Mortality and intensive care volume in ventilated patients from 1995 to 2009 in the Australian and New Zealand binational adult patient intensive care database*

Trends in ICU mortality and underlying risk over three decades among mechanically ventilated patients. A group level analysis of cohorts from infection prevention studies

BACKGROUND

Has either the underlying risk or the mortality incidence among ICU patients receiving mechanical ventilation (MV) in the literature changed in recent decades? Interpreting ICU mortality trends requires an adjusted analysis accounting for changes in underlying patient risk.

METHODS

Control and intervention groups from 147 randomized concurrent control trials (RCCT) of various VAP prevention interventions, as listed primarily within 13 Cochrane reviews and 63 observational studies listed primarily within four systematic reviews. Eligible studies were those including ICU patients with > 50% of patients receiving > 24 h of MV with mortality data available. ICU mortality (censored day 21 or before) or late (after day 21) mortality together with group-mean age, and group-mean APACHE II scores were extracted from all groups. These incidences were summarized in five meta-regression models versus publication year being variously adjusted for age, APACHE II scores, type of study intervention and other group level parameters.

RESULTS

Among 210 studies published between 1985 and 2021, 169 being found in systematic reviews, the increase per decade in mean mortality incidence, group-mean APACHE II scores, and group-mean age, were < 1 percentage point (p = 0.43), 1.83 (95% CI; 0.51-3.15) points, and 3.9 (95% CI; 1.1-6.7) years, respectively. Only in the model with risk adjustment for both group-mean age and group-mean APACHE II score was a significant decline in mortality apparent. In all models, the mortality incidence among concurrent control groups of decontamination studies was paradoxically five percentage points higher than benchmark and showed greater dispersion.

CONCLUSION

Mortality incidence has changed little over 35 years among ICU infection prevention studies whilst the patient age and underlying disease severity, measured as APACHE II, have both increased. The paradoxically high mortality among concurrent control groups within studies of decontamination methods of infection prevention remains unaccounted for.

Relationship between institutional intensive care volume prior to the COVID-19 pandemic and in-hospital death in ventilated patients with severe COVID-19

We aimed to evaluate the association between ICU patient volume before the COVID-19 pandemic and the outcomes of ventilated COVID-19 patients. We analyzed ventilated patients with COVID-19 aged > 17 years and enrolled in the J-RECOVER study, a retrospective multicenter observational study conducted in Japan between January and September 2020. Based on the number of patients admitted to the ICU between January and December 2019, the top third institutions were defined as high-volume centers, the middle third ones as middle-volume centers, and the bottom third ones as low-volume centers. The primary outcome measure was in-hospital mortality. Multivariate logistic regression analysis for in-hospital mortality and ICU patient volume was performed after adjusting for multiple propensity scores. Among 461 patients, 158, 158, and 145 patients were admitted to low-volume (20 institutions), middle-volume (14 institutions), and high-volume (13 institutions) centers, respectively. Admission to middle- and high-volume centers was not significantly associated with in-hospital death compared with admission to low-volume centers (adjusted odds ratio, 1.11 [95% confidence interval (CI): 0.55-2.25] and adjusted odds ratio, 0.81 [95% CI: 0.31-1.94], respectively). In conclusion, institutional intensive care patient volume prior to the COVID-19 pandemic was not significantly associated with in-hospital death in ventilated COVID-19 patients.

The magnitude of mortality and its determinants in Ethiopian adult intensive care units: A systematic review and meta-analysis

Introduction

Despite mortality in intensive care units being a global burden, it is higher in low-resource countries, including Ethiopia. A sufficient number of evidence is not yet established regarding mortality in the intensive care unit and its determinants. This study intended to determine the prevalence of ICU mortality and its determinants in Ethiopia.

Methods

PubMed, Google Scholar, The Cochrane Library, HINARI, and African Journals Online (AJOL) databases were systematically explored for potentially eligible studies on mortality prevalence and determinants reported by studies done in Ethiopia. Using a Microsoft Excel spreadsheet, two reviewers independently screen, select, review, and extract data for further analysis using STATA/MP version 17. A meta-analysis using a random-effects model was performed to calculate the pooled prevalence and odds ratio with a 95% confidence interval. In addition, using study region and sample size, subgroup analysis was also performed.

