Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010.
ABSTRACT: BACKGROUND: The Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD) collects voluntary data on patient admissions to Australian and New Zealand intensive care units (ICUs). This paper presents an in-depth statistical analysis of risk-adjusted mortality of ICU admissions from 2000 to 2010 for the purpose of identifying ICUs with unusual performance. METHODS: A cohort of 523,462 patients from 144 ICUs was analysed. For each ICU, the natural logarithm of the standardised mortality ratio (log-SMR) was estimated from a risk-adjusted, three-level hierarchical model. This is the first time a three-level model has been fitted to such a large ICU database anywhere. The analysis was conducted in three stages which included the estimation of a null distribution to describe usual ICU performance. Log-SMRs with appropriate estimates of standard errors are presented in a funnel plot using 5% false discovery rate thresholds. False coverage-statement rate confidence intervals are also presented. The observed numbers of deaths for ICUs identified as unusual are compared to the predicted true worst numbers of deaths under the model for usual ICU performance. RESULTS: Seven ICUs were identified as performing unusually over the period 2000 to 2010, in particular, demonstrating high risk-adjusted mortality compared to the majority of ICUs. Four of the seven were ICUs in private hospitals. Our three-stage approach to the analysis detected outlying ICUs which were not identified in a conventional (single) risk-adjusted model for mortality using SMRs to compare ICUs. We also observed a significant linear decline in mortality over the decade. Distinct yearly and weekly respiratory seasonal effects were observed across regions of Australia and New Zealand for the first time. CONCLUSIONS: The statistical approach proposed in this paper is intended to be used for the review of observed ICU and hospital mortality. Two important messages from our study are firstly, that comprehensive risk-adjustment is essential in modelling patient mortality for comparing performance, and secondly, that the appropriate statistical analysis is complicated.
Project description:Outcomes following admission to intensive care units (ICU) may vary with time and day. This study investigated associations between time of day and risk of ICU mortality and chance of ICU discharge in acute ICU admissions. Adult patients (age ≥ 18 years) who were admitted to ICUs participating in the Austrian intensive care database due to medical or surgical urgencies and emergencies between January 2012 and December 2016 were included in this retrospective study. Readmissions were excluded. Statistical analysis was conducted using the Fine-and-Gray proportional subdistribution hazards model concerning ICU mortality and ICU discharge within 30 days adjusted for SAPS 3 score. 110,628 admissions were analysed. ICU admission during late night and early morning was associated with increased hazards for ICU mortality; HR: 1.17; 95% CI: 1.08-1.28 for 00:00-03:59, HR: 1.16; 95% CI: 1.05-1.29 for 04:00-07:59. Risk of death in the ICU decreased over the day; lowest HR: 0.475, 95% CI: 0.432-0.522 for 00:00-03:59. Hazards for discharge from the ICU dropped sharply after 16:00; lowest HR: 0.024; 95% CI: 0.019-0.029 for 00:00-03:59. We conclude that there are "time effects" in ICUs. These findings may spark further quality improvement efforts.
Project description:OBJECTIVES:Pediatric cardiac ICUs should be adept at treating both critical medical and surgical conditions for patients with cardiac disease. There are no case-mix adjusted quality metrics specific to medical cardiac ICU admissions. We aimed to measure case-mix adjusted cardiac ICU medical mortality rates and assess variation across cardiac ICUs in the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Pediatric Cardiac Critical Care Consortium clinical registry. PATIENTS:All cardiac ICU admissions that did not include cardiac surgery. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary endpoint was cardiac ICU mortality. Based on multivariable logistic regression accounting for clustering, we created a case-mix adjusted model using variables present at cardiac ICU admission. Bootstrap resampling (1,000 samples) was used for model validation. We calculated a standardized mortality ratio for each cardiac ICU based on observed-to-expected mortality from the fitted model. A cardiac ICU was considered a statistically significant outlier if the 95% CI around the standardized mortality ratio did not cross 1. Of 11,042 consecutive medical admissions from 25 cardiac ICUs (August 2014 to May 2017), the observed mortality rate was 4.3% (n = 479). Final model covariates included age, underweight, prior surgery, time of and reason for cardiac ICU admission, high-risk medical diagnosis or comorbidity, mechanical ventilation or extracorporeal membrane oxygenation at admission, and pupillary reflex. The C-statistic for the validated model was 0.87, and it was well calibrated. Expected mortality ranged from 2.6% to 8.3%, reflecting important case-mix variation. Standardized mortality ratios ranged from 0.5 to 1.7 across cardiac ICUs. Three cardiac ICUs were outliers; two had lower-than-expected (standardized mortality ratio <1) and one had higher-than-expected (standardized mortality ratio >1) mortality. CONCLUSIONS:We measured case-mix adjusted mortality for cardiac ICU patients with critical medical conditions, and provide the first report of variation in this quality metric within this patient population across Pediatric Cardiac Critical Care Consortium cardiac ICUs. This metric will be used by Pediatric Cardiac Critical Care Consortium cardiac ICUs to assess and improve outcomes by identifying high-performing (low-mortality) centers and engaging in collaborative learning.
