Información de la revista
Vol. 13. Núm. 4.
Páginas 292-302 (julio - agosto 1999)
Respuestas rápidas
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Vol. 13. Núm. 4.
Páginas 292-302 (julio - agosto 1999)
Open Access
Comorbilidad crónica y homogeneidad de los grupos de diagnósticos relacionados
Visitas
10469
J. Librero1,2, S. Peiró1,2,
Autor para correspondencia
vpeiro@san.gva.es

Institut Valencià d'Estudis en Salud Pública. Salvador Peiró. Juan de Garay, 21. 46017 Valencia.
, R. Ordiñana2
1 Institut Valencià d'Estudis en Salud Pública (IVESP)
2 Instituto de Investigación en Servicios de Salud (IISS)
Este artículo ha recibido

Under a Creative Commons license
Información del artículo
Resumen
Bibliografía
Descargar PDF
Estadísticas
Resumen
Objetivo

Los Grupos de Diagnósticos Relacionados (GDR) se utilizan para comparar la eficiencia de hospitales y servicios, esperándose que los pacientes incluidos en un mismo GDR se comporten de forma homogénea respecto a la duración de la estancia. El objetivo de este estudio es valorar, en el entorno del Sistema Nacional de Salud, la variabilidad interna de determinados GDR, en función de la comorbilidad de los pacientes.

Método

Se analizó, en función de diferentes puntuaciones de comorbilidad medida por el índice de Charlson (ICh), la duración de la estancia, mortalidad intrahospitalaria y reingresos urgentes a los 30 y 365 días, en 106.673 episodios de hospitalización (excluidos menores de 17 años, obstetricia y psiquiatría) provenientes de 12 hospitales, así como en 17 GDR seleccionados por su mayor frecuencia y comorbilidad.

Resultados

En el análisis agregado, la duración de la estancia (desde 8,5 días en los pacientes sin comorbilidad, a 17,0 días en los pacientes con puntuación superior a 4) y el porcentaje de mortalidad intrahospitalaria (desde el 3,7% en los pacientes sin comorbilidad, al 17,6% en los pacientes con mayor puntuación) aumentaron significativamente con cada nivel del índice de Charlson.

La proporción de reingresos a los 30 días aumentó desde 4,7 al 10,9%, según puntuación de comorbilidad. Los ingresos al año aumentaron desde el 14,8% en los pacientes con puntuación 0, al 35,2% en los pacientes con puntuación 3-4, para disminuir hasta el 27,9% en los pacientes con puntuación superior a 4. En el análisis por GRD, 8 de los 17 grupos analizados mostraron una significativa mayor duración de la estancia al aumentar de comorbilidad. Algunos GDR también mostraron variabilidad intra-grupo respecto a la mortalidad y el reingreso, especialmente a los 365 días.

Conclusiones

Algunos GDR muestran una variabilidad interna significativa en función de la comorbilidad, que podría estar produciendo una falsa peor valoración de la eficiencia de los hospitales que tratan pacientes con mayor comorbilidad.

Palabras clave:
Grupos de Diagnósticos Relacionados
Comorbilidad
Índice de Charlson
Duración de la estancia
Mortalidad intrahospitalaria
Reingreso hospitalario
Summary
Objetive

One of the ways to compare the efficiency of different hospitals and services is to evaluate Diagnostic Related Groups (DRGs), with the hypothesis that patients in the same RDG will present homogeneous behavior with respect to length of stay. The object of this study was to evaluate in the context os the National Health System the internal variability of specific DRGs in terms of the patients' comorbidity.

Methods

On the basis of various comorbidity scores measured with the Charlson index (ChI), we analyzed length of stay, inhospital mortality and emergency readmissions at 30 and 365 days in 106.673 hospitalizations (excluding subjects younger than 17 years of age, and obstetrics and psychiatric patients) in 12 hospitals, and in 17 DRGs selected on the basis of their greater frequency and comorbidity.

Results

In the aggregated analysis, length of stay (from 8.5 days in patients with no comorbidity to 17.0 days in patients with scores higher than 4) and inhospital mortality rates (from 3.7% in patients with no comorbidity to 17.6% in patients with highest score) increased significantly with each level of the Charlson index. The readmission rate at 30 days rose from 4.7% to 10.9% also in step with increases in comorbidity scores. Readmissions at one year varied from 14.8% in patients with scores of 0 to 35.2% in patients with scores of 3-4, and dropped to 27.9% in patients with scores higher than 4. When analysing different DRGs, 8 of the 17 groups studied showed a significantly higher length of stay with increased comorbidity scores. Some DRGs also showed intra-group variability with respect to mortality and readmission, particularly at 365 days.

