Risk stratification, risk adjustment, and other risks.

“BUT our patients are sicker than yours!” is the refrain when physicians are faced with adverse comparisons of their patients’ outcomes with those of other groups, hospitals, or health systems. Meaningful and fair comparison of clinical performance requires statistical adjustment of outcomes for differences in clinical acuity of patients treated and complexity of procedures performed, among other characteristics, comprising case mix. Without such risk adjustment, comparisons are biased, physicians understandably avoid sicker patients and/or more challenging procedures, and sicker patients face barriers to access for necessary care. In this issue of ANESTHESIOLOGY, Sessler et al. describe development and validation of a “broadly applicable,” robust tool for riskadjusting mortality and length-of-stay outcomes of U.S. hospital care. To understand their achievement and its benefits and limitations, we must first appreciate the challenges. Risks are ubiquitous but not evenly distributed in time or space. A hierarchy exists in need for health services, and health status generally worsens (acuity of illness increases) sequentially from the unselected general population to outpatient settings to community hospitals and finally to academic medical centers (fig. 1A). Surgical care has a similar spatial distribution (fig. 1B). Thus, complication and death rates for ostensibly similar care are likely to differ across settings and physicians, and they should not be compared without meaningful effort to adjust outcomes for case mix. Imposition of Medicare’s Prospective Payment System for hospital care in 1983 was the stimulus, augmented later beyond Medicare, for developing risk-adjustment methods when comparing clinical outcomes, costs of care, and physician performance. Such outcomes are viewed as products of complex functions of patient-related clinical and nonclinical factors, treatment effectiveness, and random chance (fig. 2). In a particular application (e.g., comparison of hospitals), data for one outcome (e.g., postoperative myocardial infarction) is modeled in a regression analysis, with data for all relevant factors (e.g., patient demographics, comorbidities, and type of surgery) entered as candidate predictor variables for each patient. The analysis computes an expected rate of