Project description:PURPOSE: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. MATERIALS AND METHODS: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. RESULTS: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). CONCLUSION: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.
Project description:Hospitals are increasingly adopting 24-hour intensivist physician staffing as a strategy to improve intensive care unit (ICU) outcomes. However, the degree to which nighttime intensivists are associated with improvements in the quality of ICU care is unknown.We conducted a retrospective cohort study involving ICUs that participated in the Acute Physiology and Chronic Health Evaluation (APACHE) clinical information system from 2009 through 2010, linking a survey of ICU staffing practices with patient-level outcomes data from adult ICU admissions. Multivariate models were used to assess the relationship between nighttime intensivist staffing and in-hospital mortality among ICU patients, with adjustment for daytime intensivist staffing, severity of illness, and case mix. We conducted a confirmatory analysis in a second, population-based cohort of hospitals in Pennsylvania from which less detailed data were available.The analysis with the use of the APACHE database included 65,752 patients admitted to 49 ICUs in 25 hospitals. In ICUs with low-intensity daytime staffing, nighttime intensivist staffing was associated with a reduction in risk-adjusted in-hospital mortality (adjusted odds ratio for death, 0.62; P=0.04). Among ICUs with high-intensity daytime staffing, nighttime intensivist staffing conferred no benefit with respect to risk-adjusted in-hospital mortality (odds ratio, 1.08; P=0.78). In the verification cohort, there was a similar relationship among daytime staffing, nighttime staffing, and in-hospital mortality. The interaction between nighttime staffing and daytime staffing was not significant (P=0.18), yet the direction of the findings were similar to those in the APACHE cohort.The addition of nighttime intensivist staffing to a low-intensity daytime staffing model was associated with reduced mortality. However, a reduction in mortality was not seen in ICUs with high-intensity daytime staffing. (Funded by the National Heart, Lung, and Blood Institute.).
Project description:The Sepsis-3 consensus task force defined sepsis as life-threatening organ dysfunction caused by dysregulated host response to infection. However, the clinical criteria for this definition were neither designed for nor validated in children. We validated the performance of SIRS, age-adapted SOFA, quick SOFA and PELOD-2 scores as predictors of outcome in children.We performed a multicentre binational cohort study of patients < 18 years admitted with infection to ICUs in Australia and New Zealand. The primary outcome was ICU mortality. SIRS, age-adapted SOFA, quick SOFA and PELOD-2 scores were compared using crude and adjusted area under the receiver operating characteristic curve (AUROC) analysis.Of 2594 paediatric ICU admissions due to infection, 151 (5.8%) children died, and 949/2594 (36.6%) patients died or experienced an ICU length of stay ? 3 days. A ? 2-point increase in the individual score was associated with a crude mortality increase from 3.1 to 6.8% for SIRS, from 1.9 to 7.6% for age-adapted SOFA, from 1.7 to 7.3% for PELOD-2, and from 3.9 to 8.1% for qSOFA (p < 0.001). The discrimination of outcomes was significantly higher for SOFA (adjusted AUROC 0.829; 0.791-0.868) and PELOD-2 (0.816; 0.777-0.854) than for qSOFA (0.739; 0.695-0.784) and SIRS (0.710; 0.664-0.756).SIRS criteria lack specificity to identify children with infection at substantially higher risk of mortality. We demonstrate that adapting Sepsis-3 to age-specific criteria performs better than Sepsis-2-based criteria. Our findings support the translation of Sepsis-3 into paediatric-specific sepsis definitions and highlight the importance of robust paediatric organ dysfunction characterization.
