Problematic meta-analyses: Bayesian and frequentist perspectives on combining randomized controlled trials and non-randomized studies.

PURPOSE: In the literature, the propriety of the meta-analytic treatment-effect produced by combining randomized controlled trials (RCT) and non-randomized studies (NRS) is questioned, given the inherent confounding in NRS that may bias the meta-analysis. The current study compared an implicitly principled pooled Bayesian meta-analytic treatment-effect with that of frequentist pooling of RCT and NRS to determine how well each approach handled the NRS bias. MATERIALS & METHODS: Binary outcome Critical-Care meta-analyses, reflecting the importance of such outcomes in Critical-Care practice, combining RCT and NRS were identified electronically. Bayesian pooled treatment-effect and 95% credible-intervals (BCrI), posterior model probabilities indicating model plausibility and Bayes-factors (BF) were estimated using an informative heavy-tailed heterogeneity prior (half-Cauchy). Preference for pooling of RCT and NRS was indicated for Bayes-factors > 3 or < 0.333 for the converse. All pooled frequentist treatment-effects and 95% confidence intervals (FCI) were re-estimated using the popular DerSimonian-Laird (DSL) random effects model. RESULTS: Fifty meta-analyses were identified (2009-2021), reporting pooled estimates in 44; 29 were pharmaceutical-therapeutic and 21 were non-pharmaceutical therapeutic. Re-computed pooled DSL FCI excluded the null (OR or RR = 1) in 86% (43/50). In 18 meta-analyses there was an agreement between FCI and BCrI in excluding the null. In 23 meta-analyses where FCI excluded the null, BCrI embraced the null. BF supported a pooled model in 27 meta-analyses and separate models in 4. The highest density of the posterior model probabilities for 0.333 < Bayes factor < 1 was 0.8. CONCLUSIONS: In the current meta-analytic cohort, an integrated and multifaceted Bayesian approach gave support to including NRS in a pooled-estimate model. Conversely, caution should attend the reporting of naïve frequentist pooled, RCT and NRS, meta-analytic treatment effects.

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.

Hospital-acquired complications: the relative importance of hospital- and patient-related factors.

OBJECTIVE: To quantify the prevalence of hospital-acquired complications; to determine the relative influence of patient- and hospital-related factors on complication rates. DESIGN, PARTICIPANTS: Retrospective analysis of administrative data (Integrated South Australian Activity Collection; Victorian Admitted Episodes Dataset) for multiple-day acute care episodes for adults in public hospitals. SETTING: Thirty-eight major public hospitals in South Australia and Victoria, 2015-2018. MAIN OUTCOME MEASURES: Hospital-acquired complication rates, overall and by complication class, by hospital and hospital type (tertiary referral, major metropolitan service, major regional service); variance in rates (intra-class correlation coefficient, ICC) at the patient, hospital, and hospital type levels as surrogate measures of their influence on rates. RESULTS: Of 1 558 978 public hospital episodes (10 029 918 bed-days), 151 486 included a total of 214 286 hospital-acquired complications (9.72 [95% CI, 9.67-9.77] events per 100 episodes; 2.14 [95% CI, 2.13-2.15] events per 100 bed-days). Complication rates were highest in tertiary referral hospitals (12.7 [95% CI, 12.6-12.8] events per 100 episodes) and for episodes including intensive care components (37.1 [95% CI, 36.7-37.4] events per 100 episodes). For all complication classes, inter-hospital variation was determined more by patient factors (overall ICC, 0.55; 95% CI, 0.53-0.57) than by hospital factors (ICC, 0.04; 95% CI, 0.02-0.07) or hospital type (ICC, 0.01; 95% CI, 0.001-0.03). CONCLUSIONS: Hospital-acquired complications were recorded for 9.7% of hospital episodes, but patient-related factors played a greater role in determining their prevalence than the treating hospital.

Multivariate meta-analysis of critical care meta-analyses: a meta-epidemiological study.

