Dataset Information

Use of risk-adjusted CUSUM charts to monitor 30-day mortality in Danish hospitals.

ABSTRACT:

Background

Monitoring hospital outcomes and clinical processes as a measure of clinical performance is an integral part of modern health care. The risk-adjusted cumulative sum (CUSUM) chart is a frequently used sequential analysis technique that can be implemented to monitor a wide range of different types of outcomes.

Objective

The aim of this study was to describe how risk-adjusted CUSUM charts based on population-based nationwide medical registers were used to monitor 30-day mortality in Danish hospitals and to give an example on how alarms of increased hospital mortality from the charts can guide further in-depth analyses.

Materials and methods

We used routinely collected administrative data from the Danish National Patient Registry and the Danish Civil Registration System to create risk-adjusted CUSUM charts. We monitored 30-day mortality after hospital admission with one of 77 selected diagnoses in 24 hospital units in Denmark in 2015. The charts were set to detect a 50% increase in 30-day mortality, and control limits were determined by simulations.

Results

Among 1,085,576 hospital admissions, 441,352 admissions had one of the 77 selected diagnoses as their primary diagnosis and were included in the risk-adjusted CUSUM charts. The charts yielded a total of eight alarms of increased mortality. The median of the hospitals' estimated average time to detect a 50% increase in 30-day mortality was 50 days (interquartile interval, 43;54). In the selected example of an alarm, descriptive analyses indicated performance problems with 30-day mortality following hip fracture surgery and diagnosis of chronic obstructive pulmonary disease.

Conclusion

The presented implementation of risk-adjusted CUSUM charts can detect significant increases in 30-day mortality within 2 months, on average, in most Danish hospitals. Together with descriptive analyses, it was possible to use an alarm from a risk-adjusted CUSUM chart to identify potential performance problems.

SUBMITTER: Rasmussen TB

PROVIDER: S-EPMC5912378 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Use of risk-adjusted CUSUM charts to monitor 30-day mortality in Danish hospitals.

Rasmussen Thomas Bøjer TB Ulrichsen Sinna Pilgaard SP Nørgaard Mette M

Clinical epidemiology 20180418

<h4>Background</h4>Monitoring hospital outcomes and clinical processes as a measure of clinical performance is an integral part of modern health care. The risk-adjusted cumulative sum (CUSUM) chart is a frequently used sequential analysis technique that can be implemented to monitor a wide range of different types of outcomes.<h4>Objective</h4>The aim of this study was to describe how risk-adjusted CUSUM charts based on population-based nationwide medical registers were used to monitor 30-day mo ...[more]

PMID: 29713201

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Project description:BackgroundTraffic incidents are still a major contributor to hospital admissions and trauma-related mortality. The aim of this nationwide study was to examine risk-adjusted traffic injury mortality to determine whether hospital type was an independent survival factor.MethodsData on all patients admitted to Swedish hospitals with traffic-related injuries, based on International Classification of Diseases codes, between 2001 and 2011 were extracted from the Swedish inpatient and cause of death registries. Using the binary outcome measure of death or survival, data were analysed using logistic regression, adjusting for age, sex, comorbidity, severity of injury and hospital type. The severity of injury was established using the International Classification of Diseases Injury Severity Score (ICISS).ResultsThe final study population consisted of 152,693 hospital admissions. Young individuals (0-25 years of age) were overrepresented, accounting for 41% of traffic-related injuries. Men were overrepresented in all age categories. Fatalities at university hospitals had the lowest mean (SD) ICISS 0.68 (0.19). Regional and county hospitals had mean ICISS 0.75 (0.15) and 0.77 (0.15), respectively, for fatal traffic incidents. The crude overall mortality in the study population was 1193, with a mean ICISS 0.72 (0.17). Fatalities at university hospitals had the lowest mean ICISS 0.68 (0.19). Regional and county hospitals had mean ICISS 0.75 (0.15) and 0.77 (0.15), respectively, for fatal traffic incidents.When regional and county hospitals were merged into one group and its risk-adjusted mortality compared with university hospitals, no significant difference was found. A comparison between hospital groups with the most severely injured patients (ICISS ≤0.85) also did not show a significant difference (odds ratio, 1.13; 95% confidence interval, 0.97-1.32).ConclusionsThis study shows that, in Sweden, the type of hospital does not influence risk adjusted traffic related mortality, where the most severely injured patients are transported to the university hospitals and centralization of treatment is common.