Project description:BACKGROUND:Risk adjusted mortality for intensive care units (ICU) is usually estimated via logistic regression. Random effects (RE) or hierarchical models have been advocated to estimate provider risk-adjusted mortality on the basis that standard estimators increase false outlier classification. The utility of fixed effects (FE) estimators (separate ICU-specific intercepts) has not been fully explored. METHODS:Using a cohort from the Australian and New Zealand Intensive Care Society Adult Patient Database, 2009-2010, the model fit of different logistic estimators (FE, random-intercept and random-coefficient) was characterised: Bayesian Information Criterion (BIC; lower values better), receiver-operator characteristic curve area (AUC) and Hosmer-Lemeshow (H-L) statistic. ICU standardised hospital mortality ratios (SMR) and 95%CI were compared between models. ICU site performance (FE), relative to the grand observation-weighted mean (GO-WM) on odds ratio (OR), risk ratio (RR) and probability scales were assessed using model-based average marginal effects (AME). RESULTS:The data set consisted of 145355 patients in 128 ICUs, years 2009 (47.5%) & 2010 (52.5%), with mean(SD) age 60.9(18.8) years, 56% male and ICU and hospital mortalities of 7.0% and 10.9% respectively. The FE model had a BIC = 64058, AUC = 0.90 and an H-L statistic P-value = 0.22. The best-fitting random-intercept model had a BIC = 64457, AUC = 0.90 and H-L statistic P-value = 0.32 and random-coefficient model, BIC = 64556, AUC = 0.90 and H-L statistic P-value = 0.28. Across ICUs and over years no outliers (SMR 95% CI excluding null-value = 1) were identified and no model difference in SMR spread or 95%CI span was demonstrated. Using AME (OR and RR scale), ICU site-specific estimates diverged from the GO-WM, and the effect spread decreased over calendar years. On the probability scale, a majority of ICUs demonstrated calendar year decrease, but in the for-profit sector, this trend was reversed. CONCLUSIONS:The FE estimator had model advantage compared with conventional RE models. Using AME, between and over-year ICU site-effects were easily characterised.
Project description:<h4>Background</h4>Globally, mortality rates of patients admitted to the intensive care unit (ICU) have decreased over the last two decades. However, evaluations of the temporal trends in the characteristics and outcomes of ICU patients in Asia are limited. The objective of this study was to describe the characteristics and risk adjusted outcomes of all patients admitted to publicly funded ICUs in Hong Kong over a 11-year period. The secondary objective was to validate the predictive performance of Acute Physiology And Chronic Health Evaluation (APACHE) IV for ICU patients in Hong Kong.<h4>Methods</h4>This was an 11-year population-based retrospective study of all patients admitted to adult general (mixed medical-surgical) intensive care units in Hong Kong public hospitals. ICU patients were identified from a population electronic health record database. Prospectively collected APACHE IV data and clinical outcomes were analysed.<h4>Results</h4>From 1 April 2008 to 31 March 2019, there were a total of 133,858 adult ICU admissions in Hong Kong public hospitals. During this time, annual ICU admissions increased from 11,267 to 14,068, whilst hospital mortality decreased from 19.7 to 14.3%. The APACHE IV standard mortality ratio (SMR) decreased from 0.81 to 0.65 during the same period. Linear regression demonstrated that APACHE IV SMR changed by - 0.15 (95% CI - 0.18 to - 0.11) per year (Pearson's R = - 0.951, p < 0.001). Observed median ICU length of stay was shorter than that predicted by APACHE IV (1.98 vs. 4.77, p < 0.001). C-statistic for APACHE IV to predict hospital mortality was 0.889 (95% CI 0.887 to 0.891) whilst calibration was limited (Hosmer-Lemeshow test p < 0.001).<h4>Conclusions</h4>Despite relatively modest per capita health expenditure, and a small number of ICU beds per population, Hong Kong consistently provides a high-quality and efficient ICU service. Number of adult ICU admissions has increased, whilst adjusted mortality has decreased over the last decade. Although APACHE IV had good discrimination for hospital mortality, it overestimated hospital mortality of critically ill patients in Hong Kong.
