Association of patient-to-intensivist ratio with hospital mortality in Australia and New Zealand

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review

OBJECTIVES

COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients.

DATA SOURCES

PubMed, Embase, and Web of Science.

STUDY SELECTION

English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients.

DATA EXTRACTION

Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations.

DATA SYNTHESIS

Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status.

CONCLUSIONS

Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.

Association Between In-hospital Mortality and the Institutional Factors of Intensive Care Units with a Focus on the Intensivist- to-bed Ratio: A Retrospective Cohort Study

Purpose: To elucidate the relationship between in-hospital mortality and the institutional factors of intensive care units (ICUs), with a focus on the intensivist-to-bed ratio. Methods: A retrospective cohort study was conducted using a Japanese ICU database, including adult patients admitted between April 1, 2020 and March 31, 2021. We used a multilevel logistic regression model to investigate the associations between in-hospital mortality and the following institutional factors: the intensivist-to-bed ratios on weekdays or over weekends/holidays, different work shifts, hospital-to-ICU-bed ratio, annual-ICU-admission-to-bed ratio, type of hospital, and the presence of other medical staff. Results: The study population comprised 46 503 patients admitted to 65 ICUs. The in-hospital mortality rate was 8.1%. The median numbers of ICU beds and intensivists were 12 (interquartile range [IQR] 8-14) and 4 (IQR 2-9), respectively. In-hospital mortality decreased significantly as the intensivist-to-bed ratio at 10 am on weekdays increased: the average contrast indicated a 20% (95% confidence interval [CI]: 1%-38%) reduction when the ratio increased from 0 to 0.5, and a 38% (95% CI: 9%-67%) reduction when the ratio increased from 0 to 1. The other institutional factors did not present a significant effect. Conclusions: The intensivist-to-bed ratio at 10 am on weekdays had a significant effect on in-hospital mortality. Further investigation is needed to understand the processes leading to improved outcomes.

Who needs clinician attention first? A qualitative study of critical care clinicians' needs that enable the prioritization of care for populations of acutely ill patients

BACKGROUND

To adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources. Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard.

METHODS

We conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software.

RESULTS

We interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU.

CONCLUSIONS

This qualitative study explored ICU clinicians' information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU.

Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio

BACKGROUND

The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients.

METHODS

Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis.

RESULTS

Our protocol describes the processes and methods for an observational study in the ICU.

CONCLUSIONS

This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.

Intensivist physician-to-patient ratios and mortality in the intensive care unit

PURPOSE

A high daily census may hinder the ability of physicians to deliver quality care in the intensive care unit (ICU). We sought to determine the relationship between intensivist-to-patient ratios and mortality among ICU patients.

METHODS

We performed a retrospective cohort study of intensivist-to-patient ratios in 29 ICUs in 10 hospitals in the United States from 2018 to 2020. We used meta-data from progress notes in the electronic health record to determine an intensivist-specific caseload for each ICU day. We then fit a multivariable proportional hazards model with time-varying covariates to estimate the relationship between the daily intensivist-to-patient ratio and ICU mortality at 28 days.

RESULTS

The final analysis included 51,656 patients, 210,698 patient days, and 248 intensivist physicians. The average caseload per day was 11.8 (standard deviation: 5.7). There was no association between the intensivist-to-patient ratio and mortality (hazard ratio for each additional patient: 0.987, 95% confidence interval: 0.968-1.007, p = 0.2). This relationship persisted when we defined the ratio as caseload over the sample-wide average (hazard ratio: 0.907, 95% confidence interval: 0.763-1.077, p = 0.26) and cumulative days with a caseload over the sample-wide average (hazard ratio: 0.991, 95% confidence interval: 0.966-1.018, p = 0.52). The relationship was not modified by the presence of physicians-in-training, nurse practitioners, and physician assistants (p value for interaction term: 0.14).

CONCLUSIONS

Mortality for ICU patients appears resistant to high intensivist caseloads. These results may not generalize to ICUs organized differently than those in this sample, such as ICUs outside the United States.

Evaluation of Digital Health Strategy to Support Clinician-Led Critically Ill Patient Population Management: A Randomized Crossover Study

To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR).

DESIGN

Single center randomized crossover study.

SETTING

Quaternary care academic hospital.

SUBJECTS

Attending and in-training critical care physicians, and advanced practice providers.

INTERVENTIONS

AMP.

