Outcome in patients perceived as receiving excessive care across different ethical climates: a prospective study in 68 intensive care units in Europe and the USA

Abstract

Purpose: Whether the quality of the ethical climate in the intensive care unit (ICU) improves the identification of patients receiving excessive care and affects patient outcomes is unknown.

Methods: In this prospective observational study, perceptions of excessive care (PECs) by clinicians working in 68 ICUs in Europe and the USA were collected daily during a 28-day period. The quality of the ethical climate in the ICUs was assessed via a validated questionnaire. We compared the combined endpoint (death, not at home or poor quality of life at 1 year) of patients with PECs and the time from PECs until written treatment-limitation decisions (TLDs) and death across the four climates defined via cluster analysis.

Results: Of the 4747 eligible clinicians, 2992 (63%) evaluated the ethical climate in their ICU. Of the 321 and 623 patients not admitted for monitoring only in ICUs with a good (n = 12, 18%) and poor (n = 24, 35%) climate, 36 (11%) and 74 (12%), respectively were identified with PECs by at least two clinicians. Of the 35 and 71 identified patients with an available combined endpoint, 100% (95% CI 90.0-1.00) and 85.9% (75.4-92.0) (P = 0.02) attained that endpoint. The risk of death (HR 1.88, 95% CI 1.20-2.92) or receiving a written TLD (HR 2.32, CI 1.11-4.85) in patients with PECs by at least two clinicians was higher in ICUs with a good climate than in those with a poor one. The differences between ICUs with an average climate, with (n = 12, 18%) or without (n = 20, 29%) nursing involvement at the end of life, and ICUs with a poor climate were less obvious but still in favour of the former.

Conclusion: Enhancing the quality of the ethical climate in the ICU may improve both the identification of patients receiving excessive care and the decision-making process at the end of life.

Keywords: Decision-making; Ethical climate; Interdisciplinary collaboration; Patient outcomes; Perceived excessive care; Treatment-limitation decisions.

Fig. 1

Flow chart. Phase I: Recruitment and data collection of hospital and ICU characteristics, Phase II: Ethical climate data collection, Phase III: Daily perceptions of clinicians and collection of patient characteristics during the 28 days study period, Phase IV: Collection of patients’ one year outcomes. PEC(s) perception(s) of excessive care, TLDs treatment-limitation decisions

Fig. 2

Ethical climates. Factor and cluster analysis were used to obtain mutually exclusive climates. Factor analysis attributes and aggregates the 35-item ethical decision-making climate questionnaire into seven factors for each clinician, which describe different aspects of the ethical decision-making climate as perceived by that clinician. These were subsequently averaged across clinicians to obtain seven factor scores per ICU [14]. A cluster analysis based on these averages scores identified four meaningful ethical climates; good, average with⁽⁺⁾ and without⁽⁻⁾ involvement of nurses at end-of-life (EOL), and poor. The figure visualizes the average factor scores in clinicians per climate. Larger values indicate better agreement with the climate expressed by the corresponding factor. More detailed information can be found in the ESM 2

Fig. 3

a–f Competing risk analyses of time from admission until concordant perceptions of excessive care (PECs) by at least two different clinicians, written treatment-limitation-decision (TLD) and death before and after weighting for country, hospital and patients characteristics using propensity scores. The primary endpoint (dead, not at home or a utility < 0.5 according the EuroQoL-5D questionnaire [21] at one year) is visualized separately in c, d. The sudden increase at day 365 represents the proportion of patients alive with a utility < 0.5 or not living at home. The incidence of the primary endpoint differs from the text because drop-outs are taken into account in competing risk analyses. The results are expressed as (cause-specific) hazard ratios (HR) together with 95% confidence intervals (CI). To avoid type I errors, we gave priority to comparisons between the most extreme (good and poor) climates

Fig. 3

a–f Competing risk analyses of time from admission until concordant perceptions of excessive care (PECs) by at least two different clinicians, written treatment-limitation-decision (TLD) and death before and after weighting for country, hospital and patients characteristics using propensity scores. The primary endpoint (dead, not at home or a utility < 0.5 according the EuroQoL-5D questionnaire [21] at one year) is visualized separately in c, d. The sudden increase at day 365 represents the proportion of patients alive with a utility < 0.5 or not living at home. The incidence of the primary endpoint differs from the text because drop-outs are taken into account in competing risk analyses. The results are expressed as (cause-specific) hazard ratios (HR) together with 95% confidence intervals (CI). To avoid type I errors, we gave priority to comparisons between the most extreme (good and poor) climates