Project description:Fever suppression may be beneficial for patients with traumatic brain injury (TBI) and stroke, but for patients with meningitis or encephalitis [central nervous system (CNS) infection], the febrile response may be advantageous.To evaluate the relationship between peak temperature in the first 24 h of intensive care unit (ICU) admission and all-cause hospital mortality for acute neurological diseases.Retrospective cohort design from 2005 to 2013, including 934,159 admissions to 148 ICUs in Australia and New Zealand (ANZ) and 908,775 admissions to 236 ICUs in the UK.There were 53,942 (5.8 %) patients in ANZ and 56,696 (6.2 %) patients in the UK with a diagnosis of TBI, stroke or CNS infection. For both the ANZ (P = 0.02) and UK (P < 0.0001) cohorts there was a significant interaction between early peak temperature and CNS infection, indicating that the nature of the relationship between in-hospital mortality and peak temperature differed between TBI/stroke and CNS infection. For patients with CNS infection, elevated peak temperature was not associated with an increased risk of death, relative to the risk at 37-37.4 °C (normothermia). For patients with stroke and TBI, peak temperature below 37 °C and above 39 °C was associated with an increased risk of death, compared to normothermia.The relationship between peak temperature in the first 24 h after ICU admission and in-hospital mortality differs for TBI/stroke compared to CNS infection. For CNS infection, increased temperature is not associated with increased risk of death.
Project description:Variation in the use of ICUs for low-risk conditions contributes to health system inefficiency. We sought to examine the relationship between ICU use for patients with pulmonary embolism (PE) and cost, mortality, readmission, and procedure use.We performed a retrospective cohort study including 61,249 adults with PE discharged from 263 hospitals in three states between 2007 and 2010. We generated hospital-specific ICU admission rate quartiles and used a series of multilevel models to evaluate relationships between admission rates and risk-adjusted in-hospital mortality, readmission, and costs and between ICU admission rates and several critical care procedures.Hospital quartiles varied in unadjusted ICU admission rates for PE (range, ≤ 15% to > 31%). Among all patients, there was a small trend toward increased use of arterial catheterization (0.6%-1.1%, P < .01) in hospital quartiles with higher levels of ICU admission. However, use of invasive mechanical ventilation (14.4%-7.9%, P < .01), noninvasive ventilation (6.6%-3.0%, P < .01), central venous catheterization (14.6%-11.3%, P < .02), and thrombolytics (11.0%-4.7%, P < .01) in patients in the ICU declined across hospital quartiles. There was no relationship between ICU admission rate and risk-adjusted hospital mortality, costs, or readmission.Hospitals vary widely in ICU admission rates for acute PE without a detectable impact on mortality, cost, or readmission. Patients admitted to ICUs in higher-using hospitals received many critical care procedures less often, suggesting that these patients may have had weaker indications for ICU admission. Hospitals with greater ICU admission may be appropriate targets for improving efficiency in ICU admissions.
Project description:<h4>Introduction</h4>In critical illness, the association of hypoglycemia, blood glucose (BG) variability and outcome are not well understood. We describe the incidence, clinical factors and outcomes associated with an early hypoglycemia and BG variability in critically ill patients.<h4>Methods</h4>Retrospective interrogation of prospectively collected data from the Australia New Zealand Intensive Care Society Adult Patient Database on 66184 adult admissions to 24 intensive care units (ICUs) from 1 January 2000 to 31 December 2005. Primary exposure was hypoglycemia (BG < 4.5 mmol/L) and BG variability (BG < 4.5 and >or= 12.0 mmol/L) within 24 hours of admission. Primary outcome was all-cause mortality.<h4>Results</h4>The cumulative incidence of hypoglycemia and BG variability were 13.8% (95% confidence interval (CI) = 13.5 to 14.0; n = 9122) and 2.9% (95%CI = 2.8 to 3.0, n = 1913), respectively. Several clinical factors were associated with both hypoglycemia and BG variability including: co-morbid disease (P < 0.001), non-elective admissions (P < 0.001), higher illness severity (P < 0.001), and primary septic diagnosis (P < 0.001). Hypoglycemia was associated with greater odds of adjusted ICU (odds ratio (OR) = 1.41, 95% CI = 1.31 to 1.54) and hospital death (OR = 1.36, 95% CI = 1.27 to 1.46). Hypoglycemia severity was associated with 'dose-response' increases in mortality. BG variability was associated with greater odds of adjusted ICU (1.5, 95% CI = 1.4 to 1.6) and hospital (1.4, 95% CI = 1.3 to 1.5) mortality, when compared with either hypoglycemia only or neither.<h4>Conclusions</h4>In critically ill patients, both early hypoglycemia and early variability in BG are relatively common, and independently portend an increased risk for mortality.