metricas
Revista Española de Anestesiología y Reanimación Escala APACHE II para pacientes críticos con cáncer sólido. Estudio de reclas...
Información de la revista
Vol. 65. Núm. 8.
Páginas 423-482 (Octubre 2018)
Cita
Cita
Compartir
Descargar PDF
Más opciones de artículo
Visitas
1690
Vol. 65. Núm. 8.
Páginas 423-482 (Octubre 2018)
ORIGINAL

Escala APACHE II para pacientes críticos con cáncer sólido. Estudio de reclasificación

APACHE II score for critically ill patients with a solid tumor: A reclassification study
Visitas
1690
F.D. Martos-Beníteza,
Autor para correspondencia
fdmartos@infomed.sld.cu

Autor para correspondencia.
, I. Cordero-Escobarb, A. Soto-Garcíac, I. Betancourt-Plazad, I. González-Martínezd
a Unidad de Cuidados Intensivos 8B, Hospital “Hermanos Ameijeiras”, La Habana, Cuba
b Departamento de Anestesiología, Hospital “Hermanos Ameijeiras”, La Habana, Cuba
c Unidad de Cuidados Intensivos Oncológicos, Instituto de Oncología y Radiobiología, La Habana, Cuba
d Unidad de Cuidados Intensivos, Hospital Docente “Dr. Miguel Enríquez”, La Habana, Cuba
Contenido relacionado
F.D. Martos-Benítez, I. Cordero-Escobar, A. Soto-García, I. Betancourt-Plaza, I. González-Martínez
Este artículo ha recibido
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Resumen
Objetivo

Mejorar el rendimiento de la escala Acute Physiology and Chronic Health Evaluation (APACHE) II para la predicción de muerte hospitalaria en pacientes críticos con cáncer.

Materiales y métodos

Estudio prospectivo de 522 pacientes con cáncer sólido admitidos en UCI. Se creó la «escala APACHE II para pacientes con cáncer sólido» (escala APACHE IIPCC); se adicionaron variables típicas del paciente oncológico crítico a la escala APACHE II general. Se evaluó la calibración (prueba de Hosmer-Lemeshow [H-L]) y discriminación (área bajo la curva de las características operativas del receptor [ACOR]). Se utilizó la mejora en la discriminación integrada (IDI), mejora neta en la reclasificación (NRI; 20% como valor de corte en el riesgo de muerte) y NRI cuantitativo (cNRI) para evaluar la mejora en la predicción de muerte hospitalaria con el nuevo modelo.

Resultados

La mortalidad hospitalaria fue del 13%. La discriminación fue superior con la escala APACHE IICCP (ACOR = 0,91 [IC del 95% 0,87-0,94; p < 0,0001]) comparado con la escala APACHE II general (ACOR = 0,62 [IC del 95% 0,54-0,70; p = 0,002]). La calibración fue mejor con la escala APACHE IICCP (H-L p = 0,267 vs. p = 0,001). En el análisis de reclasificación se observó una mejora en la predicción de muerte con la escala APACHE IICCP (IDI = 0,2994 [p < 0,0001]; cNRI = 134,3% [IC del 95% 108,8-159,8%; p < 0,0001]; NRI = 41,5% [IC del 95% 23,7-59,3%; p < 0,0001]).

Conclusiones

La escala APACHE IICCP fue superior a la escala APACHE II general en la predicción de muerte en pacientes críticos con cáncer sólido. Se requieren otros estudios que validen este nuevo modelo predictivo.

Palabras clave:
APACHE
Cáncer
Escala pronóstica
Mortalidad
Paciente críticamente enfermo
Abstract
Objective

To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) II model for predicting hospital mortality in critically ill cancer patients.

Materials and methods

This was a prospective cohort study of 522 patients admitted to ICU with a solid tumor. We developed the “APACHE II score for critically ill patients with a solid tumor” (APACHE IICCP score), in which typical variables of critically ill cancer patients were added to general APACHE II score. Calibration and discrimination were evaluated by Hosmer-Lemeshow test (H-L) and area under receiver operating characteristic curve (AROC), respectively. The improvement in predicting hospital mortality with the new model was assessed using a reclassification analysis by integrated discrimination improvement (IDI), net reclassification improvement (NRI; cut-off point of 20% in risk of death) and quantitative NRI (qNRI).

