Expanding the Dimensions of Effectiveness Research in Sepsis
Authors: Allan J. Walkey
Journal: American Journal of Respiratory and Critical Care Medicine (2014)

The combination of increasing incidence and declining short-term mortality rates for patients with severe sepsis (1, 2) has led to a remarkable growth of severe sepsis survivors (3). Unfortunately, the gains in short-term mortality among patients with severe sepsis have not translated into long-term mortality benefits (3). Whereas 30-day severe sepsis mortality declined from approximately 45% in the early 1990s (1) to approximately 20% mortality in 2012 (2), 3-year mortality rates among severe sepsis survivors have remained fairly stagnant (3). As we achieve greater success with the acute care of patients with severe sepsis inside the hospital, our attentions appropriately expand to improving posthospitalization outcomes (4).

With an eye toward improved long-term outcomes after sepsis, Chao and colleagues report in this issue of the Journal (pp. 1003–1011) the association between receipt of posthospitalization physical therapy (PT) and long-term mortality among more than 100,000 survivors of critical illness in Taiwan (5). Chao and colleagues report that receipt of PT within 90 days of discharge from a sepsis hospitalization was associated with a modest 8% relative risk reduction in mortality at 1 year, a benefit that persisted up to 10 years after hospital discharge. The mortality benefit associated with postdischarge PT showed a potential “dose–response” relationship, which was present regardless of receipt of inpatient PT and was most marked among patients requiring more than 7 days of mechanical ventilation.

Is it feasible that PT after hospitalization for sepsis might reduce long-term mortality? Meta-analysis of trials investigating PT initiated during critical illness shows evidence of improvements in peripheral and diaphragmatic muscle strength, quality of life, and functional capacity, but not mortality (6). Randomized trials of physical therapy interventions initiated after hospital discharge—although limited in sample size and scope of the PT intervention—have been less encouraging (7). Hospital readmission rates are lower for patients with better functional capacity (8), and inflammatory markers present at hospital discharge are associated with mortality 1 year after sepsis (9). Thus, potential mechanisms for improved outcomes with PT initiated after sepsis include improved functional capacity (6) and reductions in systemic inflammation (10). Results from Chao and colleagues support the hypotheses that PT may improve recovery of muscle wasting, with stronger effect estimates for patients requiring prolonged mechanical ventilation, a group with high incidence of intensive care unit–acquired weakness.

Skeptics might question whether observational research methods such as those used by Chao and colleagues produce “reliable” (unbiased) effect estimates. In general, findings from large observational studies have agreed with those of large randomized trials, at a rate similar to the agreement between similar randomized trials (11). Well-publicized discrepancies between randomized and observational studies (such as those observed with postmenopausal hormone replacement or activated protein C) have been addressed through reanalysis of the observational data (12, 13). However, comparative effectiveness research using administrative data, such as that used by Chao and colleagues, may be fraught with potential pitfalls.

The greatest challenge to observational effectiveness research is unmeasured confounding, which may produce spurious findings. Despite methods (such as propensity scores) meant to attenuate the influence of measured confounders, findings from observational studies may remain biased by unmeasured confounders. One of the hopes of “big data” is that enough information can be accumulated about each research subject to greatly limit the chance that a confounding variable will remain unmeasured. Chao and colleagues have attempted to use almost all the “big data” available to them to construct a “high dimensional propensity score” (14). In its simplest presentation, a high dimensional propensity score attempts to use information from all potential measured confounders to assign a probability that each subject would receive the treatment of interest (in this case, PT). Chao and colleagues used more than 500 variables available from a rich population-based claims database—including diagnoses, drugs, procedures, and tests ordered.

With a large number of measured confounding variables, the likelihood for unmeasured confounding and spurious effect estimates declines—but is not zero. Using an analogy from randomized trials, the methods used by Chao and colleagues more closely represent that of a “per protocol,” rather than an “intention-to-treat,” analysis. Even if we have complete information about potentially confounding factors that might influence referral to PT, we do not know which patients were referred to PT, but did not attend. “Per protocol” methodology may magnify the absence of potentially important confounding variables such as the presence of depression, cognitive dysfunction, or do-not-resuscitate orders (12) that could decrease the likelihood that a patient follows through with a PT referral and increase the risk of death. Use of intention-to-treat methodology in observational effectiveness studies has been shown to be of particular importance, improving previous discrepancies between randomized trial results and observational studies (13).

Another potentially strong source for bias in an observational study such as this is the “immortal person-time bias” (15). Immortal person-time bias might occur because patients who received PT could not have died before their first PT session; by contrast, those who did not receive PT may have died before receiving PT. If not approached appropriately this “immortal time” would represent a potential source of bias in favor of PT. Fortunately, the authors deal with immortal person-time bias directly through a number of methods, such as starting the analysis after a time “landmark” that both PT-exposed and unexposed patients must have lived to reach and including PT exposure as a time-varying covariate. Although immortal person-time bias may creep into the dose–effect analysis (each PT course required additional “immortal time”), findings were robust to multiple different sensitivity analyses with a variety of different specifications of the underlying cohort, “immortal time” landmarks, PT exposure, and statistical methods, which strengthen their results.

Chao and colleagues provide a path forward to further test the hypothesis that posthospitalization PT may improve long-term outcomes of sepsis survivors. They leverage their results to supply sample size calculations and subgroups most likely to benefit from PT, providing useful information for the design of future randomized trials of postsepsis hospitalization PT. With the use of a high dimensional propensity score approach examining posthospitalization outcomes for critically ill patients hospitalized with sepsis, Chao and colleagues have demonstrated the expanding dimensions of critical care research.