We developed a framework for individualizing guidelines based on comorbidity. This framework is designed “from the ground up” to be compatible with clinical decision aids because it quantitatively weighs guidelines’ benefits and harms and therefore can offer clear guidance for decision making. It is substantially different from other published approaches for individualizing guidelines, which require qualitative valuations that are often complex and are less likely to be applicable at the point of care.1–5
The recent development of comorbidity-based prognostic models that are simple6
or have user-friendly Web-based interfaces22
has greatly facilitated the development of this framework. Although some clinicians may wonder why any decision rule is necessary, when it is theoretically possible to recalculate risks and benefits each time a guideline is applied, it would be prohibitively difficult (if not impossible) to iterate all possible combinations of guidelines and comorbidities and to perform a distinct, evidence-based analysis for each combination.
Our approach estimates the time until a guideline’s incremental benefits are likely to exceed its incremental harms and then asks whether this time is longer than a patient’s comorbidity-adjusted life expectancy. In our illustration of the payoff time framework, we found that individuals with HIV are likely to benefit from screening because of the relatively long life expectancy conferred by current therapies. In contrast, individuals with severe CHF may be unlikely to benefit from colorectal cancer screening because of their relatively short life expectancies.
An important strength of our framework is its potential to affect health policy. Because the payoff time provides an objective method of inferring when a guideline may confer harm rather than benefit, it may be used to delineate circumstances when guideline compliance should not count toward quality benchmarks or toward pay for performance. For example, we would argue that if a clinician elects not to screen for colorectal cancer in a 60-year-old woman with risk strata 3 or 4 CHF, it should not harm the clinician’s quality “report card” or “performance” portfolio.
The concept of a payoff time is likely to be intuitive, as expert panels sometimes specify a minimum life expectancy that is a prerequisite for a guideline’s expected benefits to exceed its expected harms. For example, the US Preventive Services Task Force advocates waiving colorectal cancer screening when a patient’s estimated life expectancy is less than 5 years. These recommendations can be thought of as payoff times that are estimated by expert opinion rather than the more data-based approach of our framework.
An important limitation of our work is that we did not consider harm and benefit data particular to the comorbidities under consideration. For example, CHF is likely to increase the harm from colorectal cancer screening by increasing susceptibility to harm from complications, and our payoff time calculations did not consider this likely impact. However, it is important to note that using data that are not comorbidity specific is not an intrinsic limitation of the payoff time approach, but rather a limitation of the data sources that were available. Once data concerning the interaction between CHF and harm from colonoscopy are available, the information may be incorporated into the payoff time estimation. Furthermore, even when comorbidity-specific data are unavailable, payoff time inferences may still be valid if the comorbidity is likely to result in an underestimate of the payoff time. For example, CHF is likely to result in an underestimation of the colorectal cancer screening payoff time because it is likely to increase the chance of harm (eg, an increase in complication rate) more than it is likely to increase the chance of benefit. We may therefore infer that patients with risk strata 3 or 4 CHF should not undergo colorectal cancer screening because their life expectancies are lower than the underestimates (and would therefore also be lower than the true payoff times).
Our framework has other limitations. It does not consider patient preferences, which are an important consideration in any clinical decision. Indeed, it is important to emphasize that the payoff time should not be interpreted as a clinical dictum, but rather as a clinical decision support tool, one among many information sources in a shared decision between patient and clinician. Another limitation of our approach is that it does not consider costs. However, it would be straightforward to consider costs for any particular willingness to pay threshold (eg, $100 000 per life-year saved) by determining when a guideline first confers the corresponding life expectancy benefit. As more stringent willingness-to-pay criteria are specified, the associated payoff times would lengthen.
In conclusion, we present a practical framework for tailoring clinical guidelines to comorbid populations that may be applied at the point of care. This method has the potential to reduce morbidity and mortality, while decreasing use of resources, and therefore has implications for health policy and clinical care.