BACKGROUND: Meta-analyses typically consider multiple outcomes and report univariate effect sizes considered as independent. Multivariate meta-analysis (MVMA) incorporates outcome correlation and synthesises direct evidence and related outcome estimates within a single analysis. In a series of meta-analyses from the critically ill literature, the current study contrasts multiple univariate effect estimates and their precision with those derived from MVMA. METHODS: A previous meta-epidemiological study was used to identify meta-analyses with either one or two secondary outcomes providing sufficient detail to structure bivariate or tri-variate MVMA, with mortality as primary outcome. Analysis was performed using a random effects model for both odds ratio (OR) and risk ratio (RR); borrowing of strength (BoS) between multivariate outcome estimates was reported. Estimate comparisons, ß coefficients, standard errors (SE) and confidence interval (CI) width, univariate versus multivariate, were performed using Lin's concordance correlation coefficient (CCC). RESULTS: In bivariate meta-analyses, for OR (n = 49) and RR (n = 48), there was substantial concordance (≥ 0.69) between estimates; but this was less so for tri-variate meta-analyses for both OR (n = 25; ≥ 0.38) and RR (≥ -0.10; n = 22). A variable change in the multivariate precision of primary mortality outcome estimates compared with univariate was present for both bivariate and tri-variate meta-analyses and for metrics. For second outcomes, precision tended to decrease and CI width increase for bivariate meta-analyses, but was variable in the tri-variate. For third outcomes, precision increased and CI width decreased. In bivariate meta-analyses, OR coefficient significance reversal, univariate versus MVMA, occurred once for mortality and 6 cases for second outcomes. RR coefficient significance reversal occurred in 4 cases; 2 were discordant with OR. For tri-variate OR meta-analyses reversal of coefficient estimate significance occurred in two cases for mortality, nine cases for second and 7 cases for third outcomes. In RR meta-analyses significance reversals occurred for mortality in 2 cases, 6 cases for second and 3 cases for third; there were 7 discordances with OR. BoS was greater in trivariate MVMAs compared with bivariate and for OR versus RR. CONCLUSIONS: MVMA would appear to be the preferred solution to multiple univariate analyses; parameter significance changes may occur. Analytic metric appears to be a determinant.

Modelling hospital outcome: problems with endogeneity.

BACKGROUND: Mortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry database, various binary outcome modelling strategies and methods to account for covariate endogeneity were explored. METHODS: Patient mortality data was sourced from the Australian & New Zealand Intensive Society Adult Patient Database for 2016. Hospital mortality was modelled using logistic, probit and linear probability (LPM) models with intensive care (ICU) providers as fixed (FE) and random (RE) effects. Model comparison entailed indices of discrimination and calibration, information criteria (AIC and BIC) and binned residual analysis. Suspect covariate and ventilation treatment assignment endogeneity was identified by correlation between predictor variable and hospital mortality error terms, using the Stata™ "eprobit" estimator. Marginal effects were used to demonstrate effect estimate differences between probit and "eprobit" models. RESULTS: The cohort comprised 92,693 patients from 124 intensive care units (ICU) in calendar year 2016. Patients mean age was 61.8 (SD 17.5) years, 41.6% were female and APACHE III severity of illness score 54.5(25.6); 43.7% were ventilated. Of the models considered in predicting hospital mortality, logistic regression (with or without ICU FE) and RE logistic regression dominated, more so the latter using information criteria indices. The LPM suffered from many predictions outside the unit [0,1] interval and both poor discrimination and calibration. Error terms of hospital length of stay, an independent risk of death score and ventilation status were correlated with the mortality error term. Marked differences in the ventilation mortality marginal effect was demonstrated between the probit and the "eprobit" models which were scenario dependent. Endogeneity was not demonstrated for the APACHE III score. CONCLUSIONS: Logistic regression accounting for provider effects was the preferred estimator for hospital mortality modelling. Endogeneity of covariates and treatment variables may be identified using appropriate modelling, but failure to do so yields problematic effect estimates.

Multivariate Meta-Analysis of the Mortality Effect of Prone Positioning in the Acute Respiratory Distress Syndrome.

