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Patient characteristics in SUPPORT: Sociodemographics, admission diagnosis, co-morbidities and acute physiology score
0895-4356/90$3.00+ 0.00 Copyri~t 0 1990Pcrgamon Press PI0
J Clia Epidaabl Vol. 43, Suppl.,29S-3lS, 1990 Printedin GreatBritain.AUrightsmrvcd
CHAPTER 4 PATIENT CHARACTERISTICS IN SUPPORT: SOCIODEMOGRAPHICS, ADMISSION DIAGNOSIS, CO-MORBIDITIES AND ACUTE PHYSIOLOGY SCORE RUSSELL S. PHILLIPS’
and WILLIAM A. KNAUS*
‘Division of Clinical Epidemiology and the Division of General Medicine and Primary Care, Dewrtment of Medicine. Beth Israel Ho&al and Harvard Medical School. the Charles A. Dana R&h Institute and. the Harvard ‘l%omdike Laboratory, Beth Is&l Hospital, Boston, MA 02215 and *ICU Research Unit, the Department of Anesthesiology, George Washington University Medical Center, Washington, DC 20037, U.S.A.
INTRODUCTION
In this chapter, several variables hypothesized to effect the length and quality of patients’ lives or the decision making process are reviewed. These variables include sociodemographics, admission diagnosis, comorbid illnesses and acute physiology score. For each, the rationale for inclusion as a study variable, the pertinent literature, and data collection instruments are described.
!XXIODEMOGRAPHICS, ADMISSION DIAGNOSIS AND CO-MORBIDITIES
Background
Sociodemographics, admission diagnosis and co-morbidities must be considered when attempting to predict or explain patients’ outcomes. Such factors as age, gender and race may impact access to care, treatment decisions and outcomes. For example, in some studies older patients have expressed preferences for less aggressive care and are less willing to undergo cardiopulmonary rescuscitation [ 1,2]. Similarly, female patients have expressed preferences for less aggressive care [ 1,2]. Race has been demonstrated to affect utilization of procedures such as coronary catheterization and bypass surgery [3]. Both age and race are associated with the decision to withdraw life-sustaining dialysis [4]. Religious and educational background may 29s
also explain patients’ preferences for care and involvement in the decision making process. In one study, patients with less education tended to prefer more aggressive care [5] while other investigators were unable to demonstrate a relation between educational background and care preferences [ 1,6]. Income and employment status may help explain a patient’8 willingness to undergo prolonged and expensive therapies. However, in a study of survivors of intensive care, family income did not influence a patient’s willingness to undergo intensive care again [5]. There are few data on patients’ preference for care at home vs in hospital care although it is likely that such factor8 as insurance coverage, living arrangements, household size and potential supports during convalescence would affect this decision. Across categories, information on diagnoses and co-morbidities is likely to be of prognostic importance. For example, as the number of comorbidities increases, it is likely that mortality will increase [q. Similarly, certain types of illnesses may be associated with decisions to withdraw or limit life-sustaining treatments. For example, patients with terminal illnesses are more likely to prefer less aggressive care [l]. Data collection
Sociodemographic information is collected from the patient or surrogate by interview and by chart review. Our preliminary experience
RUSSELLS. PHILLIPSand WILLIAMA. KNAUS
30s
revealed excellent agreement between the responses of the surrogate and patient to questions regarding demographic information. Therefore, to reduce respondent burden, these data are collected only once from the patient or surrogate, depending on who is most easily available for interview. Chart review for demogaphic information occurs at entry into the study. Specific items include patient race, household income and size, marital status, years of education and religious background. Information on age, gender, and insurance coverage is obtained directly from the medical record. Diagnosis and co-morbidities are obtained by chart review. The admission diagnosis, defined as the principal reason for hospital admission or transfer, is collected on entry into the study. These diagnoses will correspond to a standardized list (Appendix 1) and as many as four diagnoses are selected for each patient. Co-morbidities (Appendix 2) are noted if the medical record documents their presence during the 6 month period prior to the onset of the acute illness. Co-morbidities are subdivided into 7 sections: cardiovascular, neurologic, cognitive, respiratory, renal, gastrointestinal, hematologic, and general. These co-morbidities are derived from the APACHE II severity of disease classification system as described previously [8]. ACUTE
PHYSIOLOGY
SCORE
Background