Results

The hospital mortality rate was 13%. Discrimination was superior for APACHE IICCP score (AROC=0.91 [95% CI 0.87–0.94; P<.0001]) compared to general APACHE II score (AROC=0.62 [95% CI 0.54–0.70; P=.002]). Calibration was better using APACHE IICCP score (H-L; P=.267 vs. P=.001). In reclassification analysis, an improved mortality prediction was observed with APACHE IICCP score (IDI=0.2994 [P<.0001]; total qNRI=134.3% [95% CI 108.8–159.8%; P<.0001]; total NRI=41.5% [95% CI 23.7–59.3%; P<.0001]).

Conclusions

The performance of APACHE IICCP score was superior to that observed for general APACHE II score in predicting mortality in critically ill patients with a solid tumor. Other studies validating this new predictive model are required.

Keywords:
APACHE
Cancer
Critically ill patient
Mortality
Prognostic score

Artículo

Opciones para acceder a los textos completos de la publicación Revista Española de Anestesiología y Reanimación
Suscriptor
Suscriptor de la revista

Si ya tiene sus datos de acceso, clique aquí.

Si olvidó su clave de acceso puede recuperarla clicando aquí y seleccionando la opción "He olvidado mi contraseña".
Comprar
Comprar acceso al artículo

Comprando el artículo el PDF del mismo podrá ser descargado

Comprar ahora
Contactar
Teléfono para suscripciones e incidencias
De lunes a viernes de 9h a 18h (GMT+1) excepto los meses de julio y agosto que será de 9 a 15h
Llamadas desde España
932 415 960
Llamadas desde fuera de España
+34 932 415 960
E-mail
Bibliografía
[1]
P. Schellongowski, M. Kiehl, M. Kochanek, T. Staudinger, G. Beutel, on Behalf of Intensive Care in Hematologic-Oncologic Patients.
Intensive care for cancer patients. An interdisciplinary challenge for cancer specialists and intensive care physicians.
Memo., 9 (2016), pp. 39-44
[2]
M. Soares, P. Caruso, E. Silva, J.M. Teles, S.M. Lobo, G. Friedman, et al.
Characteristics and outcomes of patients with cancer requiring admission to intensive care units: A prospective multicenter study.
Crit Care Med., 38 (2010), pp. 9-15
[3]
G. Aygencel, M. Turkoglu, G. Turkoz Sucak, M. Benekli.
Prognostic factors in critically ill cancer patients admitted to the intensive care unit.
J Crit Care., 29 (2014), pp. 618-626
[4]
S. Anisoglou, C. Asteriou, N. Barbetakis, S. Kakolyris, G. Anastasiadou, I. Pnevmatikos.
Outcome of lung cancer patients admitted to the intensive care unit with acute respiratory failure.
Hippokratia., 17 (2013), pp. 60-63
[5]
M. Legrand, A. Max, V. Peigne.
Survival in neutropenic patients with severe sepsis or septic shock.
Crit Care Med., 40 (2012), pp. 43-49
[6]
M. Soares, U.V. Silva, J.M. Teles, E. Silva, P. Caruso, S.M. Lobo, et al.
Validation of 4prognostic scores in patients with cancer admitted to Brazilian intensive care units: Results from a prospective multicenter study.
Intensive Care Med., 36 (2010), pp. 1188-1195
[7]
X.Z. Xing, Y. Gao, H.J. Wang, C.L. Huang, S.N. Qu, H. Zhang, et al.
Performance of three prognostic models in patients with cancer in need of intensive care in a medical center in China.
PLoS ONE., 10 (2015), pp. e0131329
[8]
J.S. Groeger, S. Lemeshow, K. Price, D.M. Nierman, P. White Jr., J. Klar, et al.
Multicenter outcome study of cancer patients admitted to the intensive care unit: A probability of mortality model.
J Clin Oncol., 16 (1998), pp. 761-770
[9]
F.D. Martos-Benitez, A. Soto-Garcia, A. Gutierrez-Noyola.
Clinical characteristics and outcomes of cancer patients requiring intensive care unit admission: A prospective study.
J Cancer Res Clin Oncol., 144 (2018), pp. 717-723
[10]
M.J. Pencina, R.B. d’Agostino, R.S. Vasan.
Statistical methods for assessment of added usefulness of new biomarkers.
Clin Chem Lab Med., 48 (2010), pp. 1703-1711
[11]
M. Barkhordari, M. Padyab, F. Hadaegh, F. Azizi, M. Bozorgmanesh.