Conclusions

Some DRGs show significant internal variability in terms of comorbidity that may be generating a false worse evaluation of the efficiency of hospitals that treat patients with higher comorbidity.

El Texto completo está disponible en PDF
Bibliografía
[1.]
D.A. Brand, L. Quam, S. Leatheman.
Medical practice profiling: concepts and caveats.
Med Care Res Rev, 52 (1995), pp. 223-251
[2.]
F.A. Connell, P. Diehr, L.G. Hart.
The use of large data dates in health care studies.
Ann Rev Public Health, 8 (1987), pp. 51-74
[3.]
N.P. Roos, C.D. Black, N. Frohlich, C. Decoster, M.M. Cohen, D.J. Tataryn, et al.
A Population-based health information system.
Med Care, 33 (1995), pp. Ds13-Dd20
[4.]
J.P. Kassirer.
The use and abuse of practice profiles.
N Eng J Med, 330 (1994), pp. 634-636
[5.]
H.G. Welch, E.M. Miller, W.P. Welch.
Physician profiling. An analysis of impatient practice patterns in Florida and Oregon.
N Eng J Med, 330 (1994), pp. 607-612
[6.]
S. Greenfield.
The state of outcomes research.Are we on target?.
N Eng J Med, 320 (1989), pp. 1142-1144
[7.]
L.l. Iezzoni.
Risk adjustment for Medical Effectiveness Research. Data and Methods.
pp. 83-97
[8.]
R.B. Fetter, Y. Shin, J.l. Freeman, R.F. Averill, J.D. Thompson.
Case Mix Definition by Diagnosis Related Groups.
Med Care, 18 (1980), pp. 1-53
[9.]
L.l. Iezzoni.
Risk adjustment for measuring health care outcomes Ann Arbor.
Health Administration Press, (1994),
[10.]
L.L. Roos, N.P. Roos, S.M. Cageorge, J.P. Nicol, B. Comm.
How good are the data? reliability of one heath care dara bank.
Med Care, 20 (1992), pp. 266-276
[11.]
J. Green, N. Wintfeld.
How accurate are hospital discharge data for evaluating efectiveness of care?.
Med Care, 31 (1993), pp. 719-731
[12.]
D.C. Hsia, C.A. Ahern, B.P. Ritchie, L.M. Moscoe, W.M. Krushat.
Medicare Reimbursement Accuracy under the Prospective Payment System, 1985 to 1988.
JAMA, 268 (1992), pp. 896-899
[13.]
L.F. McMahon, H.L. Smits.
Can Medicare Prospective Payment Survive the ICD9-CM Disease Classification System?.
Ann Intern Med, 104 (1986), pp. 562-566
[14.]
A.R. Feinstein.
ICD, POR, and DRG. Unsolved Scientific Problems in the Nosology of Clinical Medicine.
Arch Intern Med, 148 (1988), pp. 2269-2274
[15.]
L.l. Iezzoni.
Using Administrative Diagnostic Data to Adress the Quality of Hospital Care: The Pitfalls and Potential of ICD-9-CM.
Int J Technol Assess Health Care, 6 (1991), pp. 272-281
[16.]
S.F. Jencks, J. Daley, D. Draper, N. Thomas, G. Lenhart, J. Walker.
Interpreting hospital mortality data. The role of clinical risk adjusting.
JAMA, 260 (1988), pp. 3611-3616
[17.]
K.L. Kahn, R.H. Brook, D. Draper, E.B. Keeler, L.V. Rubenstein, W.H. Rogers, et al.
Interpreting hospital mortality data. How can we proceed?.
JAMA, 260 (1988), pp. 3625-3628
[18.]
P.J. Sanazaro, D.H. Mills.
A critique of the use of generic screening in quality assessment.
JAMA, 265 (1991), pp. 1977-1981
[19.]
J. Green, L.J. Passman, N. Wintfeld.
Analyzing hospital mortality. The consequences of diversity in patient mix.
JAMA, 265 (1991), pp. 1849-1853
[20.]
R.A. Hayward, A.M. Bernard, J.S. Rosevear, J.E. Anderson, L.F. Mc-Mahon.
An evaluation of generic screens for poor quality of hospital care on a general medicine service.
Med Care, 31 (1993), pp. 394-402
[21.]
J.P. Kassirer.
The quality of care and the quality of measuring i.t.
N Eng J Med, 329 (1993), pp. 1263-1265
[22.]
P.D. Cleary, S. Greenfield.