Acute Physiology Score The (APS) (Appendix 3) is a key component of the APACHE prognostic scoring system [8]. APS, which can be treated as an ordinal risk scale, is comprised of weights assigned to 12 recorded physiologic measurements. Each recorded physiologic measure is the most abnormal reading during a 24-hour observation period (e.g. the lowest blood pressure for a patient in shock or the highest creatinine for a patient in acute renal failure). Most of the 12 variables are routinely measured on seriously ill hospitalized patients shortly after admission. If a measure is not taken during a 24-hour period, the value is assumed to be normal. This assumption has been previously validated [8]. Depending on the specific physiologic value obtained, a weighted score is assigned. Each weight was chosen by clinical judgment and review of existing literature to represent the
individual physiologic variable’s relative contribution to the patient’s overall risk of death. Missing values are assigned a weight of zero. The APS has a possible range of O-71 points but a usual range of O-40. APS scores are directly proportional to risk of in-hospital death [8]. Investigations of the relation of the APS to patients outcome within specific diagnostic groups have shown that the slope of the relationship between APS and outcome appears to be stable within many common diagnostic categories, although different diseases have different intercepts. The APS has an advantage over the mean values of common physiologic measures when comparing risks for different patient groups [9, lo]. This occurs because the APS not only provides an individual risk assessment (as opposed to a group mean) but its weighting scheme takes into account deviations on either side of a normal range which, if not taken into account, cancel each other out in a group risk assessment based on mean values. The APS has been extensively validated in a large number of clinical studies in countries throughout the world. Currently there are over 200 independent articles using APACHE II and the APS to prognosticate outcome for a wide range of acutely ill hospitalized adults. Each of these studies has demonstrated a strong and consistent relationship between APS and the patient’s risk of in-hospital death. The APS has facilitated comparing outcomes among patients in different intensive care units [l 1, 121 and has provided a firm conceptual and practical basis for outcome prediction using data available both at the time of the patient’s presentation for medical care as well as over time [13]. Currently the APS and the entire APACHE scoring system are undergoing an exhaustive review and examination [12]. As part of this review, both the selection and the relative weighting of all physiologic variables will be reviewed utilizing a nationally representative data base of 40 hospitals and approximately 17,000 U.S. patients. This database will be used to determine whether any adjustments to the physiologic weights are desirable and would improve predictive ability. Data collection
In the SUPPORT project, the current 12 basic components of the APS will be collected in their raw (i.e. non-weighted) form. We also collect data on serum albumin and serum bilirubin for
Chapter 4: So&demographics,
Admission Diagnosis, Co-morbidities and APS
all patients. These data are collected on hospital days 2, 4, 8, 15 and 26 following the patient’s fist day in the study. Because raw data will lx obtained, we will be ble to use the APS as now constructed as well as any subsequent modifications of the APS. REFERENCES Frankl D. Gye RK, Bellamy PE. Attitudes of hospitalized patients toward life support: A survey of 200 medical inpatients. Am J Med 1989; 86: 645-648. Stohnan CJ, Gregory JJ, Dunn D, Ripley B. Evaluation of do not resuscitate orders at a community hospital. Arch Intera Med 1989; 149: 1851-1856. Ford E, Cooper R, Castaner A, Simmons BN. Coronary arteriography and coronary bypass surgery among whites and other racial groups relative to hospital based incidence rates for coronary atery disease: Findings from NHDs. Am J PubI& He&h 1989; 79: 437-440. Port FK, Wolfe RA, Hawthorne VM, Ferguson CW. Discontinuation of dialysis therapy as a cause of death. Am J Nepbrol 1989; 9: 145-149.
56.
,,
8.
9.
10. 11. 12.
13.
31s
Davis M, Patrick DL, Southerland LI, Green ML. Patients’ and families’ preferences for medical intensive care. JAMA 1988; 260: 797-802. Shmerling RM, Bedell SE, Lilienfeld A, Ddbanco TL. Discwsmg cardiopulmonary resuscitation: A study of elderly outpatients. J Gca Intasn Mad 1988; 3: 317-321. Charlson ME, Pompeii P, Ales KL, Mackenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies; development and validation. J Chrao Dia 1987; 40: 373-383. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: A severity of disease classification system. CrIt Cue Mad 1985; 13: 818-829. Kahn KL, Brook RI-I, Draper D et al. Interpreting hospital mortality data. How can we proceed. JAMA 1988; 260: 3625-3628. Knaus WA, Wagner DP, Draper EA. The value of measuring severity of disease in clinical research on acutely iii patients. J Cbron MS 1984, 37: 455-463. Knaus WA. Draoer EA. Waaner DP. Zimmerman JE. An evaluation oioutcome frcm intensive care in major medical centers. Ann Iotera Med 1986; 1oQ: 410-418. Zimmerman JE, Knaus WA, Wagner DP, Draper EA er ul. APACHE III study design: Analytic plan for evaluation of severity and outcome. CrIt Care Med 1989; 17 (Suppl. 12): S169-S221. Chang RWS. Individual outcome prediction models for intensive care units. Lancet 1989; 2: 143-146.