Results

9799 potential articles were found after removing duplicates and screening for eligibility, 14 were reviewed. Ethiopia's pooled national prevalence of adult intensive care unit mortality was 39.70% (95% CI: 33.66, 45.74). Mechanical ventilation, length of staying more than two weeks, GCS below 9, and acute respiratory distress syndrome were major predictors of mortality in intensive care units of Ethiopia.

Conclusion

Mortality in adult ICU is high in Ethiopia. We strongly recommend that all health care professionals and other stakeholders should act to decrease the high mortality among critically ill patients in Ethiopia.

Association Between Hospital Volume and Mortality in Status Epilepticus: A National Cohort Study

OBJECTIVES

In various medical and surgical conditions, research has found that centers with higher patient volumes have better outcomes. This relationship has not previously been explored for status epilepticus. This study sought to examine whether centers that see higher volumes of patients with status epilepticus have lower in-hospital mortality than low-volume centers.

DESIGN

Cohort study, using 2010-2015 data from the nationwide Case Mix Programme database of the U.K.'s Intensive Care National Audit and Research Centre.

SETTING

Greater than 90% of ICUs in United Kingdom, Wales, and Northern Ireland.

PATIENTS

Twenty-thousand nine-hundred twenty-two adult critical care admissions with a primary or secondary diagnosis of status epilepticus or prolonged seizure.

INTERVENTIONS

Annual hospital status epilepticus admission volume.

MEASUREMENTS AND MAIN RESULTS

We used multiple logistic regression to evaluate the association between hospital annual status epilepticus admission volume and in-hospital mortality. Hospital volume was modeled as a nonlinear variable using restricted cubic splines, and generalized estimating equations with robust SEs were used to account for clustering by institution. There were 2,462 in-hospital deaths (11.8%). There was no significant association between treatment volume and in-hospital mortality for status epilepticus (p = 0.54). This conclusion was unchanged across a number of subgroup and sensitivity analyses, although we lacked data on seizure duration and medication use. Secondary analyses suggest that many high-risk patients were already transferred from low- to high-volume centers.

CONCLUSIONS

We find no evidence that higher volume centers are associated with lower mortality in status epilepticus overall. It is likely that national guidelines and local pathways in the United Kingdom allow efficient patient transfer from smaller centers like district general hospitals to provide satisfactory patient care in status epilepticus. Future research using more granular data should explore this association for the subgroup of patients with refractory and superrefractory status epilepticus.

Effect of Institutional Case Volume on In-Hospital and Long-Term Mortality in Critically Ill Patients Requiring Mechanical Ventilation for 48 Hours or More

BACKGROUND

The purpose of this study was to evaluate whether institutional case volume affects clinical outcomes in patients receiving mechanical ventilation for 48 hours or more.

METHODS

We conducted a nationwide retrospective cohort study using the database of Korean National Healthcare Insurance Service. Between January 2007 and December 2016, 158,712 adult patients were included at 55 centers in Korea. Centers were categorized according to the average annual number of patients: > 500, 500 to 300, and < 300.

RESULTS

In-hospital mortality rates in the high-, medium-, and low-volume centers were 32.6%, 35.1%, and 39.2%, respectively. After adjustment, in-hospital mortality was significantly higher in low-volume centers (adjusted odds ratio [OR], 1.332; 95% confidence interval [CI], 1.296-1.368; P < 0.001) and medium-volume centers (adjusted OR, 1.125; 95% CI, 1.098-1.153; P < 0.001) compared to high-volume centers. Long-term survival for up to 8 years was better in high-volume centers.

CONCLUSION

Centers with higher case volume (> 500 patients/year) showed lower in-hospital mortality and long-term mortality, compared to centers with lower case volume (< 300 patients/year) in patients who required mechanical ventilation for 48 hours or more.

ICU mortality and variables associated with ICU survival in Poland: A nationwide database study

BACKGROUND

Recently published international comparison data across European countries revealed high mortality rates in Polish ICUs.

OBJECTIVES

Estimation of the rate of ICU mortality and identification of variables associated with ICU survival in Poland.

DESIGN

Retrospective analyses of a database reporting ICU stays in Poland.

SETTINGS AND PATIENTS

The study included data from all adult patients admitted to an ICU in Poland from 1 January 2012 to 31 December 2012.