Mulley AG: Variations in lenght of stay and outcomes for six medical and surgical conditions in Massachusetts and California.
JAMA, 266 (1991), pp. 733-740
[23.]
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
[24.]
S. Greenfield, G. Apolone, B.J. McNeil, P.D. Cleary.
The importance of co-existent disease in the occurrence of postoperative complications and one-year recovery in patients undergoing total hip replacement. Comorbidity and outcomes after hip replacement.
Med Care, 31 (1993), pp. 141-154
[25.]
L. Rabeneck, A.R. Feinstein, R.I. Horwitz, C.K. Wells.
A new clinical prognostic staying system for acute pancreatitis.
AM J Med, 95 (1993), pp. 61-70
[26.]
P.S. Romano, Roosll, J.G. Jollis.
Further evidence concerning the use of a comorbidity index with ICD-9-CM administrative data.
J Clin Epidemiol, 46 (1993), pp. 1085-1090
[27.]
E.L. Siegler, M.G. Stineman, G. Maislin.
Development of complications during rehabilitation.
Arch Intern Med, 154 (1994), pp. 2185-2190
[28.]
C. Melfi, E. Holleman, D. Arthur, A. Katz.
Selecting a patient characteristics index for the prediction of medical outcomes using administrative claims data.
J Clin Epidemiol, 48 (1995), pp. 917-926
[29.]
D.J. Brailer, M.V. Kroch E Pauly, J. Huang.
Comorbidity-adjusted complication risk. A new outcome quality measure.
Med Care, 34 (1996), pp. 490-505
[30.]
M.E. Charlson, C.R. Mackenzie, J.P. Gold, K.L. Ales, M. Topkins, G.T. Shires.
Risk for postoperative congestive hear failure.
Surg Gynecol Obstet, 172 (1991), pp. 95-104
[31.]
J.N. Katz, S.J. Lipson, M.G. Larson, J.M. Mclnnes, A.H. Fossel, M.H. Liang.
The outcome of descompressive laminectomy for degenerative lumbar spinal stenosis.
J Bone Joint Surg Am, 73 (1991), pp. 809-816
[32.]
M.H. Kaplan, A.R. Feinstein.
The importance of classifying initial co-morbidity in evaluating the outcome of diabetes mellitus.
J Chron Dis, 27 (1974), pp. 387-404
[33.]
M.E. Charlson, P. Pompei, K.L. Ales, C.R. MacKenzie.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
J Chron Dis, 40 (1987), pp. 373-383
[34.]
S.F. Jencks, D.K. Williams, T.L. Kay.
Assessing hospital associated deaths from discharge data: the role of length of stay and comorbidities.
Jama, 260 (1988), pp. 2240-2246
[35.]
P. Pompei, M.E. Charlson, R.G. Douglas.
Clinical assessments as predictors of one year survival after hospitalizacion: implications for prognostic stratificación.
J Clin Epidemiol, 41 (1988), pp. 275-284
[36.]
S. Greenfield, H.U. Aronow, R.M. Elashoff, B. Watanabe.
Flaws in mortality data: the hazards of ignoring comorbid disease.
JAMA, 260 (1988), pp. 2253-2255
[37.]
N.P. Roos, J.E. Wennberg, D.J. Malenka, E.S. Fisher, K. McPherson, T. Folmer Andersen, et al.
Mortality and reoperation after open and transurethral resection of the prostate for benign prostatic hyperplasia.
N Eng J Med, 320 (1989), pp. 1120-1124
[38.]
J. Concato, R.I. Horwitz, A.R. Feinstein, A.G. Elmore, S.F. Schiff.
Problems of comorbidity in mortality after prostatectomy.
JAMA, 267 (1992), pp. 1077-1082
[39.]
P.S. Romano, L.L. Roos, J.G. Jollis.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives.
J Clin Epidemiol, 46 (1993), pp. 1075-1079
[40.]
W. D'Hoore, C. Sicotte, C. Tilquin.
Rinsk adjustmnet in outcome assessment: the Charlson Comorbidity Index.
Meth Inform Med, 32 (1993), pp. 382-387
[41.]
L.l. lezzoni, T. Heeren, S.M. Foley, J. Daley, J. Hughes, G.A. Coffman.
Chronic conditions and risk of inhospital death.
Health Serv Res, 29 (1994), pp. 435-460
[42.]
