John H. Wasson

Clinical prediction rules: applications and methodological standards

The objective of clinical prediction rules is to reduce the uncertainty inherent in medical practice by defining how to use clinical findings to make predictions. Clinical prediction rules are derived from systematic clinical observations. They can help physicians identify patients who require diagnostic tests, treatment, or hospitalization. Before adopting a prediction rule, clinicians must evaluate its applicability to their patients. We describe methodological standards that can be used to decide whether a prediction rule is suitable for adoption in a clinicians practice. We applied these standards to 33 reports of prediction rules; 42 per cent of the reports contained an adequate description of the prediction rules, the patients, and the clinical setting. The misclassification rate of the rule was measured in only 34 per cent of reports, and the effects of the rule on patient care were described in only 6 per cent of reports. If the objectives of clinical prediction rules are to be fully achieved, authors and readers need to pay close attention to basic principles of study design.

Assessment of function in routine clinical practice: description of the COOP Chart method and preliminary findings.

The COOP Project, a primary care research network, has begun development of a Chart method to screen function quickly. The COOP Charts, analogous to Snellen Charts, were pretested in two practices on adult patients (N = 117) to test feasibility, clinical utility, and validity. Patients completed questionnaires containing validated health status scales and sociodemographic variables. Practice staff filled out forms indicating COOP Chart scores and clinical data. We held debriefing interviews with staff who administered the Charts. The results indicate the Charts take 1-2 minutes to administer, are easy to use, and produce important clinical data. The patterns of correlations between the Charts and validity indicator variables provide evidence for both convergent and discriminant validity. We conclude that new measures are needed to assess function in a busy office practice and that the COOP Chart system represents one promising strategy.

Microsystems in Health Care: Part 1. Learning from High-Performing Front-Line Clinical Units

BACKGROUND Clinical microsystems are the small, functional, front-line units that provide most health care to most people. They are the essential building blocks of larger organizations and of the health system. They are the place where patients and providers meet. The quality and value of care produced by a large health system can be no better than the services generated by the small systems of which it is composed. METHODS A wide net was cast to identify and study a sampling of the best-quality, best-value small clinical units in North America. Twenty microsystems, representing different component parts of the health system, were examined from December 2000 through June 2001, using qualitative methods supplemented by medical record and finance reviews. RESULTS The study of the 20 high-performing sites generated many best practice ideas (processes and methods) that microsystems use to accomplish their goals. Nine success characteristics were related to high performance: leadership, culture, macro-organizational support of microsystems, patient focus, staff focus, interdependence of care team, information and information technology, process improvement, and performance patterns. These success factors were interrelated and together contributed to the microsystems ability to provide superior, cost-effective care and at the same time create a positive and attractive working environment. CONCLUSIONS A seamless, patient-centered, high-quality, safe, and efficient health system cannot be realized without the transformation of the essential building blocks that combine to form the care continuum.

Prostate Cancer Clinical Guidelines Panel Summary Report on the Management of Clinically Localized Prostate Cancer

PURPOSE The American Urological Association convened the Prostate Cancer Clinical Guidelines Panel to analyze the literature regarding available methods for treating locally confined prostate cancer, and to make practice policy recommendations based on the treatment outcomes data insofar as the data permit. MATERIALS AND METHODS The panel searched the MEDLINE data base for all articles from 1966 to 1993 on stage T2 (B) prostate cancer and systematically analyzed outcomes data for radical prostatectomy, radiation therapy and surveillance as treatment alternatives. Outcomes considered most important were survival at 5, 10 and 15 years, progression at 5, 10 and 15 years, and treatment complications. RESULTS The panel found the outcomes data inadequate for valid comparisons of treatments. Differences were too great among treatment series with regard to such significant characteristics as age, tumor grade and pelvic lymph node status. The panel elected to display, in tabular form and graphically, the ranges in outcomes data reported for each treatment alternative. CONCLUSIONS In making its recommendations, the panel presented treatment alternatives as options, identifying the advantages and disadvantages of each, and recommended as a standard that patients with newly diagnosed, clinically localized prostate cancer should be informed of all commonly accepted treatment options.

Estimating treatment benefits for the elderly: the effect of competing risks.