BACKGROUND: The efficacy of prone positioning (PP) as therapy of the acute respiratory distress syndrome (ARDS) has varied in recent meta-analyses. The efficacy question was reviewed using a cohesive multivariate meta-analysis model incorporating all available common time-point data. METHODS: Data from a core group of 8 randomized controlled trials (2001-2013) utilized in 8 current meta-analyses (2014-2017) was extracted for common time points. Multivariate meta-analysis and meta-regression models for prone-hours per day, mechanical ventilation tidal-volume and baseline patient PaO2/FiO2, considered as continuous and categorical predictors, determined the pooled relative risk (RR) of mortality for prone versus supine positioning. RESULTS: Mortality RR at 28-30 days, 2-3 months and 6-months was not significant overall (P > 0.05). Meta-regression of categorical predictors indicated significant mortality reduction (P ≤ 0.001) for ≥ 12 prone-hours (versus < 12), lung protective ventilation (versus none) and moderate-severe ARDS (versus all ARDS). Meta-regressions of continuous predictors were also significant (P ≤ 0.021) and yielded treatment inflection points of efficacious therapy for ≥ 12 prone-hours per day, ≤ 8.5 mL/kg tidal volume and ≤ PaO2/FiO2 ratio of 130. CONCLUSIONS: The mortality treatment effect of PP in ARDS, was not demonstrated in the unadjusted meta-analysis model. Moderator effects indicated consistent significant benefit of prone positioning. In the absence of individual patient data, multivariate models provide more decisive conclusions than individual time point analyses.

Assessing changes over time in healthcare provider performance: addressing regression to the mean over multiple time points.

In recent years, the evaluation of healthcare provider performance has become standard for governments, insurance companies, and other stakeholders. Often, performance is compared across providers using indicators in one time period, for example a year. However it is often important to assess changes in the performance of individual providers over time. Such analyses can be used to determine if any providers have significant improvements, deteriorations, unusual patterns or systematic changes in performance. Studies which monitor healthcare provider performance in this way have to date typically been limited to comparing performance in the most recent period with performance in a previous period. It is also important to consider a longer-term view of performance and assess changes over more than two periods. In this paper, we develop test statistics that account for variable numbers of prior performance indicators, and show that these are particularly useful for assessing consecutive improvements or deteriorations in performance. We apply the tests to coronary artery bypass graft mortality rates in New York State hospitals, and mortality data from Australian and New Zealand intensive care units. Although our applications are to medical data, the new tests have broad application in other areas.

Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010.

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.

Clinical features of organophosphate poisoning: A review of different classification systems and approaches.

PURPOSE: The typical toxidrome in organophosphate (OP) poisoning comprises of the Salivation, Lacrimation, Urination, Defecation, Gastric cramps, Emesis (SLUDGE) symptoms. However, several other manifestations are described. We review the spectrum of symptoms and signs in OP poisoning as well as the different approaches to clinical features in these patients. MATERIALS AND METHODS: Articles were obtained by electronic search of PubMed(®) between 1966 and April 2014 using the search terms organophosphorus compounds or phosphoric acid esters AND poison or poisoning AND manifestations. RESULTS: Of the 5026 articles on OP poisoning, 2584 articles pertained to human poisoning; 452 articles focusing on clinical manifestations in human OP poisoning were retrieved for detailed evaluation. In addition to the traditional approach of symptoms and signs of OP poisoning as peripheral (muscarinic, nicotinic) and central nervous system receptor stimulation, symptoms were alternatively approached using a time-based classification. In this, symptom onset was categorized as acute (within 24-h), delayed (24-h to 2-week) or late (beyond 2-week). Although most symptoms occur with minutes or hours following acute exposure, delayed onset symptoms occurring after a period of minimal or mild symptoms, may impact treatment and timing of the discharge following acute exposure. Symptoms and signs were also viewed as an organ specific as cardiovascular, respiratory or neurological manifestations. An organ specific approach enables focused management of individual organ dysfunction that may vary with different OP compounds. CONCLUSIONS: Different approaches to the symptoms and signs in OP poisoning may better our understanding of the underlying mechanism that in turn may assist with the management of acutely poisoned patients.

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.

The role of non-invasive positive pressure ventilation in post-extubation respiratory failure: An evaluation using meta-analytic techniques.

