When people become sick, they have a great many questions about how their illness will affect them. Is it dangerous? Could I die of it? Will there be pain? How long will I be able to continue my present activities? Will it ever go away altogether? Most patients and their families want to know what to expect, even in situations where little can be done about their illness.
Prognosis is the prediction of the course of disease following its onset.
This chapter reviews the ways in which the course of disease can be described. The intention is to give readers a better understanding of a difficult but indispensable task—predicting patients’ futures as closely as possible. The objective is to avoid expressing prognoses with vagueness when unnecessary and with certainty when misleading.
Doctors and patients want to know the general course of the illness, but they want to go further and tailor this information to their particular situation as much as possible. For example, even though ovarian cancer is usually fatal in the long run, women with this cancer may live from a few months to many years, and they want to know where on this continuum their particular case is likely to fall.
Studies of prognosis are similar to cohort studies of risk. Patients are assembled who have a particular disease or illness in common, they are followed forward in time, and clinical outcomes are measured. Patient characteristics that are associated with an outcome of the disease, called prognostic factors, are identified. Prognostic factors are analogous to risk factors, except that they represent a different part of the disease spectrum, from disease to outcomes. Case-control studies of people with the disease who do and do not have a bad outcome can also estimate the relative risk associated with various prognostic factors, but they are unable to provide information on outcome rates (see Chapter 6).
Differences in Risk and Prognostic Factors
Risk and prognostic factors differ from each other in several ways.
The Patients are Different
Studies of risk factors usually deal with healthy people, whereas studies of prognostic factors are of sick people.
The Outcomes are Different
For risk, the event being counted is usually the onset of disease. For prognosis, consequences of disease are counted, including death, complications, disability, and suffering.
The Rates are Different
Risk factors are usually for low-probability events. Yearly rates for the onset of various diseases are on the order of 1/1,000 to 1/100,000 or less. As a result, relationships between exposure and disease are difficult to confirm in the course of day-to-day clinical experiences, even for astute clinicians. Prognosis, on the other hand, describes relatively frequent events. For example, several percent of patients with acute myocardial infarction die before leaving the hospital.
The Factors May be Different
Variables associated with an increased risk are not necessarily the same as those marking a worse prognosis. Often, they are considerably different for a given disease. For example, the number of well-established risk factors for cardiovascular disease (hypertension, smoking, dyslipidemia, diabetes, and family history of coronary heart disease) is inversely related to the risk of dying in the hospital after a first myocardial infarction (Fig. 7.1) .
Figure 7.1.Risk and prognostic factors for first myocardial infarction.
(Redrawn with permission from Canto JC, Kiefe CI, Rogers WJ, et al. Number if coronary heart disease risk factors and mortality in patients with first myocardial infarction. JAMA 2011;306:2120–2127.)
Clinicians can often form good estimates of short-term prognosis from their own personal experience. However, they may be less able to sort out, without the assistance of research, the various factors that are related to long-term prognosis or the complex ways in which prognostic factors are related to one another.
Clinical Course and Natural History of Disease
Prognosis can be described as either the clinical course or natural history of disease. The term clinical course describes the evolution (prognosis) of a disease that has come under medical care and has been treated in a variety of ways that affect the subsequent course of events. Patients usually receive medical care at some time in the course of their illness when they have diseases that cause symptoms such as pain, failure to thrive, disfigurement, or unusual behavior. Examples include type 1 diabetes mellitus, carcinoma of the lung, and rabies. After such a disease is recognized, it is likely to be treated.
The prognosis of disease without medical intervention is termed the natural history of disease. Natural history describes how patients fare if nothing is done about their disease. A great many health conditions do not come under medical care, even in countries with advanced health care systems. They remain unrecognized because they are asymptomatic (e.g., many cancers of the prostate are occult and slow growing) and are, therefore, unrecognized in life. For others, such as osteoarthritis, mild depression, or low-grade anemia, people may consider their symptoms to be one of the ordinary discomforts of daily living, not a disease and, therefore, not seek medical care for them.
Irritable bowel syndrome is a common condition that involves abdominal pain and disturbed bowel habits not caused by other diseases. How often do patients with this condition visit doctors? In a British cohort of 3,875 people without irritable bowel syndrome at baseline, 15% developed the syndrome over the next 10 years 2. Of these, only 17% consulted their primary care physician with related symptoms at least once in 10 years, and 4% had consulted in the past year. In another study, characteristics of the abdominal complaints did not account for whether patients with irritable bowel syndrome sought health care for their symptoms 3.
Elements of Prognostic Studies
Figure 7.2 shows the basic design of a cohort study of prognosis. At best, studies of prognosis are of a defined clinical or geographic population, begin observation at a specified point in time in the course of disease, follow-up all patients for an adequate period of time, and measure clinically important outcomes.
Figure 7.2.Design of a cohort study of risk.
The purpose of representative sampling from a defined population is to assure that study results have the greatest possible generalizability. It is sometimes possible to study prognosis in a complete sample of patients with new-onset disease in large regions. In some countries, the existence of national medical records makes population-based studies of prognosis possible.
Dutch investigators studied the risk of complications of pregnancy in women with type 1 diabetes mellitus 4. The sample included all of the 323 women in the Netherlands with type 1 diabetes who had become pregnant during a 1-year period and had been under care in one of the nation’s 118 hospitals. Most pregnancies were planned, and during pregnancy, most women took folic acid supplements and had good control of their blood sugar. Nevertheless, complication rates in newborns were much higher than in the general population. Neonatal morbidity (one or more complications) occurred in 80% of infants and rates of congenital malformations and unusually large newborns (macrosomia) were three-fold to 12-fold higher than in the general population. This study suggests that good control of blood sugar alone was not sufficient to prevent complications of pregnancy in women with type 1 diabetes.
