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Health Serv Res. 1994 October; 29(4): 435–460.
PMCID: PMC1070016

Chronic conditions and risk of in-hospital death.


OBJECTIVE. This study examined the relationship of in-hospital death and 13 conditions likely to have been present prior to the patient's admission to the hospital, defined using secondary discharge diagnosis codes. DATA SOURCES AND STUDY SETTING. 1988 California computerized hospital discharge abstract data, including 24 secondary diagnosis coding slots, from all general, acute care hospitals. STUDY DESIGN. The odds ratio for in-hospital death associated with each of 13 chronic conditions was computed from a multivariable logistic regression using patient age and all chronic conditions to predict in-hospital death. DATA EXTRACTION. All 1,949,276 general medical and surgical admissions of persons over 17 years of age were included. Patients were assigned to four groups according to the mortality rate of their reason for admission; some analyses separated medical and surgical hospitalizations. PRINCIPAL FINDINGS. Overall mortality was 4.4 percent. For all cases, mortality varied by chronic condition, ranging from 5.3 percent for coronary artery disease to 18.6 percent for nutritional deficiencies. The odds ratios associated with the presence of a chronic condition were generally highest for patients in the rare mortality group. Although chronic conditions were more commonly listed for medical patients, the associated odds ratios were generally higher for surgical patients, particularly in lower mortality groups. CONCLUSIONS. Studies examining death rates need to consider the influence of chronic conditions. Chronic conditions had a particularly significant association with the likelihood of death for admission types generally associated with low mortality rates and for surgical hospitalizations. The accuracy and completeness of discharge diagnoses require further study, especially relating to chronic illnesses.