BACKGROUND: The use of non-invasive positive pressure ventilation (NIPPV) in post-extubation respiratory failure is not well-established. Meta-analytic techniques were used to assess the effects of prophylactic application of NIPPV (prior to the development of respiratory failure) and therapeutic application of NIPPV (subsequent to the development of respiratory failure). MATERIALS AND METHODS: Randomized controlled trials (RCTs) from 1966 to May 2010 were identified using electronic databases. RCTs, which reported the use of NIPPV in post-extubation respiratory failure with defined assessable endpoints: reintubation, mortality and length of stay, were included. RESULTS: Reintubation was the primary outcome, mortality and lengths of stay were the secondary outcomes. Risk ratios (RR) were calculated for discrete outcomes and weighted mean differences (WMD) for continuous measures. There were 13 trials with 1420 patients; 9 prophylactic with 861 patients and 4 therapeutic with 559 patients. In the prophylactic group, NIPPV was associated with lower rates of reintubation: RR 0.53 (95% confidence interval [CI], 0.28-0.98), P = 0.04. In the therapeutic group, NIPPV showed a null effect on reintubation: RR 0.79 (95% CI, 0.50-1.25), P = 0.31. The analysis on the secondary outcomes suggested significant reduction of hospital mortality with prophylactic application of NIPPV: RR 0.62 (95% CI 0.4-0.97), P = 0.03, with no effect on the other outcomes. Therapeutic application of NIPPV reduced intensive care unit length of stay: WMD -1.17 (95% CI -2.82 to -0.33), P = 0.006, but no effect on the other secondary outcomes. CONCLUSIONS: The results of this review suggested prophylactic NIPPV was beneficial with respect to reintubation and the therapeutic use of NIPPV showed a null effect.

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

OBJECTIVES: The mortality outcome of mechanical ventilation, a key intervention in the critically ill, has been variously reported to be determined by intensive care patient volume. We determined the volume-(mortality)-outcome relationship of mechanically ventilated patients whose records were contributed to the Australian and New Zealand Intensive Care Society Adult Patient Database. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 208,810 index patient admissions from 136 Australian and New Zealand intensive care units in the same number of hospitals over the course of 1995-2009. MEASUREMENTS AND MAIN RESULTS: The patient-volume effect on hospital mortality, overall and at the level of patient (nonsurgical, elective surgical, and emergency surgical) and intensive care unit (rural/regional, metropolitan, tertiary, and private) descriptors, was determined by random-effects logistic regression adjusting for illness severity and demographic and geographical predictors. Annualized patient volume was modeled both as a categorical (deciles) and, with calendar year, a continuous variable using fractional polynomials. The patients were of mean age of 59 yrs (SD, 19 yrs), Acute Physiology and Chronic Health Evaluation III score 66 (32), and 39.4% female, with a hospital mortality of 22.4%. Overall and at both the patient and intensive care unit descriptor levels, no progressive decline in mortality was demonstrated across the annual patient volume range (12-932). Over the whole database, mortality odds ratio for the last volume decile (801-932 patients) was 1.26 (95% confidence interval, 1.06-1.50; p = .009) compared with the first volume decile (12-101 patients). Calendar year mortality decreases were evident (odds ratio, 0.96; 95% confidence interval, 0.94-0.98; p = .0001). Using fractional polynomials, modest curvilinear mortality increases (range, 5%-8%) across the volume range were noted over the whole database for nonsurgical patients and at the tertiary intensive care unit level. CONCLUSION: No inverse volume-(mortality)-outcome relationship was apparent for ventilated patients in the Australian and New Zealand Intensive Care Society database. Mechanisms for mortality increments with patient volume were not identified but warrant further study.

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand Intensive Care Adult Patient Data-Base, 2008-2009.

BACKGROUND: For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. METHODS: Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator. RESULTS: The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function. CONCLUSIONS: For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

Competing-risk analysis of ESRD and death among patients with type 1 diabetes and macroalbuminuria.

Patients with both type 1 diabetes and CKD have an increased risk of adverse outcomes. The competing risks of death and ESRD may confound the estimates of risk for each outcome. Here, we sought to determine the major predictors of the cumulative incidence of ESRD and pre-ESRD mortality in patients with type 1 diabetes and macroalbuminuria while incorporating the competing risk for the alternate outcome into a Fine-Gray competing-risks analysis. We followed 592 patients with macroalbuminuria for a median of 9.9 years. During this time, 56 (9.5%) patients died and 210 (35.5%) patients developed ESRD. Predictors of incident ESRD, taking baseline renal function and the competing risk for death into account, included an elevated HbA(1c), elevated LDL cholesterol, male sex, weight-adjusted insulin dose, and a shorter duration of diabetes. By contrast, predictors of pre-ESRD death, taking baseline renal function and the competing risk for ESRD into account, included only age, the presence of established macrovascular disease, and elevated cholesterol levels. This competing-risks approach has potential to highlight the appropriate targets and strategies for preventing premature mortality in patients with type 1 diabetes.