Stata modules for calculating novel predictive performance indices for logistic models.
Int J Endocrinol Metab., 14 (2016), pp. e26707
[12]
K. Han, K. Song, B.W. Choi.
How to develop validate, and compare clinical prediction models involving radiological parameters: Study Design and Statistical methods.
Korean J Radiol., 17 (2016), pp. 339-350
[13]
D.W. Hosmer, S. Lemeshow, R.X. Sturdivant.
Applied logistic regression.
3rd ed., John Wiley and Sons, (2013), pp. 35-36
[14]
M. Pencina.
Novel metrics for evaluating improvement in discrimination: Net reclassification and integrated discrimination improvements for normal variables and nested models.
Stat Med, 31 (2012), pp. 101-113
[15]
W. Sawicka, R. Owczuk, M.A. Wujtewicz, M. Wujtewicz.
The effectiveness of the APACHE II SAPS II and SOFA prognostic scoring systems in patients with haematological malignancies in the intensive care unit.
Anaesthesiol Intensive Ther., 46 (2014), pp. 166-170
[16]
B. Du, Y. An, Y. Kang, X. Yu, M. Zhao, X. Ma, et al.
Characteristics of critically ill patients in ICUs in mainland China.
Crit Care Med., 41 (2013), pp. 84-92
[17]
H. Lee, Y.J. Shon, H. Kim, H. Paik, H.P. Park.
Validation of the APACHE IV model and its comparison with the APACHE II SAPS 3, and Korean SAPS 3 models for the prediction of hospital mortality in a Korean surgical intensive care unit.
Korean J Anesthesiol., 67 (2014), pp. 115-122
[18]
J.L. Vincent, R. Moreno.
Clinical review: Scoring systems in the critically ill.
Crit Care., 14 (2010), pp. 207
[19]
D. Juneja, O. Singh, P. Nasa, R. Dang.
Comparison of newer scoring systems with the conventional scoring systems in general intensive care population.
Minerva Anestesiol., 78 (2012), pp. 194-200
[20]
M.T. Gilani, M. Razavi, A.M. Azad.
A comparison of Simplified Acute Physiology Score II. Acute Physiology And Chronic Health Evaluation II and Acute Physiology And Chronic Health Evaluation III scoring system in predicting mortality and length of stay at surgical intensive care unit.
Niger Med J., 55 (2014), pp. 144-147
[21]
J. Zhou, C. Qian, M. Zhao, X. Yu, Y. Kang, X. Ma, et al.
Epidemiology and outcome of severe sepsis and septic shock in intensive care units in mainland China.
PLoS One., 9 (2014), pp. e107181
[22]
I.C.T. Almeida, M. Soares, F.A. Bozza, C.R. Shinotsuka, R. Bujokas, V.C. Souza-Dantaset, et al.
The impact of acute brain dysfunction in the outcomes of mechanically ventilated cancer patients.
[23]
L.C. Azevedo, P. Caruso, U.V. Silva, A.P. Torelly, E. Silva, E. Rezende, et al.
Outcomes for patients with cancer admitted to the ICU requiring ventilatory support: Results from a prospective multicenter study.
Chest., 146 (2014), pp. 257-266
[24]
C.Y. Shih, M.C. Hung, H.M. Lu, L. Chen, S.J. Huang, J.D. Wang.
Incidence, life expectancy and prognostic factors in cancer patients under prolonged mechanical ventilation: A nationwide analysis of 5,138 cases during 1998-2007.
Crit Care., 17 (2013), pp. R144
[25]
F.D. Martos-Benítez, A. Gutiérrez-Noyola, M. Badal, N.A. Dietrich.
Risk factors and outcomes of severe acute respiratory failure requiring invasive mechanical ventilation in cancer patients: A retrospective cohort study.
pii:S0210-5691(17)30226-7 [Epub ahead of print]
[26]
A.S. Slutsky, V.M. Ranieri.
Ventilator-induced lung injury.
N Engl J Med., 369 (2013), pp. 2126-2136
[27]
S.I. Blot, PoelaertJ, M. Kollef.
How to avoid microaspiration? A key element for the prevention of ventilator-associated pneumonia in intubated ICU patients.
BMC Infectious Diseases., 14 (2014), pp. 119
[28]
R. Fisher, C. Dangoisse, S. Crichton, S. Crichton, C. Whiteley, L. Camporota, et al.
Short-term and medium-term survival of critically ill patients with solid tumours admitted to the intensive care unit: A retrospective analysis.
BMJ Open., 6 (2016), pp. e011363
asdasdasd
Opciones de artículo
Herramientas
Material suplementario