MAIN OUTCOME MEASURES

ICU mortality and variables associated with ICU survival.

RESULTS

A total of 48 282 patients were treated in 347 ICUs (mean age 63.1 ± 16.8 years, 59% men) with 20 278 deaths (42.0%). Variables associated with ICU survival were: tertiary level of hospital care [relative risk (RR) 0.86, 95% confidence interval (CI) 0.80 to 0.92, P < 0.001]; high annual patient volume in the ICU (RR 0.9995 patient year, 95% CI 0.9994 to 0.9996, P < 0.001); younger patient age (RR 1.025 year, 95% CI 1.024 to 1.026, P < 0.001); female sex (RR 0.92, 95% CI 0.88 to 0.96; P < 0.001); and lower number of comorbidities (RR 1.33, 95% CI 1.31 to 1.35, P < 0.001).

CONCLUSION

ICU mortality was high in Poland. Structural variables, such as the level of hospital care and annual patient volume, may be associated with ICU survival.

Improved survival rates of AML patients following admission to the intensive care unit

Induction chemotherapy in AML patients may have life-threatening side effects requiring intensive care unit (ICU) treatment. We analyzed all AML patients receiving intensive chemotherapy at a single academic center between 01/2006-12/2016. At least one ICU admission was observed in 32% (76/240) patients, and 33% of those died following ICU admission. Whereas the ICU admission proportion remained stable, mortality after ICU admission decreased from 14% (2006-2008) to 3% (2014-2016; p = .056). The number of failing organ systems inversely correlated with surviving ICU admission (p < .001). Sepsis and renal, cardiac and pulmonary failure were each associated with higher mortality. With increasing ICU duration, survival probability decreased (p < .001), but remained >50% even after 14 days of ICU treatment. Progression-free and overall survival were comparable between ICU surviving patients and patients never needing ICU support. In conclusion, outcome after ICU admission of AML patients has substantially improved in recent years.

Outcomes comparison in patients admitted to low complexity rural and urban intensive care units in the Veterans Health Administration

PURPOSE

To evaluate mortality, length of stay, and inter-hospital transfer in the Veteran Health Administration (VHA) among low complexity Intensive Care Unit (ICU) patients.

MATERIALS AND METHOD

Retrospective study of adult ICU admissions identified in VHA Medical SAS®; 2010-2015 at Veterans Affairs (VA) Medical Centers. Facilities classified by the Rural Urban Commuting Area code algorithm as large rural (referred to as rural) (N = 6) or urban (N = 33).

RESULTS

In rural hospitals, patients (N = 9665) were less likely to have a respiratory (12.9% v. 18.9%; p < .001) diagnosis, more likely diagnosed with sepsis (17.6% v. 4.9%), and had a higher illness severity score (42.0 vs. 41.4; p = .01) compared to urban (N = 65,846) counterparts. Mortality within ICU did not vary across facility rurality. In unadjusted analyses, facility rurality (rural vs. urban) was associated with reduced inter-hospital transfers (OR = 0.74; 95% CI = [0.69, 0.80]; p < .001) and a shorter ICU length of stay (RR = 0.82; 95% CI = [0.74, 0.91]; p < .001). This did not hold when the hierarchical data was accounted for.

CONCLUSIONS

Despite challenges, low complexity ICUs in rural VA facilities fare similarly to urban counterparts. Being part of a national healthcare system may have benefits to explore in sustaining critical care access in rural areas outside the VA healthcare system.

[Cases and duration of mechanical ventilation in German hospitals : An analysis of DRG incentives and developments in respiratory medicine]

BACKGROUND

Diagnosis-related groups (DRGs) have been used to reimburse hospitals services in Germany since 2003/04. Like any other reimbursement system, DRGs offer specific incentives for hospitals that may lead to unintended consequences for patients. In the German context, specific procedures and their documentation are suspected to be primarily performed to increase hospital revenues. Mechanical ventilation of patients and particularly the duration of ventilation, which is an important variable for the DRG-classification, are often discussed to be among these procedures.

OBJECTIVES

The aim of this study was to examine incentives created by the German DRG-based payment system with regard to mechanical ventilation and to identify factors that explain the considerable increase of mechanically ventilated patients in recent years. Moreover, the assumption that hospitals perform mechanical ventilation in order to gain economic benefits was examined.