MEASUREMENTS AND MAIN RESULTS

We compared ICU clinician performance in structured clinical task completion using two electronic environments-the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7-10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (-6.11; 95% CI, -7.91 to -4.30 min and -5.38; 95% CI, -7.56 to -3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, -0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, -32.7 to -12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36-1.30; p = 0.078).

CONCLUSIONS

When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians' performance while using AMP in the live ICU setting.

Health and Well-Being of Intensive Care Unit Physicians: How to Ensure the Longevity of a Critical Specialty

A second epidemic of burnout, fatigue, anxiety, and moral distress has emerged concurrently with the coronavirus disease 2019 (COVID-19) pandemic, and critical care physicians are especially affected. This article reviews the history of burnout in health care workers, presents the signs and symptoms, discusses the specific impact of the COVID-19 pandemic on intensive care unit caregivers, and attempts to identify potential strategies to combat the Great Resignation disproportionately affecting health care workers. The article also focuses on how the specialty can amplify the voices and highlight the leadership potential of underrepresented minorities, physicians with disabilities, and the aging physician population.

Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study

Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed.

OBJECTIVES

To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition.

DESIGN SETTING AND PARTICIPANTS

This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016-May 2017) and after (June 2017-November 2017) the transition to a high-intensity ICU staffing model.

MAIN OUTCOMES AND MEASURES

The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission.

RESULTS

For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61-1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57-1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001).

CONCLUSIONS AND RELEVANCE

Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.

Strain on the ICU resources and patient outcomes in the COVID-19 pandemic: A Swedish national registry cohort study

BACKGROUND

The Coronavirus 2019 (COVID-19) pandemic has led to an unprecedented strain on the ICU resources. It is not known how the ICU resources employed in treating COVID-19 patients are related to inpatient characteristics, use of organ support or mortality.

OBJECTIVES

To investigate how the use of ICU resources relate to use of organ support and mortality in COVID-19 patients.

DESIGN

A national register-based cohort study.

SETTING

All Swedish ICUs from March 2020 to November 2021.

PATIENTS

All patients admitted to Swedish ICUs with a primary diagnosis of COVID-19 reported to the national Swedish Intensive Care Register (SIR).

MAIN OUTCOME MEASURES

Organ support (mechanical ventilation, noninvasive ventilation, high-flow oxygen therapy, prone positioning, surgical and percutaneous tracheostomy, central venous catheterisation, continuous renal replacement therapy and intermittent haemodialysis), discharge at night, re-admission, transfer and ICU and 30-day mortality.

RESULTS

Seven thousand nine hundred and sixty-nine patients had a median age of 63 years, and 70% were men. Median daily census was 167% of habitual census, daily new admissions were 20% of habitual census and the median occupancy was 82%. Census and new admissions were associated with mechanical ventilation, OR 1.37 (95% CI 1.28 to 1.48) and OR 1.44 (95% CI 1.13 to 1.84), respectively, but negatively associated with noninvasive ventilation, OR 0.83 (95% CI 0.77 to 0.89) and OR 0.40 (95% CI 0.30 to 52) and high-flow oxygen therapy, OR 0.72 (95% CI 0.67 to 0.77) and OR 0.77 (95% CI 0.61 to 0.97). Occupancy above 90% of available beds was not associated with mechanical ventilation or noninvasive ventilation, but with high-flow oxygen therapy, OR 1.36 (95% CI 1.21 to 1.53). All measures of pressure on resources were associated with transfer to other hospitals, but none were associated with discharge at night, ICU mortality or 30-day mortality.

CONCLUSIONS

Pressure on ICU resources was associated with more invasive respiratory support, indicating that during these times, ICU resources were reserved for sicker patients.

Variation in Bed-to-Physician Ratios During Weekday Daytime Hours in ICUs in Australia and New Zealand

OBJECTIVES

To determine common "bed-to-physician" ratios during weekday hours across ICUs and assess factors associated with variability in this ratio.

DESIGN

Retrospective cohort study.

SETTING

All ICUs in Australia/New Zealand that participated in a staffing survey administered in 2017-2018.

PATIENTS

ICU admissions from 2016 to 2018.