The current practice of encouraging patients to participate in treatment decisions requires that clinicians be facile in communicating the risks and benefits of therapy. Sharing numeric data can foster the process. However, because the format in which data are presented influences their interpretation [1-3], clinicians need to consider which format best describes the outcomes their patients face. Consider the tension between relative and absolute risk reduction. The interpretation of even a large relative risk reduction is highly dependent on the baseline risk for the specific disease. A 50% reduction in mortality with early intervention, for example, appears different when the risk for death from disease is changed from 2 per 1000 to 200 per 1000. When the mortality risk is low, the absolute survival benefit is small0.1% (2/1000 to 1/1000); when the risk is high, the absolute benefit is great10% (200/1000 to 100/1000). In the former scenario, patients might reasonably choose to forego a noxious intervention. In the latter, however, patients might be more likely to accept the morbidity of treatment. Because this distinction between relative and absolute risk reduction is concealed when benefit is expressed in only relative terms, many have argued that relative risk reductions should be anchored by baseline risk so that the absolute benefit of treatment is clear [2, 4, 5] However, an absolute measure of disease risk (or risk reduction from therapy) is not the ultimate outcome of interest to patients. Overall risk is more important. The difference is the risks patients face from other conditionsthat is, competing risks. When competing risks are great, they matter. The importance of even a 10% absolute survival benefit from treatment is markedly diminished for a patient who is at greater risk for death from other causes, regardless of the proposed therapy. Such great competing risks are most prevalent among the elderly. Although physicians intuitively understand the relevance of competing risks, they may be less sure about how to quantify the effect. We provide a framework to help physicians gauge the effect of competing risks in their elderly patients. Methods Overview To quantify the effect of competing risks, we used age-specific mortality data from U.S. vital statistics and the declining exponential approximation for life expectancy (DEALE) to model age-specific expectations for persons faced with a particular disease-related mortality. We sought to determine, for example, how a new disease with a 5-year mortality rate of 25% would affect the life expectancy of an average 70-year-old man. We then considered two refinements: the first, to better adjust for the individual patient (using self-reported health status), and the second, to describe more thoroughly the outcome (by including disabling events). Modeling the Effect of a New Disease on Life Expectancy Life expectancy and mortality are fundamentally related to probability estimates. In the general population, life expectancy decreases with increasing age, and annual mortality increases. Gompertz was the first to describe this complex mathematic relation using an exponential function that now bears his name. As life expectancy decreases, mortality rates become almost constant over time. When this occurs, the relation between survival and mortality rates can be approximated with a much simpler mathematic relation: a declining exponential function (the DEALE). This approximation was first validated and popularized by Beck and colleagues [6, 7] and is particularly suited to calculating the effect that a new risk has in older patients. The fundamental assumption behind this technique is that life expectancy equals the inverse of the annual mortality rate: Equation 1 Because mortality rates are essentially constant probability estimates when assessed over relatively short time horizons, patient-specific mortality rates can be expressed as the sum of the disease-independent mortality rate (also known as age-specific mortality rate) and a disease-related mortality rate (also known as case-fatality or excess mortality rate): Equation 2 Note that when disease-related mortality is zero (that is, when the patient does not have the disease or when the disease has no effect on survival), the patient-specific mortality rate (and thus life expectancy) is determined solely by the patients age. Calculation of the life expectancy estimates used in Figure 1 and Figure 2 is relatively simple. Because Figure 1 is the central portion of our paper, we now describe it in detail. Normal life expectancy (the top curves) was determined from the most recent data (1991) from the National Center for Health Statistics, U.S. Department of Health and Human Services [8]. On the basis of remaining life expectancy and the DEALE [6, 7], we calculated the age-specific mortality rate for each age cohort from 65 to 85 years of age. Combining the age-specific mortality with the hypothetical