Even without national medical records, population-based studies are possible. In the United States, the Network of Organ Sharing collects data on all patients with transplants, and the Surveillance, Epidemiology, and End Results (SEER) program collects incidence and survival data on all patients with new-onset cancers in several large areas of the country, comprising 28% of the U.S. population. For primary care questions, in the United States and elsewhere, individual practices in communities have banded together into “primary care research networks” to collect research data on their patients’ care.
Most studies of prognosis, especially for less common diseases, are of local patients. For these studies, it is especially important to provide the information that users can rely on to decide whether the results generalize to their own situation: patients’ characteristics (e.g., age, severity of disease, and comorbidity), the setting where they were found (e.g., primary care practices, community hospitals, or referral centers), and how they were sampled (e.g., complete, random, or convenience sampling). Often, this information is sufficient to establish wide generalizability, for example, in studies of community acquired pneumonia or thrombophlebitis in a local hospital.
Cohorts in prognostic studies should begin from a common point in time in the course of disease, called zero time, such as at the time of the onset of symptoms, diagnosis, or the beginning of treatment. If observation begins at different points in the course of disease for the various patients in a cohort, the description of their prognosis will lack precision, and the timing of recovery, recurrence, death, and other outcome events will be difficult to interpret or will be misleading. The term inception cohort is used to describe a group of patients that is assembled at the onset (inception) of their disease.
Prognosis of cancer is often described separately according to patients’ clinical stage (extent of spread) at the beginning of follow-up. If it is, a systematic change in how stage at zero time is established can result in a different prognosis for each stage even if the course of disease is unchanged for each patient in the cohort. This has been shown to happen during staging of cancer—assessing the extent of disease, with higher stages corresponding to more advanced cancer, which is done for the purposes of prognosis and choice of treatment. Stage migration occurs when a newer technology is able to detect the spread of cancer better than an older staging method.
Patients who used to be classified in a lower stage are, with the newer technology, classified as being in a higher (more advanced) stage. Removal of patients with more advanced disease from lower stages results in an apparent improvement in prognosis for each stage, regardless of whether treatment is more effective or prognosis for these patients as a whole is better. Stage migration has been called the “Will Rogers phenomenon” after the humorist who said of the geographic migration in the United States during the economic depression of the 1930s, “When the Okies left Oklahoma and moved to California, they raised the average intelligence in both states” .
Positron emission tomography (PET) scans, a sensitive test for metastases, are now used to stage non–small cell lung cancers. Investigators compared cancer stages before and after PET scans were in general use and found a 5.4% decline in the number of patients with stage III disease (cancer spread within the chest) and a 8.4% increase in patients with stage IV disease (distant metastases) 6. PET staging was associated with better survival in stage III and stage IV disease, but not earlier stages. The authors concluded that stage migration was responsible for at least some of the apparent improvement in survival in patients with stage III and stage IV lung cancer that occurred after PET scan staging was introduced.
Patients must be followed for a long enough period of time for most of the clinically important outcome events to have occurred. Otherwise, the observed rate will understate the true one. The appropriate length of follow-up depends on the disease. For studies of surgical site infections, the follow-up period should last for a few weeks, and for studies of the onset of AIDS and its complications in patients with HIV infection, the follow-up period should last several years.
Outcomes of Disease
Descriptions of prognosis should include the full range of manifestations of disease that would be considered important to patients. This means not only death and disease but also pain, anguish, and the inability to care for one’s self or pursue usual activities. The 5 Ds—death, disease, discomfort, disability, and dissatisfaction—are a simple way to summarize important clinical outcomes (see Table 1.2).
In their efforts to be “scientific,” physicians tend to value precise or technologically measured outcomes, sometimes at the expense of clinical relevance. As discussed in Chapter 1, clinical effects that cannot be directly perceived by patients, such as radiologic reduction in tumor size, normalization of blood chemistries, improvement in ejection fraction, or change in serology, are not clinically useful ends in themselves.
It is appropriate to substitute these biologic phenomena for clinical outcomes only when the two are known to be related to each other. Thus, in patients with pneumonia, short-term persistence of abnormalities on chest radiographs may not be alarming if the patient’s fever has subsided, energy has returned, and cough has diminished.
Ways to measure patient-centered outcomes are now used in clinical research. Table 7.1 shows a simple measure of quality of life used in studies of cancer treatment. There are also research measures for performance status, health-related quality of life, pain, and other aspects of patient well-being.
Table 7.1.A Simple Measure of Quality of Life. The Eastern Collaborative Oncology Group’s Performance Scale
|1||Symptomatic, fully ambulatory|
|2||Symptomatic, in bed <50% of the day|
|3||Symptomatic, in bed >50% of the day|
It is convenient to summarize the course of disease as a single rate—the proportion of people experiencing an event during a fixed time period. Some rates used for this purpose are shown in Table 7.2. These rates have in common the same basic components of incidence: events arising in a cohort of patients over time.
Table 7.2.Rates Commonly Used to Describe Prognosis
|5-year survival||Percent of patients surviving 5 years from some point in the course of their disease|
|Case fatality||Percent of patients with a disease who die of it|
|Disease-specific mortality||Number of people per 10,000 (or 100,000) population dying of a specific disease|
|Response||Percent of patients showing some evidence of improvement following an intervention|
|Remission||Percent of patients entering a phase in which disease is no longer detectable|
|Recurrence||Percent of patients who have return of disease after a disease-free interval|