239 (2004), pp. 475-482
Copyright © 2010. Asociación Española de Cirujanos
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