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 Epidermiol, 49 (1996), pp. 743-747
[43.]
L.l. lezzoni, M. Shwartz, A.S. Ash, J.S. Hugues, J. Daley, Y.D. Mackiernan.
Using severity-adjusted stroke mortality rates to judge hospitals.
Int J Qual Health Care, 7 (1995), pp. 81-94
[44.]
L.l. lezzoni, M. Shwartz, A.S. Ash, J.S. Hughes, J. Daley, Y.D. Mackiernan.
Severity measurement methods and judging hospital death rates for pneumonia.
Med Care, 34 (1996), pp. 11-28
[45.]
B.S. Linn, M.W. Linn, L. Gurel.
Cumulative illness rating scale.
J Am Geriatr Soc, 16 (1968), pp. 622-628
[46.]
A. Lahad, Y. Yodfat.
Impact of comorbidity on well-being in hypertension: case control study.
J Hum Hypertens, 7 (1993), pp. 611-614
[47.]
J.N. Katz, E.A. Wrigth, E. Guadagnoli, M.H. Liang, E.W. Jarlson, P.D. Cleary.
Differences between men and women undergoing major orthopedic surgery for degenerative arthritis Rheumatism, 37 (1994), pp. 687
[48.]
G. Stucki, M.H. Liang, S.J. Lipson, A.H. Fossel, J.N. Katz.
Contribution of neuromuscular impairment tophysical functional status in patients with spinal stenosis.
J Rheumatology, 21 (1994), pp. 1338-1343
[49.]
C.D. Mulrow, M.B. Gerety, J.E. Cornell, V.A. Lawrence, D.N. Kanten.
The relationship between disease and function and perceived health in very frail elders.
J Am Geriart Soc, 42 (1994), pp. 374-380
[50.]
S. Peiró, J. Librero, A. Benages Martínez.
Factores asociados al reingreso hospitalario urgente en patología digestiva y hepatobiliar.
Med Clin (Barc), 107 (1996), pp. 4-13
[51.]
K. Waite, E. Oddone, M. Weinberger, G. Samsa, M. Foy, W. Henderson.
Lack of association between patient's measured burden of disease and risk for hospital readmission.
J Clin Epidemiol, 47 (1994), pp. 1229-1236
[52.]
P.A. Parmelee, P.D. Thuras, I.R. Katz, M.P. Lawton.
Validation of the Cumullative Illness Rating Scale in geriatric residential population.
J Am Geriatr Soc, 43 (1995), pp. 130-137
[53.]
J.N. Katz, L.C. Chang, O. Sangha, A.H. Fossel, D.W. Bates.
Can comorbidity be measured by questionnaire rather than medical record review?.
Med Care, 34 (1996), pp. 73-84
[54.]
S. Greenfield, D.M. Blanco, R.M. Elashoff.
Developing and testing of a new index of comorbility.
Clin Res, 35 (1987), pp. 346.A
[55.]
W.A. Knaus, E.A. Draper, D.P. Wagner, J.E. Zimmerman.
APACHE II: a severity of disease classification system.
Crit Care Med, 13 (1985), pp. 818-829
[56.]
E. Keeler, K.L. Kahn, D. Draper, M.J. Sherwood, L.V. Rubenstein, E.J. Reinisch, et al.
Changes in sickness at admission following the introduction of the Prospective Payment System.
JAMA, 264 (1990), pp. 1962-1968
[57.]
K.C. Shestak, N.F. Jones, W. Wu, J.T. Johnson, E.N. Myers.
Effect of advanced age and medical disease on the outcome of microvascular reconstruction for head and neck defects.
Head Neck, 14 (1992), pp. 14-18
[58.]
G.R. Parkerson, W.E. Broadhead, C.K. Tse.
The Duke severity of Illiness Checklist (DUSOI) for measurement of severity and comorbidity.
J Clin Epidemiol, 46 (1993), pp. 379-393
[59.]
The frequency of «do not resuscitate» order in aged-patients: effect of patient-and non-patient-related factors. Neth J Med 1994;44:78–83
[60.]
Dirección General del Servicio Valenciano de Salud.
Memoria de actividad – Assistencia especializada 1993.
Generalitat Valenciana, Conselleria de Sanitat i Consum, (1995),
[61.]
J. Librero, S. Peiró.
Medición de la efectividad hospitalaria: Calidad de las fuentes de información. El Conjunto Mínimo de Datos Básicos de la Comunidad Valenciana. Gac.