METHODS

We linked survey data with patient-level data. We defined: 1) bed-to-intensivist ratio as the number of usually available ICU beds divided by the number of onsite weekday daytime intensivists; and 2) bed-to-physician ratio as the number of available ICU beds divided by the total number of physicians (intensivists + nonintensivists, including trainees). We calculated the median and interquartile range (IQR) of bed-to-intensivist ratio and bed-to-physician ratios during weekday hours. We assessed variability in each by type of hospital and ICU and by severity of illness of patients, defined by the predicted hospital mortality.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the 123 (87.2%) of Australia/New Zealand ICUs that returned staffing surveys, 114 (92.7%) had an intensivist present during weekday daytime hours, and 116 (94.3%) reported at least one nonintensivist physician. The median bed-to-intensivist ratio was 8.0 (IQR, 6.0-11.4), which decreased to a bed-to-physician ratio of 3.0 (IQR, 2.2-4.9). These ratios varied with mean severity of illness of the patients in the unit. The median bed-to-intensivist ratio was highest (13.5) for ICUs with a mean predicted mortality > 2-4%, and the median bed-to-physician ratio was highest (5.7) for ICUs with a mean predicted mortality of > 4-6%. Both ratios decreased and plateaued in ICUs with a mean predicted mortality for patients greater than 8% (median bed-to-intensivist ratio range, 6.8-8.0, and bed-to-physician ratio range of 2.4-2.7).

CONCLUSIONS

Weekday bed-to-physician ratios in Australia/New Zealand ICUs are lower than the bed-to-intensivist ratios and have a relatively fixed ratio of less than 3 for units taking care of patients with a higher average severity of illness. These relationships may be different in other countries or healthcare systems.

SWEAT ICU-An Observational Study of Physician Workload and the Association of Physician Outcomes in Academic ICUs

The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality.

DESIGN

Cross-sectional observational study.

SETTING

Fourteen academic centers in the United States from August 2020 to July 2021.

SUBJECTS

We enrolled ICU physicians and collected data on adult ICU patients under the physician's care on the single physician-selected study day for each physician.

MEASUREMENTS and MAIN RESULTS

The primary exposure was workload (self-reported number of patients' physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10-14), and the overall prevalence of burnout was 26.4% (n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24-2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92-1.91).

CONCLUSIONS

In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.

SepsEast Registry indicates high mortality associated with COVID-19 caused acute respiratory failure in Central-Eastern European intensive care units

The coronavirus disease (COVID-19) pandemic caused unprecedented research activity all around the world but publications from Central-Eastern European countries remain scarce. Therefore, our aim was to characterise the features of the pandemic in the intensive care units (ICUs) among members of the SepsEast (Central-Eastern European Sepsis Forum) initiative. We conducted a retrospective, international, multicentre study between March 2020 and February 2021. All adult patients admitted to the ICU with pneumonia caused by COVID-19 were enrolled. Data on baseline and treatment characteristics, organ support and mortality were collected. Eleven centres from six countries provided data from 2139 patients. Patient characteristics were: median 68, [IQR 60-75] years of age; males: 67%; body mass index: 30.1 [27.0-34.7]; and 88% comorbidities. Overall mortality was 55%, which increased from 2020 to 2021 (p = 0.004). The major causes of death were respiratory (37%), cardiovascular (26%) and sepsis with multiorgan failure (21%). 1061 patients received invasive mechanical ventilation (mortality: 66%) without extracorporeal membrane oxygenation (n = 54). The rest of the patients received non-invasive ventilation (n = 129), high flow nasal oxygen (n = 317), conventional oxygen therapy (n = 122), as the highest level of ventilatory support, with mortality of 50%, 39% and 22%, respectively. This is the largest COVID-19 dataset from Central-Eastern European ICUs to date. The high mortality observed especially in those receiving invasive mechanical ventilation renders the need of establishing national-international ICU registries and audits in the region that could provide high quality, transparent data, not only during the pandemic, but also on a regular basis.

Association of Anesthesiologist Staffing Ratio With Surgical Patient Morbidity and Mortality

Importance

Recent studies have investigated the effect of overlapping surgeon responsibilities or nurse to patient staffing ratios on patient outcomes, but the association of overlapping anesthesiologist responsibilities with patient outcomes remains unexplored to our knowledge.

Objective

To examine the association between different levels of anesthesiologist staffing ratios and surgical patient morbidity and mortality.

