Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2024 Mar 1;52(3):387-395.
doi: 10.1097/CCM.0000000000006098. Epub 2023 Nov 9.

The Influence of Potential Organ Donors on Standardized Mortality Ratios and ICU Benchmarking

Anssi Pölkki  1   2 , André Moser  3 , Rahul Raj  4 , Jukka Takala  5 , Stepani Bendel  1 , Stephan M Jakob  5 , Matti Reinikainen  1   2
Affiliations
Multicenter Study

The Influence of Potential Organ Donors on Standardized Mortality Ratios and ICU Benchmarking

Anssi Pölkki et al. Crit Care Med. .

Abstract

Objectives: The standardized mortality ratio (SMR) is a common metric to benchmark ICUs. However, SMR may be artificially distorted by the admission of potential organ donors (POD), who have nearly 100% mortality, although risk prediction models may not identify them as high-risk patients. We aimed to evaluate the impact of PODs on SMR.

Design: Retrospective registry-based multicenter study.

Setting: Twenty ICUs in Finland, Estonia, and Switzerland in 2015-2017.

Patients: Sixty thousand forty-seven ICU patients.

Interventions: None.

Measurements and main results: We used a previously validated mortality risk model to calculate the SMRs. We investigated the impact of PODs on the overall SMR, individual ICU SMR and ICU benchmarking. Of the 60,047 patients admitted to the ICUs, 514 (0.9%) were PODs, and 477 (93%) of them died. POD deaths accounted for 7% of the total 6738 in-hospital deaths. POD admission rates varied from 0.5 to 18.3 per 1000 admissions across ICUs. The risk prediction model predicted a 39% in-hospital mortality for PODs, but the observed mortality was 93%. The ratio of the SMR of the cohort without PODs to the SMR of the cohort with PODs was 0.96 (95% CI, 0.93-0.99). Benchmarking results changed in 70% of ICUs after excluding PODs.

Conclusions: Despite their relatively small overall number, PODs make up a large proportion of ICU patients who die. PODs cause bias in SMRs and in ICU benchmarking. We suggest excluding PODs when benchmarking ICUs with SMR.

PubMed Disclaimer

Conflict of interest statement

Dr. Pölkki was supported by institutional funding from Kuopio University Hospital, University of Eastern Finland, and The Finnish Society of Anaesthesiologists. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Ratios of standardized mortality ratios (SMRs) comparing the cohort without potential organ donors (PODs) to the cohort with PODs (SMRPOD excluded/SMRPOD included) in each ICU during the entire study period. The error bars represent the 95% CIs. The vertical dashed line represents the average ratio in the whole study population (0.96). The dark gray area represents the 95% CI (0.93–0.99).
Figure 2.
Figure 2.
Impact of exclusion of potential organ donors (PODs). The filled circles represent the standardized mortality ratios (SMRs) of each ICU during the whole study period with PODs included. The triangles represent the SMRs of the ICUs with PODs excluded. The error bars represent the 95% CIs. The ICUs listed on the y-axis are arranged by increasing SMRs with PODs included. L1–L6 represent the ICUs of large nonuniversity hospitals, S1–S6 those of small nonuniversity hospitals, and U1–U8 those of university hospitals.
Figure 3.
Figure 3.
Alterations to standardized mortality ratio-based ranking of ICUs caused by excluding the potential organ donors (PODs) during the whole study period (first panel, left), and alterations during each study year separately (second to fourth panel). The size of the symbol indicates the proportion of PODs of all admissions in the ICU.
See this image and copyright information in PMC

Comment in

References

    1. Salluh JIF, Soares M, Keegan MT: Understanding intensive care unit benchmarking. Intensive Care Med. 2017; 43:1703–1707 - PubMed
    1. Zimmerman JE, Kramer AA, McNair DS, et al. : Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients. Crit Care Med. 2006; 34:1297–1310 - PubMed
    1. Metnitz PG, Moreno RP, Almeida E, et al. ; SAPS 3 Investigators: SAPS 3--from evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description. Intensive Care Med. 2005; 31:1336–1344 - PMC - PubMed
    1. Moreno RP, Metnitz PG, Almeida E, et al. ; SAPS 3 Investigators: SAPS 3--from evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005; 31:1345–1355 - PMC - PubMed
    1. Higgins T, Teres D, Nathanson B, et al. : Updated mortality probability model - MPM0-III. Chest. 2005; 128(4 Suppl):348S–348S

Publication types

LinkOut - more resources