Clinical prediction of postoperative respiratory failure.

1247 June 2013 C LINICAL research in Anesthesiology increasingly focuses on prediction of myriad perioperative complications. This research has been driven by development of databases that capture various measures of patient health, anesthetic care, surgical procedure, and outcomes. One of the strengths of this approach is that it allows analysis of outcomes that were previously understudied due to their rarity or complexity. Postoperative respiratory failure is a perfect target for such researches because it is relatively rare and has a significant impact on healthcare costs1 and patient mortality.2,3 Previous investigators have developed several screening tools for the prediction of postoperative respiratory failure,2–4 but most have focused on 30-day outcomes, a period that may well exceed the immediate influence of anesthesia technique and perioperative respiratory management. For example, the first 24 h after surgery represent the highest risk of unanticipated respiratory failure due to opioids,5,6 whereas postoperative hypoxemia has been shown to peak by the third night after major surgery.7–10 In this issue of the Journal, Eikermann et al. report the development and validation of a Score for Prediction of Postoperative Respiratory Complications (SPORC) focusing on the early postoperative period of 3 days after surgery.11 The investigators identified several independent predictors for reintubation such as planned postoperative hospital admission, preoperative history of congestive heart failure, chronic pulmonary or cerebrovascular disease, emergency surgery, American Society of Anesthesiologists score of 3 or more, and high-risk surgical service. By using a weighted point system, the SPORC yielded a calculated area under the receiver operating characteristics curve of 0.84–0.87, with a step-wise increase in the odds for reintubation with increasing number of risk factors. As previously reported, the development of respiratory failure was associated with a large increase in 30-day mortality.2 The SPORC tool is, thus, a simple way to identify high-risk patients in future studies and prospectively evaluate the effectiveness of interventions in preventing or reducing the incidence and severity of postoperative respiratory failure. For example, success of continuous positive airway pressure therapy in patients recovering from major abdominal surgery suggests that screening tool such as SPORC may have a role in identifying the patients who are likely to benefit from this therapy.12 There are, however, several limitations to the authors’ approach to screening. Bayes Theorem describes the relation between the prevalence of a disease and the accuracy of prediction tools.13–15 For rare events, even highly accurate tests will generate many false positives, with the potential consequences of excess resource utilization and complications from unnecessary treatment. Most prediction models derived from outcomes databases have positive predictive values less than 10% (low clinical precision) as the outcomes of interest are typically rare (0.1–4%).2,16 As most patients who underwent surgery have low risk and will not develop the complication in question, high specificity is given; but, because only a small fraction of patients will screen positively as high-risk, the sensitivity typically tends to be low. A tool with sensitivity of less than 50% will, by definition, fail to identify the majority of patients who will develop the complication. This is the case with the majority of prediction models derived from outcomes databases for perioperative complications (low sensitivity and low technical precision). A consequence is that policies or care processes that preferentially allocate treatments to high-risk patients, based on these screening tools, may potentially place more patients at harm due to misdiagnoses (i.e., patients who should be given treatment do not receive it). Furthermore, heterogeneity in patient populations is high in most prediction models, resulting in significant variability Clinical Prediction of Postoperative Respiratory Failure