J Clia Epidaabl Vol. 43, Suppl.,29S-3lS, 1990 Printedin GreatBritain.AUrightsmrvcd
CHAPTER 4 PATIENT CHARACTERISTICS IN SUPPORT: SOCIODEMOGRAPHICS, ADMISSION DIAGNOSIS, CO-MORBIDITIES AND ACUTE PHYSIOLOGY SCORE RUSSELL S. PHILLIPS’
and WILLIAM A. KNAUS*
‘Division of Clinical Epidemiology and the Division of General Medicine and Primary Care, Dewrtment of Medicine. Beth Israel Ho&al and Harvard Medical School. the Charles A. Dana R&h Institute and. the Harvard ‘l%omdike Laboratory, Beth Is&l Hospital, Boston, MA 02215 and *ICU Research Unit, the Department of Anesthesiology, George Washington University Medical Center, Washington, DC 20037, U.S.A.
INTRODUCTION
In this chapter, several variables hypothesized to effect the length and quality of patients’ lives or the decision making process are reviewed. These variables include sociodemographics, admission diagnosis, comorbid illnesses and acute physiology score. For each, the rationale for inclusion as a study variable, the pertinent literature, and data collection instruments are described.
!XXIODEMOGRAPHICS, ADMISSION DIAGNOSIS AND CO-MORBIDITIES
Background
Sociodemographics, admission diagnosis and co-morbidities must be considered when attempting to predict or explain patients’ outcomes. Such factors as age, gender and race may impact access to care, treatment decisions and outcomes. For example, in some studies older patients have expressed preferences for less aggressive care and are less willing to undergo cardiopulmonary rescuscitation [ 1,2]. Similarly, female patients have expressed preferences for less aggressive care [ 1,2]. Race has been demonstrated to affect utilization of procedures such as coronary catheterization and bypass surgery [3]. Both age and race are associated with the decision to withdraw life-sustaining dialysis [4]. Religious and educational background may 29s
also explain patients’ preferences for care and involvement in the decision making process. In one study, patients with less education tended to prefer more aggressive care [5] while other investigators were unable to demonstrate a relation between educational background and care preferences [ 1,6]. Income and employment status may help explain a patient’8 willingness to undergo prolonged and expensive therapies. However, in a study of survivors of intensive care, family income did not influence a patient’s willingness to undergo intensive care again [5]. There are few data on patients’ preference for care at home vs in hospital care although it is likely that such factor8 as insurance coverage, living arrangements, household size and potential supports during convalescence would affect this decision. Across categories, information on diagnoses and co-morbidities is likely to be of prognostic importance. For example, as the number of comorbidities increases, it is likely that mortality will increase [q. Similarly, certain types of illnesses may be associated with decisions to withdraw or limit life-sustaining treatments. For example, patients with terminal illnesses are more likely to prefer less aggressive care [l]. Data collection
Sociodemographic information is collected from the patient or surrogate by interview and by chart review. Our preliminary experience
RUSSELLS. PHILLIPSand WILLIAMA. KNAUS
30s
revealed excellent agreement between the responses of the surrogate and patient to questions regarding demographic information. Therefore, to reduce respondent burden, these data are collected only once from the patient or surrogate, depending on who is most easily available for interview. Chart review for demogaphic information occurs at entry into the study. Specific items include patient race, household income and size, marital status, years of education and religious background. Information on age, gender, and insurance coverage is obtained directly from the medical record. Diagnosis and co-morbidities are obtained by chart review. The admission diagnosis, defined as the principal reason for hospital admission or transfer, is collected on entry into the study. These diagnoses will correspond to a standardized list (Appendix 1) and as many as four diagnoses are selected for each patient. Co-morbidities (Appendix 2) are noted if the medical record documents their presence during the 6 month period prior to the onset of the acute illness. Co-morbidities are subdivided into 7 sections: cardiovascular, neurologic, cognitive, respiratory, renal, gastrointestinal, hematologic, and general. These co-morbidities are derived from the APACHE II severity of disease classification system as described previously [8]. ACUTE
PHYSIOLOGY
SCORE
Background
Acute Physiology Score The (APS) (Appendix 3) is a key component of the APACHE prognostic scoring system [8]. APS, which can be treated as an ordinal risk scale, is comprised of weights assigned to 12 recorded physiologic measurements. Each recorded physiologic measure is the most abnormal reading during a 24-hour observation period (e.g. the lowest blood pressure for a patient in shock or the highest creatinine for a patient in acute renal failure). Most of the 12 variables are routinely measured on seriously ill hospitalized patients shortly after admission. If a measure is not taken during a 24-hour period, the value is assumed to be normal. This assumption has been previously validated [8]. Depending on the specific physiologic value obtained, a weighted score is assigned. Each weight was chosen by clinical judgment and review of existing literature to represent the