Long-term clinical outcomes in patients with a working diagnosis of myocardial infarction with non-obstructed coronary arteries (MINOCA) assessed by cardiovascular magnetic resonance imaging.

BACKGROUND: Myocardial infarction with non-obstructed coronary arteries (MINOCA) is a distinct entity among patients presenting with troponin-positive acute chest pain. We have previously reported on the incremental diagnostic capability of cardiovascular magnetic resonance (CMR) in this cohort. There is paucity of evidence on the long-term (> 5 years) clinical outcomes of these patients as graded by their acute CMR diagnosis. METHODS AND RESULTS: A total of 229 patients with a working diagnosis of MINOCA who underwent CMR assessment during the acute admission (2010-2017) were prospectively studied. The primary endpoint was major adverse cardiac events (MACE) defined as a composite of all-cause mortality and cardiovascular readmissions, identified from hospital and primary care records. CMR performed at a median of 6 days (IQR 2, 8) from presentation provided a diagnosis in 85% of the patients (38% myocarditis, 28% acute myocardial infarction and 19% Takotsubo cardiomyopathy). Over a median follow-up of 7.1 years (IQR 3.7, 8.2), 56 (24%) patients experienced a MACE. We found a strong association between CMR diagnosis and MACE (log rank 30.47, p < 0.001). In multivariate analysis, age (hazard ratio = 1.07; 95% confidence interval = 1.05, 1.10; p < 0.001) and CMR diagnosis of acute myocardial infarction (hazard ratio = 8.87; 95% confidence interval = 2.58, 30.4; p = 0.001) were independent predictors of MACE. CONCLUSIONS: In a large cohort of patients with a working diagnosis of MINOCA, one in four suffer a MACE during long-term clinical follow-up. CMR diagnosis of acute myocardial infarction and age were significant predictors of MACE even in the absence of significant coronary artery obstruction.

Safety of the endotracheal tube for prolonged mechanical ventilation.

RATIONALE: The endotracheal tube (ETT) is the most common route for invasive mechanical ventilation (MV) yet controversy attends its long-term safety. OBJECTIVE: Assess the safety of ETT compared with tracheostomy tube (TT) for MV support in the intensive care unit (ICU). METHODS: Retrospective analysis of five year national dataset of 128,977 adults (age > 15-years) admitted for MV therapy with tracheostomy tube (TT; n = 4772) or without (ETT; n = 124,204), excluding those with neurological diagnoses or likely to require a surgical airway (n = 27,466), in 93 public health service ICUs across Australia, between July 2013-June 2018. MEASUREMENTS: Hospital survival (including liberation from MV) for ETT Group compared with TT Group using a probit regression model adjusted for confounding using fixed, endogenous and non-random treatment assignment covariates, and their interactions; analysed and plotted as marginal effects by duration of MV. RESULTS: Median duration of MV was 2 (IQR =1-4) days, predominantly via ETT (124,205; 96.3%), and 21,620 (16.7%) died. Temporal trend for ETT increased (OR = 1.06 per year, 95%CI =1.03-1.10) compared to TT, even for prolonged (>3 weeks) MV (38.1%). Higher risk-adjusted mortality was associated with longer duration of MV and after 9 days of MV with retention of ETT compared with TT - average (mortality) treatment effect 12.6% (95%CI =10.7-14.5). The latter was not significant after 30 days of MV. CONCLUSIONS: The safety of ETT compared with TT beyond short-term MV (≤9-days) is uncertain and requires prospective evaluation with additional data.

Nebulised heparin for patients with or at risk of acute respiratory distress syndrome: a multicentre, randomised, double-blind, placebo-controlled phase 3 trial.

