Date Published: June 13, 2018
Publisher: Public Library of Science
Author(s): Ilona W. M. Verburg, Evert de Jonge, Niels Peek, Nicolette F. de Keizer, Pierre Moine.
To assess and improve the effectiveness of ICU care, in-hospital mortality rates are often used as principal quality indicator for benchmarking purposes. Two other often used, easily quantifiable, quality indicators to assess the efficiency of ICU care are based on readmission to the ICU and ICU length of stay. Our aim was to examine whether there is an association between case-mix adjusted outcome-based quality indicators in the general ICU population as well as within specific subgroups.
We included patients admitted in 2015 of all Dutch ICUs. We derived the standardized in-hospital mortality ratio (SMR); the standardized readmission ratio (SRR); and the standardized length of stay ratio (SLOSR). We expressed association through Pearson’s correlation coefficients.
The SMR ranged from 0.6 to 1.5; the SRR ranged from 0.7 to 2.1; and the SLOSR ranged from 0.7 to 1.3. For the total ICU population we found no significant associations. We found a positive, non-significant, association between SMR and SLOSR for admissions with low-mortality risk, (r = 0.25; p = 0.024), and a negative association between these indicators for admissions with high-mortality risk (r = -0.49; p<0.001). Overall, we found no association at ICU population level. Differential associations were found between performance on mortality and length of stay within different risk strata. We recommend users of quality information to take these three outcome indicators into account when benchmarking ICUs as they capture different aspects of ICU performance. Furthermore, we suggest to report quality indicators for patient subgroups.
In recent years attention to quality of healthcare as expressed by quality indicators has increased [1, 2]. Treatment at the intensive care unit (ICU) is very complex and delivered in a highly technical and labor-intensive environment. Along with these developments the cost of intensive care has increased substantially resulting in a high proportion of the health care expenditure accountable to ICUs . This all makes ICUs a particularly interesting part of the hospital to assess and improve performance.
Results on the performance of the prediction models are shown in Table D and Figs A- C of S1 File. Fig 2 presents the pairs of SMR, SRR and SLOSR plotted against each other for the entire cohort. Table 2 presents Pearson’s correlation coefficients for the entire cohort as well as for subgroups. No significant associations were identified between SRR and SMR or SRR and SLOSR. This means that ICUs with lower than expected readmission rates did not have lower or higher mortality, or shorter or longer length of stays, than expected. No significant associations were identified at cohort level between SMR and SLOSR. However, a negative significant association (r = -0.49; p<0.001) was found for the high mortality risk subgroup (i.e., recalibrated APACHE IV probability of mortality larger than 0.7), meaning better performance on SMR (lower mortality) was associated with worse performance on SLOSR (longer ICU length of stay). Conversely, a positive association was found for the low mortality risk subgroup (i.e., recalibrated APACHE IV probability of mortality smaller than 0.3). This association was not significant using Pearson’s correlation coefficient (r = 0.25; p = 0.024), but was significant using the Spearman’s rank correlation coefficient (ρ = 0.29; p = 0.009). Fig 3 presents the pairs of SMR and SLOSR for these subgroups based on probability of mortality. In this study we examined the association between SMR, SLOSR and SRR as individual outcome quality indicators for benchmarking ICU quality of care. We did not find significant correlations between SRR and SMR or SRR and SLOSR. This means that ICUs with lower than expected readmission rates did not have lower or higher mortality, or shorter or longer length of stays, than expected. We also did not find a significant correlation between SMR and SLOSR for the general ICU population. However, subgroup analyses based on probability of mortality showed associations in opposite directions. We identified no significant association between quality indicators for in-hospital mortality, readmission to the ICU within 48 hours after ICU discharge and ICU length of stay at ICU population level. Differential associations were found between performance on mortality and length of stay within different risk strata. We recommend users of quality information for benchmarking purposes to take these three outcome indicators into account when judging or monitoring ICU quality of care as they capture different aspects of ICU performance. Furthermore, we suggest that users of quality information also receive quality data for patient subgroups, especially low-risk and high-risk patients groups. Source: http://doi.org/10.1371/journal.pone.0198522