individual physiologic variable’s relative contribution to the patient’s overall risk of death. Missing values are assigned a weight of zero. The APS has a possible range of O-71 points but a usual range of O-40. APS scores are directly proportional to risk of in-hospital death [8]. Investigations of the relation of the APS to patients outcome within specific diagnostic groups have shown that the slope of the relationship between APS and outcome appears to be stable within many common diagnostic categories, although different diseases have different intercepts. The APS has an advantage over the mean values of common physiologic measures when comparing risks for different patient groups [9, lo]. This occurs because the APS not only provides an individual risk assessment (as opposed to a group mean) but its weighting scheme takes into account deviations on either side of a normal range which, if not taken into account, cancel each other out in a group risk assessment based on mean values. The APS has been extensively validated in a large number of clinical studies in countries throughout the world. Currently there are over 200 independent articles using APACHE II and the APS to prognosticate outcome for a wide range of acutely ill hospitalized adults. Each of these studies has demonstrated a strong and consistent relationship between APS and the patient’s risk of in-hospital death. The APS has facilitated comparing outcomes among patients in different intensive care units [l 1, 121 and has provided a firm conceptual and practical basis for outcome prediction using data available both at the time of the patient’s presentation for medical care as well as over time [13]. Currently the APS and the entire APACHE scoring system are undergoing an exhaustive review and examination [12]. As part of this review, both the selection and the relative weighting of all physiologic variables will be reviewed utilizing a nationally representative data base of 40 hospitals and approximately 17,000 U.S. patients. This database will be used to determine whether any adjustments to the physiologic weights are desirable and would improve predictive ability. Data collection
In the SUPPORT project, the current 12 basic components of the APS will be collected in their raw (i.e. non-weighted) form. We also collect data on serum albumin and serum bilirubin for
Chapter 4: So&demographics,
Admission Diagnosis, Co-morbidities and APS
all patients. These data are collected on hospital days 2, 4, 8, 15 and 26 following the patient’s fist day in the study. Because raw data will lx obtained, we will be ble to use the APS as now constructed as well as any subsequent modifications of the APS. REFERENCES Frankl D. Gye RK, Bellamy PE. Attitudes of hospitalized patients toward life support: A survey of 200 medical inpatients. Am J Med 1989; 86: 645-648. Stohnan CJ, Gregory JJ, Dunn D, Ripley B. Evaluation of do not resuscitate orders at a community hospital. Arch Intera Med 1989; 149: 1851-1856. Ford E, Cooper R, Castaner A, Simmons BN. Coronary arteriography and coronary bypass surgery among whites and other racial groups relative to hospital based incidence rates for coronary atery disease: Findings from NHDs. Am J PubI& He&h 1989; 79: 437-440. Port FK, Wolfe RA, Hawthorne VM, Ferguson CW. Discontinuation of dialysis therapy as a cause of death. Am J Nepbrol 1989; 9: 145-149.
56.
,,
8.
9.
10. 11. 12.
13.
31s
Davis M, Patrick DL, Southerland LI, Green ML. Patients’ and families’ preferences for medical intensive care. JAMA 1988; 260: 797-802. Shmerling RM, Bedell SE, Lilienfeld A, Ddbanco TL. Discwsmg cardiopulmonary resuscitation: A study of elderly outpatients. J Gca Intasn Mad 1988; 3: 317-321. Charlson ME, Pompeii P, Ales KL, Mackenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies; development and validation. J Chrao Dia 1987; 40: 373-383. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: A severity of disease classification system. CrIt Cue Mad 1985; 13: 818-829. Kahn KL, Brook RI-I, Draper D et al. Interpreting hospital mortality data. How can we proceed. JAMA 1988; 260: 3625-3628. Knaus WA, Wagner DP, Draper EA. The value of measuring severity of disease in clinical research on acutely iii patients. J Cbron MS 1984, 37: 455-463. Knaus WA. Draoer EA. Waaner DP. Zimmerman JE. An evaluation oioutcome frcm intensive care in major medical centers. Ann Iotera Med 1986; 1oQ: 410-418. Zimmerman JE, Knaus WA, Wagner DP, Draper EA er ul. APACHE III study design: Analytic plan for evaluation of severity and outcome. CrIt Care Med 1989; 17 (Suppl. 12): S169-S221. Chang RWS. Individual outcome prediction models for intensive care units. Lancet 1989; 2: 143-146.