We found that clinically important bleeding was associated with an increase in mortality (absolute risk increase, 20–30%; RR increase, 1–4) and an increased length of ICU stay (approximately 4–8 days) using three different analytic methods. We used two different control groups for matching in the two matched cohort methods to improve the robustness of the results [20
]. To control for age, illness severity, admitting diagnosis, ventilation status, bleeding status and organ dysfunction, we used multivariate methods. We considered the potential bias of ICU-acquired confounding factors [21
] by controlling for mechanical ventilation status and organ dysfunction in all six MODS domains as time-dependent variables until 3 days prior to the bleed. We used the RR to generate more clinically interpretable and conservative associations than those expressed by odds ratios [22
]. We presented median rather than mean durations of ICU stay to avoid inflated estimates of length of stay [23
]. We used objective criteria for clinically important upper gastrointestinal bleeding, which were determined in duplicate by blinded adjudicators unaware of this study hypothesis [24
Other studies have estimated the attributable mortality and length of ICU stay for complications of critical illness such as catheter-related infection [21
]. Interpreting such studies requires examination of issues such as population characteristics, case definition, and statistical strategies. In considering statistical strategies to determine the attributable mortality of another ICU outcome (VAP), two basic approaches have been used: logistic regression and comparative methods (with or without matching). The regression method commonly used to determine the attributable mortality of a condition such as VAP considers mortality status as a dependent variable, then selects independent variables (including VAP and other events) to test their association with mortality. Regression analysis can potentially yield distorted associations by selection of only a subset of variables that influence mortality risk, and by omission of key variables that would alter the findings if they were included. Nevertheless, in sufficiently powered studies, VAP was found to be an independent predictor of mortality after adjusting for other characteristics and ICU-acquired events using regression analysis [28
In addition to regression methods, studies of the attributable morbidity and mortality of VAP have been conducted using comparative methods. In addition to issues of population differences and case definition [32
], another explanation for the discordant results is the analytic approaches used. These approaches include unmatched crude comparison of patients with and without VAP [33
], simple matching of patients with and without VAP based on baseline characteristics [34
], matching of patients based on baseline characteristics and discharge diagnosis [35
], and matched cohort analyses that incorporate baseline characteristics and time-dependent variables that could influence outcome [32
Our analysis of the attributable morbidity and mortality of clinically important gastrointestinal bleeding used both comparison and regression methods. In the absence of a well-accepted single approach or guidelines for an optimal analysis, we summarize the advantages and disadvantages of the methods used to estimate the attributable mortality and length of ICU stay associated with clinically important bleeding (Table ). The crude comparison method ignores the influence of confounding factors and inevitably yields inaccurate estimates. The three other approaches (the matched cohort method, the model-based matched cohort method, and the regression method) are superior to a crude comparison method because adjustment for potentially important confounding factors is more likely to yield valid estimates.
Advantages and disadvantages of different approaches to estimating attributable mortality and length of intensive care unit stay
Although the matched cohort method is founded on biologic rationale to match patients, clinical judgement may sometimes fail to adjust for all important determinants of outcome. In contrast, both the model-based matched cohort method and the regression method allow for adjustment of additional potentially important confounders. Moreover, they both can be customized to the database in which they are developed. Finally, there is the issue of the effect of bleeding over time. The matched cohort method and model-based matched cohort method both use the Mantel-Haenszel technique, which is founded on an assumption of constant odds over time. The initial regression method used Cox proportional hazards to estimate the risk of mortality, which generates valid estimates if the assumption of proportional hazard holds (e.g. if the risk is stable over time). However, when we tested whether the risk of mortality attributable to bleeding was constant over time using the regression model, we found that it was not. When bleeding occurred in the first 3 weeks of ICU stay, there was a trend toward a decreased risk of death in bleeding patients compared with non-bleeding patients. When bleeding occurred 4 weeks or longer following ICU admission, the risk of death was significantly increased in bleeding patients compared with non-bleeding patients. This interesting finding may reflect the more serious pathophysiology of late onset bleeding due to a longstanding ischaemic gastropathy, which is often considered a manifestation of multiple organ dysfunction [13
In summary, in this population of 1666 ICU patients ventilated for >48 hours, we have demonstrated that clinically important gastrointestinal bleeding is associated with a significant increase in attributable mortality (full range of RRs, 1–4) and length of ICU stay (approximately 4–8 days). These results build on previous work estimating the clinical and economic consequences of bleeding in which we used a hierarchical matched cohort study of 64 patients. We previously found a trend toward an increased risk of mortality (RR = 1.14, 95% CI = 0.7–2.0), and a trend toward an increased length of ICU stay of 6.5 days (95% CI = -12.3 to 25.3 days) associated with clinically important bleeding [11
]. We also found that each episode resulted in a mean of seven additional haema-tology tests, 11 blood product transfusions, and 24 days of treatment, resulting in an overall cost of clinically important bleeding of $12,000. This analysis is limited in that patients who were admitted to ICU with a diagnosis of pneumonia or patients who had two or more doses of prophylaxis were excluded from the first database. Although it possible that the attributable morbidity and mortality of clinically important bleeding in such patients may differ from other ICU patients ventilated for at least 48 hours, this seems very unlikely. Of course, these results do not show that bleeding events themselves directly lead to increased length of stay or death; thus, causation cannot be inferred from these analyses.
In two multicentre studies, the Canadian Critical Care Trials Group has found that clinically important bleeding occurs in 4% of mechanically ventilated patients [4
]. However, bleeding rates vary; 2.4% of patients had macroscopic bleeding in one study in the Netherlands, in which all patients received selective digestive decontamination, dopamine, nitroglycerin, and ketanserin while mechanically ventilated, and tapering doses of dexamethasone [39
]. Prevention may be unnecessary in populations in which clinically important bleeding is documented to be very rare, [40
], and thus bleeding prophy-laxis strategies may need to be customized to different settings. A formal cost-effectiveness analysis would best address the impact of alternative stress ulcer prophylaxis policies including targeted prevention for patients at highest risk, which requires accurate estimates and a plausible range of estimates for the target event. The analyses we report for the attributable length of ICU stay and mortality associated with clinically important bleeding can thus be integrated with evidence about incidence, risk factors, and the advantages and disadvantages of various preventive strategies, to better understand the consequences of different approaches to stress ulcer prophylaxis.