MATERIAL AND METHODS

In order to gain insights on the development of the number of mechanically ventilated patients, patient-level data provided by the German Federal Statistical Office and the German Institute for the Hospital Remuneration System were analyzed. The type of performed ventilation, the total number of ventilation hours, the age distribution, mortality and the DRG distribution for mechanical ventilation were calculated, using methods of descriptive and inferential statistics. Furthermore, changes in DRG-definitions and changes in respiratory medicine were compared for the years 2005-2012.

RESULTS

Since the introduction of the DRG-based payment system in Germany, the hours of ventilation and the number of mechanically ventilated patients have substantially increased, while mortality has decreased. During the same period there has been a switch to less invasive ventilation methods. The age distribution has shifted to higher age-groups. A ventilation duration determined by DRG definitions could not be found.

CONCLUSION

Due to advances in respiratory medicine, new ventilation methods have been introduced that are less prone to complications. This development has simultaneously improved survival rates. There was no evidence supporting the assumption that the duration of mechanical ventilation is influenced by the time intervals relevant for DRG grouping. However, presumably operational routines such as staff availability within early and late shifts of the hospital have a significant impact on the termination of mechanical ventilation.

Relationship between Annualized Case Volume and Mortality in Sepsis

Background

The relationship between annualized case volume and mortality in patients with sepsis is not fully understood. The authors performed a dose–response meta-analysis to assess the effect of annualized case volume on mortality among patients with sepsis in the intensive care unit, emergency department, or hospital, hypothesizing that higher annualized case volume may lead to lower mortality.

Methods

The authors searched PubMed and Embase through July 2015 to identify observational studies that examined the relationship between annualized case volume and mortality in sepsis. The predefined outcome was mortality. Odds ratios with 95% CIs were pooled using a random-effects model.

Results

Ten studies involving 3,495,921 participants and 834,009 deaths were included. The pooled estimate suggested that annualized case volume was inversely associated with mortality (odds ratio, 0.76; 95% CI, 0.65 to 0.89; P = 0.001), with high heterogeneity (I2 = 96.6%). The relationship was consistent in most subgroup analyses and robust in sensitivity analysis. Dose–response analysis identified a nonlinear relationship between annualized case volume and mortality (P for nonlinearity less than 0.001).

Conclusions

This meta-analysis confirmed the study hypothesis and provided strong evidence for an inverse and a nonlinear dose–response relationship between annualized case volume and mortality in patients with sepsis. Variations in cutoff values of category for annualized case volume across studies may mainly result in the overall heterogeneity. Future studies should uncover the mechanism of volume–mortality relationship and standardize the cutoff values of category for annualized case volume in patients with sepsis.

The association between ICU level of care and mortality in the Netherlands

PURPOSE

The relationship between the number of patients admitted to an intensive care unit (ICU) volume and mortality is currently the subject of debate. After implementation of a national guideline in 2006, all Dutch ICUs have been classified into three levels based on ICU size, patient volume, ventilation days, and staffing. The goal of this study is to investigate the association between ICU level and mortality of ICU patients in the Netherlands.

METHODS

We analyzed data from 132,159 patients admitted to 87 ICUs between January 1, 2009 and October 1, 2011. Logistic GEE analyses were performed to assess the influence of ICU level on in-hospital mortality and 90-day mortality in the total ICU population and in different ICU subgroups while adjusting for severity of illness by APACHE IV.

RESULTS

No significant differences were found in the adjusted in-hospital mortality of the total ICU population and in different subgroups admitted to level 1, 2 and 3 ICUs. In-hospital mortality in level 2 and 3 ICUs as opposed to level 1 ICUs was 1.06 (0.93-1.22) and 1.10 (0.94-1.29), respectively, and 90-day mortality was 0.92 (0.80-1.06) and 1.01 (0.88-1.17).

CONCLUSION

We demonstrated that ICU level was not associated with significant differences in the case-mix adjusted in-hospital and long-term mortality of ICU patients. This finding is in contrast with some earlier studies suggesting a volume-outcome relationship. Our results may be explained by the successful implementation of nationwide mandatory quality requirements and adequate staffing in all three levels of ICUs over the last years.