Project description:BACKGROUND:Continuous monitoring of surgical outcomes after joint replacement is needed to detect which brands' components have a higher than expected failure rate and are therefore no longer recommended to be used in surgical practice. We developed a monitoring method based on cumulative sum (CUSUM) chart specifically for this application. METHODS:Our method entails the use of the competing risks model with the Weibull and the Gompertz hazard functions adjusted for observed covariates to approximate the baseline time-to-revision and time-to-death distributions, respectively. The correlated shared frailty terms for competing risks, corresponding to the operating unit, are also included in the model. A bootstrap-based boundary adjustment is then required for risk-adjusted CUSUM charts to guarantee a given probability of the false alarm rates. We propose a method to evaluate the CUSUM scores and the adjusted boundary for a survival model with the shared frailty terms. We also introduce a unit performance quality score based on the posterior frailty distribution. This method is illustrated using the 2003-2012 hip replacement data from the UK National Joint Registry (NJR). RESULTS:We found that the best model included the shared frailty for revision but not for death. This means that the competing risks of revision and death are independent in NJR data. Our method was superior to the standard NJR methodology. For one of the two monitored components, it produced alarms four years before the increased failure rate came to the attention of the UK regulatory authorities. The hazard ratios of revision across the units varied from 0.38 to 2.28. CONCLUSIONS:An earlier detection of failure signal by our method in comparison to the standard method used by the NJR may be explained by proper risk-adjustment and the ability to accommodate time-dependent hazards. The continuous monitoring of hip replacement outcomes should include risk adjustment at both the individual and unit level.

Project description:IntroductionA common quality indicator for monitoring and comparing hospitals is based on death within 30 days of admission. An important use is to determine whether a hospital has higher or lower mortality than other hospitals. Thus, the ability to identify such outliers correctly is essential. Two approaches for detection are: 1) calculating the ratio of observed to expected number of deaths (OE) per hospital and 2) including all hospitals in a logistic regression (LR) comparing each hospital to a form of average over all hospitals. The aim of this study was to compare OE and LR with respect to correctly identifying 30-day mortality outliers. Modifications of the methods, i.e., variance corrected approach of OE (OE-Faris), bias corrected LR (LR-Firth), and trimmed mean variants of LR and LR-Firth were also studied.Materials and methodsTo study the properties of OE and LR and their variants, we performed a simulation study by generating patient data from hospitals with known outlier status (low mortality, high mortality, non-outlier). Data from simulated scenarios with varying number of hospitals, hospital volume, and mortality outlier status, were analysed by the different methods and compared by level of significance (ability to falsely claim an outlier) and power (ability to reveal an outlier). Moreover, administrative data for patients with acute myocardial infarction (AMI), stroke, and hip fracture from Norwegian hospitals for 2012-2014 were analysed.ResultsNone of the methods achieved the nominal (test) level of significance for both low and high mortality outliers. For low mortality outliers, the levels of significance were increased four- to fivefold for OE and OE-Faris. For high mortality outliers, OE and OE-Faris, LR 25% trimmed and LR-Firth 10% and 25% trimmed maintained approximately the nominal level. The methods agreed with respect to outlier status for 94.1% of the AMI hospitals, 98.0% of the stroke, and 97.8% of the hip fracture hospitals.ConclusionWe recommend, on the balance, LR-Firth 10% or 25% trimmed for detection of both low and high mortality outliers.

Project description:INTRODUCTION: A health care system is a complex adaptive system. The effect of a single intervention, incorporated into a complex clinical environment, may be different from that expected. A national database such as the Intensive Care National Audit & Research Centre (ICNARC) Case Mix Programme in the UK represents a centralised monitoring, surveillance and reporting system for retrospective quality and comparative audit. This can be supplemented with real-time process monitoring at a local level for continuous process improvement, allowing early detection of the impact of both unplanned and deliberately imposed changes in the clinical environment. METHODS: Demographic and UK Acute Physiology and Chronic Health Evaluation II (APACHE II) data were prospectively collected on all patients admitted to a UK regional hospital between 1 January 2003 and 30 June 2004 in accordance with the ICNARC Case Mix Programme. We present a cumulative expected minus observed (E-O) plot and the risk-adjusted p chart as methods of continuous process monitoring. We describe the construction and interpretation of these charts and show how they can be used to detect planned or unplanned organisational process changes affecting mortality outcomes. RESULTS: Five hundred and eighty-nine adult patients were included. The overall death rate was 0.78 of predicted. Calibration showed excess survival in ranges above 30% risk of death. The E-O plot confirmed a survival above that predicted. Small transient variations were seen in the slope that could represent random effects, or real but transient changes in the quality of care. The risk-adjusted p chart showed several observations below the 2 SD control limits of the expected mortality rate. These plots provide rapid analysis of risk-adjusted performance suitable for local application and interpretation. The E-O chart provided rapid easily visible feedback of changes in risk-adjusted mortality, while the risk-adjusted p chart allowed statistical evaluation. CONCLUSION: Local analysis of risk-adjusted mortality data with an E-O plot and a risk-adjusted p chart is feasible and allows the rapid detection of changes in risk-adjusted outcome of intensive care patients. This complements the centralised national database, which is more archival and comparative in nature.

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Dataset Information

Use of risk-adjusted CUSUM charts to monitor 30-day mortality in Danish hospitals.

Background

Objective

Materials and methods

Results

Conclusion

Publications

Use of risk-adjusted CUSUM charts to monitor 30-day mortality in Danish hospitals.

Similar Datasets

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