disease-related mortality allowed us to calculate the other four curves. The disease-related annual mortality rate can be calculated from 5-year disease-specific survival using the following equation: Equation 3 Figure 1. The effect of selected disease-related mortality rates on the remaining life expectancy of women (left) and men (right) at the time of diagnosis. Figure 2. The effect of age on the distribution of health states in the future. Thus, if the disease-related 5-year mortality rate is 25% (and the 5-year survival rate is 75%), then the disease-related annual mortality rate is 0.06. Equation 4 A 70-year-old man, for example, has a life expectancy of 12.2 years or an annual age-specific mortality rate of 0.08. Equation 5 Given the foregoing disease, the mans all-cause annual mortality rate is 0.14 (= 0.06 + 0.08), and his life expectancy is 7.2 years. Equation 6 Thus, the sum of the age-specific and disease-related mortality rates gives the patient-specific mortality rate, the inverse of which is life expectancy. Normal life expectancy serves as our proxy for disease-independent data. The mortality reflected in this measure is, of course, itself the result of several diseases in the elderlyprimarily cardiovascular disease and cancer. The method we describe produces a valid approximation whenever the disease in question is not a major contributor to the age-specific mortality rate. For example, if the disease in question was all cardiovascular disease or all cancer, then much of the age-specific mortality rate would already account for the mortality from the disease. Completely successful therapy for such a broad category of disease would move a patient well above his or her normal life expectancy by removing the common causes of death. Thus, the method we describe should be applied only when the physician is considering more discrete diagnoses (for example, aortic aneurysm or breast cancer), which make a relatively small contribution to overall mortality. To provide some quantitative data on how great a contributor to all-cause mortality a given disease can be without affecting our method, we did a sensitivity analysis that removed the contribution of a particular disease from normal life expectancy and accordingly revised the estimate of perfect treatment on life expectancy. For example, for a disease that accounts for 40% of all-cause mortality (such as all cardiovascular diagnoses), revised treatment benefit (in years) was three times the benefit estimated by our method. For a disease that accounts for 30% of all-cause mortality (for example, all cancers considered together), the revised benefit was twice as high as the benefit estimated by our method. However, for a disease that constitutes less than 10% of all-cause mortality (this is the case for any individual cancer), the revised benefit is small (for example, less than 20% higher than that estimated by our method). Adjustments for Health Status The adjustments for health status shown in Table 1 are based on data from the East Boston Senior Health Project. All participants were asked the following question: Compared with others your age, would you rate your overall health as excellent, good, fair, or poor? Analyzing the 1437 men and 2332 women separately, we used 5-year follow-up data to calculate, for each health status self-rating, the proportion of patients who died. The ratio of this health status-specific survival to overall survival served as our health status weight. A more precise analysis for men and women, using five age cohorts (ages 65 to 69 years, 70 to 74 years, 75 to 79 years, 80 to 84 years, and 85 years and older) produced essentially the same weights. Table 1. Estimated Physiologic Age of Elderly Patients Adjusted for Their Self-Reported Health Status* Overall, men who described themselves as in excellent health had a lower mortality rate than average (health status weight, 0.52). Men who reported themselves as in good, fair, and poor health had health status weights of 0.89, 1.26, and 1.88, respectively. The analysis for women showed health status weights of 0.64, 0.88, 1.08, and 1.82 for self-reported health status of excellent, good, fair, and poor, respectively. To approximate a physiologic age to reflect health status, we applied the health status weights to four chronologic ages: 65, 70, 75, and 80 years. Using the age-specific annual mortality from U.S. Vital Statistics data [8] and the health status weight, we calculated a health status-adjusted mortality rate as the following: Equation 7 We then returned to the Vital Statistics data to determine the age at which an average person would have this annual mortality rate. These data do not provide annual mortality rates for persons older than 85 years, forcing us to report 85 years and older for the highest mortality rates. The process was done separately for men and women. Future Disabling Events The expectation of future disabling events (Figure 3) is based on cros