Fixed effects modelling for provider mortality outcomes: Analysis of the Australia and New Zealand Intensive Care Society (ANZICS) Adult Patient Data-base

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.

Trends in admission prevalence, illness severity and survival of haematological patients treated in Dutch intensive care units

PURPOSE

To explore trends over time in admission prevalence and (risk-adjusted) mortality of critically ill haematological patients and compare these trends to those of several subgroups of patients admitted to the medical intensive care unit (medical ICU patients).

METHODS

A total of 1,741 haematological and 60,954 non-haematological patients admitted to the medical ICU were analysed. Trends over time and differences between two subgroups of haematological medical ICU patients and four subgroups of non-haematological medical ICU patients were assessed, as well as the influence of leukocytopenia.

RESULTS

The proportion of haematological patients among all medical ICU patients increased over time [odds ratio (OR) 1.06; 95 % confidence interval (CI) 1.03-1.10 per year; p < 0.001]. Risk-adjusted mortality was significantly higher for haematological patients admitted to the ICU with white blood cell (WBC) counts of <1.0 × 10(9)/L (47 %; 95 % CI 41-54 %) and ≥1.0 × 10(9)/L (45 %; 95 % CI 42-49 %), respectively, than for patients admitted with chronic heart failure (27 %; 95 % CI 26-28 %) and with chronic liver cirrhosis (38 %; 95 % CI 35-42 %), but was not significantly different from patients admitted with solid tumours (40 %; 95 % CI 36-45 %). Over the years, the risk-adjusted hospital mortality rate significantly decreased in both the haematological and non-haematological group with an OR of 0.93 (95 % CI 0.92-0.95) per year. After correction for case-mix using the APACHE-II score (with WBC omitted), a WBC <1.0 × 10(9)/L was not a predictor of mortality in haematological patients (OR 0.86; 95 % CI 0.46-1.64; p = 0.65). We found no case-volume effect on mortality for haematological ICU patients.

CONCLUSIONS

An increasing number of haematological patients are being admitted to Dutch ICUs. While mortality is significantly higher in this group of medical ICU patients than in subgroups of non-haematological ones, the former show a similar decrease in raw and risk-adjusted mortality rate over time, while leukocytopenia is not a predictor of mortality. These results suggest that haematological ICU patients have benefitted from improved intensive care support during the last decade.

Is the volume of mechanically ventilated admissions to UK critical care units associated with improved outcomes?

BACKGROUND

It is unknown whether a volume-outcome relationship exists for mechanically ventilated admissions to UK critical care units. This study was conducted to evaluate the volume-outcome relationship for mechanically ventilated admissions to adult, general critical care units in the UK with a view to informing policy, service delivery and organisation of specialist, advanced respiratory care.

METHODS

A retrospective cohort study using data from the Case Mix Programme Database was conducted. The primary exposure of interest was annual volume (absolute number) of mechanically ventilated admissions per critical care unit per year. The primary outcome was ultimate acute hospital mortality. A multivariable analysis was performed to assess the relationship between annual volume and outcome while adjusting for a priori selected confounders. Two interaction tests were performed. The first interaction test was between annual volume and admission type and the second between annual volume and initial acute severity of respiratory failure. Sensitivity analysis excluding volume outlier units and using restricted cubic splines to model volume was also performed.

RESULTS

After adjusting for confounding, there was a significant relationship between annual volume and ultimate acute hospital mortality (p < 0.02). The first interaction test revealed a strong interaction between annual volume and admission type, with a more pronounced volume-outcome relationship for non-surgical admissions (p < 0.001). The second interaction test between annual volume and initial acute severity of respiratory failure was not statistically significant (p = 0.12). The analysis using restricted cubic splines demonstrated a similar graphical relationship but the results were not statistically significant (p = 0.87).

CONCLUSIONS

A volume-outcome relationship was demonstrated for mechanically ventilated admissions to adult, general critical care units in the UK. The relationship is sensitive to the modelling approach used.

Effect of procedure volume on outcomes after iliac artery angioplasty and stenting

Background

Service reorganization to concentrate complex vascular services in hospitals with high caseload volume aims to reduce mortality and complication rates. The present study assessed the relationship between caseload volume and outcome for iliac artery angioplasty and stenting in England using a routinely available national data set (Hospital Episode Statistics, HES).

