Concordance of effects of medical interventions on hospital admission and readmission rates with effects on mortality

Background:

Many clinical trials examine a composite outcome of admission to hospital and death, or infer a relationship between hospital admission and survival benefit. This assumes concordance of the outcomes “hospital admission” and “death.” However, whether the effects of a treatment on hospital admissions and readmissions correlate to its effect on serious outcomes such as death is unknown. We aimed to assess the correlation and concordance of effects of medical interventions on admission rates and mortality.

Methods:

We searched the Cochrane Database of Systematic Reviews from its inception to January 2012 (issue 1, 2012) for systematic reviews of treatment comparisons that included meta-analyses for both admission and mortality outcomes. For each meta-analysis, we synthesized treatment effects on admissions and death, from respective randomized trials reporting those outcomes, using random-effects models. We then measured the concordance of directions of effect sizes and the correlation of summary estimates for the 2 outcomes.

Results:

We identified 61 meta-analyses including 398 trials reporting mortality and 182 trials reporting admission rates; 125 trials reported both outcomes. In 27.9% of comparisons, the point estimates of treatment effects for the 2 outcomes were in opposite directions; in 8.2% of trials, the 95% confidence intervals did not overlap. We found no significant correlation between effect sizes for admission and death (Pearson r = 0.07, p = 0.6). Our results were similar when we limited our analysis to trials reporting both outcomes.

Interpretation:

In this metaepidemiological study, admission and mortality outcomes did not correlate, and discordances occurred in about one-third of the treatment comparisons included in our analyses. Both outcomes convey useful information and should be reported separately, but extrapolating the benefits of admission to survival is unreliable and should be avoided.Health care decisions often rely on effects of interventions described using rates of admission or readmission to hospital.^1,2 These outcomes are typically regarded as indicators of insufficient quality of care and inefficient spending of health care resources;^1,2 however, whether they can predict other serious clinical outcomes, such as death, is unknown.Although effects on admission or readmission rates are often analyzed using large sets of routinely collected data, such as from administrative databases and electronic health records, many randomized controlled trials (RCTs) also collect data on admission rates, and some RCTs collect mortality data. Moreover, some trials combine death and admission to hospital as the primary composite outcome³ to increase the study’s power to detect significant differences and reduce the required study size.⁴ However, the interpretation of such a combination is difficult when the treatment effects on the 2 components are not concordant,⁵ for example, when more patients survive but rates of admission increase. In such cases, composite outcomes may dilute or obscure clinically significant treatment effects on important individual components,^4,6 and incomplete disclosure of individual effects may mislead the interpretation of the results.⁴We investigated systematic reviews of treatment comparisons that included meta-analyses of RCTs assessing effects on both rates of admission and mortality. We used the reported trial data to assess whether effects on admission rates were concordant with effects on mortality or whether it was possible to identify interventions and diseases in which these 2 outcomes would provide differing pictures of the merits of the tested interventions.