Outcomes of external-beam radiation therapy for prostate cancer: a study of Medicare beneficiaries in three surveillance, epidemiology, and end results areas.

PURPOSE This study was designed to obtain representative estimates of the quality of life and probabilities of possible adverse effects among Medicare-age patients treated with external-beam radiation therapy for prostate cancer. METHODS Patients treated for local or regional prostate cancer with high-energy external-beam radiation between 1989 and 1991 were sampled from a claims data base of the Surveillance, Epidemiology, and End Results (SEER) program from three regions. Patients were surveyed primarily by mail, with telephone follow-up evaluation of non-respondents. There were 621 respondents (83% response rate). The results were compared with data from a previously published national survey of Medicare-age men who had undergone radical prostatectomy. RESULTS Although they were older at the time of treatment, radiation patients were less likely than surgical patients to wear pads for wetness (7% v 32%) and had a lower rate of impotence (23% v 56% for men < 70 years), while they were more likely to report problems with bowel dysfunction (10% v 4%). Both groups reported generally positive feelings about their treatments. Radiation and surgical patients reported similar rates of additional subsequent treatment (24% v 26% at 3 years after primary treatment). However, radiation patients were less likely to say they were cancer-free, and they reported more worry about cancer than did surgical patients. CONCLUSION The health-related quality of life of radiation and surgical patients, on average, is similar, but the pattern of experience with adverse consequences of treatment differs by treatment.

Health status assessment for elderly patients. Report of the Society of General Internal Medicine Task Force on Health Assessment.

A brief but systematic assessment of functional status should be incorporated into the routine medical management of elderly patients, because of its demonstrated usefulness.

Patient reported outcome measures in practice

Scores of tools to measure outcomes that matter to patients have been developed over the past 30 years but few are used routinely at the point of care. Nelson and colleagues describe examples where they are used in primary and secondary care and argue for their wider uptake to improve quality of care

Clinical Microsystems, Part 1. The Building Blocks of Health Systems

BACKGROUND Wherever, however, and whenever health care is delivered-no matter the setting or population of patients-the body of knowledge on clinical microsystems can guide and support innovation and peak performance. Many health care leaders and staff at all levels of their organizations in many countries have adapted microsystem knowledge to their local settings. CLINICAL MICROSYSTEMS A PANORAMIC VIEW: HOW DO CLINICAL MICROSYSTEMS FIT TOGETHER? As the patients journey of care seeking and care delivery takes place over time, he or she will move into and out of an assortment of clinical microsystems, such as a family practitioners office, an emergency department, and an intensive care unit. This assortment of clinical microsystems-combined with the patients own actions to improve or maintain health--can be viewed as the patients unique health system. This patient-centric view of a health system is the foundation of second-generation development for clinical microsystems. LESSONS FROM THE FIELD These lessons, which are not comprehensive, can be organized under the familiar commands that are used to start a race: On Your Mark, Get Set, Go! ... with a fourth category added-Reflect: Reviewing the Race. These insights are intended as guidance to organizations ready to strategically transform themselves. CONCLUSION Beginning to master and make use of microsystem principles and methods to attain macrosystem peak performance can help us knit together care in a fragmented health system, eschew archipelago building in favor of nation-building strategies, achieve safe and efficient care with reliable handoffs, and provide the best possible care and attain the best possible health outcomes.

Prostate Cancer Screening and Beliefs about Treatment Efficacy: A National Survey of Primary Care Physicians and Urologists

PURPOSE To describe practice patterns and beliefs of primary care physicians and urologists regarding early detection and treatment of prostate cancer. SUBJECTS AND METHODS National probability samples of primary care physicians (n=444) and urologists (n=394) completed mail survey instruments in 1995. Physicians were asked about their use of prostate-specific antigen (PSA) testing for men of different ages and their beliefs about the value of radical prostatectomy, external-beam radiation therapy, and watchful waiting for men with differing life expectancies. RESULTS Most primary care physicians report doing PSA tests during routine examination of men older than 50 years of age. The majority say they continue to do them on patients over 80 years and to refer men with abnormal values for biopsy. In contrast, only a minority of urologists would recommend PSA tests or biopsy for abnormal values for men over 75 years of age. More than 80% of primary care physicians and urologists doubt the value of radical prostatectomy for men with < 10 years of life expectancy; more primary care physicians than urologists see probable survival benefit in radiation therapy for patients with life expectancy < 10 years (48% versus 36%) or > 10 years (67% versus 53%). Thirteen percent of primary care physicians and only 3% of urologists consider watchful waiting to be as appropriate as aggressive therapy for men with > 10 years of life expectancy. CONCLUSIONS Primary care physicians are more aggressive about PSA testing and referral for biopsy than most urologists recommend. Both groups recommend PSA testing and believe that aggressive treatment is more beneficial than existing evidence indicates.