Methods

Routine administrative data for iliac artery angioplasty and stent procedures performed in England between 2007 and 2011 were analysed. Associations between centre volume and outcomes (death, complications and duration of hospital stay) were tested and compared for two methods of stratification (quartiles and quintiles) and two statistical tests (odds ratios and the Cochran–Armitage test for trend). Multivariable analysis was also performed.

Results

There were 23 308 episodes of care recorded in HES with Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, fourth revision, codes L54.1 or L54.4 corresponding to iliac artery intervention. There was a gradual increase year by year in number of procedures performed. Univariable and multivariable analysis showed no association between centre volume and either death or complications (multivariable odds ratio, OR 1·00, 95 per cent confidence interval 1·00 to 1·00) for elective and non-elective procedures. Age was associated with higher mortality and complication rates in elective procedures, and with mortality in non-elective procedures. The risk of death after elective iliac angioplasty or stenting was significantly higher in women (multivariable OR 4·98, 2·09 to 13·26).

Conclusion

There was no association between the outcomes of endovascular iliac artery intervention and centre volume, but outcomes were significantly worse with increasing age and female sex.

Reliability of in-hospital mortality as a quality indicator in clinical quality registries. A case study in an intensive care quality register

OBJECTIVES

Errors in the registration or extraction of patient outcome data, such as in-hospital mortality, may lower the reliability of the quality indicator that uses this (partly) incorrect data. Our aim was to measure the reliability of in-hospital mortality registration in the Dutch National Intensive Care Evaluation (NICE) registry.

METHODS

We linked data of the NICE registry with an insurance claims database, resulting in a list of discrepancies in in-hospital mortality. Eleven Intensive Care Units (ICUs) were visited where local data sources were investigated to find the true in-hospital mortality status of the discrepancies and to identify the causes of the data errors in the NICE registry. Original and corrected Standardized Mortality Ratios (SMRs) were calculated to determine if conclusions about quality of care changed compared to the national benchmark.

RESULTS

In eleven ICUs, 23,855 records with 460 discrepancies were identified of which 255 discrepancies (1.1% of all linked records) were due to incorrect in-hospital mortality registration in the NICE registry. Two programming errors in computer software of six ICUs caused 78% of errors, the remainder was caused by manual transcription errors and failure to record patient outcomes. For one ICU the performance became concordant with the national benchmark after correction, instead of being better.

CONCLUSIONS

The reliability of in-hospital mortality registration in the NICE registry was good. This was reflected by the low number of data errors and by the fact that conclusions about the quality of care were only affected for one ICU due to systematic data errors. We recommend that registries frequently verify the software used in the registration process, and compare mortality data with an external source to assure consistent quality of data.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

Background

Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency.

Methods

Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance.

Results

The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag₄₀ and 35% had autocorrelation through to lag₄₀; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model.

Conclusions

The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.

Evaluating the performance of Australian and New Zealand intensive care units in 2009 and 2010

The Australian and New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) is one of the largest databases of its kind in the world and collects individual admissions' data from intensive care units (ICUs) around Australia and New Zealand. Use of this database for monitoring and comparing the performance of ICUs, quantified by the standardised mortality ratio, poses several theoretical and computational challenges, which are addressed in this paper. In particular, the expected number of deaths must be appropriately estimated, the ICU casemix adjustment must be adequate, statistical variation must be fully accounted for, and appropriate adjustment for multiple comparisons must be made. Typically, one or more of these issues have been neglected in ICU comparison studies. Our approach to the analysis proceeds by fitting a random coefficient hierarchical logistic regression model for the inhospital death of each patient, with patients clustered within ICUs. We anticipate the majority of ICUs will be estimated as performing 'usually' after adjusting for important clinical covariates. We take as a starting point the ideas in Ohlssen et al and estimate an appropriate null model that we expect these ICUs to follow, taking a frequentist rather than a Bayesian approach. This methodology allows us to rigorously account for the aforementioned statistical issues and to determine if there are any ICUs contributing to the Australian and New Zealand Intensive Care Society database that have comparatively unusual performance. In addition to investigating the yearly performance of the ICUs, we also estimate changes in individual ICU performance between 2009 and 2010 by adjusting for regression-to-the-mean.