Design, Setting, and Participants

A retrospective, matched cohort study consisting of major noncardiac inpatient surgical procedures performed from January 1, 2010, to October 31, 2017, was conducted in 23 US academic and private hospitals. A total of 866 453 adult patients (aged ≥18 years) undergoing major inpatient surgery within the Multicenter Perioperative Outcomes Group electronic health record registry were included. Anesthesiologist sign-in and sign-out times were used to calculate a continuous time-weighted average staffing ratio variable for each operation. Propensity score-matching methods were applied to create balanced sample groups with respect to patient-, operative-, and hospital-level confounders and resulted in 4 groups based on anesthesiologist staffing ratio. Groups consisted of patients receiving care from an anesthesiologist covering 1 operation (group 1), more than 1 to no more than 2 overlapping operations (group 1-2), more than 2 to no more than 3 overlapping operations (group 2-3), and more than 3 to no more than 4 overlapping operations (group 3-4). Data analysis was performed from October 2019 to October 2021.

Exposure

Undergoing a major inpatient surgical operation that involved an anesthesiologist providing care for up to 4 overlapping operations.

Main Outcomes and Measures

The primary composite outcome was 30-day mortality and 6 major surgical morbidities (cardiac, respiratory, gastrointestinal, urinary, bleeding, and infectious complications) derived from International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision discharge diagnosis codes.

Results

In all, 578 815 adult patients (mean [SD] age, 55.7 [16.2] years; 55.1% female) were analyzed. After matching operations according to anesthesiologist staffing ratio, 48 555 patients were in group 1; 247 057, group 1-2; 216 193, group 2-3; and 67 010, group 3-4. Increasing anesthesiologist coverage responsibilities was associated with an increase in risk-adjusted surgical patient morbidity and mortality. Compared with patients in group 1-2, those in group 2-3 had a 4% relative increase in risk-adjusted mortality and morbidity (5.06% vs 5.25%; adjusted odds ratio [AOR], 1.04; 95% CI, 1.01-1.08; P = .02) and those in group 3-4 had a 14% increase in risk-adjusted mortality and morbidity (5.06% vs 5.75%; AOR, 1.15; 95% CI, 1.09-1.21; P < .001).

Conclusions and Relevance

This study's findings suggest that increasing overlapping coverage by anesthesiologists is associated with increased surgical patient morbidity and mortality. Therefore, the potential effects of staffing ratios in perioperative team models should be considered in clinical coverage efforts.

Outcomes in Temporary ICUs Versus Conventional ICUs: An Observational Cohort of Mechanically Ventilated Patients With COVID-19–Induced Acute Respiratory Distress Syndrome

Throughout the COVID-19 pandemic, thousands of temporary ICUs have been established worldwide. The outcomes and management of mechanically ventilated patients in these areas remain unknown.

Association of patient-to-intensivist ratio with hospital mortality in Australia and New Zealand

Purpose

The impact of intensivist workload on intensive care unit (ICU) outcomes is incompletely described and assessed across healthcare systems and countries. We sought to examine the association of patient-to-intensivist ratio (PIR) with hospital mortality in Australia/New Zealand (ANZ) ICUs.

Methods

We conducted a retrospective study of adult admissions to ANZ ICUs (August 2016–June 2018) using two cohorts: “narrow”, based on previously used criteria including restriction to ICUs with a single daytime intensivist; and “broad”, refined by individual ICU daytime staffing information. The exposure was average daily PIR and the outcome was hospital mortality. We used summary statistics to describe both cohorts and multilevel multivariable logistic regression models to assess the association of PIR with mortality. In each, PIR was modeled using restricted cubic splines to allow for non-linear associations. The broad cohort model included non-PIR physician and non-physician staffing covariables.

Results

The narrow cohort of 27,380 patients across 67 ICUs (predicted mortality: median 1.2% [IQR 0.4–1.4%]; mean 5.9% [sd 13.2%]) had a median PIR of 10.1 (IQR 7–14). The broad cohort of 91,206 patients across 73 ICUs (predicted mortality: 1.9% [0.6–6.5%]; 7.6% [14.9%]) had a median PIR of 7.8 (IQR 5.8–10.2). We found no association of PIR with mortality in either the narrow (PIR 1st spline term odds ratio [95% CI]: 1 [0.94, 1.06], Wald testing of spline terms p = 0.61) or the broad (1.02 [0.97, 1.07], p = 0.4) cohort.

Conclusion

We found no association of PIR with hospital mortality across ANZ ICUs. The low cohort predicted mortality may limit external validity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-021-06575-z.