BACKGROUND: Mechanical ventilation in intensive care for 48 h or longer is associated with the acute respiratory distress syndrome (ARDS), which might be present at the time ventilatory support is instituted or develop afterwards, predominantly during the first 5 days. Survivors of prolonged mechanical ventilation and ARDS are at risk of considerably impaired physical function that can persist for years. An early pathogenic mechanism of lung injury in mechanically ventilated, critically ill patients is inflammation-induced pulmonary fibrin deposition, leading to thrombosis of the microvasculature and hyaline membrane formation in the air sacs. The main aim of this study was to determine if nebulised heparin, which targets fibrin deposition, would limit lung injury and thereby accelerate recovery of physical function in patients with or at risk of ARDS. METHODS: The Can Heparin Administration Reduce Lung Injury (CHARLI) study was an investigator-initiated, multicentre, double-blind, randomised phase 3 trial across nine hospitals in Australia. Adult intensive care patients on invasive ventilation, with impaired oxygenation defined by a PaO2/FiO2 ratio of less than 300, and with the expectation of invasive ventilation beyond the next calendar day were recruited. Key exclusion criteria were heparin allergy, pulmonary bleeding, and platelet count less than 50 X 109/L. Patients were randomly assigned 1:1, with stratification by site and using blocks of variable size and random seed, via a web-based system, to either unfractionated heparin sodium 25 000 IU in 5 mL or identical placebo (sodium chloride 0·9% 5 mL), administered using a vibrating mesh membrane nebuliser every 6 h to day 10 while invasively ventilated. Patients, clinicians, and investigators were masked to treatment allocation. The primary outcome was the Short Form 36 Health Survey Physical Function Score (out of 100) of survivors at day 60. Prespecified secondary outcomes, which are exploratory, included development of ARDS to day 5 among at-risk patients, deterioration of the Murray Lung Injury Score (MLIS) to day 5, mortality at day 60, residence of survivors at day 60, and serious adverse events. Analyses followed the intention-to-treat principle. There was no imputation of missing data. The trial is registered with the Australian and New Zealand Clinical Trials Register, number ACTRN12612000418875 . FINDINGS: Between Sept 4, 2012, and Aug 23, 2018, 256 patients were randomised. Final follow-up was on Feb 25, 2019. We excluded three patients who revoked consent and one ineligible participant who received no intervention. Of 252 patients included in data analysis, the mean age was 58 years (SD 15), 157 (62%) were men, and 118 (47%) had ARDS. 128 (51%) patients were assigned to the heparin group and 124 (49%) to the placebo group, all of whom received their assigned intervention. Survivors in the heparin group (n=97) had similar SF-36 Physical Function Scores at day 60 compared to the placebo group (n=94; mean 53·6 [SD 31·6] vs 48·7 [35·7]; difference 4·9 [95% CI -4·8 to 14·5]; p=0·32). Compared with the placebo group, the heparin group had fewer cases of ARDS develop to day 5 among the at-risk patients (nine [15%] of 62 patients vs 21 [30%] of 71 patients; hazard ratio 0·46 [95% CI 0·22 to 0·98]; p=0·0431), less deterioration of the MLIS to day 5 (difference -0·14 [-0·26 to -0·02]; p=0·0215), similar day 60 mortality (23 [18%] of 127 patients vs 18 [15%] of 123 patients; odds ratio [OR] 1·29 [95% CI 0·66 to 2·53]; p=0·46), and more day 60 survivors at home (86 [87%] of 99 patients vs 73 [73%] of 100 patients; OR 2·45 [1·18 to 5·08]; p=0·0165). A similar number of serious adverse events occurred in each group (seven [5%] of 128 patients in the heparin group vs three [2%] of 124 patients in the placebo group; OR 2·33 [0·59 to 9·24]; p=0·23), which were a transient increase in airway pressure during nebulisation (n=3 in the heparin group), major non-pulmonary bleeding (n=2 in each group), haemoptysis (n=1 in the heparin group), tracheotomy site bleeding (n=1 in the heparin group), and hypoxaemia during nebulisation (n=1 in the placebo group). INTERPRETATION: In patients with or at risk of ARDS, nebulised heparin did not improve self-reported performance of daily physical activities, but was well tolerated and exploratory outcomes suggest less progression of lung injury and earlier return home. Further research is justified to establish if nebulised heparin accelerates recovery in those who have or are at risk of ARDS. FUNDING: Rowe Family Foundation, TR and RB Ditchfield Medical Research Endowment Fund, Patricia Madigan Charitable Trust, and The J and R McGauran Trust Fund.

Global quantitative indices reflecting provider process-of-care: data-base derivation.