Sanit, 9 (1995), pp. 104-105.A
[62.]
J. Librero, R. Ordiñana, S. Peiró.
Análisis automatizado de la calidad del conjunto mínimo de datos básicos. Implicaciones para los sistemas de ajuste riesgos.
Gac Sanit, 12 (1998), pp. 9-21
[63.]
S.D. Horn, P.D. Bulkley, A.F. Chambers, R.A. Horn, C.J. Schramm.
Interhospital Differences in Severity of Illnes: Problems for Problems for Prospective Payment Based on Diagnosis-Related Groups (DRGs).
N Eng J Meg, 313 (1985), pp. 20-24
[64.]
S.D. Horn, R.A. Horn, P.D. Sharkey.
The Severity of Illness Index as a Severity Adjustment to Diagnosis-Related Groups.
Health Care Financ Rev, (1984), pp. 33-45
[65.]
L.F. McMahon, J.E. Billi.
Measurement of Severity of Illness and the medicare Prospective Payment System: State of the Art and Future Directions.
J Gen Intern Med, 3 (1988), pp. 482-490
[66.]
H.L. Smits, R.B. Fetter, L.F. McMahon.
Variation in resourse Use within Diagnosis. Related Groups: The Severity Issue.
Health Care Financing Review, (1984), pp. 71-78
[67.]
T.E. McGuire.
An Evaluation of Diagnosis-Related Group Severity and Complexity Refinement.
Health Care Financing Review, 12 (1991), pp. 49-60
[68.]
J.L. Freeman, R.B. Fetter, H. Park, K.C. Schneider, J.L. Lichtenstein, et al.
Diagnosis-Related Groups refinement with diagnosis-and procedure-specific comorbidities and complications.
Med Care, 33 (1995), pp. 806-827
[69.]
F. Pradas, S. Peiró, J. Librero, E. Bernal.
Variabilidad interna del sistema AP-DGR en relación a resultados de atención primaria hos-pitalaria. En: La reforma de los modelos sanitarios, la definición del producto y las necesidades de gestión.
pp. 257-267
[70.]
L.l. Iezzoni, S.M. Foley, J. Daley, J. Hughes, E.S. Fisher, T. Heeren.
Comorbidities, complications and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality?.
JAMA, (1992), pp. 2197-2203
[71.]
J. Librero, S. Peiró, R. Ordiñana.
¿Previenen las enfermedades crónicas la mortalidad intrahospitalaria?.
Paradojas y sesgos de información en el Conjunto Mínimo de Datos Básico al alta hospitalaria. Gac Sanit, 12 (1998), pp. 199-206
[72.]
S. Peiró, J. Librero.
Selection bias, indexes of «burden» and risk.
J Clin Epidemiol, 49 (1996), pp. 495
[73.]
D.M. Smith, J.A. Norton, C.J. McDonald.
Nonelective readmissions of medical patients.
J Chronic Dis, 38 (1985), pp. 213-224
[74.]
C.C. Fethke, I.M. Smith, N. Johnson.
Risk factors affecting readmission on the elderly into the health care system.
Med Care, 24 (1986), pp. 429-437
[75.]
R.L. Evans, R.D. Hendricks, K.V. Lawrence, D.S. Bishop.
Identifyging factors associated with health care use: a hospital-based risk screening index.
Soc Sci Med, 27 (1988), pp. 947-954
[76.]
J.M. Corrigan, J.B. Martin.
Identification of factors associated with hospital readmission and development of a predictive model.
Health Serv Res, 27 (1992), pp. 81-101
[77.]
M.E. Charlson.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: a response.
J Clin Epidemiol, 46 (1993), pp. 1083-1084
[78.]
R.A. Deyo.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: a response:.
J Clin Epidemiol, 46 (1993), pp. 1081-1082
[79.]
M. Charlson, T.P. Szatrowsky, J. Peterson, J. Gold.
Validation of a combined comorbidity index.
J Clin Epidemiol, 47 (1994), pp. 1245-1251
Copyright © 1999. Sociedad Española de Salud Pública y Administración Sanitaria
Descargar PDF
Idiomas
Gaceta Sanitaria
Opciones de artículo
Herramientas
es en

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?