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  • Anderson G, Steinberg EP, Whittle J, Powe NR, Antebi S, Herbert R. Development of clinical and economic prognoses from Medicare claims data. JAMA. 1990 Feb 16;263(7):967–972. [PubMed]
  • Brewster AC, Karlin BG, Hyde LA, Jacobs CM, Bradbury RC, Chae YM. MEDISGRPS: a clinically based approach to classifying hospital patients at admission. Inquiry. 1985 Winter;22(4):377–387. [PubMed]
  • Browner WS, Li J, Mangano DT. In-hospital and long-term mortality in male veterans following noncardiac surgery. The Study of Perioperative Ischemia Research Group. JAMA. 1992 Jul 8;268(2):228–232. [PubMed]
  • 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(5):373–383. [PubMed]
  • Connell FA, Diehr P, Hart LG. The use of large data bases in health care studies. Annu Rev Public Health. 1987;8:51–74. [PubMed]
  • Daley J, Jencks S, Draper D, Lenhart G, Thomas N, Walker J. Predicting hospital-associated mortality for Medicare patients. A method for patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure. JAMA. 1988 Dec 23;260(24):3617–3624. [PubMed]
  • Detsky AS, Abrams HB, McLaughlin JR, Drucker DJ, Sasson Z, Johnston N, Scott JG, Forbath N, Hilliard JR. Predicting cardiac complications in patients undergoing non-cardiac surgery. J Gen Intern Med. 1986 Jul-Aug;1(4):211–219. [PubMed]
  • Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992 Jun;45(6):613–619. [PubMed]
  • Dubois RW, Rogers WH, Moxley JH, 3rd, Draper D, Brook RH. Hospital inpatient mortality. Is it a predictor of quality? N Engl J Med. 1987 Dec 24;317(26):1674–1680. [PubMed]
  • Dubois RW, Brook RH, Rogers WH. Adjusted hospital death rates: a potential screen for quality of medical care. Am J Public Health. 1987 Sep;77(9):1162–1166. [PubMed]
  • Fisher ES, Whaley FS, Krushat WM, Malenka DJ, Fleming C, Baron JA, Hsia DC. The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. Am J Public Health. 1992 Feb;82(2):243–248. [PubMed]
  • Goldman L. Cardiac risks and complications of noncardiac surgery. Ann Intern Med. 1983 Apr;98(4):504–513. [PubMed]
  • Goldman L, Caldera DL, Nussbaum SR, Southwick FS, Krogstad D, Murray B, Burke DS, O'Malley TA, Goroll AH, Caplan CH, et al. Multifactorial index of cardiac risk in noncardiac surgical procedures. N Engl J Med. 1977 Oct 20;297(16):845–850. [PubMed]
  • Green J, Passman LJ, Wintfeld N. Analyzing hospital mortality. The consequences of diversity in patient mix. JAMA. 1991 Apr 10;265(14):1849–1853. [PubMed]
  • Green J, Wintfeld N, Sharkey P, Passman LJ. The importance of severity of illness in assessing hospital mortality. JAMA. 1990 Jan 12;263(2):241–246. [PubMed]
  • Greenfield S, Blanco DM, Elashoff RM, Ganz PA. Patterns of care related to age of breast cancer patients. JAMA. 1987 May 22;257(20):2766–2770. [PubMed]
  • Greenfield S, Aronow HU, Elashoff RM, Watanabe D. Flaws in mortality data. The hazards of ignoring comorbid disease. JAMA. 1988 Oct 21;260(15):2253–2255. [PubMed]
  • Greenfield S, Apolone G, McNeil BJ, Cleary PD. 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. 1993 Feb;31(2):141–154. [PubMed]
  • Hollenberg M, Mangano DT, Browner WS, London MJ, Tubau JF, Tateo IM. Predictors of postoperative myocardial ischemia in patients undergoing noncardiac surgery. The Study of Perioperative Ischemia Research Group. JAMA. 1992 Jul 8;268(2):205–209. [PubMed]
  • Hsia DC, Krushat WM, Fagan AB, Tebbutt JA, Kusserow RP. Accuracy of diagnostic coding for Medicare patients under the prospective-payment system. N Engl J Med. 1988 Feb 11;318(6):352–355. [PubMed]
  • Hsia DC, Ahern CA, Ritchie BP, Moscoe LM, Krushat WM. Medicare reimbursement accuracy under the prospective payment system, 1985 to 1988. JAMA. 1992 Aug 19;268(7):896–899. [PubMed]
  • Iezzoni LI. Using administrative diagnostic data to assess the quality of hospital care. Pitfalls and potential of ICD-9-CM. Int J Technol Assess Health Care. 1990;6(2):272–281. [PubMed]
  • Iezzoni LI, Ash AS, Coffman G, Moskowitz MA. Admission and mid-stay MedisGroups scores as predictors of death within 30 days of hospital admission. Am J Public Health. 1991 Jan;81(1):74–78. [PubMed]
  • Iezzoni LI, Ash AS, Coffman GA, Moskowitz MA. Predicting in-hospital mortality. A comparison of severity measurement approaches. Med Care. 1992 Apr;30(4):347–359. [PubMed]
  • Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA. 1992 Apr 22;267(16):2197–2203. [PubMed]
  • Jencks SF, Daley J, Draper D, Thomas N, Lenhart G, Walker J. Interpreting hospital mortality data. The role of clinical risk adjustment. JAMA. 1988 Dec 23;260(24):3611–3616. [PubMed]
  • Jencks SF, Williams DK, Kay TL. Assessing hospital-associated deaths from discharge data. The role of length of stay and comorbidities. JAMA. 1988 Oct 21;260(15):2240–2246. [PubMed]
  • Jencks SF. Accuracy in recorded diagnoses. JAMA. 1992 Apr 22;267(16):2238–2239. [PubMed]
  • Jewell ER, Persson AV. Preoperative evaluation of the high-risk patient. Surg Clin North Am. 1985 Feb;65(1):3–19. [PubMed]
  • Keeler EB, Kahn KL, Draper D, Sherwood MJ, Rubenstein LV, Reinisch EJ, Kosecoff J, Brook RH. Changes in sickness at admission following the introduction of the prospective payment system. JAMA. 1990 Oct 17;264(15):1962–1968. [PubMed]
  • Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985 Oct;13(10):818–829. [PubMed]
  • Knaus WA, Wagner DP, Zimmerman JE, Draper EA. Variations in mortality and length of stay in intensive care units. Ann Intern Med. 1993 May 15;118(10):753–761. [PubMed]
  • Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991 Dec;100(6):1619–1636. [PubMed]
  • Lemeshow S, Teres D, Klar J, Avrunin JS, Gehlbach SH, Rapoport J. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA. 1993 Nov 24;270(20):2478–2486. [PubMed]
  • Lloyd SS, Rissing JP. Physician and coding errors in patient records. JAMA. 1985 Sep 13;254(10):1330–1336. [PubMed]
  • McMahon LF, Jr, Smits HL. Can Medicare prospective payment survive the ICD-9-CM disease classification system? Ann Intern Med. 1986 Apr;104(4):562–566. [PubMed]
  • Morris JA, Jr, MacKenzie EJ, Edelstein SL. The effect of preexisting conditions on mortality in trauma patients. JAMA. 1990 Apr 11;263(14):1942–1946. [PubMed]
  • Mullin RL. Diagnosis-related groups and severity. ICD-9-CM, the real problem. JAMA. 1985 Sep 6;254(9):1208–1210. [PubMed]
  • Park RE, Brook RH, Kosecoff J, Keesey J, Rubenstein L, Keeler E, Kahn KL, Rogers WH, Chassin MR. Explaining variations in hospital death rates. Randomness, severity of illness, quality of care. JAMA. 1990 Jul 25;264(4):484–490. [PubMed]
  • Romano PS, Mark DH. Bias in the coding of hospital discharge data and its implications for quality assessment. Med Care. 1994 Jan;32(1):81–90. [PubMed]
  • Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993 Oct;46(10):1075–1090. [PubMed]
  • Roos LL, Roos NP, Sharp SM. Monitoring adverse outcomes of surgery using administrative data. Health Care Financ Rev. 1987 Dec;Spec No:5–16. [PubMed]
  • Roos LL, Sharp SM, Cohen MM, Wajda A. Risk adjustment in claims-based research: the search for efficient approaches. J Clin Epidemiol. 1989;42(12):1193–1206. [PubMed]
  • Roos NP, Roos LL, Mossey J, Havens B. Using administrative data to predict important health outcomes. Entry to hospital, nursing home, and death. Med Care. 1988 Mar;26(3):221–239. [PubMed]
  • Savino JA, Del Guercio LR. Preoperative assessment of high-risk surgical patients. Surg Clin North Am. 1985 Aug;65(4):763–791. [PubMed]
  • Schneider AJ. Assessment of risk factors and surgical outcome. Surg Clin North Am. 1983 Oct;63(5):1113–1126. [PubMed]
  • Simborg DW. DRG creep: a new hospital-acquired disease. N Engl J Med. 1981 Jun 25;304(26):1602–1604. [PubMed]
  • Steinwald B, Dummit LA. Hospital case-mix change: sicker patients or DRG creep? Health Aff (Millwood) 1989 Summer;8(2):35–47. [PubMed]
  • Thomas JW, Ashcraft ML. Measuring severity of illness: six severity systems and their ability to explain cost variations. Inquiry. 1991 Spring;28(1):39–55. [PubMed]
  • Wennberg JE, Roos N, Sola L, Schori A, Jaffe R. Use of claims data systems to evaluate health care outcomes. Mortality and reoperation following prostatectomy. JAMA. 1987 Feb 20;257(7):933–936. [PubMed]

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