Measuring efficiency in Australian and New Zealand paediatric intensive care units

Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise

BACKGROUND

Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared.

METHODS

From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established.

RESULTS

The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics.

CONCLUSIONS

Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no "one best model"; thus, ICU "performance" is determined by model choice and any rankings thereupon should be circumspect.

Severity of illness and organ dysfunction scoring systems in pediatric critical care: The impacts on clinician's practices and the future

Severity and organ dysfunction (OD) scores are increasingly used in pediatric intensive care units (PICU). Therefore, this review aims to provide 1/ an updated state-of-the-art of severity scoring systems and OD scores in pediatric critical care, which explains 2/ the performance measurement tools and the significance of each tool in clinical practice and provides 3/ the usefulness, limits, and impact on future scores in PICU. The following two pediatric systems have been proposed: the PRISMIV, is used to collect data between 2 h before PICU admission and the first 4 h after PICU admission; the PIM3, is used to collect data during the first hour after PICU admission. The PELOD-2 and SOFApediatric scores were the most common OD scores available. Scores used in the PICU should help clinicians answer the following three questions: 1/ Are the most severely ill patients dying in my service: a good discrimination allow us to interpret that there are the most severe patients who died in my service. 2/ Does the overall number of deaths observed in my department consistent with the severity of patients? The standard mortality ratio allow us to determine whether the total number of deaths observed in our service over a given period is in adequacy with the number of deaths predicted, by considering the severity of patients on admission? 3/ Does the number of deaths observed by severity level in my department consistent with the severity of patients? The calibration enabled us to determine whether the number of deaths observed according to the severity of patients at PICU admission in a department over a given period is in adequacy with the number of deaths predicted, according to the severity of the patients at PICU admission. These scoring systems are not interpretable at the patient level. Scoring systems are used to describe patients with PICU in research and evaluate the service's case mix and performance. Therefore, the prospect of automated data collection, which permits their calculation, facilitated by the computerization of services, is a necessity that manufacturers should consider.

Long-stay patients in pediatric intensive care units. Five-years, 2-points, cross-sectional study

Objectives:

To explore the changing patterns of long-stay patients (LSP) to improve the utilization of pediatric intensive care units (PICUs) resources.

Methods:

This is a 2-points cross-sectional study (5 years apart; 2014-2019) conducted among PICUs and SCICUs in Riyadh, Saudi Arabia. Children who have stayed in PICU for more than 21 days were included.

Results:

Out of the 11 units approached, 10 (90%) agreed to participate. The prevalence of LSP in all these hospitals decreased from 32% (48/150) in 2014 to 23.4% (35/149) in 2019. The length of stay ranged from 22 days to 13.5 years. The majority of LSP had a neuromuscular or cardiac disease and were admitted with respiratory compromise. Ventilator-associated pneumonia was the most prevalent complication (37.5%). The most commonly used resources were mechanical ventilation (93.8%), antibiotics (60.4%), and blood-products transfusions (35.4%). The most common reason for the extended stay was medical reasons (51.1%), followed by a lack of family resources (26.5%) or lack of referral to long-term care facilities (22.4%).

Conclusion:

A long-stay is associated with significant critical care bed occupancy, complications, and utilization of resources that could be otherwise utilized as surge capacity for critical care services. Decreasing occupancy in this multicenter study deserves further engagement of the healthcare leaders and families to maximize the utilization of resources.

Number of intensivists per bed is associated with efficiency of Dutch intensive care units

PURPOSE

To measure efficiency in Intensive Care Units (ICUs) and to determine which organizational factors are associated with ICU efficiency, taking confounding factors into account.

MATERIALS AND METHODS

We used data of all consecutive admissions to Dutch ICUs between January 1, 2016 and January 1, 2019 and recorded ICU organizational factors. We calculated efficiency for each ICU by averaging the Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU) and examined the relationship between various organizational factors and ICU efficiency. We thereby compared the results of linear regression models before and after covariate adjustment using propensity scores.

RESULTS

We included 164,399 admissions from 83 ICUs. ICU efficiency ranged from 0.51-1.42 (average 0.99, 0.15 SD). The unadjusted model as well as the propensity score adjusted model showed a significant association between the ratio of employed intensivists per ICU bed and ICU efficiency. Other organizational factors had no statistically significant association with ICU efficiency after adjustment.

CONCLUSIONS

We found marked variability in efficiency in Dutch ICUs. After applying covariate adjustment using propensity scores, we identified one organizational factor, ratio intensivists per bed, having an association with ICU efficiency.

Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs

PURPOSE

Comparisons between institutions of intensive care unit (ICU) length of stay (LOS) are significantly confounded by individual patient characteristics, and currently there is a paucity of methods available to calculate risk-adjusted metrics.

METHODS

We extracted de-identified data from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for admissions between January 1 2011 and December 31 2015. We used a mixed-effects log-normal regression model to predict LOS using patient and admission characteristics. We calculated a risk-adjusted LOS ratio (RALOSR) by dividing the geometric mean observed LOS by the exponent of the expected Ln-LOS for each site and year. The RALOSR is scaled such that values <1 indicate a LOS shorter than expected, while values >1 indicate a LOS longer than expected. Secondary mixed effects regression modelling was used to assess the stability of the estimate in units over time.

RESULTS

During the study there were a total of 662,525 admissions to 168 units (median annual admissions = 767, IQR:426-1121). The mean observed LOS was 3.21 days (median = 1.79 IQR = 0.92-3.52) over the entire period, and declined on average 1.97 hours per year (95%CI:1.76-2.18) from 2011 to 2015. The RALOSR varied considerably between units, ranging from 0.35 to 2.34 indicating large differences after accounting for case-mix.

CONCLUSIONS

There are large disparities in risk-adjusted LOS among Australian and New Zealand ICUs which may reflect differences in resource utilization.

High-flow nasal cannula (HFNC) support in interhospital transport of critically ill children

PURPOSE

Optimal respiratory support for interhospital transport of critically ill children is challenging and has been scarcely investigated. High-flow nasal cannula (HFNC) therapy has emerged as a promising support mode in the paediatric intensive care unit (PICU), but no data are available on HFNC used during interhospital transport. We aimed to assess the safety of HFNC during retrievals of critically ill children and its impact on the need for invasive ventilation (IV).

METHODS

This was a retrospective, single-centre study of children under 2 years old transported by a specialized paediatric retrieval team to PICU. We compared IV rates before (2005-2008) and after introduction of HFNC therapy (2009-2012).

RESULTS

A total of 793 infants were transported. The mean transport duration was 1.4 h (range 0.25-8), with a mean distance of 205 km (2-2,856). Before introduction of HFNC, 7 % (n = 23) were retrieved on non-invasive ventilation (NIV) and 49 % (n = 163) on IV. After introduction of HFNC, 33 % (n = 150) were retrieved on HFNC, 2 % (n = 10) on NIV, whereas IV decreased to 35 % (n = 162, p < 0.001). No patients retrieved on HFNC required intubation during retrieval, or developed pneumothorax or cardiac arrest. Using HFNC was associated with a significant reduction in IV initiated by the retrieval team (multivariate OR 0.51; 95 % CI 0.27-0.95; p = 0.032).

CONCLUSIONS

We report on a major change of practice in transport of critically ill children in our retrieval system. HFNC therapy was increasingly used and was not inferior to low-flow oxygen or NIV. Randomized trials are needed to assess whether HFNC can reduce the need for IV in interhospital transport of critically ill children.