BACKGROUND: Controversy has attended the relationship between risk-adjusted mortality and process-of-care. There would be advantage in the establishment, at the data-base level, of global quantitative indices subsuming the diversity of process-of-care. METHODS: A retrospective, cohort study of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 1993-2003, at the level of geographic and ICU-level descriptors (n = 35), for both hospital survivors and non-survivors. Process-of-care indices were established by analysis of: (i) the smoothed time-hazard curve of individual patient discharge and determined by pharmaco-kinetic methods as area under the hazard-curve (AUC), reflecting the integrated experience of the discharge process, and time-to-peak-hazard (TMAX, in days), reflecting the time to maximum rate of hospital discharge; and (ii) individual patient ability to optimize output (as length-of-stay) for recorded data-base physiological inputs; estimated as a technical production-efficiency (TE, scaled [0,(maximum)1]), via the econometric technique of stochastic frontier analysis. For each descriptor, multivariate correlation-relationships between indices and summed mortality probability were determined. RESULTS: The data-set consisted of 223129 patients from 99 ICUs with mean (SD) age and APACHE III score of 59.2(18.9) years and 52.7(30.6) respectively; 41.7% were female and 45.7% were mechanically ventilated within the first 24 hours post-admission. For survivors, AUC was maximal in rural and for-profit ICUs, whereas TMAX (>or= 7.8 days) and TE (>or= 0.74) were maximal in tertiary-ICUs. For non-survivors, AUC was maximal in tertiary-ICUs, but TMAX (>or= 4.2 days) and TE (>or= 0.69) were maximal in for-profit ICUs. Across descriptors, significant differences in indices were demonstrated (analysis-of-variance, P

Updating the evidence for the role of corticosteroids in severe sepsis and septic shock: a Bayesian meta-analytic perspective.

INTRODUCTION: Current low (stress) dose corticosteroid regimens may have therapeutic advantage in severe sepsis and septic shock despite conflicting results from two landmark randomised controlled trials (RCT). We systematically reviewed the efficacy of corticosteroid therapy in severe sepsis and septic shock. METHODS: RCTs were identified (1950-September 2008) by multiple data-base electronic search (MEDLINE via OVID, OVID PreMedline, OVID Embase, Cochrane Central Register of Controlled trials, Cochrane database of systematic reviews, Health Technology Assessment Database and Database of Abstracts of Reviews of Effects) and hand search of references, reviews and scientific society proceedings. Three investigators independently assessed trial inclusion and data extraction into standardised forms; differences resolved by consensus. RESULTS: Corticosteroid efficacy, compared with control, for hospital-mortality, proportion of patients experiencing shock-resolution, and infective and non-infective complications was assessed using Bayesian random-effects models; expressed as odds ratio (OR, (95% credible-interval)). Bayesian outcome probabilities were calculated as the probability (P) that OR ≥1. Fourteen RCTs were identified. High-dose (>1000 mg hydrocortisone (equivalent) per day) corticosteroid trials were associated with a null (n = 5; OR 0.91(0.31-1.25)) or higher (n = 4, OR 1.46(0.73-2.16), outlier excluded) mortality probability (P = 42.0% and 89.3%, respectively). Low-dose trials (<1000 mg hydrocortisone per day) were associated with a lower (n = 9, OR 0.80(0.40-1.39); n = 8 OR 0.71(0.37-1.10), outlier excluded) mortality probability (20.4% and 5.8%, respectively). OR for shock-resolution was increased in the low dose trials (n = 7; OR 1.20(1.07-4.55); P = 98.2%). Patient responsiveness to corticotrophin stimulation was non-determinant. A high probability of risk-related treatment efficacy (decrease in log-odds mortality with increased control arm risk) was identified by metaregression in the low dose trials (n = 9, slope coefficient -0.49(-1.14, 0.27); P = 92.2%). Odds of complications were not increased with corticosteroids. CONCLUSIONS: Although a null effect for mortality treatment efficacy of low dose corticosteroid therapy in severe sepsis and septic shock was not excluded, there remained a high probability of treatment efficacy, more so with outlier exclusion. Similarly, although a null effect was not excluded, advantageous effects of low dose steroids had a high probability of dependence upon patient underlying risk. Low dose steroid efficacy was not demonstrated in corticotrophin non-responders. Further large-scale trials appear mandated.